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Tbarkin121/GuardDog/isaac/assets/urdf/TorquePole/package.xml | <package format="2">
<name>TorquePole</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for TorquePole</p>
<p>This package contains configuration data, 3D models and launch files
for TorquePole robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 625 | XML | 28.809522 | 75 | 0.7104 |
Tbarkin121/GuardDog/python/SanityCheck.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 30 02:54:59 2023
@author: PandorasBox
"""
# https://studywolf.wordpress.com/2013/09/02/robot-control-jacobians-velocity-and-force/
import torch
theta1 = torch.tensor(torch.pi/4)
theta2 = torch.tensor(3*torch.pi/8)*5
t1 = -torch.sin(theta1) - torch.sin(theta1 + theta2)
t2 = torch.cos(theta1) + torch.cos(theta1 + theta2)
t3 = -torch.sin(theta1+theta2)
t4 = torch.cos(theta1+theta2)
J = torch.tensor([[t1,t2],[t3,t4]])
F = torch.tensor([[1.3],[1.0]])
t = torch.matmul(J,F)
print(t)
| 559 | Python | 20.538461 | 88 | 0.67263 |
Tbarkin121/GuardDog/python/pendulum_dynamic_jacobian.py | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 01:37:05 2023
@author: Plutonium
"""
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 21 09:58:15 2023
@author: Plutonium
"""
import torch
import matplotlib.pyplot as plt
import numpy as np
import time
from torchviz import make_dot
torch.set_default_device('cuda')
# torch.autograd.set_detect_anomaly(True)
#%%
class Pendulum:
def __init__(self):
print('Init Arm')
self.num_segments = 10
self.angle_offset = 3.1415/2 # So gravity is down
self.joint_angles = torch.zeros(self.num_segments, requires_grad=True)
self.joint_velocity = torch.ones(self.num_segments, requires_grad=True)
self.joint_acceleration = torch.zeros(self.num_segments, requires_grad=True)
self.link_lengths = torch.ones(self.num_segments, requires_grad=False)/self.num_segments
self.link_mass = torch.ones(self.num_segments, requires_grad=False)
self.xs = torch.zeros(self.num_segments+1, requires_grad=False)
self.ys = torch.zeros(self.num_segments+1, requires_grad=False)
self.x_targ=torch.tensor(-0.33, requires_grad=False)
self.y_targ=torch.tensor(0.44, requires_grad=False)
self.I = (1/3)*self.link_mass*self.link_lengths
plt.close('all')
xp = torch.cat((torch.tensor([0.0]), self.xs)).detach().cpu().numpy()
yp = torch.cat((torch.tensor([0.0]), self.ys)).detach().cpu().numpy()
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.ax.grid(True)
self.ax.set_xlim([-2, 2])
self.ax.set_ylim([-2, 2])
self.line1, = self.ax.plot(xp, yp, 'r-') # Returns a tuple of line objects, thus the comma
self.line2, = self.ax.plot(self.x_targ.detach().cpu().numpy(),self.y_targ.detach().cpu().numpy(), 'o') # Returns a tuple of line objects, thus the comma
def forward_kinematics(self):
self.xs = torch.zeros(self.num_segments+1, requires_grad=False)
self.ys = torch.zeros(self.num_segments+1, requires_grad=False)
for s in range(1, self.num_segments+1):
self.xs[s] = self.xs[s-1] + self.link_lengths[s-1]*torch.cos(torch.sum(self.joint_angles[0:s]))
self.ys[s] = self.ys[s-1] + self.link_lengths[s-1]*torch.sin(torch.sum(self.joint_angles[0:s]))
def ComputeLagrange(self):
# Reset energies to zero
self.T = 0.0
self.U = 0.0
# Cumulative angle for kinematics
cumulative_angle = 0.0
# Cumulative velocity for translational kinetic energy
cumulative_velocity = 0.0 # Cumulative velocity for translational kinetic energy
cumulative_x = 0.0
cumulative_y = 0.0
for i in range(self.num_segments):
# Update the cumulative angle
cumulative_angle = cumulative_angle + self.joint_angles[i]
cumulative_velocity = cumulative_velocity + self.joint_velocity[i]
# Position of the COM of the current segment
com_x = cumulative_x + self.link_lengths[i] / 2 * torch.cos(cumulative_angle)
com_y = cumulative_y + self.link_lengths[i] / 2 * torch.sin(cumulative_angle)
# Update cumulative_x and cumulative_y for the next segment
cumulative_x = cumulative_x + self.link_lengths[i] * torch.cos(cumulative_angle)
cumulative_y = cumulative_y + self.link_lengths[i] * torch.sin(cumulative_angle)
# Translational velocity of the segment's center of mass
# This requires calculating the derivative of com_x and com_y with respect to time
# For simplicity, we'll assume constant velocity for this example
# translational_velocity = torch.sqrt(com_x**2 + com_y**2) * cumulative_velocity
# Translational kinetic energy
# self.T += 0.5 * self.link_mass[i] * translational_velocity**2
# Rotational Kinetic energy
self.T += 0.5 * self.I[i] * self.joint_velocity[i]**2
# Height of the segment's center of mass
# h = (self.link_lengths[i] / 2) * (1 - torch.cos(cumulative_angle + self.angle_offset))
h = com_y-1 # Height of the center of mass
# Potential energy
self.U += self.link_mass[i] * 9.81 * h
self.F = self.joint_velocity * 0.01 # Friction Energy Lost
# I think I can just add the friction in here?
self.L = self.T-self.U # Legrangeian
def EulerLagrange(self, delta_t): #(d/dt)(dL/dthetadot)-(dL/dtheta) = tau
# self.tau = self.I*self.joint_acceleration + torch.sin(self.joint_angles)*self.link_mass*9.81*self.link_lengths/2
# Compute current Lagrangian
self.ComputeLagrange()
# Save current values of dL/dthetadot
# current_dL_dthetadot = self.I * self.joint_velocity
self.ClearGrads()
self.L.backward()
current_dL_dthetadot = self.joint_velocity.grad.clone()
manual_dL_dthetadot = self.I*self.joint_velocity
# print("Automatic differentiation:", current_dL_dthetadot)
# print("Manual calculation:", manual_dL_dthetadot)
# Update state to t + delta_t
self.UpdateState(delta_t)
# Compute new Lagrangian at t + delta_t
self.ComputeLagrange()
# New value of dL/dthetadot at t + delta_t
self.ClearGrads()
self.L.backward()
# new_dL_dthetadot = self.I * self.joint_velocity
new_dL_dthetadot = self.joint_velocity.grad.clone()
# Numerical approximation of time derivative
dL_dthetadot_dt = (new_dL_dthetadot - current_dL_dthetadot) / delta_t
manual_dL_dthetadot_dt = self.I*self.joint_acceleration
# print("Automatic differentiation:", dL_dthetadot_dt)
# print("Manual calculation:", manual_dL_dthetadot_dt)
# dL/dtheta
dL_dtheta = self.joint_angles.grad
manual_dL_dtheta = -self.link_mass*9.81*self.link_lengths/2*torch.sin(self.joint_angles)
# print("Automatic differentiation:", dL_dtheta)
# print("Manual calculation:", manual_dL_dtheta)
# Euler-Lagrange equation
self.tau = dL_dthetadot_dt - dL_dtheta
friction_torque = self.joint_velocity * 0.1
# input_tau = self.tau + friction_torque
input_tau = 0
total_tau = input_tau-self.tau-friction_torque
self.joint_acceleration = total_tau/self.I
def UpdateState(self, delta_t):
# Update joint angles and velocities using a simple Euler integration
with torch.no_grad():
# Update joint angles and velocities using a simple Euler integration
self.joint_angles += self.joint_velocity * delta_t
self.joint_velocity += self.joint_acceleration * delta_t
def ClearGrads(self):
if self.joint_angles.grad is not None:
self.joint_angles.grad = None
if self.joint_velocity.grad is not None:
self.joint_velocity.grad = None
if self.joint_acceleration.grad is not None:
self.joint_acceleration.grad = None
def Plot(self):
self.forward_kinematics()
xp = torch.cat((torch.tensor([0.0]), self.xs)).detach().cpu().numpy()
yp = torch.cat((torch.tensor([0.0]), self.ys)).detach().cpu().numpy()
self.line1.set_xdata(xp)
self.line1.set_ydata(yp)
self.line2.set_xdata(self.x_targ.detach().cpu().numpy())
self.line2.set_ydata(self.y_targ.detach().cpu().numpy())
self.fig.canvas.draw()
self.fig.canvas.flush_events()
# plt.connect('motion_notify_event', self.mouse_move)
def mouse_move(self, event):
x, y = event.xdata, event.ydata
if(x and y):
self.x_targ = torch.tensor(x, dtype=torch.float, requires_grad=False)
self.y_targ = torch.tensor(y, dtype=torch.float, requires_grad=False)
#%%
env = Pendulum()
env.Plot()
#%%
for _ in range(10000):
t1 = time.perf_counter()
env.EulerLagrange(0.01)
if ( (_ % 10) == 0):
env.Plot()
t2 = time.perf_counter()
print(t2-t1)
# print(env.T)
# print(env.U)
# print(env.L)
# print(env.tau)
| 8,733 | Python | 38.342342 | 160 | 0.582503 |
Tbarkin121/GuardDog/python/kinematic_jacobian_test.py | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 01:37:05 2023
@author: Plutonium
"""
import torch
import matplotlib.pyplot as plt
import numpy as np
import time
import torchviz
torch.set_default_device('cpu')
#%%
class PlanarArm:
def __init__(self, num_segments):
print('Init Arm')
self.num_segments = num_segments
self.joint_angles = torch.zeros(num_segments, requires_grad=True)
with torch.no_grad():
self.joint_angles[0] = torch.pi/4
self.joint_angles[1] = torch.pi/4
self.joint_lengths = torch.ones(num_segments, requires_grad=False)*1.0
with torch.no_grad():
self.joint_lengths[1] = self.joint_lengths[1]/2
self.xs = torch.zeros(num_segments+1, requires_grad=False)
self.ys = torch.zeros(num_segments+1, requires_grad=False)
self.x_targ=torch.tensor(-0.33, requires_grad=False)
self.y_targ=torch.tensor(0.44, requires_grad=False)
self.weights = torch.ones([num_segments,1])
self.weights[0] = 0
plt.close('all')
xp = torch.cat((torch.tensor([0.0]), self.xs)).detach().cpu().numpy()
yp = torch.cat((torch.tensor([0.0]), self.ys)).detach().cpu().numpy()
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.ax.grid(True)
self.ax.set_xlim([-2, 2])
self.ax.set_ylim([-2, 2])
self.line1, = self.ax.plot(xp, yp, 'r-') # Returns a tuple of line objects, thus the comma
self.line2, = self.ax.plot(self.x_targ.detach().cpu().numpy(),self.y_targ.detach().cpu().numpy(), 'o') # Returns a tuple of line objects, thus the comma
self.line3, = self.ax.plot([0,0],[0,0], 'm-') # Returns a tuple of line objects, thus the comma
def forward_kinematics(self):
self.xs = torch.zeros(self.num_segments+1, requires_grad=False)
self.ys = torch.zeros(self.num_segments+1, requires_grad=False)
for s in range(1, self.num_segments+1):
self.xs[s] = self.xs[s-1] + self.joint_lengths[s-1]*torch.cos(torch.sum(self.joint_angles[0:s]))
self.ys[s] = self.ys[s-1] + self.joint_lengths[s-1]*torch.sin(torch.sum(self.joint_angles[0:s]))
def get_residual(self):
self.dx = self.xs[-1] - self.x_targ
self.dy = self.ys[-1] - self.y_targ
# error = torch.sqrt(dx**2 + dy**2)
def compute_jacobian(self):
# Compute forward kinematics
self.forward_kinematics()
self.get_residual()
if self.joint_angles.grad is not None:
self.joint_angles.grad = None
self.dx.backward()
self.jacobian_x = self.joint_angles.grad.clone()
# Zero out the gradients before computing the next one
# self.joint_angles.grad = None
if self.joint_angles.grad is not None:
self.joint_angles.grad = None
self.dy.backward()
self.jacobian_y = self.joint_angles.grad.clone()
self.J = torch.stack((env.jacobian_x, env.jacobian_y))
# Manual 2 segment jacobian calc (Checked out vs torch, it matches)
# self.test_J = torch.zeros(2,2)
# self.test_J[0,0]= - self.joint_lengths[0]*torch.sin(self.joint_angles[0]) - self.joint_lengths[1]*torch.sin(self.joint_angles[0] + self.joint_angles[1])
# self.test_J[0,1]= - self.joint_lengths[1]*torch.sin(self.joint_angles[0] + self.joint_angles[1])
# self.test_J[1,0]= self.joint_lengths[0]*torch.cos(self.joint_angles[0]) + self.joint_lengths[1]*torch.cos(self.joint_angles[0] + self.joint_angles[1])
# self.test_J[1,1]= self.joint_lengths[1]*torch.cos(self.joint_angles[0] + self.joint_angles[1])
def update_angles(self, dtheta):
with torch.no_grad():
# self.joint_angles -= dtheta.view(-1)
self.joint_angles[1] -= dtheta[1][0]
def plot(self):
# self.forward_kinematics()
xp = torch.cat((torch.tensor([0.0]), self.xs)).detach().cpu().numpy()
yp = torch.cat((torch.tensor([0.0]), self.ys)).detach().cpu().numpy()
self.line1.set_xdata(xp)
self.line1.set_ydata(yp)
self.line2.set_xdata(self.x_targ.detach().cpu().numpy())
self.line2.set_ydata(self.y_targ.detach().cpu().numpy())
self.line3.set_xdata([xp[-1], xp[-1] + 0.1*self.EndEffector_F[0].detach().cpu().numpy()[0]])
self.line3.set_ydata([yp[-1], yp[-1] + 0.1*self.EndEffector_F[1].detach().cpu().numpy()[0]])
self.fig.canvas.draw()
self.fig.canvas.flush_events()
plt.connect('motion_notify_event', self.mouse_move)
def control(self):
m = 2
n = self.num_segments
gamma = .5
self.compute_jacobian()
# self.J_inv = torch.linalg.pinv(self.J)
# self.delta_theta = torch.matmul(self.J_inv, torch.tensor([self.dx, self.dy]))
# self.update_angles(self.delta_theta)
JJT = torch.matmul(self.J + torch.eye(self.J.shape[0])*0.00001, self.J.permute([1,0]) + torch.eye(self.J.shape[0])*0.00001)
Im = torch.eye(m)
R = torch.stack((env.dx, env.dy)).view(-1,1)
M1 = torch.linalg.solve(JJT, self.J)
M2 = torch.linalg.solve(JJT+gamma**2*Im, R)
In = torch.eye(n)
Zp = In - torch.matmul(self.J.permute([1,0]), M1)
DeltaThetaPrimary = torch.matmul(self.J.permute([1,0]), M2)
DeltaThetaSecondary = torch.matmul(Zp, self.joint_angles.view(-1,1) * self.weights)
DeltaTheta = DeltaThetaPrimary + DeltaThetaSecondary
self.update_angles(DeltaTheta)
def mouse_move(self, event):
x, y = event.xdata, event.ydata
if(x and y):
self.x_targ = torch.tensor(x, dtype=torch.float, requires_grad=False)
self.y_targ = torch.tensor(y, dtype=torch.float, requires_grad=False)
def endeffector_forces(self):
with torch.no_grad():
self.J_inv = torch.linalg.pinv(self.J.T + torch.eye(self.J.shape[0])*0.00001)
# self.J_inv = torch.linalg.pinv(self.J.T)
# Matches the numbers from :
# https://studywolf.wordpress.com/2013/09/02/robot-control-jacobians-velocity-and-force/
end_effector_force = torch.tensor([[1.0],[1.0]])
joint_torques = torch.matmul(self.J.T, end_effector_force)
recalc_force = torch.matmul(self.J_inv, joint_torques)
print(joint_torques)
print(recalc_force)
demand_force = torch.tensor([[1.0],[0.0]])
demand_torques = torch.matmul(self.J.T, demand_force)
# demand_torques = torch.tensor([[3.0],[0.0]])
calc_forces = torch.matmul(self.J_inv, demand_torques)
# print(self.J)
# print(self.J_inv)
print('----')
print(demand_torques)
print(calc_forces)
self.EndEffector_F = calc_forces
pass
#%%
env = PlanarArm(2)
#%%
for i in range(10000):
ang = torch.tensor(i*torch.pi/180)
# env.control(-2.0, -1.5)
start = time.perf_counter()
env.control()
env.endeffector_forces()
end = time.perf_counter()
dt = end-start
# print(f"Control Time : {dt}")
# print(f"end effector pos : ({env.xs[-1]},{env.ys[-1]})")
env.plot()
| 7,612 | Python | 36.502463 | 167 | 0.565423 |
Tbarkin121/GuardDog/urdf/Biped_NoWheels/package.xml | <package format="2">
<name>Biped_NoWheels</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped_NoWheels</p>
<p>This package contains configuration data, 3D models and launch files
for Biped_NoWheels robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 637 | XML | 29.380951 | 75 | 0.711146 |
Tbarkin121/GuardDog/urdf/TorquePole/package.xml | <package format="2">
<name>TorquePole</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for TorquePole</p>
<p>This package contains configuration data, 3D models and launch files
for TorquePole robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 625 | XML | 28.809522 | 75 | 0.7104 |
Tbarkin121/GuardDog/urdf/Quad/package.xml | <package format="2">
<name>Quad</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Quad</p>
<p>This package contains configuration data, 3D models and launch files
for Quad robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 607 | XML | 27.95238 | 75 | 0.701812 |
Tbarkin121/GuardDog/urdf/Quad/config/joint_names_Quad.yaml | controller_joint_names: ['', 'FR_J1', 'FR_J2', 'FR_J3', 'FL_J1', 'FL_J2', 'FL_J3', 'BR_J1', 'BR_J2', 'BR_J3', 'BL_J1', 'BL_J2', 'BL_J3', ]
| 139 | YAML | 68.999966 | 138 | 0.489209 |
Tbarkin121/GuardDog/urdf/Biped_SphereFoot/package.xml | <package format="2">
<name>Biped_SphereFoot</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped_SphereFoot</p>
<p>This package contains configuration data, 3D models and launch files
for Biped_SphereFoot robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 643 | XML | 29.666665 | 75 | 0.713841 |
Tbarkin121/GuardDog/urdf/Biped_Longfoot/package.xml | <package format="2">
<name>Biped_Longfoot</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped_Longfoot</p>
<p>This package contains configuration data, 3D models and launch files
for Biped_Longfoot robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 637 | XML | 29.380951 | 75 | 0.711146 |
Tbarkin121/GuardDog/urdf/Biped_Longfoot/config/joint_names_Biped_Longfoot.yaml | controller_joint_names: ['', 'L_J1', 'L_J2', 'L_J3', 'R_J1', 'R_J2', 'R_J3', 'Wheel_X', 'Wheel_Y', ]
| 101 | YAML | 49.999975 | 100 | 0.49505 |
Tbarkin121/GuardDog/urdf/Biped/package.xml | <package format="2">
<name>Biped</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped</p>
<p>This package contains configuration data, 3D models and launch files
for Biped robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 610 | XML | 28.095237 | 75 | 0.703279 |
Tbarkin121/GuardDog/urdf/QuadCoordFix/package.xml | <package format="2">
<name>QuadCoordFix</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for QuadCoordFix</p>
<p>This package contains configuration data, 3D models and launch files
for QuadCoordFix robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 631 | XML | 29.095237 | 75 | 0.713154 |
Tbarkin121/GuardDog/urdf/QuadCoordFix/config/joint_names_QuadCoordFix.yaml | controller_joint_names: ['', 'FR_J1', 'FR_J2', 'FR_J3', 'FL_J1', 'FL_J2', 'FL_J3', 'BR_J1', 'BR_J2', 'BR_J3', 'BL_J1', 'BL_J2', 'BL_J3', ]
| 139 | YAML | 68.999966 | 138 | 0.489209 |
Tbarkin121/GuardDog/urdf/Jumpy/package.xml | <package format="2">
<name>Jumpy</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Jumpy</p>
<p>This package contains configuration data, 3D models and launch files
for Jumpy robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 610 | XML | 28.095237 | 75 | 0.703279 |
Tbarkin121/GuardDog/urdf/Quad_Foot/package.xml | <package format="2">
<name>Quad_Foot</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Quad_Foot</p>
<p>This package contains configuration data, 3D models and launch files
for Quad_Foot robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 622 | XML | 28.666665 | 75 | 0.70418 |
Tbarkin121/GuardDog/urdf/Quad_Foot/config/joint_names_Quad_Foot.yaml | controller_joint_names: ['', 'FR_J1', 'FR_J2', 'FR_J3', 'FL_J1', 'FL_J2', 'FL_J3', 'BR_J1', 'BR_J2', 'BR_J3', 'BL_J1', 'BL_J2', 'BL_J3', ]
| 139 | YAML | 68.999966 | 138 | 0.489209 |
Tbarkin121/GuardDog/urdf/Biped_10B/package.xml | <package format="2">
<name>Biped_10B</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped_10B</p>
<p>This package contains configuration data, 3D models and launch files
for Biped_10B robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 622 | XML | 28.666665 | 75 | 0.70418 |
Tbarkin121/GuardDog/urdf/Biped_10B/config/joint_names_Biped_10B.yaml | controller_joint_names: ['', 'L_J1', 'L_J2', 'L_J3', 'R_J1', 'R_J2', 'R_J3', 'Wheel_X', 'Wheel_Y', ]
| 101 | YAML | 49.999975 | 100 | 0.49505 |
Tbarkin121/GuardDog/urdf/Quad_LimitHip/package.xml | <package format="2">
<name>Quad_LimitHip</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Quad_LimitHip</p>
<p>This package contains configuration data, 3D models and launch files
for Quad_LimitHip robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 634 | XML | 29.238094 | 75 | 0.709779 |
Tbarkin121/GuardDog/urdf/Quad_LimitHip/config/joint_names_Quad_LimitHip.yaml | controller_joint_names: ['', 'FR_J1', 'FR_J2', 'FR_J3', 'FL_J1', 'FL_J2', 'FL_J3', 'BR_J1', 'BR_J2', 'BR_J3', 'BL_J1', 'BL_J2', 'BL_J3', ]
| 139 | YAML | 68.999966 | 138 | 0.489209 |
Tbarkin121/GuardDog/urdf/Biped_CenterHip/package.xml | <package format="2">
<name>Biped_CenterHip</name>
<version>1.0.0</version>
<description>
<p>URDF Description package for Biped_CenterHip</p>
<p>This package contains configuration data, 3D models and launch files
for Biped_CenterHip robot</p>
</description>
<author>TODO</author>
<maintainer email="[email protected]" />
<license>BSD</license>
<buildtool_depend>catkin</buildtool_depend>
<depend>roslaunch</depend>
<depend>robot_state_publisher</depend>
<depend>rviz</depend>
<depend>joint_state_publisher_gui</depend>
<depend>gazebo</depend>
<export>
<architecture_independent />
</export>
</package> | 640 | XML | 29.523808 | 75 | 0.7125 |
Tbarkin121/GuardDog/stm32/onnx_test.py | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 5 18:38:17 2024
@author: tylerbarkin
"""
import numpy as np
import onnx
import onnxruntime as ort
onnx_model = onnx.load("pendulum.onnx")
# Check that the model is well formed
onnx.checker.check_model(onnx_model)
ort_model = ort.InferenceSession("pendulum.onnx")
in_data = np.zeros((1,2))
in_data[0,0] = 0.52
in_data[0,1] = -0.32
outputs = ort_model.run(
None,
{"obs": in_data.astype(np.float32)},
)
print(outputs)
#Results from STM32 with 0.52 and -0.32 as inputs
# 1.80677, -0.67291, 0.82512
# Results from python with 0.52 and -0.32 as inputs
# 1.80461, -0.67291, 0.82535
# Minor differences due to quantization and compression? The more compression the worse the inference does
| 748 | Python | 20.399999 | 107 | 0.697861 |
Tbarkin121/GuardDog/stm32/stm32_net_test.py | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 4 12:52:12 2024
@author: tylerbarkin
"""
import serial
import struct
import time
import numpy as np
import platform
class MCU_Comms():
def __init__(self):
self.act_data = np.zeros(4)
self.out_data = np.zeros(4)
if platform.system() == 'Windows':
self.port = 'COM6'
else:
self.port = '/dev/ttyACM0'
print('Using Port : {}'.format(self.port))
self.open_port()
def open_port(self):
# Configure the serial connection
self.ser = serial.Serial(
port=self.port, # Serial port
baudrate=115200, # Baud rate, should match STM32 setting
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1 # Read timeout in seconds
)
def close_port(self):
if self.ser.is_open:
self.ser.close()
print("Serial port closed")
def read_data(self):
try:
# Read 16 bytes from the serial port (size of 4 floats)
data = self.ser.read(4 * 4)
# Check if we received 16 bytes
if len(data) == 16:
# Unpack the bytes to four floats
float_values = struct.unpack('4f', data)
self.in_data = np.array(float_values)
print(f"Received floats: {float_values}")
else:
print("Incomplete data received")
except KeyboardInterrupt:
print("Exiting...")
def write_data(self):
# Pack the floats into bytes
data_to_send = struct.pack('4f', *self.out_data)
try:
# Send the packed bytes over the serial connection
self.ser.write(data_to_send)
print("Data sent")
except Exception as e:
print(f"Error: {e}")
def __del__(self):
# Destructor: close the serial port
self.close_port()
comm_obj = MCU_Comms()
comm_obj.out_data = np.array([0.05, -0.03, 0.0, 0.0])
for _ in range(1):
start_time = time.perf_counter()
comm_obj.write_data()
comm_obj.read_data()
elapsed_time = time.perf_counter() - start_time
print('Total Time = {}'.format(elapsed_time))
comm_obj.close_port()
| 2,589 | Python | 26.263158 | 100 | 0.495172 |
Tbarkin121/GuardDog/stm32/stm32_anymal_net_test.py | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 4 12:52:12 2024
@author: tylerbarkin
"""
import serial
import struct
import time
import numpy as np
import platform
class MCU_Comms():
def __init__(self):
self.act_data = np.zeros(12)
self.obs_data = np.zeros(48)
if platform.system() == 'Windows':
self.port = 'COM6'
else:
self.port = '/dev/ttyACM1'
print('Using Port : {}'.format(self.port))
self.open_port()
def open_port(self):
# Configure the serial connection
self.ser = serial.Serial(
port=self.port, # Serial port
baudrate=460800, # Baud rate, should match STM32 setting
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1 # Read timeout in seconds
)
def close_port(self):
if self.ser.is_open:
self.ser.close()
print("Serial port closed")
def read_data(self):
try:
# Read 16 bytes from the serial port (size of 4 floats)
data = self.ser.read(12 * 4)
# Check if we received 48 bytes
if len(data) == 48:
# Unpack the bytes to four floats
float_values = struct.unpack('12f', data)
self.act_data = np.array(float_values)
print(f"Received floats: {float_values}")
else:
print("Incomplete data received")
except KeyboardInterrupt:
print("Exiting...")
def write_data(self):
# Pack the floats into bytes
data_to_send = struct.pack('48f', *self.obs_data)
try:
# Send the packed bytes over the serial connection
self.ser.write(data_to_send)
print("Data sent")
except Exception as e:
print(f"Error: {e}")
def __del__(self):
# Destructor: close the serial port
self.close_port()
comm_obj = MCU_Comms()
comm_obj.obs_data = np.zeros((48))
for i in range(48):
comm_obj.obs_data[i] = i
for _ in range(1):
start_time = time.perf_counter()
comm_obj.write_data()
comm_obj.read_data()
elapsed_time = time.perf_counter() - start_time
print('Total Time = {} : Freq {}'.format(elapsed_time, 1/elapsed_time))
comm_obj.close_port()
| 2,659 | Python | 26.42268 | 100 | 0.498308 |
Tbarkin121/GuardDog/stm32/serial_comms.py | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 4 12:52:12 2024
@author: tylerbarkin
"""
import serial
import struct
import time
import numpy as np
class MCU_Comms():
def __init__(self):
self.in_data = np.zeros(5)
self.out_data = np.zeros(4)
self.open_port()
def open_port(self):
# Configure the serial connection
self.ser = serial.Serial(
port='COM5', # Serial port
baudrate=1843200, # Baud rate, should match STM32 setting
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS,
timeout=1 # Read timeout in seconds
)
def close_port(self):
if self.ser.is_open:
self.ser.close()
print("Serial port closed")
def read_data(self):
try:
# Read 16 bytes from the serial port (size of 4 floats)
data = self.ser.read(5 * 4)
# Check if we received 20 bytes
if len(data) == 20:
# Unpack the bytes to four floats
float_values = struct.unpack('5f', data)
self.in_data = np.array(float_values)
print(f"Received floats: {float_values}")
else:
print("Incomplete data received")
except KeyboardInterrupt:
print("Exiting...")
def write_data(self):
# Pack the floats into bytes
data_to_send = struct.pack('4f', *self.out_data)
try:
# Send the packed bytes over the serial connection
self.ser.write(data_to_send)
print("Data sent")
time.sleep(0.5)
except Exception as e:
print(f"Error: {e}")
def __del__(self):
# Destructor: close the serial port
self.close_port()
comm_obj = MCU_Comms()
comm_obj.read_data()
comm_obj.read_data()
comm_obj.read_data()
comm_obj.read_data()
comm_obj.read_data()
comm_obj.close_port()
| 2,250 | Python | 25.482353 | 101 | 0.489778 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/system_stm32g4xx.c | /**
******************************************************************************
* @file system_stm32g4xx.c
* @author MCD Application Team
* @brief CMSIS Cortex-M4 Device Peripheral Access Layer System Source File
*
* This file provides two functions and one global variable to be called from
* user application:
* - SystemInit(): This function is called at startup just after reset and
* before branch to main program. This call is made inside
* the "startup_stm32g4xx.s" file.
*
* - SystemCoreClock variable: Contains the core clock (HCLK), it can be used
* by the user application to setup the SysTick
* timer or configure other parameters.
*
* - SystemCoreClockUpdate(): Updates the variable SystemCoreClock and must
* be called whenever the core clock is changed
* during program execution.
*
* After each device reset the HSI (16 MHz) is used as system clock source.
* Then SystemInit() function is called, in "startup_stm32g4xx.s" file, to
* configure the system clock before to branch to main program.
*
* This file configures the system clock as follows:
*=============================================================================
*-----------------------------------------------------------------------------
* System Clock source | HSI
*-----------------------------------------------------------------------------
* SYSCLK(Hz) | 16000000
*-----------------------------------------------------------------------------
* HCLK(Hz) | 16000000
*-----------------------------------------------------------------------------
* AHB Prescaler | 1
*-----------------------------------------------------------------------------
* APB1 Prescaler | 1
*-----------------------------------------------------------------------------
* APB2 Prescaler | 1
*-----------------------------------------------------------------------------
* PLL_M | 1
*-----------------------------------------------------------------------------
* PLL_N | 16
*-----------------------------------------------------------------------------
* PLL_P | 7
*-----------------------------------------------------------------------------
* PLL_Q | 2
*-----------------------------------------------------------------------------
* PLL_R | 2
*-----------------------------------------------------------------------------
* Require 48MHz for RNG | Disabled
*-----------------------------------------------------------------------------
*=============================================================================
******************************************************************************
* @attention
*
* Copyright (c) 2019 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/** @addtogroup CMSIS
* @{
*/
/** @addtogroup stm32g4xx_system
* @{
*/
/** @addtogroup STM32G4xx_System_Private_Includes
* @{
*/
#include "stm32g4xx.h"
#if !defined (HSE_VALUE)
#define HSE_VALUE 24000000U /*!< Value of the External oscillator in Hz */
#endif /* HSE_VALUE */
#if !defined (HSI_VALUE)
#define HSI_VALUE 16000000U /*!< Value of the Internal oscillator in Hz*/
#endif /* HSI_VALUE */
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_TypesDefinitions
* @{
*/
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_Defines
* @{
*/
/************************* Miscellaneous Configuration ************************/
/* Note: Following vector table addresses must be defined in line with linker
configuration. */
/*!< Uncomment the following line if you need to relocate the vector table
anywhere in Flash or Sram, else the vector table is kept at the automatic
remap of boot address selected */
/* #define USER_VECT_TAB_ADDRESS */
#if defined(USER_VECT_TAB_ADDRESS)
/*!< Uncomment the following line if you need to relocate your vector Table
in Sram else user remap will be done in Flash. */
/* #define VECT_TAB_SRAM */
#if defined(VECT_TAB_SRAM)
#define VECT_TAB_BASE_ADDRESS SRAM_BASE /*!< Vector Table base address field.
This value must be a multiple of 0x200. */
#define VECT_TAB_OFFSET 0x00000000U /*!< Vector Table base offset field.
This value must be a multiple of 0x200. */
#else
#define VECT_TAB_BASE_ADDRESS FLASH_BASE /*!< Vector Table base address field.
This value must be a multiple of 0x200. */
#define VECT_TAB_OFFSET 0x00000000U /*!< Vector Table base offset field.
This value must be a multiple of 0x200. */
#endif /* VECT_TAB_SRAM */
#endif /* USER_VECT_TAB_ADDRESS */
/******************************************************************************/
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_Macros
* @{
*/
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_Variables
* @{
*/
/* The SystemCoreClock variable is updated in three ways:
1) by calling CMSIS function SystemCoreClockUpdate()
2) by calling HAL API function HAL_RCC_GetHCLKFreq()
3) each time HAL_RCC_ClockConfig() is called to configure the system clock frequency
Note: If you use this function to configure the system clock; then there
is no need to call the 2 first functions listed above, since SystemCoreClock
variable is updated automatically.
*/
uint32_t SystemCoreClock = HSI_VALUE;
const uint8_t AHBPrescTable[16] = {0U, 0U, 0U, 0U, 0U, 0U, 0U, 0U, 1U, 2U, 3U, 4U, 6U, 7U, 8U, 9U};
const uint8_t APBPrescTable[8] = {0U, 0U, 0U, 0U, 1U, 2U, 3U, 4U};
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_FunctionPrototypes
* @{
*/
/**
* @}
*/
/** @addtogroup STM32G4xx_System_Private_Functions
* @{
*/
/**
* @brief Setup the microcontroller system.
* @param None
* @retval None
*/
void SystemInit(void)
{
/* FPU settings ------------------------------------------------------------*/
#if (__FPU_PRESENT == 1) && (__FPU_USED == 1)
SCB->CPACR |= ((3UL << (10*2))|(3UL << (11*2))); /* set CP10 and CP11 Full Access */
#endif
/* Configure the Vector Table location add offset address ------------------*/
#if defined(USER_VECT_TAB_ADDRESS)
SCB->VTOR = VECT_TAB_BASE_ADDRESS | VECT_TAB_OFFSET; /* Vector Table Relocation in Internal SRAM */
#endif /* USER_VECT_TAB_ADDRESS */
}
/**
* @brief Update SystemCoreClock variable according to Clock Register Values.
* The SystemCoreClock variable contains the core clock (HCLK), it can
* be used by the user application to setup the SysTick timer or configure
* other parameters.
*
* @note Each time the core clock (HCLK) changes, this function must be called
* to update SystemCoreClock variable value. Otherwise, any configuration
* based on this variable will be incorrect.
*
* @note - The system frequency computed by this function is not the real
* frequency in the chip. It is calculated based on the predefined
* constant and the selected clock source:
*
* - If SYSCLK source is HSI, SystemCoreClock will contain the HSI_VALUE(**)
*
* - If SYSCLK source is HSE, SystemCoreClock will contain the HSE_VALUE(***)
*
* - If SYSCLK source is PLL, SystemCoreClock will contain the HSE_VALUE(***)
* or HSI_VALUE(*) multiplied/divided by the PLL factors.
*
* (**) HSI_VALUE is a constant defined in stm32g4xx_hal.h file (default value
* 16 MHz) but the real value may vary depending on the variations
* in voltage and temperature.
*
* (***) HSE_VALUE is a constant defined in stm32g4xx_hal.h file (default value
* 24 MHz), user has to ensure that HSE_VALUE is same as the real
* frequency of the crystal used. Otherwise, this function may
* have wrong result.
*
* - The result of this function could be not correct when using fractional
* value for HSE crystal.
*
* @param None
* @retval None
*/
void SystemCoreClockUpdate(void)
{
uint32_t tmp, pllvco, pllr, pllsource, pllm;
/* Get SYSCLK source -------------------------------------------------------*/
switch (RCC->CFGR & RCC_CFGR_SWS)
{
case 0x04: /* HSI used as system clock source */
SystemCoreClock = HSI_VALUE;
break;
case 0x08: /* HSE used as system clock source */
SystemCoreClock = HSE_VALUE;
break;
case 0x0C: /* PLL used as system clock source */
/* PLL_VCO = (HSE_VALUE or HSI_VALUE / PLLM) * PLLN
SYSCLK = PLL_VCO / PLLR
*/
pllsource = (RCC->PLLCFGR & RCC_PLLCFGR_PLLSRC);
pllm = ((RCC->PLLCFGR & RCC_PLLCFGR_PLLM) >> 4) + 1U ;
if (pllsource == 0x02UL) /* HSI used as PLL clock source */
{
pllvco = (HSI_VALUE / pllm);
}
else /* HSE used as PLL clock source */
{
pllvco = (HSE_VALUE / pllm);
}
pllvco = pllvco * ((RCC->PLLCFGR & RCC_PLLCFGR_PLLN) >> 8);
pllr = (((RCC->PLLCFGR & RCC_PLLCFGR_PLLR) >> 25) + 1U) * 2U;
SystemCoreClock = pllvco/pllr;
break;
default:
break;
}
/* Compute HCLK clock frequency --------------------------------------------*/
/* Get HCLK prescaler */
tmp = AHBPrescTable[((RCC->CFGR & RCC_CFGR_HPRE) >> 4)];
/* HCLK clock frequency */
SystemCoreClock >>= tmp;
}
/**
* @}
*/
/**
* @}
*/
/**
* @}
*/
| 10,571 | C | 35.965035 | 101 | 0.4781 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/stm32g4xx_hal_msp.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file stm32g4xx_hal_msp.c
* @brief This file provides code for the MSP Initialization
* and de-Initialization codes.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "main.h"
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
/* Private typedef -----------------------------------------------------------*/
/* USER CODE BEGIN TD */
/* USER CODE END TD */
/* Private define ------------------------------------------------------------*/
/* USER CODE BEGIN Define */
/* USER CODE END Define */
/* Private macro -------------------------------------------------------------*/
/* USER CODE BEGIN Macro */
/* USER CODE END Macro */
/* Private variables ---------------------------------------------------------*/
/* USER CODE BEGIN PV */
/* USER CODE END PV */
/* Private function prototypes -----------------------------------------------*/
/* USER CODE BEGIN PFP */
/* USER CODE END PFP */
/* External functions --------------------------------------------------------*/
/* USER CODE BEGIN ExternalFunctions */
/* USER CODE END ExternalFunctions */
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
/**
* Initializes the Global MSP.
*/
void HAL_MspInit(void)
{
/* USER CODE BEGIN MspInit 0 */
/* USER CODE END MspInit 0 */
__HAL_RCC_SYSCFG_CLK_ENABLE();
__HAL_RCC_PWR_CLK_ENABLE();
/* System interrupt init*/
/** Disable the internal Pull-Up in Dead Battery pins of UCPD peripheral
*/
HAL_PWREx_DisableUCPDDeadBattery();
/* USER CODE BEGIN MspInit 1 */
/* USER CODE END MspInit 1 */
}
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
| 2,268 | C | 25.080459 | 80 | 0.454145 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/sysmem.c | /**
******************************************************************************
* @file sysmem.c
* @author Generated by STM32CubeIDE
* @brief STM32CubeIDE System Memory calls file
*
* For more information about which C functions
* need which of these lowlevel functions
* please consult the newlib libc manual
******************************************************************************
* @attention
*
* Copyright (c) 2023 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* Includes */
#include <errno.h>
#include <stdint.h>
/**
* Pointer to the current high watermark of the heap usage
*/
static uint8_t *__sbrk_heap_end = NULL;
/**
* @brief _sbrk() allocates memory to the newlib heap and is used by malloc
* and others from the C library
*
* @verbatim
* ############################################################################
* # .data # .bss # newlib heap # MSP stack #
* # # # # Reserved by _Min_Stack_Size #
* ############################################################################
* ^-- RAM start ^-- _end _estack, RAM end --^
* @endverbatim
*
* This implementation starts allocating at the '_end' linker symbol
* The '_Min_Stack_Size' linker symbol reserves a memory for the MSP stack
* The implementation considers '_estack' linker symbol to be RAM end
* NOTE: If the MSP stack, at any point during execution, grows larger than the
* reserved size, please increase the '_Min_Stack_Size'.
*
* @param incr Memory size
* @return Pointer to allocated memory
*/
void *_sbrk(ptrdiff_t incr)
{
extern uint8_t _end; /* Symbol defined in the linker script */
extern uint8_t _estack; /* Symbol defined in the linker script */
extern uint32_t _Min_Stack_Size; /* Symbol defined in the linker script */
const uint32_t stack_limit = (uint32_t)&_estack - (uint32_t)&_Min_Stack_Size;
const uint8_t *max_heap = (uint8_t *)stack_limit;
uint8_t *prev_heap_end;
/* Initialize heap end at first call */
if (NULL == __sbrk_heap_end)
{
__sbrk_heap_end = &_end;
}
/* Protect heap from growing into the reserved MSP stack */
if (__sbrk_heap_end + incr > max_heap)
{
errno = ENOMEM;
return (void *)-1;
}
prev_heap_end = __sbrk_heap_end;
__sbrk_heap_end += incr;
return (void *)prev_heap_end;
}
| 2,726 | C | 33.0875 | 79 | 0.537784 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/tim.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file tim.c
* @brief This file provides code for the configuration
* of the TIM instances.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "tim.h"
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
TIM_HandleTypeDef htim2;
/* TIM2 init function */
void MX_TIM2_Init(void)
{
/* USER CODE BEGIN TIM2_Init 0 */
/* USER CODE END TIM2_Init 0 */
TIM_ClockConfigTypeDef sClockSourceConfig = {0};
TIM_MasterConfigTypeDef sMasterConfig = {0};
/* USER CODE BEGIN TIM2_Init 1 */
/* USER CODE END TIM2_Init 1 */
htim2.Instance = TIM2;
htim2.Init.Prescaler = 170;
htim2.Init.CounterMode = TIM_COUNTERMODE_UP;
htim2.Init.Period = 4.294967295E9;
htim2.Init.ClockDivision = TIM_CLOCKDIVISION_DIV1;
htim2.Init.AutoReloadPreload = TIM_AUTORELOAD_PRELOAD_DISABLE;
if (HAL_TIM_Base_Init(&htim2) != HAL_OK)
{
Error_Handler();
}
sClockSourceConfig.ClockSource = TIM_CLOCKSOURCE_INTERNAL;
if (HAL_TIM_ConfigClockSource(&htim2, &sClockSourceConfig) != HAL_OK)
{
Error_Handler();
}
sMasterConfig.MasterOutputTrigger = TIM_TRGO_RESET;
sMasterConfig.MasterSlaveMode = TIM_MASTERSLAVEMODE_DISABLE;
if (HAL_TIMEx_MasterConfigSynchronization(&htim2, &sMasterConfig) != HAL_OK)
{
Error_Handler();
}
/* USER CODE BEGIN TIM2_Init 2 */
/* USER CODE END TIM2_Init 2 */
}
void HAL_TIM_Base_MspInit(TIM_HandleTypeDef* tim_baseHandle)
{
if(tim_baseHandle->Instance==TIM2)
{
/* USER CODE BEGIN TIM2_MspInit 0 */
/* USER CODE END TIM2_MspInit 0 */
/* TIM2 clock enable */
__HAL_RCC_TIM2_CLK_ENABLE();
/* USER CODE BEGIN TIM2_MspInit 1 */
/* USER CODE END TIM2_MspInit 1 */
}
}
void HAL_TIM_Base_MspDeInit(TIM_HandleTypeDef* tim_baseHandle)
{
if(tim_baseHandle->Instance==TIM2)
{
/* USER CODE BEGIN TIM2_MspDeInit 0 */
/* USER CODE END TIM2_MspDeInit 0 */
/* Peripheral clock disable */
__HAL_RCC_TIM2_CLK_DISABLE();
/* USER CODE BEGIN TIM2_MspDeInit 1 */
/* USER CODE END TIM2_MspDeInit 1 */
}
}
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
| 2,698 | C | 24.704762 | 80 | 0.59192 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/stm32g4xx_it.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file stm32g4xx_it.c
* @brief Interrupt Service Routines.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "main.h"
#include "stm32g4xx_it.h"
/* Private includes ----------------------------------------------------------*/
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
/* Private typedef -----------------------------------------------------------*/
/* USER CODE BEGIN TD */
/* USER CODE END TD */
/* Private define ------------------------------------------------------------*/
/* USER CODE BEGIN PD */
/* USER CODE END PD */
/* Private macro -------------------------------------------------------------*/
/* USER CODE BEGIN PM */
/* USER CODE END PM */
/* Private variables ---------------------------------------------------------*/
/* USER CODE BEGIN PV */
/* USER CODE END PV */
/* Private function prototypes -----------------------------------------------*/
/* USER CODE BEGIN PFP */
/* USER CODE END PFP */
/* Private user code ---------------------------------------------------------*/
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
/* External variables --------------------------------------------------------*/
extern UART_HandleTypeDef huart2;
/* USER CODE BEGIN EV */
/* USER CODE END EV */
/******************************************************************************/
/* Cortex-M4 Processor Interruption and Exception Handlers */
/******************************************************************************/
/**
* @brief This function handles Non maskable interrupt.
*/
void NMI_Handler(void)
{
/* USER CODE BEGIN NonMaskableInt_IRQn 0 */
/* USER CODE END NonMaskableInt_IRQn 0 */
/* USER CODE BEGIN NonMaskableInt_IRQn 1 */
while (1)
{
}
/* USER CODE END NonMaskableInt_IRQn 1 */
}
/**
* @brief This function handles Hard fault interrupt.
*/
void HardFault_Handler(void)
{
/* USER CODE BEGIN HardFault_IRQn 0 */
/* USER CODE END HardFault_IRQn 0 */
while (1)
{
/* USER CODE BEGIN W1_HardFault_IRQn 0 */
/* USER CODE END W1_HardFault_IRQn 0 */
}
}
/**
* @brief This function handles Memory management fault.
*/
void MemManage_Handler(void)
{
/* USER CODE BEGIN MemoryManagement_IRQn 0 */
/* USER CODE END MemoryManagement_IRQn 0 */
while (1)
{
/* USER CODE BEGIN W1_MemoryManagement_IRQn 0 */
/* USER CODE END W1_MemoryManagement_IRQn 0 */
}
}
/**
* @brief This function handles Prefetch fault, memory access fault.
*/
void BusFault_Handler(void)
{
/* USER CODE BEGIN BusFault_IRQn 0 */
/* USER CODE END BusFault_IRQn 0 */
while (1)
{
/* USER CODE BEGIN W1_BusFault_IRQn 0 */
/* USER CODE END W1_BusFault_IRQn 0 */
}
}
/**
* @brief This function handles Undefined instruction or illegal state.
*/
void UsageFault_Handler(void)
{
/* USER CODE BEGIN UsageFault_IRQn 0 */
/* USER CODE END UsageFault_IRQn 0 */
while (1)
{
/* USER CODE BEGIN W1_UsageFault_IRQn 0 */
/* USER CODE END W1_UsageFault_IRQn 0 */
}
}
/**
* @brief This function handles System service call via SWI instruction.
*/
void SVC_Handler(void)
{
/* USER CODE BEGIN SVCall_IRQn 0 */
/* USER CODE END SVCall_IRQn 0 */
/* USER CODE BEGIN SVCall_IRQn 1 */
/* USER CODE END SVCall_IRQn 1 */
}
/**
* @brief This function handles Debug monitor.
*/
void DebugMon_Handler(void)
{
/* USER CODE BEGIN DebugMonitor_IRQn 0 */
/* USER CODE END DebugMonitor_IRQn 0 */
/* USER CODE BEGIN DebugMonitor_IRQn 1 */
/* USER CODE END DebugMonitor_IRQn 1 */
}
/**
* @brief This function handles Pendable request for system service.
*/
void PendSV_Handler(void)
{
/* USER CODE BEGIN PendSV_IRQn 0 */
/* USER CODE END PendSV_IRQn 0 */
/* USER CODE BEGIN PendSV_IRQn 1 */
/* USER CODE END PendSV_IRQn 1 */
}
/**
* @brief This function handles System tick timer.
*/
void SysTick_Handler(void)
{
/* USER CODE BEGIN SysTick_IRQn 0 */
/* USER CODE END SysTick_IRQn 0 */
HAL_IncTick();
/* USER CODE BEGIN SysTick_IRQn 1 */
/* USER CODE END SysTick_IRQn 1 */
}
/******************************************************************************/
/* STM32G4xx Peripheral Interrupt Handlers */
/* Add here the Interrupt Handlers for the used peripherals. */
/* For the available peripheral interrupt handler names, */
/* please refer to the startup file (startup_stm32g4xx.s). */
/******************************************************************************/
/**
* @brief This function handles USART2 global interrupt / USART2 wake-up interrupt through EXTI line 26.
*/
void USART2_IRQHandler(void)
{
/* USER CODE BEGIN USART2_IRQn 0 */
/* USER CODE END USART2_IRQn 0 */
HAL_UART_IRQHandler(&huart2);
/* USER CODE BEGIN USART2_IRQn 1 */
/* USER CODE END USART2_IRQn 1 */
}
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
| 5,569 | C | 24.550459 | 105 | 0.510864 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/usart.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file usart.c
* @brief This file provides code for the configuration
* of the USART instances.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "usart.h"
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
UART_HandleTypeDef huart2;
/* USART2 init function */
void MX_USART2_UART_Init(void)
{
/* USER CODE BEGIN USART2_Init 0 */
/* USER CODE END USART2_Init 0 */
/* USER CODE BEGIN USART2_Init 1 */
/* USER CODE END USART2_Init 1 */
huart2.Instance = USART2;
huart2.Init.BaudRate = 115200;
huart2.Init.WordLength = UART_WORDLENGTH_8B;
huart2.Init.StopBits = UART_STOPBITS_1;
huart2.Init.Parity = UART_PARITY_NONE;
huart2.Init.Mode = UART_MODE_TX_RX;
huart2.Init.HwFlowCtl = UART_HWCONTROL_NONE;
huart2.Init.OverSampling = UART_OVERSAMPLING_16;
huart2.Init.OneBitSampling = UART_ONE_BIT_SAMPLE_DISABLE;
huart2.Init.ClockPrescaler = UART_PRESCALER_DIV1;
huart2.AdvancedInit.AdvFeatureInit = UART_ADVFEATURE_NO_INIT;
if (HAL_UART_Init(&huart2) != HAL_OK)
{
Error_Handler();
}
if (HAL_UARTEx_SetTxFifoThreshold(&huart2, UART_TXFIFO_THRESHOLD_1_8) != HAL_OK)
{
Error_Handler();
}
if (HAL_UARTEx_SetRxFifoThreshold(&huart2, UART_RXFIFO_THRESHOLD_1_8) != HAL_OK)
{
Error_Handler();
}
if (HAL_UARTEx_DisableFifoMode(&huart2) != HAL_OK)
{
Error_Handler();
}
/* USER CODE BEGIN USART2_Init 2 */
/* USER CODE END USART2_Init 2 */
}
void HAL_UART_MspInit(UART_HandleTypeDef* uartHandle)
{
GPIO_InitTypeDef GPIO_InitStruct = {0};
RCC_PeriphCLKInitTypeDef PeriphClkInit = {0};
if(uartHandle->Instance==USART2)
{
/* USER CODE BEGIN USART2_MspInit 0 */
/* USER CODE END USART2_MspInit 0 */
/** Initializes the peripherals clocks
*/
PeriphClkInit.PeriphClockSelection = RCC_PERIPHCLK_USART2;
PeriphClkInit.Usart2ClockSelection = RCC_USART2CLKSOURCE_PCLK1;
if (HAL_RCCEx_PeriphCLKConfig(&PeriphClkInit) != HAL_OK)
{
Error_Handler();
}
/* USART2 clock enable */
__HAL_RCC_USART2_CLK_ENABLE();
__HAL_RCC_GPIOA_CLK_ENABLE();
/**USART2 GPIO Configuration
PA2 ------> USART2_TX
PA3 ------> USART2_RX
*/
GPIO_InitStruct.Pin = USART2_TX_Pin|USART2_RX_Pin;
GPIO_InitStruct.Mode = GPIO_MODE_AF_PP;
GPIO_InitStruct.Pull = GPIO_NOPULL;
GPIO_InitStruct.Speed = GPIO_SPEED_FREQ_LOW;
GPIO_InitStruct.Alternate = GPIO_AF7_USART2;
HAL_GPIO_Init(GPIOA, &GPIO_InitStruct);
/* USART2 interrupt Init */
HAL_NVIC_SetPriority(USART2_IRQn, 0, 0);
HAL_NVIC_EnableIRQ(USART2_IRQn);
/* USER CODE BEGIN USART2_MspInit 1 */
/* USER CODE END USART2_MspInit 1 */
}
}
void HAL_UART_MspDeInit(UART_HandleTypeDef* uartHandle)
{
if(uartHandle->Instance==USART2)
{
/* USER CODE BEGIN USART2_MspDeInit 0 */
/* USER CODE END USART2_MspDeInit 0 */
/* Peripheral clock disable */
__HAL_RCC_USART2_CLK_DISABLE();
/**USART2 GPIO Configuration
PA2 ------> USART2_TX
PA3 ------> USART2_RX
*/
HAL_GPIO_DeInit(GPIOA, USART2_TX_Pin|USART2_RX_Pin);
/* USART2 interrupt Deinit */
HAL_NVIC_DisableIRQ(USART2_IRQn);
/* USER CODE BEGIN USART2_MspDeInit 1 */
/* USER CODE END USART2_MspDeInit 1 */
}
}
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
| 3,922 | C | 25.869863 | 82 | 0.608108 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/gpio.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file gpio.c
* @brief This file provides code for the configuration
* of all used GPIO pins.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "gpio.h"
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
/*----------------------------------------------------------------------------*/
/* Configure GPIO */
/*----------------------------------------------------------------------------*/
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
/** Configure pins as
* Analog
* Input
* Output
* EVENT_OUT
* EXTI
*/
void MX_GPIO_Init(void)
{
GPIO_InitTypeDef GPIO_InitStruct = {0};
/* GPIO Ports Clock Enable */
__HAL_RCC_GPIOA_CLK_ENABLE();
__HAL_RCC_GPIOB_CLK_ENABLE();
/*Configure GPIO pin Output Level */
HAL_GPIO_WritePin(LD2_GPIO_Port, LD2_Pin, GPIO_PIN_RESET);
/*Configure GPIO pin : PtPin */
GPIO_InitStruct.Pin = LD2_Pin;
GPIO_InitStruct.Mode = GPIO_MODE_OUTPUT_PP;
GPIO_InitStruct.Pull = GPIO_NOPULL;
GPIO_InitStruct.Speed = GPIO_SPEED_FREQ_LOW;
HAL_GPIO_Init(LD2_GPIO_Port, &GPIO_InitStruct);
}
/* USER CODE BEGIN 2 */
/* USER CODE END 2 */
| 1,840 | C | 26.893939 | 80 | 0.451087 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/main.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file : main.c
* @brief : Main program body
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "main.h"
#include "crc.h"
#include "tim.h"
#include "usart.h"
#include "gpio.h"
#include "app_x-cube-ai.h"
/* Private includes ----------------------------------------------------------*/
/* USER CODE BEGIN Includes */
#include <stdint.h>
#include "network.h"
/* USER CODE END Includes */
/* Private typedef -----------------------------------------------------------*/
/* USER CODE BEGIN PTD */
/* USER CODE END PTD */
/* Private define ------------------------------------------------------------*/
/* USER CODE BEGIN PD */
/* USER CODE END PD */
/* Private macro -------------------------------------------------------------*/
/* USER CODE BEGIN PM */
/* USER CODE END PM */
/* Private variables ---------------------------------------------------------*/
/* USER CODE BEGIN PV */
union floatUnion {
float floatValue[4];
uint8_t bytes[4 * sizeof(float)];
};
union floatUnion data;
ai_float in_data1[AI_NETWORK_IN_1_SIZE];
ai_float out_data1[AI_NETWORK_OUT_1_SIZE];
ai_float out_data2[AI_NETWORK_OUT_2_SIZE];
ai_float out_data3[AI_NETWORK_OUT_3_SIZE];
uint8_t data_flag;
/* USER CODE END PV */
/* Private function prototypes -----------------------------------------------*/
void SystemClock_Config(void);
/* USER CODE BEGIN PFP */
/* USER CODE END PFP */
/* Private user code ---------------------------------------------------------*/
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
/**
* @brief The application entry point.
* @retval int
*/
int main(void)
{
/* USER CODE BEGIN 1 */
data_flag=0;
/* USER CODE END 1 */
/* MCU Configuration--------------------------------------------------------*/
/* Reset of all peripherals, Initializes the Flash interface and the Systick. */
HAL_Init();
/* USER CODE BEGIN Init */
/* USER CODE END Init */
/* Configure the system clock */
SystemClock_Config();
/* USER CODE BEGIN SysInit */
/* USER CODE END SysInit */
/* Initialize all configured peripherals */
MX_GPIO_Init();
MX_USART2_UART_Init();
MX_CRC_Init();
MX_TIM2_Init();
MX_X_CUBE_AI_Init();
/* USER CODE BEGIN 2 */
HAL_UART_Receive_IT(&huart2, data.bytes, sizeof(data.bytes));
/* USER CODE END 2 */
/* Infinite loop */
/* USER CODE BEGIN WHILE */
while (1)
{
// HAL_UART_Transmit(&huart2, data.bytes, sizeof(data.bytes), 100);
// Read 16 bytes into data.bytes
// HAL_UART_Receive(&huart2, data.bytes, sizeof(data.bytes), 1000);
if(data_flag)
{
TIM2->CNT = 0;
HAL_TIM_Base_Start(&htim2);
in_data1[0] = data.floatValue[0]; // Sin Encoding
in_data1[1] = data.floatValue[1]; // Cosine Encoding
in_data1[2] = data.floatValue[2]; // Velocity
MX_X_CUBE_AI_Process();
HAL_TIM_Base_Stop(&htim2);
data.floatValue[0] = out_data1[0]; // Value
data.floatValue[1] = out_data2[0]; // Mu
data.floatValue[2] = out_data3[0]; // STD
data.floatValue[3] = (float)TIM2->CNT/(1000000); // Execution Time
data_flag = 0;
HAL_UART_Transmit(&huart2, data.bytes, sizeof(data.bytes), 100);
HAL_UART_Receive_IT(&huart2, data.bytes, sizeof(data.bytes));
}
/* USER CODE END WHILE */
/* USER CODE BEGIN 3 */
}
/* USER CODE END 3 */
}
/**
* @brief System Clock Configuration
* @retval None
*/
void SystemClock_Config(void)
{
RCC_OscInitTypeDef RCC_OscInitStruct = {0};
RCC_ClkInitTypeDef RCC_ClkInitStruct = {0};
/** Configure the main internal regulator output voltage
*/
HAL_PWREx_ControlVoltageScaling(PWR_REGULATOR_VOLTAGE_SCALE1_BOOST);
/** Initializes the RCC Oscillators according to the specified parameters
* in the RCC_OscInitTypeDef structure.
*/
RCC_OscInitStruct.OscillatorType = RCC_OSCILLATORTYPE_HSI;
RCC_OscInitStruct.HSIState = RCC_HSI_ON;
RCC_OscInitStruct.HSICalibrationValue = RCC_HSICALIBRATION_DEFAULT;
RCC_OscInitStruct.PLL.PLLState = RCC_PLL_ON;
RCC_OscInitStruct.PLL.PLLSource = RCC_PLLSOURCE_HSI;
RCC_OscInitStruct.PLL.PLLM = RCC_PLLM_DIV4;
RCC_OscInitStruct.PLL.PLLN = 85;
RCC_OscInitStruct.PLL.PLLP = RCC_PLLP_DIV2;
RCC_OscInitStruct.PLL.PLLQ = RCC_PLLQ_DIV2;
RCC_OscInitStruct.PLL.PLLR = RCC_PLLR_DIV2;
if (HAL_RCC_OscConfig(&RCC_OscInitStruct) != HAL_OK)
{
Error_Handler();
}
/** Initializes the CPU, AHB and APB buses clocks
*/
RCC_ClkInitStruct.ClockType = RCC_CLOCKTYPE_HCLK|RCC_CLOCKTYPE_SYSCLK
|RCC_CLOCKTYPE_PCLK1|RCC_CLOCKTYPE_PCLK2;
RCC_ClkInitStruct.SYSCLKSource = RCC_SYSCLKSOURCE_PLLCLK;
RCC_ClkInitStruct.AHBCLKDivider = RCC_SYSCLK_DIV1;
RCC_ClkInitStruct.APB1CLKDivider = RCC_HCLK_DIV1;
RCC_ClkInitStruct.APB2CLKDivider = RCC_HCLK_DIV1;
if (HAL_RCC_ClockConfig(&RCC_ClkInitStruct, FLASH_LATENCY_4) != HAL_OK)
{
Error_Handler();
}
}
/* USER CODE BEGIN 4 */
void HAL_UART_RxCpltCallback(UART_HandleTypeDef *huart)
{
// We will set a data flag here and execute in the main loop
data_flag = 1;
}
void HAL_UART_TxCpltCallback(UART_HandleTypeDef *huart)
{
}
/* USER CODE END 4 */
/**
* @brief This function is executed in case of error occurrence.
* @retval None
*/
void Error_Handler(void)
{
/* USER CODE BEGIN Error_Handler_Debug */
/* User can add his own implementation to report the HAL error return state */
__disable_irq();
while (1)
{
}
/* USER CODE END Error_Handler_Debug */
}
#ifdef USE_FULL_ASSERT
/**
* @brief Reports the name of the source file and the source line number
* where the assert_param error has occurred.
* @param file: pointer to the source file name
* @param line: assert_param error line source number
* @retval None
*/
void assert_failed(uint8_t *file, uint32_t line)
{
/* USER CODE BEGIN 6 */
/* User can add his own implementation to report the file name and line number,
ex: printf("Wrong parameters value: file %s on line %d\r\n", file, line) */
/* USER CODE END 6 */
}
#endif /* USE_FULL_ASSERT */
| 6,694 | C | 27.248945 | 82 | 0.582761 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Src/crc.c | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file crc.c
* @brief This file provides code for the configuration
* of the CRC instances.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Includes ------------------------------------------------------------------*/
#include "crc.h"
/* USER CODE BEGIN 0 */
/* USER CODE END 0 */
CRC_HandleTypeDef hcrc;
/* CRC init function */
void MX_CRC_Init(void)
{
/* USER CODE BEGIN CRC_Init 0 */
/* USER CODE END CRC_Init 0 */
/* USER CODE BEGIN CRC_Init 1 */
/* USER CODE END CRC_Init 1 */
hcrc.Instance = CRC;
hcrc.Init.DefaultPolynomialUse = DEFAULT_POLYNOMIAL_ENABLE;
hcrc.Init.DefaultInitValueUse = DEFAULT_INIT_VALUE_ENABLE;
hcrc.Init.InputDataInversionMode = CRC_INPUTDATA_INVERSION_NONE;
hcrc.Init.OutputDataInversionMode = CRC_OUTPUTDATA_INVERSION_DISABLE;
hcrc.InputDataFormat = CRC_INPUTDATA_FORMAT_BYTES;
if (HAL_CRC_Init(&hcrc) != HAL_OK)
{
Error_Handler();
}
/* USER CODE BEGIN CRC_Init 2 */
/* USER CODE END CRC_Init 2 */
}
void HAL_CRC_MspInit(CRC_HandleTypeDef* crcHandle)
{
if(crcHandle->Instance==CRC)
{
/* USER CODE BEGIN CRC_MspInit 0 */
/* USER CODE END CRC_MspInit 0 */
/* CRC clock enable */
__HAL_RCC_CRC_CLK_ENABLE();
/* USER CODE BEGIN CRC_MspInit 1 */
/* USER CODE END CRC_MspInit 1 */
}
}
void HAL_CRC_MspDeInit(CRC_HandleTypeDef* crcHandle)
{
if(crcHandle->Instance==CRC)
{
/* USER CODE BEGIN CRC_MspDeInit 0 */
/* USER CODE END CRC_MspDeInit 0 */
/* Peripheral clock disable */
__HAL_RCC_CRC_CLK_DISABLE();
/* USER CODE BEGIN CRC_MspDeInit 1 */
/* USER CODE END CRC_MspDeInit 1 */
}
}
/* USER CODE BEGIN 1 */
/* USER CODE END 1 */
| 2,235 | C | 23.571428 | 80 | 0.564206 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/stm32g4xx_it.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file stm32g4xx_it.h
* @brief This file contains the headers of the interrupt handlers.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __STM32G4xx_IT_H
#define __STM32G4xx_IT_H
#ifdef __cplusplus
extern "C" {
#endif
/* Private includes ----------------------------------------------------------*/
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
/* Exported types ------------------------------------------------------------*/
/* USER CODE BEGIN ET */
/* USER CODE END ET */
/* Exported constants --------------------------------------------------------*/
/* USER CODE BEGIN EC */
/* USER CODE END EC */
/* Exported macro ------------------------------------------------------------*/
/* USER CODE BEGIN EM */
/* USER CODE END EM */
/* Exported functions prototypes ---------------------------------------------*/
void NMI_Handler(void);
void HardFault_Handler(void);
void MemManage_Handler(void);
void BusFault_Handler(void);
void UsageFault_Handler(void);
void SVC_Handler(void);
void DebugMon_Handler(void);
void PendSV_Handler(void);
void SysTick_Handler(void);
void USART2_IRQHandler(void);
/* USER CODE BEGIN EFP */
/* USER CODE END EFP */
#ifdef __cplusplus
}
#endif
#endif /* __STM32G4xx_IT_H */
| 1,890 | C | 26.808823 | 80 | 0.481481 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/gpio.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file gpio.h
* @brief This file contains all the function prototypes for
* the gpio.c file
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __GPIO_H__
#define __GPIO_H__
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#include "main.h"
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
/* USER CODE BEGIN Private defines */
/* USER CODE END Private defines */
void MX_GPIO_Init(void);
/* USER CODE BEGIN Prototypes */
/* USER CODE END Prototypes */
#ifdef __cplusplus
}
#endif
#endif /*__ GPIO_H__ */
| 1,264 | C | 24.3 | 80 | 0.476266 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/crc.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file crc.h
* @brief This file contains all the function prototypes for
* the crc.c file
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __CRC_H__
#define __CRC_H__
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#include "main.h"
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
extern CRC_HandleTypeDef hcrc;
/* USER CODE BEGIN Private defines */
/* USER CODE END Private defines */
void MX_CRC_Init(void);
/* USER CODE BEGIN Prototypes */
/* USER CODE END Prototypes */
#ifdef __cplusplus
}
#endif
#endif /* __CRC_H__ */
| 1,291 | C | 23.377358 | 80 | 0.481797 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/stm32g4xx_hal_conf.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file stm32g4xx_hal_conf.h
* @author MCD Application Team
* @brief HAL configuration file
******************************************************************************
* @attention
*
* Copyright (c) 2019 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef STM32G4xx_HAL_CONF_H
#define STM32G4xx_HAL_CONF_H
#ifdef __cplusplus
extern "C" {
#endif
/* Exported types ------------------------------------------------------------*/
/* Exported constants --------------------------------------------------------*/
/* ########################## Module Selection ############################## */
/**
* @brief This is the list of modules to be used in the HAL driver
*/
#define HAL_MODULE_ENABLED
/*#define HAL_ADC_MODULE_ENABLED */
/*#define HAL_COMP_MODULE_ENABLED */
/*#define HAL_CORDIC_MODULE_ENABLED */
#define HAL_CRC_MODULE_ENABLED
/*#define HAL_CRYP_MODULE_ENABLED */
/*#define HAL_DAC_MODULE_ENABLED */
/*#define HAL_FDCAN_MODULE_ENABLED */
/*#define HAL_FMAC_MODULE_ENABLED */
/*#define HAL_HRTIM_MODULE_ENABLED */
/*#define HAL_IRDA_MODULE_ENABLED */
/*#define HAL_IWDG_MODULE_ENABLED */
/*#define HAL_I2C_MODULE_ENABLED */
/*#define HAL_I2S_MODULE_ENABLED */
/*#define HAL_LPTIM_MODULE_ENABLED */
/*#define HAL_NAND_MODULE_ENABLED */
/*#define HAL_NOR_MODULE_ENABLED */
/*#define HAL_OPAMP_MODULE_ENABLED */
/*#define HAL_PCD_MODULE_ENABLED */
/*#define HAL_QSPI_MODULE_ENABLED */
/*#define HAL_RNG_MODULE_ENABLED */
/*#define HAL_RTC_MODULE_ENABLED */
/*#define HAL_SAI_MODULE_ENABLED */
/*#define HAL_SMARTCARD_MODULE_ENABLED */
/*#define HAL_SMBUS_MODULE_ENABLED */
/*#define HAL_SPI_MODULE_ENABLED */
/*#define HAL_SRAM_MODULE_ENABLED */
#define HAL_TIM_MODULE_ENABLED
#define HAL_UART_MODULE_ENABLED
/*#define HAL_USART_MODULE_ENABLED */
/*#define HAL_WWDG_MODULE_ENABLED */
#define HAL_GPIO_MODULE_ENABLED
#define HAL_EXTI_MODULE_ENABLED
#define HAL_DMA_MODULE_ENABLED
#define HAL_RCC_MODULE_ENABLED
#define HAL_FLASH_MODULE_ENABLED
#define HAL_PWR_MODULE_ENABLED
#define HAL_CORTEX_MODULE_ENABLED
/* ########################## Register Callbacks selection ############################## */
/**
* @brief This is the list of modules where register callback can be used
*/
#define USE_HAL_ADC_REGISTER_CALLBACKS 0U
#define USE_HAL_COMP_REGISTER_CALLBACKS 0U
#define USE_HAL_CORDIC_REGISTER_CALLBACKS 0U
#define USE_HAL_CRYP_REGISTER_CALLBACKS 0U
#define USE_HAL_DAC_REGISTER_CALLBACKS 0U
#define USE_HAL_EXTI_REGISTER_CALLBACKS 0U
#define USE_HAL_FDCAN_REGISTER_CALLBACKS 0U
#define USE_HAL_FMAC_REGISTER_CALLBACKS 0U
#define USE_HAL_HRTIM_REGISTER_CALLBACKS 0U
#define USE_HAL_I2C_REGISTER_CALLBACKS 0U
#define USE_HAL_I2S_REGISTER_CALLBACKS 0U
#define USE_HAL_IRDA_REGISTER_CALLBACKS 0U
#define USE_HAL_LPTIM_REGISTER_CALLBACKS 0U
#define USE_HAL_NAND_REGISTER_CALLBACKS 0U
#define USE_HAL_NOR_REGISTER_CALLBACKS 0U
#define USE_HAL_OPAMP_REGISTER_CALLBACKS 0U
#define USE_HAL_PCD_REGISTER_CALLBACKS 0U
#define USE_HAL_QSPI_REGISTER_CALLBACKS 0U
#define USE_HAL_RNG_REGISTER_CALLBACKS 0U
#define USE_HAL_RTC_REGISTER_CALLBACKS 0U
#define USE_HAL_SAI_REGISTER_CALLBACKS 0U
#define USE_HAL_SMARTCARD_REGISTER_CALLBACKS 0U
#define USE_HAL_SMBUS_REGISTER_CALLBACKS 0U
#define USE_HAL_SPI_REGISTER_CALLBACKS 0U
#define USE_HAL_SRAM_REGISTER_CALLBACKS 0U
#define USE_HAL_TIM_REGISTER_CALLBACKS 0U
#define USE_HAL_UART_REGISTER_CALLBACKS 0U
#define USE_HAL_USART_REGISTER_CALLBACKS 0U
#define USE_HAL_WWDG_REGISTER_CALLBACKS 0U
/* ########################## Oscillator Values adaptation ####################*/
/**
* @brief Adjust the value of External High Speed oscillator (HSE) used in your application.
* This value is used by the RCC HAL module to compute the system frequency
* (when HSE is used as system clock source, directly or through the PLL).
*/
#if !defined (HSE_VALUE)
#define HSE_VALUE (8000000UL) /*!< Value of the External oscillator in Hz */
#endif /* HSE_VALUE */
#if !defined (HSE_STARTUP_TIMEOUT)
#define HSE_STARTUP_TIMEOUT (100UL) /*!< Time out for HSE start up, in ms */
#endif /* HSE_STARTUP_TIMEOUT */
/**
* @brief Internal High Speed oscillator (HSI) value.
* This value is used by the RCC HAL module to compute the system frequency
* (when HSI is used as system clock source, directly or through the PLL).
*/
#if !defined (HSI_VALUE)
#define HSI_VALUE (16000000UL) /*!< Value of the Internal oscillator in Hz*/
#endif /* HSI_VALUE */
/**
* @brief Internal High Speed oscillator (HSI48) value for USB FS and RNG.
* This internal oscillator is mainly dedicated to provide a high precision clock to
* the USB peripheral by means of a special Clock Recovery System (CRS) circuitry.
* When the CRS is not used, the HSI48 RC oscillator runs on it default frequency
* which is subject to manufacturing process variations.
*/
#if !defined (HSI48_VALUE)
#define HSI48_VALUE (48000000UL) /*!< Value of the Internal High Speed oscillator for USB FS/RNG in Hz.
The real value my vary depending on manufacturing process variations.*/
#endif /* HSI48_VALUE */
/**
* @brief Internal Low Speed oscillator (LSI) value.
*/
#if !defined (LSI_VALUE)
/*!< Value of the Internal Low Speed oscillator in Hz
The real value may vary depending on the variations in voltage and temperature.*/
#define LSI_VALUE (32000UL) /*!< LSI Typical Value in Hz*/
#endif /* LSI_VALUE */
/**
* @brief External Low Speed oscillator (LSE) value.
* This value is used by the UART, RTC HAL module to compute the system frequency
*/
#if !defined (LSE_VALUE)
#define LSE_VALUE (32768UL) /*!< Value of the External Low Speed oscillator in Hz */
#endif /* LSE_VALUE */
#if !defined (LSE_STARTUP_TIMEOUT)
#define LSE_STARTUP_TIMEOUT (5000UL) /*!< Time out for LSE start up, in ms */
#endif /* LSE_STARTUP_TIMEOUT */
/**
* @brief External clock source for I2S and SAI peripherals
* This value is used by the I2S and SAI HAL modules to compute the I2S and SAI clock source
* frequency, this source is inserted directly through I2S_CKIN pad.
*/
#if !defined (EXTERNAL_CLOCK_VALUE)
#define EXTERNAL_CLOCK_VALUE (12288000UL) /*!< Value of the External oscillator in Hz*/
#endif /* EXTERNAL_CLOCK_VALUE */
/* Tip: To avoid modifying this file each time you need to use different HSE,
=== you can define the HSE value in your toolchain compiler preprocessor. */
/* ########################### System Configuration ######################### */
/**
* @brief This is the HAL system configuration section
*/
#define VDD_VALUE (3300UL) /*!< Value of VDD in mv */
#define TICK_INT_PRIORITY (0UL) /*!< tick interrupt priority (lowest by default) */
#define USE_RTOS 0U
#define PREFETCH_ENABLE 0U
#define INSTRUCTION_CACHE_ENABLE 1U
#define DATA_CACHE_ENABLE 1U
/* ########################## Assert Selection ############################## */
/**
* @brief Uncomment the line below to expanse the "assert_param" macro in the
* HAL drivers code
*/
/* #define USE_FULL_ASSERT 1U */
/* ################## SPI peripheral configuration ########################## */
/* CRC FEATURE: Use to activate CRC feature inside HAL SPI Driver
* Activated: CRC code is present inside driver
* Deactivated: CRC code cleaned from driver
*/
#define USE_SPI_CRC 0U
/* Includes ------------------------------------------------------------------*/
/**
* @brief Include module's header file
*/
#ifdef HAL_RCC_MODULE_ENABLED
#include "stm32g4xx_hal_rcc.h"
#endif /* HAL_RCC_MODULE_ENABLED */
#ifdef HAL_GPIO_MODULE_ENABLED
#include "stm32g4xx_hal_gpio.h"
#endif /* HAL_GPIO_MODULE_ENABLED */
#ifdef HAL_DMA_MODULE_ENABLED
#include "stm32g4xx_hal_dma.h"
#endif /* HAL_DMA_MODULE_ENABLED */
#ifdef HAL_CORTEX_MODULE_ENABLED
#include "stm32g4xx_hal_cortex.h"
#endif /* HAL_CORTEX_MODULE_ENABLED */
#ifdef HAL_ADC_MODULE_ENABLED
#include "stm32g4xx_hal_adc.h"
#endif /* HAL_ADC_MODULE_ENABLED */
#ifdef HAL_COMP_MODULE_ENABLED
#include "stm32g4xx_hal_comp.h"
#endif /* HAL_COMP_MODULE_ENABLED */
#ifdef HAL_CORDIC_MODULE_ENABLED
#include "stm32g4xx_hal_cordic.h"
#endif /* HAL_CORDIC_MODULE_ENABLED */
#ifdef HAL_CRC_MODULE_ENABLED
#include "stm32g4xx_hal_crc.h"
#endif /* HAL_CRC_MODULE_ENABLED */
#ifdef HAL_CRYP_MODULE_ENABLED
#include "stm32g4xx_hal_cryp.h"
#endif /* HAL_CRYP_MODULE_ENABLED */
#ifdef HAL_DAC_MODULE_ENABLED
#include "stm32g4xx_hal_dac.h"
#endif /* HAL_DAC_MODULE_ENABLED */
#ifdef HAL_EXTI_MODULE_ENABLED
#include "stm32g4xx_hal_exti.h"
#endif /* HAL_EXTI_MODULE_ENABLED */
#ifdef HAL_FDCAN_MODULE_ENABLED
#include "stm32g4xx_hal_fdcan.h"
#endif /* HAL_FDCAN_MODULE_ENABLED */
#ifdef HAL_FLASH_MODULE_ENABLED
#include "stm32g4xx_hal_flash.h"
#endif /* HAL_FLASH_MODULE_ENABLED */
#ifdef HAL_FMAC_MODULE_ENABLED
#include "stm32g4xx_hal_fmac.h"
#endif /* HAL_FMAC_MODULE_ENABLED */
#ifdef HAL_HRTIM_MODULE_ENABLED
#include "stm32g4xx_hal_hrtim.h"
#endif /* HAL_HRTIM_MODULE_ENABLED */
#ifdef HAL_IRDA_MODULE_ENABLED
#include "stm32g4xx_hal_irda.h"
#endif /* HAL_IRDA_MODULE_ENABLED */
#ifdef HAL_IWDG_MODULE_ENABLED
#include "stm32g4xx_hal_iwdg.h"
#endif /* HAL_IWDG_MODULE_ENABLED */
#ifdef HAL_I2C_MODULE_ENABLED
#include "stm32g4xx_hal_i2c.h"
#endif /* HAL_I2C_MODULE_ENABLED */
#ifdef HAL_I2S_MODULE_ENABLED
#include "stm32g4xx_hal_i2s.h"
#endif /* HAL_I2S_MODULE_ENABLED */
#ifdef HAL_LPTIM_MODULE_ENABLED
#include "stm32g4xx_hal_lptim.h"
#endif /* HAL_LPTIM_MODULE_ENABLED */
#ifdef HAL_NAND_MODULE_ENABLED
#include "stm32g4xx_hal_nand.h"
#endif /* HAL_NAND_MODULE_ENABLED */
#ifdef HAL_NOR_MODULE_ENABLED
#include "stm32g4xx_hal_nor.h"
#endif /* HAL_NOR_MODULE_ENABLED */
#ifdef HAL_OPAMP_MODULE_ENABLED
#include "stm32g4xx_hal_opamp.h"
#endif /* HAL_OPAMP_MODULE_ENABLED */
#ifdef HAL_PCD_MODULE_ENABLED
#include "stm32g4xx_hal_pcd.h"
#endif /* HAL_PCD_MODULE_ENABLED */
#ifdef HAL_PWR_MODULE_ENABLED
#include "stm32g4xx_hal_pwr.h"
#endif /* HAL_PWR_MODULE_ENABLED */
#ifdef HAL_QSPI_MODULE_ENABLED
#include "stm32g4xx_hal_qspi.h"
#endif /* HAL_QSPI_MODULE_ENABLED */
#ifdef HAL_RNG_MODULE_ENABLED
#include "stm32g4xx_hal_rng.h"
#endif /* HAL_RNG_MODULE_ENABLED */
#ifdef HAL_RTC_MODULE_ENABLED
#include "stm32g4xx_hal_rtc.h"
#endif /* HAL_RTC_MODULE_ENABLED */
#ifdef HAL_SAI_MODULE_ENABLED
#include "stm32g4xx_hal_sai.h"
#endif /* HAL_SAI_MODULE_ENABLED */
#ifdef HAL_SMARTCARD_MODULE_ENABLED
#include "stm32g4xx_hal_smartcard.h"
#endif /* HAL_SMARTCARD_MODULE_ENABLED */
#ifdef HAL_SMBUS_MODULE_ENABLED
#include "stm32g4xx_hal_smbus.h"
#endif /* HAL_SMBUS_MODULE_ENABLED */
#ifdef HAL_SPI_MODULE_ENABLED
#include "stm32g4xx_hal_spi.h"
#endif /* HAL_SPI_MODULE_ENABLED */
#ifdef HAL_SRAM_MODULE_ENABLED
#include "stm32g4xx_hal_sram.h"
#endif /* HAL_SRAM_MODULE_ENABLED */
#ifdef HAL_TIM_MODULE_ENABLED
#include "stm32g4xx_hal_tim.h"
#endif /* HAL_TIM_MODULE_ENABLED */
#ifdef HAL_UART_MODULE_ENABLED
#include "stm32g4xx_hal_uart.h"
#endif /* HAL_UART_MODULE_ENABLED */
#ifdef HAL_USART_MODULE_ENABLED
#include "stm32g4xx_hal_usart.h"
#endif /* HAL_USART_MODULE_ENABLED */
#ifdef HAL_WWDG_MODULE_ENABLED
#include "stm32g4xx_hal_wwdg.h"
#endif /* HAL_WWDG_MODULE_ENABLED */
/* Exported macro ------------------------------------------------------------*/
#ifdef USE_FULL_ASSERT
/**
* @brief The assert_param macro is used for function's parameters check.
* @param expr: If expr is false, it calls assert_failed function
* which reports the name of the source file and the source
* line number of the call that failed.
* If expr is true, it returns no value.
* @retval None
*/
#define assert_param(expr) ((expr) ? (void)0U : assert_failed((uint8_t *)__FILE__, __LINE__))
/* Exported functions ------------------------------------------------------- */
void assert_failed(uint8_t *file, uint32_t line);
#else
#define assert_param(expr) ((void)0U)
#endif /* USE_FULL_ASSERT */
#ifdef __cplusplus
}
#endif
#endif /* STM32G4xx_HAL_CONF_H */
| 12,935 | C | 32.952756 | 118 | 0.647313 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/usart.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file usart.h
* @brief This file contains all the function prototypes for
* the usart.c file
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __USART_H__
#define __USART_H__
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#include "main.h"
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
extern UART_HandleTypeDef huart2;
/* USER CODE BEGIN Private defines */
/* USER CODE END Private defines */
void MX_USART2_UART_Init(void);
/* USER CODE BEGIN Prototypes */
/* USER CODE END Prototypes */
#ifdef __cplusplus
}
#endif
#endif /* __USART_H__ */
| 1,312 | C | 23.773584 | 80 | 0.489329 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/tim.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file tim.h
* @brief This file contains all the function prototypes for
* the tim.c file
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __TIM_H__
#define __TIM_H__
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#include "main.h"
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
extern TIM_HandleTypeDef htim2;
/* USER CODE BEGIN Private defines */
/* USER CODE END Private defines */
void MX_TIM2_Init(void);
/* USER CODE BEGIN Prototypes */
/* USER CODE END Prototypes */
#ifdef __cplusplus
}
#endif
#endif /* __TIM_H__ */
| 1,293 | C | 23.415094 | 80 | 0.482599 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/RTE_Components.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file
* @author MCD Application Team
* @version V2.0.0
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __RTE_COMPONENTS_H__
#define __RTE_COMPONENTS_H__
/* Defines ------------------------------------------------------------------*/
/* STMicroelectronics.X-CUBE-AI.8.1.0 */
#define AI_ApplicationTemplate
#endif /* __RTE_COMPONENTS_H__ */
| 1,003 | C | 33.620688 | 82 | 0.440678 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Core/Inc/main.h | /* USER CODE BEGIN Header */
/**
******************************************************************************
* @file : main.h
* @brief : Header for main.c file.
* This file contains the common defines of the application.
******************************************************************************
* @attention
*
* Copyright (c) 2024 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
*/
/* USER CODE END Header */
/* Define to prevent recursive inclusion -------------------------------------*/
#ifndef __MAIN_H
#define __MAIN_H
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#include "stm32g4xx_hal.h"
/* Private includes ----------------------------------------------------------*/
/* USER CODE BEGIN Includes */
/* USER CODE END Includes */
/* Exported types ------------------------------------------------------------*/
/* USER CODE BEGIN ET */
/* USER CODE END ET */
/* Exported constants --------------------------------------------------------*/
/* USER CODE BEGIN EC */
/* USER CODE END EC */
/* Exported macro ------------------------------------------------------------*/
/* USER CODE BEGIN EM */
/* USER CODE END EM */
/* Exported functions prototypes ---------------------------------------------*/
void Error_Handler(void);
/* USER CODE BEGIN EFP */
/* USER CODE END EFP */
/* Private defines -----------------------------------------------------------*/
#define USART2_TX_Pin GPIO_PIN_2
#define USART2_TX_GPIO_Port GPIOA
#define USART2_RX_Pin GPIO_PIN_3
#define USART2_RX_GPIO_Port GPIOA
#define T_SWDIO_Pin GPIO_PIN_13
#define T_SWDIO_GPIO_Port GPIOA
#define T_SWCLK_Pin GPIO_PIN_14
#define T_SWCLK_GPIO_Port GPIOA
#define T_SWO_Pin GPIO_PIN_3
#define T_SWO_GPIO_Port GPIOB
#define LD2_Pin GPIO_PIN_8
#define LD2_GPIO_Port GPIOB
/* USER CODE BEGIN Private defines */
/* USER CODE END Private defines */
#ifdef __cplusplus
}
#endif
#endif /* __MAIN_H */
| 2,310 | C | 27.182926 | 80 | 0.467532 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/formats_list.h | /**
******************************************************************************
* @file format_list.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform public APIs types
******************************************************************************
* @attention
*
* Copyright (c) 2019 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
/* FMT_ENTRY( exp_(0/1 only), name_, type_id_,
* sign_bit_, float_bit_, pmask_, bits_, fbits_, ldiv_bits_)
* Specifications (in order of the bit fields, little endian):
- name_ : it is the enum used to define both the ai_array_format and
ai_buffer_format.
- exp_ (1bit) : it is a boolean flag (0 or 1) indicating whether the format
is available as a public APIs ai_buffer format. in this case the field
exp_name_ indicates the enum name of the ai_buffer format
- (7 bits): reserved for flags
- sign_bit_ (1bit) : codes whether or not the format is of a signed type
- float_bit_ (1bit) : codes if the format is float
- ldiv_bits (2 bits) : right shift value for computing the byte size of the
format
- type_id_ (4bits) : it is used to define the "family" of the format:
see @ref AI_FMT_Q as an example. Currently supported types are:
AI_FMT_Q (fixed point types), AI_FMT_FLOAT (floating point values),
AI_FMT_LUT4 or AI_FMT_LUT8 (compressed formats)
- pmask_ (3bits) : padding mask bits for the format
- bits_ (7bits) : size in bits of the format (NB: integer+fractional bits)
- fbits_ (7bits) : number of fractional bits for the format
(for AI_FMT_Q only)
*/
/* Format none entry */
FMT_ENTRY(1, NONE, AI_FMT_NONE, 0, 0, 0x0, 0, 0, 0)
/* Floating point formats */
FMT_ENTRY(1, FLOAT, AI_FMT_FLOAT, 1, 1, 0x0, 32, 0, 0)
FMT_ENTRY(0, FLOAT64, AI_FMT_FLOAT, 1, 1, 0x0, 64, 0, 0)
FMT_ENTRY(0, FLOAT16, AI_FMT_FLOAT, 1, 1, 0x0, 16, 0, 0)
/* Integer formats (i.e. fractional bits = 0!) */
FMT_ENTRY(1, U8, AI_FMT_Q, 0, 0, 0x0, 8, 0, 0)
FMT_ENTRY(1, U16, AI_FMT_Q, 0, 0, 0x0, 16, 0, 0)
FMT_ENTRY(1, U32, AI_FMT_Q, 0, 0, 0x0, 32, 0, 0)
FMT_ENTRY(0, U64, AI_FMT_Q, 0, 0, 0x0, 64, 0, 0)
FMT_ENTRY(1, U1, AI_FMT_Q, 0, 0, 0x0, 1, 0, 0)
FMT_ENTRY(0, U4, AI_FMT_Q, 0, 0, 0x0, 4, 0, 0)
FMT_ENTRY(1, S8, AI_FMT_Q, 1, 0, 0x0, 8, 0, 0)
FMT_ENTRY(1, S16, AI_FMT_Q, 1, 0, 0x0, 16, 0, 0)
FMT_ENTRY(1, S32, AI_FMT_Q, 1, 0, 0x0, 32, 0, 0)
FMT_ENTRY(0, S64, AI_FMT_Q, 1, 0, 0x0, 64, 0, 0)
FMT_ENTRY(1, S1, AI_FMT_Q, 1, 0, 0x0, 1, 0, 0)
FMT_ENTRY(0, S4, AI_FMT_Q, 1, 0, 0x0, 4, 0, 0)
/* Fixed-point formats including ARM CMSIS Q7, Q15, Q31 ones */
FMT_ENTRY(1, Q, AI_FMT_Q, 1, 0, 0x0, 0, 0, 0)
FMT_ENTRY(1, Q7, AI_FMT_Q, 1, 0, 0x0, 8, 7, 0)
FMT_ENTRY(1, Q15, AI_FMT_Q, 1, 0, 0x0, 16, 15, 0)
FMT_ENTRY(0, Q31, AI_FMT_Q, 1, 0, 0x0, 32, 31, 0)
FMT_ENTRY(1, UQ, AI_FMT_Q, 0, 0, 0x0, 0, 0, 0)
FMT_ENTRY(1, UQ7, AI_FMT_Q, 0, 0, 0x0, 8, 7, 0)
FMT_ENTRY(1, UQ15, AI_FMT_Q, 0, 0, 0x0, 16, 15, 0)
FMT_ENTRY(0, UQ31, AI_FMT_Q, 0, 0, 0x0, 32, 31, 0)
/* Compressed formats */
FMT_ENTRY(0, LUT4_FLOAT, AI_FMT_LUT4, 1, 1, 0x0, 32, 0, 3)
FMT_ENTRY(0, LUT8_FLOAT, AI_FMT_LUT8, 1, 1, 0x0, 32, 0, 2)
FMT_ENTRY(0, LUT4_Q15, AI_FMT_LUT4, 1, 0, 0x0, 16, 15, 2)
FMT_ENTRY(0, LUT8_Q15, AI_FMT_LUT8, 1, 0, 0x0, 16, 15, 1)
FMT_ENTRY(0, LUT4_UQ15, AI_FMT_LUT4, 0, 0, 0x0, 16, 15, 2)
FMT_ENTRY(0, LUT8_UQ15, AI_FMT_LUT8, 0, 0, 0x0, 16, 15, 1)
/* Boolean format */
FMT_ENTRY(1, BOOL, AI_FMT_BOOL, 0, 0, 0x0, 8, 0, 0)
#undef FMT_ENTRY
| 3,930 | C | 41.72826 | 80 | 0.564885 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_convert.h | /**
******************************************************************************
* @file core_convert.h
* @author AST Embedded Analytics Research Platform
* @brief header file of core utils routines
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef CORE_CONVERT_H
#define CORE_CONVERT_H
#pragma once
#include "ai_platform.h"
#include "ai_platform_interface.h"
#include "core_common.h"
AI_API_DECLARE_BEGIN
/*!
* @defgroup core_convert Core Convert Routines
* @brief Implementation of core node format convertion routines
* (Q7 to float, ... etc.)
*/
/*!
* @brief Convert tensors from float to quantized or viceversa
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert(ai_node *pNode);
/*!
* @brief Convert integer tensors between QM.N formats (8/16 bits)
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_fixed(ai_node *pNode);
/*!
* @brief Convert integer tensors between signed and usigned (int8/uint8) formats
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_integer(ai_node *pNode);
/*!
* @brief Convert float tensor to binary
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_if32os1(ai_node *pNode);
/*!
* @brief Convert binary tensor to float
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is8os1(ai_node *pNode);
/*!
* @brief Convert binary tensor to signed int 8 bit
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is1os8(ai_node *pNode);
/*!
* @brief Convert binary tensor to signed int 16 bit
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is1os16(ai_node *pNode);
/*!
* @brief Convert binary tensor to float
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is1of32(ai_node *pNode);
/*!
* @brief Convert signed int 16 bit tensor to float
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is16of32(ai_node *pNode);
/*!
* @brief Convert unsigned int 16 bit tensor to float
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_iu16of32(ai_node *pNode);
/*!
* @brief Convert float tensor to signed int 16 bit
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_if32os16(ai_node *pNode);
/*!
* @brief Convert float tensor to unsigned int 16 bit
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_if32ou16(ai_node *pNode);
/*!
* @brief Convert signed int 16 bit tensor to unsigned int 16 bit
* @ingroup core_convert
* @param[in] pNode in a handler to node (layer or operator)
*/
AI_INTERNAL_API
void node_convert_is16ou16(ai_node *pNode);
/*!
* @brief Convert a shape struct into a stride struct
* @ingroup core_convert
* @param[in] in a pointer to a shape to convert
* @return a condverted stride datastruct
*/
AI_INTERNAL_API
void core_shape_to_stride(ai_stride* out, const ai_shape* in);
#endif /*CORE_CONVERT_H*/
| 4,123 | C | 23.993939 | 81 | 0.65171 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_conv2d.h | /**
******************************************************************************
* @file layers_conv2d.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform conv2d layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_CONV2D_H
#define LAYERS_CONV2D_H
#pragma once
#include "layers_nl.h"
#include "layers_pool.h"
#define AI_LAYER_CONV2D_FIELDS_DECLARE \
AI_LAYER_COMMON_FIELDS_DECLARE \
ai_u32 groups; /*!< groups for separable convolution */ \
AI_CONST ai_array* nl_params; /*!< array pointer to non linear parameters */ \
func_nl nl_func; /*!< function pointer to non linear transform */ \
ai_shape_2d filter_stride; /*!< filter stride, how much the filter moves */ \
ai_shape_2d dilation; /*!< dilation value along axis of the filter */ \
ai_shape filter_pad; /*!< filter pad 4d */ \
ai_layer_format_type in_ch_format; /*!< Input format (Channel 1st vs Channel last */ \
ai_layer_format_type out_ch_format; /*!< Output format (Channel 1st vs Channel last */
/*!
* @defgroup layers_conv2d Convolutive Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_dense
* @ingroup layers_conv2d
* @brief Dense (fully connected) layer
*/
typedef ai_layer_base ai_layer_dense;
/*!
* @struct ai_layer_gemm
* @ingroup layers_conv2d
* @brief layer for General Matrix Multiplication
*
* Layer for General Matrix Multiplication (GEMM):
* \f{equation}{ Y = \alpha A \cdot B + \beta C \f}
* \f$\alpha\f$ and \f$\beta\f$ are paramaters, A and B are matrices,
* C is a matrix or an array. Size checks for A, B, C, and Y are performed and
* broadcast is applied on C if necessary.
* This is a sequential layer (see @ref ai_layer).
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_gemm_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_float alpha; /*!< alpha coefficient */
ai_float beta; /*!< beta coefficient */
ai_u8 tA; /*!< transpose A flag */
ai_u8 tB; /*!< transpose B flag */
} ai_layer_gemm;
/*!
* @struct ai_layer_conv2d
* @ingroup layers_conv2d
* @brief 2D convolutional layer with strides and pads
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_conv2d_ {
AI_LAYER_CONV2D_FIELDS_DECLARE
} ai_layer_conv2d;
/*!
* @struct ai_layer_conv2d_nl_pool
* @ingroup layers_conv2d
* @brief 2D convolutional layer + nl + pooling with strides and pads
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_conv2d_nl_pool_ {
AI_LAYER_CONV2D_FIELDS_DECLARE
ai_shape_2d pool_size; /*!< pooling size */
ai_shape_2d pool_stride; /*!< pooling stride */
ai_shape pool_pad; /*!< pooling pad */
ai_handle pool_func; /*!< function pointer to pooling transform */
} ai_layer_conv2d_nl_pool;
AI_INTERNAL_API
void ai_dict8_dot_array_f32(ai_handle out, ai_ptr_const data0, ai_ptr_const lut,
const ai_float* data1, const ai_size data_size);
AI_INTERNAL_API
void ai_dict4_dot_array_f32(ai_handle out, ai_ptr_const data0, ai_ptr_const lut,
const ai_float* data1, const ai_size data_size);
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Computes the activations of a floating point 32 2D convolutional layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_if32of32wf32(ai_layer* layer);
/*!
* @brief Computes the activations of a floating point 32 2D dw layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_if32of32wf32(ai_layer* layer);
/*!
* @brief Computes the activations of a floating point 32 2D convolutional group layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_if32of32wf32_group(ai_layer* layer);
/*!
* @brief Computes the activations of a 2D floating point 32 pool fused convolutional layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_if32of32wf32_pool(ai_layer* layer);
/*!
* @brief Computes the activations of a 2D floating point 32 pool fused dw layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_if32of32wf32_pool(ai_layer* layer);
/*!
* @brief Computes the activations of a 2D floating point 32 pool fused convolutional group layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_if32of32wf32_group_pool(ai_layer* layer);
/*!
* @brief Computes the activations of a GEMM layer.
* @ingroup layers
* @param layer the layer including output and input tensors
*/
AI_INTERNAL_API
void forward_gemm(ai_layer* layer);
/*!
* @brief Computes matmul layer, intended as numpy.matmul(A,B).
* @ingroup layers
* @param layer the layer including output and input tensors
*/
AI_INTERNAL_API
void forward_matmul(ai_layer* layer);
/*!
* @brief Computes the activations of a dense (fully connected) layer.
* @ingroup layers_conv2d
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point 2D convolutional layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a fixed point @ref ai_layer_conv2d_nl_pool
* layer.
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for SSSA per layer quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer_SSSA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is8os8ws8_sssa_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme, with 3x3 kernels
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_3x3_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme, with 3x3 kernels and input are
* channel first
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_3x3_ch1st_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme with depth multiplier > 1
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_dm_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of int8 quantized DW layers.
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_all_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized PW layer
* for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_pw_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding)
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_dilated_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding)
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_deep_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding)
* number of output channel is greater than 8
* Kernels shall be 3x3 and stride is (1,1)
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_deep_3x3_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid or same padding)
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized Conv2d layer
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_all_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized RGB Conv2d layer
* for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_rgb_sssa8_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme, with 3x3 kernels,
* with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_3x3_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme, with 3x3 kernels,
* with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_3x3_ch1st_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized DW layer
* for SSSA per channel quantized scheme with depth multiplier > 1
* with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_dm_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of int8 quantized DW layers, with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_all_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized PW layer,
* with pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_pw_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding) and pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_dilated_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding) and pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_deep_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid padding) and pooling fused
* number of output channel is greater than 8
* Kernels shall be 3x3 and stride is (1,1)
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_deep_3x3_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized non dilated Conv2d layer
* for SSSA per channel quantized scheme (valid or same padding) and pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a int8 quantized Conv2d layer and pooling fused
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_all_sssa8_ch_nl_pool(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for SSUA per layer quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer_SSUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for SSUA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer_SSUA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for UAUA per layer quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer_UAUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for UAUA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_integer_UAUA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer.
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for SSSA per layer quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_SSSA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for SSSA per channel quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_SSSA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for SSUA per layer quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_SSUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for SSUA per channel quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_SSUA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for UAUA per layer quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_UAUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer @ref ai_layer_conv2d_nl_pool layer
* for UAUA per channel quantized scheme
* The @ref ai_layer_conv2d_nl_pool is a fused conv2D + optional nonlinear
* layer + optional pooling / nonlinearity (average, max)
* @ingroup layers_conv2d
* @param layer see @ai_layer_conv2d_nl_pool
*/
AI_INTERNAL_API
void forward_conv2d_nl_pool_integer_UAUA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer.
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for SSSA per layer quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_SSSA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for SSSA per channel quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_SSSA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for SSUA per layer quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_SSUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for SSUA per channel quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_SSUA_ch(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for UAUA per layer quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_UAUA(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer dense (fully connected) layer
* for UAUA per channel quantized scheme
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_integer_UAUA_ch(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_CONV2D_H*/
| 19,921 | C | 30.8752 | 98 | 0.69359 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_lite_math_helpers.h | #ifndef AI_LITE_MATH_HELPERS_H
#define AI_LITE_MATH_HELPERS_H
#include <math.h>
#include "ai_platform.h"
#include "ai_platform_interface.h"
#include "ai_datatypes_defines.h"
#define AI_FLOAT_TOLERANCE (6.19209290e-5F) /* Used for small calculation
noise issues */
#define AI_FLOAT_EPSILON (1.19209290e-7F)
#define AI_I8_EPSILON (0.00787401F) /* 1/(2^7 - 1) */
#define AI_I16_EPSILON (3.051851e-5F) /* 1/(2^15 - 1) */
#define AI_FLT_MAX (3.40282346638528859812e+38f)
#define AI_MIN(x,y) ( ((x)<(y)) ? (x) : (y) )
#define AI_MAX(x,y) ( ((x)>(y)) ? (x) : (y) )
#define AI_SIGN(x) (((x)>0) ? 1 : -1)
#define AI_CLAMP(x, min, max) AI_MIN(AI_MAX(x,min), max)
#define AI_ABS(x) fabsf(x)
#define AI_ABS_DIFF(x, y) ( ((x)>(y)) ? ((x)-(y)) : ((y)-(x)) )
#define AI_NEG(x) ( -1 * (x) )
#define AI_NOT(x) ( ((x)==true) ? false : true)
#define AI_RECIPROCAL(x) ( 1.0f / (x) )
#define AI_CEIL(x) ceilf(x)
#define AI_FLOOR(x) floorf(x)
#define AI_FLOOR_DIV(x, y) AI_FLOOR((x)/(y)) /* floor division: x // y */
#define AI_FLOOR_MOD(x, y) fmodf(x, y)
#define AI_ROUND(x) roundf(x)
#define AI_POW(x,y) powf(x, y)
#define AI_SQUARED_DIFF(x, y) (((x)-(y)) * ((x)-(y)))
#define AI_FLOAT_NEGATIVE_HALF (-0.5f + AI_FLOAT_EPSILON)
#define AI_FLOAT_POSITIVE_HALF (0.5f)
#define AI_MATH_ACOS(x) acosf(x)
#define AI_MATH_ACOSH(x) acoshf(x)
#define AI_MATH_ASIN(x) asinf(x)
#define AI_MATH_ASINH(x) asinhf(x)
#define AI_MATH_ATAN(x) atanf(x)
#define AI_MATH_ATANH(x) atanhf(x)
#define AI_MATH_COS(x) cosf(x)
#define AI_MATH_COSH(x) coshf(x)
#define AI_MATH_ERF(x) erff(x)
#define AI_MATH_EXP(x) expf(x)
#define AI_MATH_LOG(x) logf(x)
#define AI_MATH_POW(x, e) powf((x), (e))
#define AI_MATH_RSQRT(x) (1.0f / AI_MATH_SQRT(x))
#define AI_MATH_SIN(x) sinf(x)
#define AI_MATH_SINH(x) sinhf(x)
#define AI_MATH_SQRT(x) ai_math_sqrt(x)
#define AI_MATH_TAN(x) tanf(x)
#define AI_MATH_TANH(x) tanhf(x)
#define AI_MATH_SQUARE(x) AI_MATH_POW(x, 2.0f)
#define AI_MATH_ACOS(x) acosf(x)
#define AI_MATH_ACOSH(x) acoshf(x)
#define AI_MATH_ASIN(x) asinf(x)
#define AI_MATH_ASINH(x) asinhf(x)
#define AI_MATH_ATAN(x) atanf(x)
#define AI_MATH_ATANH(x) atanhf(x)
#define AI_MATH_COS(x) cosf(x)
#define AI_MATH_COSH(x) coshf(x)
#define AI_MATH_ERF(x) erff(x)
#define AI_MATH_EXP(x) expf(x)
#define AI_MATH_LOG(x) logf(x)
#define AI_MATH_POW(x, e) powf((x), (e))
#define AI_MATH_RSQRT(x) (1.0f / AI_MATH_SQRT(x))
#define AI_MATH_SIN(x) sinf(x)
#define AI_MATH_SINH(x) sinhf(x)
#define AI_MATH_SQRT(x) ai_math_sqrt(x)
#define AI_MATH_TAN(x) tanf(x)
#define AI_MATH_TANH(x) tanhf(x)
#define AI_MATH_SQUARE(x) AI_MATH_POW(x, 2.0f)
#define AI_MATH_RELU_TEST(x, thr, min, max) \
(((x)<=(thr)) ? (min) : (max))
#define AI_MATH_CLIP_LINEAR_REMAP(x, alpha, beta) \
(AI_MAX(0, AI_MIN(1, ((x) * (alpha) + (beta)))))
#define AI_MATH_RELU_GENERIC(x, thr, alpha, max) \
AI_MATH_RELU_TEST(x, max, AI_MATH_RELU_GENERIC_NO_MAX(x, thr, alpha), max)
#define AI_MATH_RELU_GENERIC_NO_MAX(x, thr, alpha) \
AI_MATH_RELU_TEST(x, thr, ((alpha)*((x)-(thr))), x)
#define AI_MATH_RELU_THRESHOLDED(x, thr) \
AI_MATH_RELU_TEST(x, thr, 0, (x))
#define AI_MATH_LEAKY_RELU(x, neg_slope, pos_slope) \
AI_MATH_RELU_TEST(x, 0, (x)*(neg_slope), (x)*(pos_slope))
// ( ((x)>0) ? (x)*(pos_slope) : (x)*(neg_slope) )
#define AI_MATH_PRELU(x, slope) \
AI_MATH_RELU_TEST(x, 0, (x)*(slope), (x))
// AI_MATH_LEAKY_RELU(x, slope, 1)
#define AI_MATH_RELU(x) \
AI_MATH_RELU_TEST(x, 0, 0, x)
// AI_MAX(x, 0)
#define AI_MATH_ELU(x, alpha) \
(AI_MAX(0.0f, (x)) + AI_MIN(0.0f, (alpha) * (AI_MATH_EXP(x)-1.0f)))
#define AI_MATH_SELU(x, alpha, scale) \
((scale)*AI_MATH_ELU(x, alpha))
#define AI_MATH_SCALED_TANH(x, alpha, beta) \
((alpha)*AI_MATH_TANH((beta)*(x)))
#define AI_MATH_SIGMOID(x) \
(1.0f / (1.0f + AI_MATH_EXP(-(x))))
#define AI_MATH_LOGISTIC(x)\
(x < 0) ? (1.0f -(1.0f / (1.0f + AI_MATH_EXP(-AI_ABS(x))))) :\
(1.0f / (1.0f + AI_MATH_EXP(-AI_ABS(x))))
#define AI_MATH_HARD_SIGMOID(x, alpha, beta) \
AI_MATH_CLIP_LINEAR_REMAP(x, alpha, beta)
/* Formula with higher accuracy */
#define AI_MATH_SWISH(x) \
((x) * AI_MATH_SIGMOID(x))
#define AI_MATH_HARD_SWISH(x) \
((x) * AI_MATH_CLIP_LINEAR_REMAP(x, 1.0f/6, 0.5f))
#define AI_MATH_SOFT_PLUS(x) \
AI_MATH_LOG(1.0f + AI_MATH_EXP(x))
#define AI_MATH_SOFT_SIGN(x) \
((x) / (1.0f + AI_ABS(x)))
AI_API_DECLARE_BEGIN
/*!
* @brief platform optimized square root on a float value
* @ingroup math_helpers
* @param x input value
* @return square root of the value
*/
AI_INTERFACE_ENTRY ai_float ai_math_sqrt(const ai_float x);
AI_API_DECLARE_END
#endif /* AI_LITE_MATH_HELPERS_H */
| 5,197 | C | 33.197368 | 79 | 0.556667 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_pad_dqnn.h | /**
******************************************************************************
* @file lite_pad_dqnn.h
* @author AIS
* @brief header file of AI platform lite padding kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_PADDING_DQNN_H
#define LITE_PADDING_DQNN_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles padding with binary input and binary output - Lite I/F
* @ingroup lite_padding_dqnn
*/
LITE_API_ENTRY
void forward_lite_pad_is1os1(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_i32 width_in,
const ai_i32 width_out,
const ai_i32 height_in,
const ai_i32 height_out,
const ai_u32 n_channel_out,
const ai_i32 mode,
const ai_u16 pads_x,
const ai_u16 pads_y,
const ai_u16 pads_x_r,
const ai_u16 pads_y_b,
const ai_u32 pad_value);
#endif /*LITE_PADDING_DQNN_H*/
| 2,016 | C | 37.788461 | 80 | 0.381448 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_conv2d_dqnn.h | /**
******************************************************************************
* @file lite_conv2d_dqnn.h
* @author AIS
* @brief header file of AI platform lite conv kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_CONV2D_DQNN_H
#define LITE_CONV2D_DQNN_H
#pragma once
#include "ai_lite_interface.h"
# define AI_16_OVERFLOW_CHECK(val_) (val_ <= 32767)
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
AI_API_DECLARE_BEGIN
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os1ws1_bn_pad0(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* - Optimized thanks to Optim0 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os1ws1_bn_pad0_optim0(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os8ws1_bn_pad0(const ai_u32 *pDataIn_init,
ai_i8 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale,
const ai_float *pOffset);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os1ws1_bn_pad1(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim2 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os1ws1_bn_pad1_optim2(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os8ws1_bn_pad1(const ai_u32 *pDataIn_init,
ai_i8 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale,
const ai_float *pOffset,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim1 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os8ws1_bn_pad1_optim1(const ai_u32 *pDataIn_init,
ai_i8 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale,
const ai_float *pOffset,
const ai_i32 pad_value);
/**
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os16ws1_bn_pad0_fxp(const ai_u32 *pDataIn_init,
ai_i16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os16ws1_bn_pad1_fxp(const ai_u32 *pDataIn_init,
ai_i16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim1 assumptions
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1os16ws1_bn_pad1_optim1_fxp(const ai_u32 *pDataIn_init,
ai_i16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init,
const ai_i32 pad_value);
/**
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsigned output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1ou16ws1_bn_pad1_fxp(const ai_u32 *pDataIn_init,
ai_u16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsigned output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1ou16ws1_bn_pad0_fxp(const ai_u32 *pDataIn_init,
ai_u16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsigned output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F.
* - Optimized thanks to Optim1 assumptions
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is1ou16ws1_bn_pad1_optim1_fxp(const ai_u32 *pDataIn_init,
ai_u16 *pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_float *pScale_init,
const ai_float *pOffset_init,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output - Lite I/F
* @ingroup lite_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
LITE_API_ENTRY
void forward_lite_conv2d_is8os1ws8(const ai_i8 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_i8 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i8 in_zeropoint);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output - Lite I/F - Optimized thanks to Optim2 assumptions
* @ingroup lite_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
LITE_API_ENTRY
void forward_lite_conv2d_is8os1ws8_optim2(const ai_i8 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_i8 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i8 in_zeropoint);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output - quantized with DoReFa SotA quantizer, lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_dorefa_is8os1ws8(const ai_i8 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u8 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i8 in_zeropoint);
/*!
* @brief Handles 2D convolution with 8-bits quantized input, output and weights
* - quantized with with different quantization for channel
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is8os8ws8_sssa_ch(const ai_i8 *pData_in,
ai_i8 *pData_out,
const ai_i8 *pWeights,
const ai_i32 *pBias,
ai_u16 *pBuffer_a,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_float in_scale,
const ai_float out_scale,
const ai_float *pWt_scale,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_i32 scratch_size);
/*!
* @brief Handles 2D convolution with 16-bits quantized inputs, binary outputs and binary weights - Lite I/F.
* Vanilla version.
* @ingroup lite_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
LITE_API_ENTRY
void forward_lite_conv2d_is16os1ws1_bn_fxp(const ai_i16 *pIn,
ai_u32 *pOut_32,
const ai_u32 *pWeights,
const ai_i32 *pThreshold,
ai_i8 *pBufferA,
const ai_i32 dim_kernel,
const ai_i16 dim_im_in_x,
const ai_i16 dim_im_in_y,
const ai_i16 dim_im_out_x,
const ai_i16 dim_im_out_y,
const ai_i16 ch_im_in,
const ai_i16 ch_im_out,
const ai_i16 dim_kernel_x,
const ai_i16 dim_kernel_y,
const ai_i16 padding_x,
const ai_i16 padding_y,
const ai_i16 stride_x,
const ai_i16 stride_y,
const ai_i16 dilation_x,
const ai_i16 dilation_y,
const ai_i16 in_zeropoint);
/**
* @brief Handles 2D convolution with 16-bits quantized inputs, 16-bits quantized outputs and binary weights - Lite I/F
*
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_conv2d_is16os16ws1_fxp(const ai_i16 *pIn,
ai_i16 *pOut,
const ai_u32 *pWeights,
ai_i8 *pBufferA,
const ai_i16 dim_im_in_x,
const ai_i16 dim_im_in_y,
const ai_i16 dim_im_out_x,
const ai_i16 dim_im_out_y,
const ai_i16 ch_im_in,
const ai_i16 ch_im_out,
const ai_u32 dim_kernel,
const ai_i16 dim_kernel_x,
const ai_i16 dim_kernel_y,
const ai_i16 padding_x,
const ai_i16 padding_y,
const ai_i16 stride_x,
const ai_i16 stride_y,
const ai_i16 dilation_x,
const ai_i16 dilation_y,
const ai_i16 in_zeropoint);
AI_API_DECLARE_END
#endif /*LITE_CONV2D_DQNN_H*/
| 30,870 | C | 55.333942 | 119 | 0.351085 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_private.h | /**
******************************************************************************
* @file core_private.h
* @author AST Embedded Analytics Research Platform
* @brief private header file of common private core module defines
******************************************************************************
* @attention
*
* Copyright (c) 2019 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef CORE_PRIVATE_H
#define CORE_PRIVATE_H
#pragma once
#include "ai_math_helpers.h"
#include "ai_datatypes_internal.h"
#include "core_log.h"
/*!
* @defgroup core_private Core Library Private macros and datatypes
* @brief Common macros, datatypes and routines for core private rounites
* @details This module contains the definitons and implementations of some
* internal routines and datatypes that are supposed to not be exposed as
* public headers. So usually this file should be include only on .c files or
* headers that are private as well
*/
/*** Foreground Colors ****************************************************/
#define CORE_COLOR_BLACK "\x1b[30m"
#define CORE_COLOR_RED "\x1b[31m"
#define CORE_COLOR_GREEN "\x1b[32m"
#define CORE_COLOR_YELLOW "\x1b[33m"
#define CORE_COLOR_BLUE "\x1b[94m"
#define CORE_COLOR_MAGENTA "\x1b[35m"
#define CORE_COLOR_CYAN "\x1b[36m"
#define CORE_COLOR_WHYTE "\x1b[37m"
#define CORE_COLOR_DEFAULT "\x1b[39m"
#define CORE_COLOR_LGRAY "\x1b[90m"
#define CORE_COLOR_LRED "\x1b[91m"
#define CORE_COLOR_LGREEN "\x1b[92m"
#define CORE_COLOR_LYELLOW "\x1b[93m"
#define CORE_COLOR_LBLUE "\x1b[94m"
#define CORE_COLOR_LMAGENTA "\x1b[95m"
#define CORE_COLOR_LCYAN "\x1b[96m"
#define CORE_COLOR_LWHITE "\x1b[97m"
/*** Text Attributes Colors *********************************************/
#define CORE_COLOR_OFF "\x1b[0m"
#define CORE_COLOR_BOLD "\x1b[1m"
#define CORE_COLOR_UNDERLINE "\x1b[4m"
#define CORE_COLOR_BLINK "\x1b[5m"
#define CORE_COLOR_BOLD_OFF "\x1b[21m"
#define CORE_COLOR_UNDERLINE_OFF "\x1b[24m"
#define CORE_COLOR_BLINK_OFF "\x1b[25m"
/*** Background Colors ****************************************************/
#define CORE_COLOR_BG_BLACK "\x1b[40m"
#define CORE_COLOR_BG_RED "\x1b[41m"
#define CORE_COLOR_BG_GREEN "\x1b[42m"
#define CORE_COLOR_BG_YELLOW "\x1b[43m"
#define CORE_COLOR_BG_BLUE "\x1b[44m"
#define CORE_COLOR_BG_MAGENTA "\x1b[45m"
#define CORE_COLOR_BG_CYAN "\x1b[46m"
#define CORE_COLOR_BG_WHITE "\x1b[47m"
#define CORE_COLOR_BG_DEFAULT "\x1b[49m"
#define CORE_COLOR_BG_LGRAY "\x1b[100m"
#define CORE_COLOR_BG_LRED "\x1b[101m"
#define CORE_COLOR_BG_LGREEN "\x1b[102m"
#define CORE_COLOR_BG_LYELLOW "\x1b[103m"
#define CORE_COLOR_BG_LBLUE "\x1b[104m"
#define CORE_COLOR_BG_LMAGENTA "\x1b[105m"
#define CORE_COLOR_BG_LCYAN "\x1b[106m"
#define CORE_COLOR_BG_LWHITE "\x1b[107m"
/*****************************************************************************/
#define CORE_ADDRESS_RANGE_INIT(start_, end_) \
core_address_range_init(start_, end_)
#define CORE_GET_BUFFER_META_INFO(meta_info_, tensor_ptr_) \
core_get_buffer_meta_info(meta_info_, tensor_ptr_)
#define CORE_ADDRESS_RANGE_END(range_) \
( (ai_ptr)(((range_)->start)+((range_)->size)) )
#define CORE_ADDRESS_RANGE_OVERLAP(overlap_) \
( ((overlap_)->start) && (((overlap_)->size)>0) )
#define CORE_ADDRESS_RANGE_OVERLAP_PARTIAL(overlap_, ref_) \
( ((overlap_)->start) && (((overlap_)->size)<((ref_)->size)) )
#define CORE_MEMORY_OVERLAP_INIT(partial_, range_, chain_id_, tensor_id_) { \
.partial = (partial_), .range = AI_PACK(range_), \
.chain_id = (chain_id_), .tensor_id = (tensor_id_) \
}
#define CORE_OFFSET(offset_, max_) \
((ai_i32)(((offset_)<0) ? AI_MAX((max_) - (offset_), 0) : AI_MIN(offset_, max_)))
/*****************************************************************************/
/** Network Context Handlers **/
/*****************************************************************************/
/*****************************************************************************/
/** Network Tensors Handlers **/
/*****************************************************************************/
#define AI_TENSOR_HAS_INTQ_INFO \
AI_BUFFER_META_HAS_INTQ_INFO
#define CORE_TENSOR_GET_SHAPE_SIZE(tensor_) \
ai_shape_get_size(AI_TENSOR_SHAPE(tensor_))
#define CORE_ASSERT_SHAPE_MATCH(x, y) \
do { \
AI_ASSERT(AI_SHAPE_H(y) == 1 || AI_SHAPE_H(x)==1 || AI_SHAPE_H(y)==AI_SHAPE_H(x)) \
AI_ASSERT(AI_SHAPE_W(y) == 1 || AI_SHAPE_W(x)==1 || AI_SHAPE_W(y)==AI_SHAPE_W(x)) \
AI_ASSERT(AI_SHAPE_D(y) == 1 || AI_SHAPE_D(x)==1 || AI_SHAPE_D(y)==AI_SHAPE_D(x)) \
AI_ASSERT(AI_SHAPE_E(y) == 1 || AI_SHAPE_E(x)==1 || AI_SHAPE_E(y)==AI_SHAPE_E(x)) \
AI_ASSERT(AI_SHAPE_CH(y) == 1 || AI_SHAPE_CH(x)==1|| AI_SHAPE_CH(y)==AI_SHAPE_CH(x)) \
AI_ASSERT(AI_SHAPE_IN_CH(y) == 1 || AI_SHAPE_IN_CH(x)==1|| AI_SHAPE_IN_CH(y)==AI_SHAPE_IN_CH(x)) \
} while(0);
#define AI_TENSOR_ARRAY_BYTE_SIZE(t_) \
AI_ARRAY_OBJ_BYTE_SIZE(AI_ARRAY_OBJ(t_->data))
#define AI_TENSOR_ARRAY_GET_DATA_ADDR(t_) \
AI_HANDLE_PTR(AI_ARRAY_OBJ_DATA_START(t_->data, void))
#define AI_TENSOR_ARRAY_UPDATE_DATA_ADDR(t_, addr_) \
{ ai_array *arr_ = AI_ARRAY_OBJ(t_->data); \
const uintptr_t off_ = (uintptr_t)arr_->data - (uintptr_t)arr_->data_start; \
arr_->data_start = AI_PTR(addr_); \
arr_->data = AI_PTR((uintptr_t)addr_ + off_); \
}
#define AI_TENSOR_INTEGER_GET_SIZE(t_) \
((t_->klass) ? (AI_KLASS_GET_INTQ_INFO_LIST(t_))->size : 0)
#define AI_TENSOR_INTEGER_GET_SCALE(t_, idx_) \
AI_INTQ_INFO_LIST_SCALE(AI_KLASS_GET_INTQ_INFO_LIST(t_), ai_float, idx_)
#define AI_TENSOR_INTEGER_GET_ZEROPOINT_I8(t_, idx_) \
AI_INTQ_INFO_LIST_ZEROPOINT(AI_KLASS_GET_INTQ_INFO_LIST(t_), ai_i8, idx_)
#define AI_TENSOR_INTEGER_GET_ZEROPOINT_U8(t_, idx_) \
AI_INTQ_INFO_LIST_ZEROPOINT(AI_KLASS_GET_INTQ_INFO_LIST(t_), ai_u8, idx_)
#define AI_TENSOR_FMT_GET_SIGN(t_) \
AI_BUFFER_FMT_GET_SIGN(AI_ARRAY_OBJ(t_->data)->format)
#define AI_TENSOR_FMT_GET_BITS(t_) \
AI_BUFFER_FMT_GET_BITS(AI_ARRAY_OBJ(t_->data)->format)
#define AI_TENSOR_FMT_GET_FBITS(t_) \
AI_BUFFER_FMT_GET_FBITS(AI_ARRAY_OBJ(t_->data)->format)
#define AI_TENSOR_FMT_GET_TYPE(t_) \
AI_BUFFER_FMT_GET_TYPE(AI_ARRAY_OBJ(t_->data)->format)
#define AI_TENSOR_GET_FMT(t_) \
(AI_ARRAY_OBJ(t_->data)->format)
/*****************************************************************************/
/** Network Buffers Handlers **/
/*****************************************************************************/
#define AI_FOR_EACH_BUFFER_ARRAY_ITEM(buffer_ptr_, buffer_array_ptr_, start_pos_, end_pos_) \
ai_buffer* buffer_ptr_ = AI_BUFFER_ARRAY_ITEM(buffer_array_ptr_, \
CORE_OFFSET(end_pos_, AI_BUFFER_ARRAY_SIZE(buffer_array_ptr_))); \
for ( ; buffer_ptr_ && AI_BUFFER_ARRAY_SIZE(buffer_array_ptr_) && \
(buffer_ptr_>=AI_BUFFER_ARRAY_ITEM(buffer_array_ptr_, \
CORE_OFFSET(start_pos_, AI_BUFFER_ARRAY_SIZE(buffer_array_ptr_)))); buffer_ptr_--)
/*****************************************************************************/
/** Network Arrays Handlers **/
/*****************************************************************************/
#define AI_ARRAY_OBJ_FMT(array_) \
AI_CAST(ai_array_format, AI_ARRAY_OBJ(array_)->format)
#define AI_ARRAY_OBJ_SIZE(array_) \
(AI_ARRAY_OBJ(array_)->size)
#define AI_ARRAY_OBJ_BYTE_SIZE(array_) \
AI_SIZE(AI_ARRAY_GET_BYTE_SIZE(AI_ARRAY_OBJ_FMT(array_), \
AI_ARRAY_OBJ_SIZE(array_)))
#define AI_ARRAY_OBJ_DATA_SIZE(array_) \
AI_ARRAY_GET_DATA_BYTE_SIZE(AI_ARRAY_OBJ_FMT(array_), \
AI_ARRAY_OBJ_SIZE(array_))
#define AI_ARRAY_OBJ_DATA(array_, type_) \
AI_CAST(type_*, AI_ARRAY_OBJ(array_)->data)
#define AI_ARRAY_OBJ_DATA_START(array_, type_) \
AI_CAST(type_*, AI_ARRAY_OBJ(array_)->data_start)
#define AI_ARRAY_OBJ_ELEM(array_, type_, pos_) \
AI_ARRAY_OBJ_DATA(array_, type_)[(pos_)]
/*****************************************************************************/
/** Network Tensors Chains / Lists Handlers **/
/*****************************************************************************/
#define SET_TENSOR_IN(chain_, pos_) \
(GET_TENSOR_LIST_IN(chain_)->tensor[(pos_)])
#define SET_TENSOR_OUT(chain_, pos_) \
(GET_TENSOR_LIST_OUT(chain_)->tensor[(pos_)])
#define AI_NODE_IO_GET(node_, in_, out_) \
ASSERT_NODE_SANITY(node_) \
ai_tensor* in_ = GET_TENSOR_IN((node_)->tensors, 0); \
ai_tensor* out_ = GET_TENSOR_OUT((node_)->tensors, 0); \
ASSERT_TENSOR_SANITY(in_) \
ASSERT_TENSOR_SANITY(out_)
/*****************************************************************************/
#define AI_BITS_TO_BYTES(bits_) \
(((bits_)+0x7) >> 3)
#define AI_BYTES_TO_BITS(bytes_) \
((bytes_) << 3)
/*****************************************************************************/
/** Network Nodes Handlers **/
/*****************************************************************************/
#define AI_NODE_IS_FIRST(node) \
(AI_NODE_OBJ(node)==AI_NODE_OBJ(AI_NODE_OBJ(node)->network->input_node))
#define AI_NODE_IS_LAST(node_) \
((AI_NODE_OBJ(node_)==AI_NODE_OBJ(node_)->next) || \
(AI_NODE_OBJ(node_)->next==NULL))
#define AI_FOR_EACH_NODE_DO(node_, nodes_) \
for (ai_node* node_ = AI_NODE_OBJ(nodes_); (node_); \
node_ = ((AI_NODE_IS_LAST(node_)) ? NULL : (node_)->next))
/*****************************************************************************/
typedef struct {
ai_ptr start;
ai_size size;
} ai_address_range;
typedef struct {
ai_address_range range;
ai_u16 chain_id;
ai_u16 tensor_id;
ai_bool partial;
} ai_memory_overlap;
/*****************************************************************************/
AI_DECLARE_STATIC
ai_address_range core_address_range_init(
const ai_handle start, const ai_handle end)
{
ai_address_range r;
r.start = (start<end) ? start : end;
r.size = (ai_size) ((start<end)
? ((ai_uptr)end-(ai_uptr)start) : ((ai_uptr)start-(ai_uptr)end));
return r;
}
AI_DECLARE_STATIC
ai_buffer_meta_info* core_get_buffer_meta_info(
ai_buffer_meta_info* meta,
const ai_tensor* t)
{
if (!meta) return NULL;
AI_ASSERT(t && t->data)
ai_bool ok;
meta->flags = 0x0;
meta->intq_info = AI_KLASS_GET_INTQ_INFO_LIST(t);
ok = (meta->intq_info && (meta->intq_info->size>0));
meta->flags |= (ok) ? AI_BUFFER_META_HAS_INTQ_INFO : 0x0;
return (ok) ? meta : NULL;
}
#if 0
#include <stdio.h>
#include <stdarg.h>
AI_DECLARE_STATIC
void _dump_file_print(
const char* fname, const char* fmt, ...)
{
static FILE* fp = NULL;
if (fname) {
if (!fp) {
fp = fopen(fname, "a");
}
}
if (fp) {
va_list args;
va_start(args, fmt);
vfprintf(fp, fmt, args);
va_end(args);
fflush(fp);
}
}
AI_DECLARE_STATIC
void _dump_bytearray(
const char* fname,
const ai_handle src, const ai_size src_size, const ai_u8 src_id,
const char* name)
{
static FILE* fp = NULL;
if (fname && src && (src_size>0)) {
if (!fp) {
fp = fopen(fname, "a");
}
}
if (fp) {
switch (src_id) {
case 1:
{
const ai_float* src_value = (const ai_float*)src;
fprintf(fp, "ai_float %s[%u] = {%f", name, src_size, src_value[0]);
for (ai_size i=1; i<src_size; i++) { fprintf(fp, ", %f", src_value[i]); }
} break;
case 2:
{
const ai_i8* src_value = (const ai_i8*)src;
fprintf(fp, "ai_i8 %s[%u] = {%d", name, src_size, src_value[0]);
for (ai_size i=1; i<src_size; i++) { fprintf(fp, ", %d", src_value[i]); }
} break;
case 3:
{
const ai_u8* src_value = (const ai_u8*)src;
fprintf(fp, "ai_u8 %s[%u] = {%u", name, src_size, src_value[0]);
for (ai_size i=1; i<src_size; i++) { fprintf(fp, ", %u", src_value[i]); }
} break;
default:
fprintf(fp, "format not supported: %u {", src_id);
break;
}
fprintf(fp, "};\n");
fflush(fp);
}
}
#endif
#endif /* CORE_PRIVATE_H */
| 13,110 | C | 34.822404 | 114 | 0.510297 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_operators.h | #ifndef LITE_OPERATORS_H
#define LITE_OPERATORS_H
#pragma once
#include "lite_bn_f32.h"
#include "lite_bn_integer.h"
#include "lite_conv2d.h"
#include "lite_conv2d_dqnn.h"
#include "lite_convert_dqnn.h"
#include "lite_dense_if32.h"
#include "lite_dense_is1.h"
#include "lite_dense_is1ws1.h"
#include "lite_dense_ws1.h"
#include "lite_gru_f32.h"
#include "lite_dw_dqnn.h"
#include "lite_pw_dqnn.h"
#include "lite_dense_is8os8ws8.h"
#include "lite_generic_float.h"
#include "lite_pool_f32.h"
#include "lite_maxpool_dqnn.h"
#include "lite_nl_generic_float.h"
#include "lite_nl_generic_integer.h"
#include "lite_pad_generic.h"
#include "lite_pad_dqnn.h"
#include "lite_upsample_generic.h"
#endif /* LITE_OPERATORS_H */
| 718 | C | 23.793103 | 36 | 0.727019 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_norm.h | /**
******************************************************************************
* @file layers_norm.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform normalization layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_NORM_H
#define LAYERS_NORM_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_norm Normalization Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_bn
* @ingroup layers_norm
* @brief Batch normalization (scale with bias) layer
*/
typedef ai_layer_base ai_layer_bn;
/*!
* @struct ai_layer_lrn
* @ingroup layers_norm
* @brief Local Response Normalization layer
*
* Divides each element by a scale factor computed
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_lrn_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_u32 local_size; /*!< size of the normalization window */
ai_float k; /*!< bias term */
ai_float alpha; /*!< input scale */
ai_float beta; /*!< scale exponent */
} ai_layer_lrn;
/*!
* @enum ai_norm_type_e
* @ingroup layers_norm
* @brief store the type of normalization algorithm to apply
*/
typedef enum ai_norm_type_ {
NONE = 0,
L1 = 1,
L2 = 2,
MAX = 3,
} ai_norm_type_e;
/*!
* @struct ai_layer_norm
* @ingroup layers_norm
* @brief Lp Normalization layer
*
* Normalizes the tensor along the 'axis' direction using the Lp norm.
* Optionally divides the result by the number of the elements.
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_norm_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_shape_idx axis; /*! normalization axis */
ai_float exponent; /*!< normalization exponent p */
ai_bool scale; /*!< multiplies by the pth root of the number of elements */
ai_norm_type_e norm_type;
} ai_layer_norm;
/*!
* @brief Local response normalization computed on a float array
* @ingroup layers_norm
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param pad amount of padding for the channels
*/
AI_INTERNAL_API
void func_lrn_array_f32(ai_handle out, const ai_handle in,
const ai_size in_size, const ai_size channel_size,
const ai_i32 pad, const ai_float k,
const ai_float alpha, const ai_float beta);
/*!
* @brief Lp normalization computed on a float array
* @ingroup layers_norm
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param exponent p exponent for the Lp normalization
* @param axis_stride stride (in array elements) of the normalization axis
* @param axis_size size of the normalization axis
* @param outer_size number of tensor slices (including the normalization axis)
* on which compute the normalization
*/
AI_INTERNAL_API
void func_norm_array_f32(ai_handle out, const ai_handle in,
const ai_float exponent,
const ai_float norm,
const ai_size axis_stride,
const ai_size axis_size,
const ai_size outer_size);
/*!
* @brief Max normalization computed on float array
* @ingroup layers_norm
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param axis_stride stride (in array elements) of the normalization axis
* @param axis_size size of the normalization axis
* @param outer_size number of tensor slices (including the normalization axis)
*/
AI_INTERNAL_API
void func_norm_max_array_f32(ai_handle out, const ai_handle in,
const ai_float norm,
const ai_size axis_size,
const ai_size n_el);
/*!
* @brief Fast L2 normalization computed on a float array
* @ingroup layers_norm
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param axis_size size of the normalization axis
* @param n_el total number of elements in the tensor
*/
AI_INTERNAL_API
void func_norm_l2_fast_array_f32(ai_handle out, const ai_handle in,
const ai_float norm,
const ai_size axis_size,
const ai_size outer_size);
/*!
* @brief Fast L1 normalization computed on a float array
* @ingroup layers_norm
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param axis_size size of the normalization axis
* @param n_el total number of elements in the tensor
*/
AI_INTERNAL_API
void func_norm_l1_fast_array_f32(ai_handle out, const ai_handle in,
const ai_float norm,
const ai_size axis_size,
const ai_size n_el);
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Computes the activations of a batchnorm (scale + bias) layer.
* @ingroup layers_norm
* @param layer the batch normalization (bn) layer
*/
AI_INTERNAL_API
void forward_bn(ai_layer* layer);
/*!
* @brief Computes the activations of a batchnorm (scale + bias) layer with
* integer format
* @ingroup layers_norm
* @param layer the batch normalization (bn) layer
*/
AI_INTERNAL_API
void forward_bn_integer(ai_layer* layer);
/*!
* @brief Computes the activations of a Local Response Normalization Layer.
* @ingroup layers_norm
* @param layer the local response normalization (lrn) layer
*/
AI_INTERNAL_API
void forward_lrn(ai_layer* layer);
/*!
* @brief Computes the activations of a normalization layer.
* @ingroup layers_norm
* @param layer the normalization (norm) layer
*/
AI_INTERNAL_API
void forward_norm(ai_layer* layer);
/*!
* @brief Batch Normalization with 16-bit input, 16-bit threshold and binary output.
* It is implemented using a threshold, and this is possible because the output is binary.
* @param layer the batch normalization layer
*/
AI_INTERNAL_API
void forward_bn_is16os1ws16(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_NORM_H*/
| 6,910 | C | 31.909524 | 97 | 0.612156 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_conv2d_dqnn.h | /**
******************************************************************************
* @file layers_conv2d_dqnn.h
* @author AIS
* @brief header file of AI platform DQNN conv datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_CONV2D_DQNN_H
#define LAYERS_CONV2D_DQNN_H
#pragma once
#include "layers_common.h"
#include "layers_conv2d.h"
/*!
* @defgroup layers_conv2d_dqnn Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
#define AI_DQNN_PAD_1_KEY (1)
#define AI_DQNN_PAD_M1_KEY (-1)
#define AI_DQNN_PAD_0_KEY (0)
#define AI_DQNN_PAD_1_VALUE (0x0)
#define AI_DQNN_PAD_M1_VALUE (0xFFFFFFFF)
#define AI_DQNN_PAD_0_VALUE (0x2)
/*!
* @struct ai_layer_conv2d_dqnn
* @ingroup layers_conv2d_dqnn
* @brief conv2d_dqnn layer
*
* @ref forward_conv2d_is1os1ws1
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_conv2d_dqnn_ {
AI_LAYER_CONV2D_FIELDS_DECLARE
ai_i32 pad_value;
} ai_layer_conv2d_dqnn;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles point wise convolution with binary input, binary output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1os1ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles point wise convolution with binary input, binary output and
* binary weights - Optimized thanks to Optim2 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1os1ws1_bn_optim2(ai_layer *pLayer);
/*!
* @brief Handles point wise convolution with binary input, 8-bits output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1os8ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles point wise convolution with binary input, 8-bits output and
* binary weights - Optimized thanks to Optim1 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1os8ws1_bn_optim1(ai_layer *pLayer);
/*!
* @brief Handles point-wise convolution with binary input, float32 output
* and binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1of32ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles point-wise convolution with binary input, float32 output
* and binary weights - Optimized thanks to Optim1 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_pw_is1of32ws1_bn_optim1(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - Optimized thanks to Optim2 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn_optim2(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os8ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - Optimized thanks to Optim1 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os8ws1_bn_optim1(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn_pad0(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Optimized thanks to
* Optim0 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn_pad0_optim0(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with 0 padding (QKeras like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os8ws1_bn_pad0(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn_pad1(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Optimized thanks
* to Optim2 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os1ws1_bn_pad1_optim2(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with +1/-1 padding (Larq like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os8ws1_bn_pad1(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with +1/-1 padding (Larq like) - Optimized thanks
* to Optim1 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os8ws1_bn_pad1_optim1(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is8os1ws8(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output - Optimized thanks to Optim2 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is8os1ws8_optim2(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and weights and
* binary output - quantized with DoReFa SotA quantizer
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_dorefa_is8os1ws8(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 16-bits quantized input, binary weights
and binary output
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is16os1ws1_bn_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 16-bits quantized input, binary weights
and 16-bits quantized output
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is16os16ws1_fxp(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights - Optimized thanks to Optim3 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn_optim3(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn_pad0(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Optimized thanks to
* Optim3 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn_pad0_optim3(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like)
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn_pad1(ai_layer *pLayer);
/*!
* @brief Handles depth-wise convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Optimized thanks to
* Optim3 assumptions
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_dw_is1os1ws1_bn_pad1_optim3(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with 8-bits quantized Input and output and
* binary weights
* @ingroup layers_conv2d_dqnn
* @param layer conv2d_dqnn layer
*/
AI_INTERNAL_API
void forward_conv2d_is8os8ws1(ai_layer *pLayer);
/**
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os16ws1_bn_pad0_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os16ws1_bn_pad1_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim1 assumptions
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1os16ws1_bn_pad1_optim1_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsigned output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1ou16ws1_bn_pad0_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsigned output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* @ingroup lite_conv2d_dqnn
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1ou16ws1_bn_pad1_fxp(ai_layer *pLayer);
/*!
* @brief Handles 2D convolution with binary input, fixed point 16-bits unsiged output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim1 assumptions
* @ingroup lite_conv2d_dqnn
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is1ou16ws1_bn_pad1_optim1_fxp(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer quantized 2D convolutional layer
* for SSSA per channel quantized RGB scheme using n_channel_in = 3
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_conv2d_is8os8ws8_sssa_ch_rgb(const ai_i8 *pData_in,
ai_i8 *pData_out,
const ai_i8 *pWeights,
const ai_i32 *pBias,
ai_u16 *pBuffer_a,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_float in_scale,
const ai_float out_scale,
const ai_float *pWt_scale,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_bool out_ch_format,
ai_i16 *p_out_r_shift,
ai_i32 *p_out_factor);
/*!
* @brief Computes the activations of a point-wise integer quantized convolution
for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_pw_is8os8ws8_sssa_ch(const ai_i8 *pData_in,
ai_i8 *pData_out,
const ai_i8 *pWeights,
const ai_i32 *pBias,
ai_u16 *pBuffer_a,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_float in_scale,
const ai_float out_scale,
const ai_float *pWt_scale,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
ai_i16 *p_out_r_shift,
ai_i32 *p_out_factor,
ai_i32 AI_PWOverlay,
ai_i16 *bufferA,
ai_i32 scratch_size);
// st_nn_context_t context);
/*!
* @brief Computes the activations of a depth-wise integer quantized convolution
for SSSA per channel quantized scheme
* @ingroup layers_conv2d
* @param layer the convolutional (conv) layer
*/
AI_INTERNAL_API
void forward_dw_is8os8ws8_sssa_ch(const ai_i8 *pData_in,
ai_i8 *pData_out,
const ai_i8 *pWeights,
const ai_i32 *pBias,
ai_u16 *pBuffer_a,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_float in_scale,
const ai_float out_scale,
const ai_float *pWt_scale,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
ai_i16 *p_out_r_shift,
ai_i32 *p_out_factor);
AI_API_DECLARE_END
#endif /*LAYERS_CONV2D_DQNN_H*/
| 17,573 | C | 34.647059 | 91 | 0.579981 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_upsample_generic.h | /**
******************************************************************************
* @file layers_upsample_generic.h
* @author Cyril Enault
* @brief header file of AI platform padding generic datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_UPSAMPLE_H
#define LAYERS_UPSAMPLE_H
#pragma once
#include "layers_generic.h"
/*!
* @defgroup layers_pad_generic Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles generic upsmapling in nearest mode
* @ingroup layers_generic
* @param layer upsample layer
*/
AI_INTERNAL_API
void forward_upsample_nearest(ai_layer *pLayer);
/*!
* @brief Handles generic upsmapling in zeros mode
* @ingroup layers_generic
* @param layer upsample layer
*/
AI_INTERNAL_API
void forward_upsample_zeros(ai_layer *pLayer);
/*!
* @brief Handles generic upsmapling in bilinear mode
* @ingroup layers_generic
* @param layer upsample layer
*/
AI_INTERNAL_API
void forward_upsample_bilinear(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_PAD_GENERIC_H*/
| 1,845 | C | 26.552238 | 80 | 0.509485 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_bn_f32.h | #ifndef LITE_BN_F32_H
#define LITE_BN_F32_H
#pragma once
#include "ai_lite_interface.h"
/*!
* @brief Forward function for a batch normalization (BN) layer with
* signed float input, signed float output, and float parameters.
* @ingroup lite_bn_f32
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param scale The pointer to BN scale param.
* @param bias The pointer to bias.
* @param n_elements The number of elements in the input tensor.
* @param n_channel_in The number of channel in the input tensor.
*/
LITE_API_ENTRY
void forward_lite_bn_if32of32wf32(
ai_float* output, const ai_float* input,
const ai_float* scale, const ai_float* bias,
const ai_u32 n_elements, const ai_u32 n_channel_in);
#endif /* LITE_BN_F32_H */
| 791 | C | 29.461537 | 69 | 0.718078 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dense_is1ws1.h | #ifndef _LITE_DENSE_IS1WS1_H
#define _LITE_DENSE_IS1WS1_H
#pragma once
#include "ai_lite_interface.h"
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed binary output, and signed binary weights.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param scratch The pointer to the scratch buffer.
* @param n_channel_in The number of channels of the input.
* @param n_channel_ouy The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1os1ws1(
ai_pbits *output, const ai_pbits *input, const ai_pbits *weights,
const ai_pbits *bias, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out
);
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed binary output, and signed binary weights.
* The BN is fused, i.e., the layer requires weights, scale, and offset, where
* weights are those of the dense layer, scale is that of the BN, and the offset
* corresponds to dense bias * bn scale + bn offset. If the parameters do not
* agree with such convention, the behavior is undefined.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param scale The pointer to scale.
* @param offset The pointer to offset.
* @param scratch The pointer to the scratch buffer.
* @param n_channel_in The number of channels of the input.
* @param n_channel_ouy The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1os1ws1_bn(
ai_pbits *output, const ai_pbits *input, const ai_pbits *weights,
const ai_float *scale, const ai_float *offset, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out
);
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed binary output, and signed 16bit weights.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param scratch The pointer to the scratch buffer (signed 32bit).
* @param n_channel_in The number of channels of the input.
* @param n_channel_ouy The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1os16ws1(
ai_i16 *output, const ai_pbits *input, const ai_pbits *weights,
const ai_pbits *bias, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out);
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed binary output, and signed 16bit weights.
* The BN is fused, i.e., the layer requires weights, scale, and offset, where
* weights are those of the dense layer, scale is that of the BN, and the offset
* corresponds to dense bias * bn scale + bn offset. If the parameters do not
* agree with such convention, the behavior is undefined.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param scratch The pointer to the scratch buffer (signed 32bit).
* @param n_channel_in The number of channels of the input.
* @param n_channel_ouy The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1os16ws1_bn(
ai_i16 *output, const ai_pbits *input, const ai_pbits *weights,
const ai_float *scale, const ai_float *offset, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out);
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed float output, and signed binary weights.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param scratch The pointer to the scratch buffer (unused).
* @param n_channel_in The number of channels of the input.
* @param n_channel_ouy The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1of32ws1(
ai_float *output, const ai_pbits *input, const ai_pbits *weights,
const ai_pbits *bias, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out
);
/*!
* @brief Forward function for a dense layer with signed binary input,
* signed float output, and signed binary weights.
* The BN is fused, i.e., the layer requires weights, scale, and offset, where
* weights are those of the dense layer, scale is that of the BN, and the offset
* corresponds to dense bias * bn scale + bn offset. If the parameters do not
* agree with such convention, the behavior is undefined.
* @ingroup lite_dense_is1ws1
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param scale The pointer to scale.
* @param offset The pointer to offset.
* @param scratch The pointer to the scratch buffer (unused).
* @param n_channel_in The number of channels of the input.
* @param n_channel_out The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_is1of32ws1_bn(
ai_float *output, const ai_pbits *input, const ai_pbits *weights,
const ai_float *scale, const ai_float *offset, ai_i32 *scratch,
const ai_u32 n_channel_in, const ai_u32 n_channel_out
);
#endif /*_LITE_DENSE_IS1WS1_H*/
| 5,999 | C | 40.666666 | 80 | 0.724287 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_custom.h | /**
******************************************************************************
* @file layers_custom.h
* @author Marco Lattuada
* @brief header file of AI platform custom layers datatype
******************************************************************************
* @attention
*
* Copyright (c) 2020 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_CUSTOM_H
#define LAYERS_CUSTOM_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_custom Custom layer definitions
* @brief Definition of structures custom layers
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_custom
* @ingroup layers_custom
* @brief Custom layer wrapper
*
* The custom layer wrapper
*/
typedef ai_layer_stateful ai_layer_custom;
AI_API_DECLARE_END
#endif /*LAYERS_CUSTOM_H*/
| 1,217 | C | 24.914893 | 80 | 0.518488 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_conv2d.h | /**
******************************************************************************
* @file lite_conv2d.h
* @author AIS
* @brief header file of AI platform lite conv2d kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_CONV2D_H
#define LITE_CONV2D_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles 2D convolution with float input, float output and
* float weights
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_conv2d_if32of32wf32(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_ptr_const pWeights_init,
const ai_ptr_const pBias_init,
const ai_size n_channel_in,
const ai_size n_channel_out,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_size filt_height_dilated,
const ai_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_size n_groups);
/*!
* @brief Handles 2D depthwise convolution with float input, float output and
* float weights
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_dw_if32of32wf32(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_ptr_const pWeights_init,
const ai_ptr_const pBias_init,
const ai_size n_channel_in,
const ai_size n_channel_out,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_size filt_height_dilated,
const ai_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_size n_groups);
/*!
* @brief Handles 2D grouped convolution with float input, float output and
* float weights
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_conv2d_if32of32wf32_group(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_ptr_const pWeights_init,
const ai_ptr_const pBias_init,
const ai_size n_channel_in,
const ai_size n_channel_out,
const ai_size width_in,
const ai_size height_in,
const ai_size width_out,
const ai_size height_out,
const ai_size filt_width,
const ai_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_size filt_height_dilated,
const ai_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_size n_groups);
/*!
* @brief Handles dilated conv2d convolutions (valid padding)
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void
forward_lite_conv2d_dilated_sssa8_ch(const ai_i8 *pData_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 n_channel_in,
const ai_i8 *pWeights,
const ai_u16 n_channel_out,
const ai_u16 dim_kernel_x,
const ai_u16 dim_kernel_y,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_layer_format_type out_ch_format,
ai_i8 *pData_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
ai_u32 height_loop_cnt,
const ai_u16 weights_prefetch_enabled,
ai_i32 scratch_size,
ai_i16 *pBuffer_a);
/*!
* @brief Handles conv2d convolutions (valid padding) with number of channels >= 8
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void
forward_lite_conv2d_deep_sssa8_ch(const ai_i8 *pData_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 n_channel_in,
const ai_i8 *pWeights,
const ai_u16 n_channel_out,
const ai_u16 dim_kernel_x,
const ai_u16 dim_kernel_y,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_layer_format_type out_ch_format,
ai_i8 *pData_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
ai_u32 height_loop_cnt,
const ai_u16 weights_prefetch_enabled,
ai_i32 scratch_size,
ai_i16 *pBuffer_a);
/*!
* @brief Handles conv2d convolutions (valid padding) with number of channels >= 8
* Special forward function for 3x3 kernels and Stride = 1
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void
forward_lite_conv2d_deep_3x3_sssa8_ch(const ai_i8 *pData_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 n_channel_in,
const ai_i8 *pWeights,
const ai_u16 n_channel_out,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_layer_format_type out_ch_format,
ai_i8 *pData_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
ai_u32 height_loop_cnt,
ai_i32 scratch_size,
ai_i16 *pBuffer_a);
/*!
* @brief Handles conv2d convolutions with same padding or with number of channels < 8
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void
forward_lite_conv2d_sssa8_ch(const ai_i8 *pData_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 n_channel_in,
const ai_i8 *pWeights,
const ai_u16 n_channel_out,
const ai_u16 dim_kernel_x,
const ai_u16 dim_kernel_y,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_u16 padding_x,
const ai_u16 padding_y,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_layer_format_type out_ch_format,
ai_i8 *pData_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
const ai_u16 weights_prefetch_enabled,
ai_i32 scratch_size,
ai_i16 *pBuffer_a);
/*!
* @brief Handles rgb conv2d convolutions
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void
forward_lite_conv2d_rgb_sssa8_ch(const ai_i8 *pData_in,
const ai_u16 dim_im_in,
const ai_i8 *pWeights,
const ai_u16 n_channel_out,
const ai_u16 dim_kernel,
const ai_u16 padding,
const ai_u16 stride,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_layer_format_type out_ch_format,
ai_i8 *pData_out,
const ai_u16 dim_im_out,
ai_i32 scratch_size,
ai_i16 *pBuffer_a);
/*!
* @brief Handles 2D convolution with float input, float output and
* float weights with pool fused
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_conv2d_if32of32wf32_pool(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_float * pWeights_init,
const ai_float *pBias_init,
ai_float *pScratch_init,
const ai_short_size n_channel_in,
const ai_short_size n_channel_out,
const ai_short_size width_in,
const ai_short_size height_in,
const ai_short_size width_out,
const ai_short_size height_out,
const ai_short_size filt_width,
const ai_short_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_short_size filt_height_dilated,
const ai_short_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_short_size n_groups,
const ai_short_size width_conv_out,
const ai_short_size height_conv_out,
ai_handle pool_func,
const ai_short_size pool_width,
const ai_short_size pool_height,
const ai_short_size pool_stride_x,
const ai_short_size pool_stride_y,
const ai_short_size pool_pad_x,
const ai_short_size pool_pad_y);
/*!
* @brief Handles 2D depthwise convolution with float input, float output and
* float weights with pool fused
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_dw_if32of32wf32_pool(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_float *pWeights_init,
const ai_float *pBias_init,
ai_float *pScratch_init,
const ai_short_size n_channel_in,
const ai_short_size n_channel_out,
const ai_short_size width_in,
const ai_short_size height_in,
const ai_short_size width_out,
const ai_short_size height_out,
const ai_short_size filt_width,
const ai_short_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_short_size filt_height_dilated,
const ai_short_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_short_size n_groups,
const ai_short_size width_conv_out,
const ai_short_size height_conv_out,
ai_handle pool_func,
const ai_short_size pool_width,
const ai_short_size pool_height,
const ai_short_size pool_stride_x,
const ai_short_size pool_stride_y,
const ai_short_size pool_pad_x,
const ai_short_size pool_pad_y);
/*!
* @brief Handles 2D grouped convolution with float input, float output and
* float weights with pool fused
* @ingroup lite_conv2d
*/
LITE_API_ENTRY
void forward_lite_conv2d_if32of32wf32_group_pool(const ai_float *pDataIn_init,
ai_float *pDataOut_init,
const ai_float *pWeights_init,
const ai_float *pBias_init,
ai_float *pScratch_init,
const ai_short_size n_channel_in,
const ai_short_size n_channel_out,
const ai_short_size width_in,
const ai_short_size height_in,
const ai_short_size width_out,
const ai_short_size height_out,
const ai_short_size filt_width,
const ai_short_size filt_height,
const ai_u16 filt_pad_x,
const ai_u16 filt_pad_y,
const ai_u16 filt_stride_x,
const ai_u16 filt_stride_y,
const ai_short_size filt_height_dilated,
const ai_short_size filt_width_dilated,
const ai_u16 dilation_x,
const ai_u16 dilation_y,
const ai_short_size n_groups,
const ai_short_size width_conv_out,
const ai_short_size height_conv_out,
ai_handle pool_func,
const ai_short_size pool_width,
const ai_short_size pool_height,
const ai_short_size pool_stride_x,
const ai_short_size pool_stride_y,
const ai_short_size pool_pad_x,
const ai_short_size pool_pad_y);
#endif /*LITE_CONV2D_H*/
| 18,200 | C | 49.418282 | 86 | 0.398407 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_formats_converters.h | /**
******************************************************************************
* @file layers_formats_converters.h
* @author AST Embedded Analytics Research Platform
* @brief header file of formats converters layers
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_FORMATS_CONVERTERS_H
#define LAYERS_FORMATS_CONVERTERS_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_formats_converters Formats Converters Layers Definition
* @brief this group implements formats converter layers (cast, etc.)
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_cast
* @ingroup layers_formats_converters
* @brief C Implementation of cast layer
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_cast_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_array_format to_format; /*!< cast output format */
} ai_layer_cast;
/*****************************************************************************/
/* Forward Functions Section */
/*****************************************************************************/
/*!
* @brief forward function for cast layer.
* @ingroup layers_
* @param layer template layer as an opaque pointer
*/
AI_INTERNAL_API
void forward_cast(ai_layer* layer);
AI_API_DECLARE_END
#endif /*LAYERS_FORMATS_CONVERTERS_H*/
| 1,862 | C | 29.048387 | 80 | 0.50913 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_pool.h | /**
******************************************************************************
* @file layers_pool.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform pooling layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_POOL_H
#define LAYERS_POOL_H
#pragma once
#include "layers_common.h"
#include "lite_maxpool_dqnn.h"
#include "lite_pool_f32.h"
/*!
* @defgroup layers_pool Pooling Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_pool
* @ingroup layers_pool
* @brief Pooling layer
*
* The type of pooling function is handled by the specific forward function
* @ref forward_pool
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_pool_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_shape_2d pool_size; /*!< pooling size */
ai_shape_2d pool_stride; /*!< pooling stride */
ai_shape pool_pad; /*!< pooling pad, y,x border sizes */
ai_u8 count_include_pad; /*!< include pad flag */
} ai_layer_pool;
/*!
* @brief Max Pooling on a 8/16 bits fixed point data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to output data
*/
AI_INTERNAL_API
void pool_func_mp_array_fixed(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Max Pooling on a 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to output data
*/
AI_INTERNAL_API
void pool_func_mp_array_integer(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Max Pooling on a signed 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to output data
*/
AI_INTERNAL_API
void pool_func_mp_array_integer_INT8(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Max Pooling on a unsigned 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to output data
*/
AI_INTERNAL_API
void pool_func_mp_array_integer_UINT8(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Average Pooling on a 8/16 bits fixed point data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to scratch memory
*/
AI_INTERNAL_API
void pool_func_ap_array_fixed(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Average Pooling on a 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to scratch memory
*/
AI_INTERNAL_API
void pool_func_ap_array_integer(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Average Pooling on a signed 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to scratch memory
*/
AI_INTERNAL_API
void pool_func_ap_array_integer_INT8(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/*!
* @brief Average Pooling on a unsigned 8-bits integer quantized data array
* @ingroup layers_pool
* @param in opaque handler to input data to process
* @param dim_im_in_x input feature map width
* @param dim_im_in_y input feature map height
* @param ch_im_in number of input channels
* @param dim_kernel_x kernel width
* @param dim_kernel_y kernel height
* @param padding_x right padding value
* @param padding_y top padding value
* @param stride_x stride value on x dimension
* @param stride_y stride value on y dimension
* @param dim_im_out_x output feature map width
* @param dim_im_out_y output feature map height
* @param out opaque handler to scratch memory
*/
AI_INTERNAL_API
void pool_func_ap_array_integer_UINT8(ai_handle in,
const ai_u16 dim_im_in_x, const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_u16 dim_kernel_x, const ai_u16 dim_kernel_y,
const ai_u16 padding_x, const ai_u16 padding_y,
const ai_u16 stride_x, const ai_u16 stride_y,
const ai_u16 dim_im_out_x, const ai_u16 dim_im_out_y,
ai_handle out);
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Computes the activations of a max pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point max pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized max pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized max pooling layer
* with int8 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_integer_INT8(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized max pooling layer
* with uint8 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_integer_UINT8(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized max pooling layer
* with int16 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_integer_INT16(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized max pooling layer
* with uint16 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_mp_integer_UINT16(ai_layer *pLayer);
/*!
* @brief Computes the activations of an average pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_ap(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point average pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_ap_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized average pooling layer.
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_ap_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized average pooling layer
* with int8 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_ap_integer_INT8(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer-quantized average pooling layer
* with uint8 I/O
* @ingroup layers_pool
* @param layer the pooling (pool) layer
*/
AI_INTERNAL_API
void forward_ap_integer_UINT8(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_POOL_H*/
| 14,171 | C | 36.294737 | 81 | 0.624656 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_pad_generic.h | /**
******************************************************************************
* @file layers_pad_generic.h
* @author Marco Forleo
* @brief header file of AI platform padding generic datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_PADDING_DQNN_H
#define LAYERS_PADDING_DQNN_H
#pragma once
#include "layers_generic.h"
/*!
* @defgroup layers_pad_generic Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles generic padding in constant mode
* @ingroup layers_generic_dqnn
* @param layer pad layer
*/
AI_INTERNAL_API
void forward_pad_constant(ai_layer *pLayer);
/*!
* @brief Handles generic padding in edge mode
* @ingroup layers_generic_dqnn
* @param layer pad layer
*/
AI_INTERNAL_API
void forward_pad_edge(ai_layer *pLayer);
/*!
* @brief Handles generic padding in reflect mode
* @ingroup layers_generic_dqnn
* @param layer pad layer
*/
AI_INTERNAL_API
void forward_pad_reflect(ai_layer *pLayer);
/*!
* @brief Handles generic padding in constant mode Channel 1st 8bit
* @ingroup layers_generic_dqnn
* @param layer pad layer
*/
AI_INTERNAL_API
void forward_pad_8bit_ch1st_3x3_constant(ai_layer* pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_PAD_GENERIC_H*/
| 2,034 | C | 25.776315 | 80 | 0.525074 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_ml_treeensembleclassifier.h | /**
******************************************************************************
* @file layers_ml_treeensembleclassifier.h
* @author AIS
* @brief header file of AI platform TreeEnsembleClassifier datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021-2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_TREE_ENSEMBLE_CLASSIFIER_H
#define LAYERS_TREE_ENSEMBLE_CLASSIFIER_H
#pragma once
#include "layers_common.h"
#include "layers_nl.h"
/*!
* @defgroup layers_ml_treensembleclassifier Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/* Error return codes */
#define AI_TREE_ENSEMBLE_CLASSIFIER_ERROR_NO 0
#define AI_TREE_ENSEMBLE_CLASSIFIER_ERROR_WRONG_IDX_FMT -1
#define AI_TREE_ENSEMBLE_CLASSIFIER_ERROR_UNFOUND_LEAF -2
#define AI_TREE_ENSEMBLE_CLASSIFIER_ERROR_UNSUPPORTED_BRANCH -3
#define AI_TREE_ENSEMBLE_CLASSIFIER_ERROR_UNSUPPORTED_FEATURE -4
#define AI_TREE_ENSEMBLE_CLASSIFIER_DEPTH_MAX 10000
/* Type of condition in the TreeEnsembleClassifier*/
typedef enum
{
AI_TREE_ENSEMBLE_CLASSIFIER_BRANCH_LT_IDX = 0,
AI_TREE_ENSEMBLE_CLASSIFIER_BRANCH_LEQ_IDX,
AI_TREE_ENSEMBLE_CLASSIFIER_BRANCH_EQ_IDX,
AI_TREE_ENSEMBLE_CLASSIFIER_BRANCH_END,
} ai_tree_ensenble_classifier_branch_e;
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_tree_ensemble_classifier_ {
AI_LAYER_COMMON_FIELDS_DECLARE
func_nl nl_func;
uint8_t all_weights_are_positive;
ai_float nodes_values_scale;
ai_float nodes_values_offset;
ai_float class_weights_scale;
ai_float class_weights_offset;
} ai_layer_tree_ensemble_classifier;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Decodes the TreeEnsembleClassifier ML operator.
* @ingroup layers_svmreg
* @param layer tree ensemble classifier layer
*/
AI_INTERNAL_API
void forward_tree_ensemble_classifier(ai_layer *pLayer);
AI_INTERNAL_API
ai_i32 decodeEstimator_LEQ_8Bits(const ai_float *pDataIn,
ai_float *pOutDataScores,
const ai_u8 *pFeatureIdxForEstimator,
const ai_float *pValuesForEstimator,
const ai_u8 *pTrueIdxForEstimator,
const ai_u8 *pFalseIdxForEstimator,
const ai_handle pClassWeightsForEstimator,
const ai_array_format classWeightsFormat,
const ai_u8 *pClassNodeIdsForEstimator,
const ai_u16 nbClassWithCurrentEstimator,
const ai_u8 *pClassIdsForEstimator);
AI_INTERNAL_API
ai_i32 decodeEstimator_LEQ_16Bits(const ai_float *pDataIn,
ai_float *pOutDataScores,
const ai_u8 *pFeatureIdxForEstimator,
const ai_float *pValuesForEstimator,
const ai_u16 *pTrueIdxForEstimator,
const ai_u16 *pFalseIdxForEstimator,
ai_handle pClassWeightsForEstimator,
const ai_array_format classWeightsFormat,
const ai_u16 *pClassNodeIdsForEstimator,
const ai_u16 nbClassWithCurrentEstimator,
const ai_u16 *pClassIdsForEstimator);
AI_API_DECLARE_END
#endif /*LAYERS_TREE_ENSEMBLE_CLASSIFIER_H*/
| 4,238 | C | 36.513274 | 80 | 0.542237 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dense_is8os8ws8.h | /**
******************************************************************************
* @file lite_dense_is8os8ws8.h
* @author Marco Forleo
* @brief header file of AI platform lite dense kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_DENSE_IS8OS8WS8_H
#define LITE_DENSE_IS8OS8WS8_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Forward function for a dense layer with signed input,
* signed output and signed weights all at 8 bits.
* @ingroup lite_dense_is8os8ws8
* @param input The pointer to input buffer.
* @param output The pointer to output buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param in_zeropoint The value of the zero point of the input.
* @param out_zeropoint TThe value of the zero point of the output.
* @param n_channel_in The number of channels of the input.
* @param n_channel_out The number of channels of the output, i.e.,
* the number of dense hidden neurons.
* @param n_pixels Total number of pixels.
*/
LITE_API_ENTRY
void forward_lite_dense_is8os8ws8(ai_i8 * pDataOut,
const ai_i8 *pDataIn,
const ai_i8 *pWeights,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size n_pixels,
const ai_float in_scale,
const ai_float out_scale,
const ai_float Wt_scale,
ai_i16 *pBuffer_a);
void forward_lite_dense_is8os8ws8_ch(ai_i8 * pDataOut,
const ai_i8 *pDataIn,
const ai_i8 *pWeights,
const ai_i32 *pBias,
const ai_i8 in_zeropoint,
const ai_i8 out_zeropoint,
const ai_u16 n_channel_in,
const ai_u16 n_channel_out,
const ai_size n_pixels,
const ai_float in_scale,
const ai_float out_scale,
const ai_float *pWt_scale,
ai_i16 *pBuffer_a);
#endif /*LITE_DENSE_IS8OS8WS8_H*/
| 3,465 | C | 44.605263 | 80 | 0.43088 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_platform_interface.h | /**
******************************************************************************
* @file ai_platform_interface.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform interface APIs types
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_PLATFORM_INTERFACE_H
#define AI_PLATFORM_INTERFACE_H
#pragma once
#include "ai_platform.h"
#include "datatypes_network.h"
#include "ai_datatypes.h"
#include "ai_datatypes_format.h"
/*!
* @defgroup datatypes_interface Interface Datatypes
* @brief Data structures and defines used to implement neural networks
*/
/******************************************************************************/
#define AI_ERROR_TRAP(net_, type_, code_) \
ai_platform_network_set_error((net_), AI_CONCAT(AI_ERROR_,type_), \
AI_CONCAT(AI_ERROR_CODE_,code_))
/*! AI_PTR HANDLERS SECTION ************************************/
#define AI_PTR(ptr_) AI_CAST(ai_ptr, ptr_)
#define AI_PTR_CONST(ptr_) AI_CAST(ai_ptr_const, ptr_)
/*! STATIC ARRAYS ALLOCATOR SECTION ************************************/
#define AI_PACK_STORAGE_ARRAY(type_, dim_, ...) \
(type_[dim_]) { AI_PACK(__VA_ARGS__) }
/*! AI_STORAGE_KLASS SECTION ************************************/
#define AI_STORAGE_KLASS_PACK(type_, dim_, ...) \
AI_PACK_STORAGE_ARRAY(type_, dim_, __VA_ARGS__)
#define AI_STORAGE_KLASS_INIT(type_, size_, data_) \
{ \
.type = (type_), \
.size = (size_), \
.data = (ai_handle)(data_), \
}
/*!
* @enum ai_storage_klass_type
* @ingroup ai_platform_interface
* @brief @ref ai_storage_class types enum
*/
typedef enum {
AI_STORAGE_KLASS_NONE = 0x00,
AI_STORAGE_KLASS_SHAPE = 0x01,
AI_STORAGE_KLASS_STRIDE = 0x02,
} ai_storage_klass_type;
/*!
* @struct ai_storage_klass
* @ingroup ai_platform_interface
* @brief Generic "Template" klass for generic storage arrays containers
* from this klass several typed containers are derived (see e.g. @ref ai_shape)
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_storage_klass_s {
ai_u32 type : 8;
ai_u32 size : 24;
ai_handle data;
} ai_storage_klass;
AI_PACKED_STRUCT_END
/*! AI_SHAPES SECTION ************************************/
#define AI_SHAPE_MAX_DIMENSION (6)
#define AI_SHAPE_2D_INIT(w_, h_) \
{ .data = { (w_), (h_) } }
#define AI_SHAPE_INIT(dim_, ...) \
AI_STORAGE_KLASS_INIT( \
AI_STORAGE_KLASS_SHAPE, \
dim_, \
AI_STORAGE_KLASS_PACK(ai_shape_dimension, dim_, ## __VA_ARGS__))
#define AI_SHAPE_INIT_FROM_BUFFER(dim_, buffer_) \
AI_STORAGE_KLASS_INIT( \
AI_STORAGE_KLASS_SHAPE, \
dim_, \
buffer_)
#define AI_SHAPE_ALLOCATE_STATIC(num_dim_) \
AI_SHAPE_INIT((num_dim_), 0)
typedef ai_u8 ai_shape_idx;
/*!
* @struct ai_shape
* @ingroup ai_platform_interface
* @brief Dimensions for generic 4D tensors
*/
typedef ai_storage_klass ai_shape;
/*! AI_STRIDES HANDLERS SECTION ************************************/
#define AI_STRIDE_INIT(dim_, ...) \
AI_STORAGE_KLASS_INIT( \
AI_STORAGE_KLASS_STRIDE, \
dim_, \
AI_STORAGE_KLASS_PACK(ai_stride_dimension, dim_, ## __VA_ARGS__))
#define AI_STRIDE_INIT_FROM_BUFFER(dim_, buffer_) \
AI_STORAGE_KLASS_INIT( \
AI_STORAGE_KLASS_STRIDE, \
dim_, \
buffer_)
#define AI_STRIDE_ALLOCATE_STATIC(num_dims_) \
AI_STRIDE_INIT((num_dims_), 0)
/*!
* @struct ai_stride
* @ingroup ai_platform_interface
* @brief Stride dimensions for generic 4D tensors (in number of elements)
*/
typedef ai_storage_klass ai_stride;
/*! BASIC_TYPES HANDLERS SECTION ************************************/
#define AI_SIZE(value_) \
AI_CAST(ai_size, value_)
/*! AI_KLASS_OBJ HANDLERS SECTION ************************************/
#define AI_KLASS_OBJ(obj_) \
AI_CAST(ai_klass_obj, obj_)
/*! GENERIC HANDLERS SECTION ************************************/
#define AI_OBJ_DATA(obj_, type_) \
AI_CAST(type_, (obj_)->data)
/*! AI_BUFFER HANDLERS SECTION ************************************/
#define AI_BUFFER_OBJ(ptr_) \
AI_CAST(ai_buffer*, ptr_)
/*! AI_ARRAY HANDLERS SECTION ************************************/
#define AI_ARRAY_OBJ(ptr_) \
AI_CAST(ai_array*, ptr_)
#define AI_ARRAY_OBJ_INIT_STATIC(type_, format_, size_, ...) { \
.format = AI_FMT_OBJ(format_), \
.size = (ai_array_size)(size_), \
.data = (ai_ptr)((type_[]){ __VA_ARGS__ }), \
.data_start = AI_PTR(0), \
}
#define AI_ARRAY_OBJ_INIT(format_, data_, data_start_, size_) { \
.format = AI_FMT_OBJ(format_), \
.size = AI_CAST(ai_array_size, size_), \
.data = AI_PTR(data_), \
.data_start = AI_PTR(data_start_) }
#define AI_ARRAY_OBJ_DECLARE_STATIC(name_, type_, format_, attr_, size_, ...) \
AI_ALIGNED(4) \
attr_ ai_array name_ = AI_ARRAY_OBJ_INIT_STATIC(type_, format_, size_, __VA_ARGS__);
#define AI_ARRAY_OBJ_DECLARE(name_, format_, data_, data_start_, size_, attr_) \
AI_ALIGNED(4) \
attr_ ai_array name_ = AI_ARRAY_OBJ_INIT(format_, data_, data_start_, size_);
/********************************* ai_array macros ***************************/
#define AI_PACK_ARRAYS(...) \
(ai_array[]) { AI_PACK(__VA_ARGS__) }
#define AI_ARRAY_LIST_OBJ_INIT(arrays_ptr_) \
((ai_array*)(arrays_ptr_))
#define AI_ARRAY_LIST_FLAGS(list_) \
((list_) ? (list_)->flags : 0x0)
#define AI_ARRAY_LIST_SIZE(list_) \
((list_) ? (list_)->size : 0)
#define AI_ARRAY_LIST_DATA(list_, pos_) \
((list_) ? &((list_)->data[pos_]) : NULL)
/********************************* ai_tensor macros **************************/
#define AI_TENSOR_OBJ(obj_) \
AI_CAST(ai_tensor*, obj_)
#define AI_TENSOR_INFO_OBJ_INIT(id_, flags_, data_size_) { \
.id = (id_), \
.flags = (flags_), \
.data_size = (data_size_) \
}
#define AI_TENSOR_OBJ_INIT(id_, flags_, shape_, stride_, arrays_size_, arrays_ptr_, klass_obj_) { \
.klass = (ai_klass_obj)(klass_obj_), \
.info = AI_TENSOR_INFO_OBJ_INIT(id_, flags_, arrays_size_), \
.shape = shape_, \
.stride = stride_, \
.data = AI_ARRAY_LIST_OBJ_INIT(AI_PACK(arrays_ptr_)), \
}
#define AI_TENSOR_OBJ_DECLARE(name_, attr_, id_, flags_, shape_, stride_, \
arrays_size_, arrays_ptr_, klass_obj_) \
AI_ALIGNED(4) \
attr_ ai_tensor name_ = AI_TENSOR_OBJ_INIT(id_, flags_, AI_PACK(shape_), AI_PACK(stride_), \
arrays_size_, AI_PACK(arrays_ptr_), AI_PACK(klass_obj_));
/********************************* TENSOR STATE MACROS ***********************/
#define AI_TENSOR_STATE_OBJ_INIT(end_ptr_ , curr_ptr_, stride_, size_) \
{ (end_ptr_), (curr_ptr_), (stride_), (size_) }
/********************************* TENSOR LIST MACROS ************************/
#if (AI_TOOLS_API_VERSION <= AI_TOOLS_API_VERSION_1_3)
#pragma message ("Including deprecated AI_TENSOR_LIST_ENTRY, AI_TENSOR_LIST_EMPTY, AI_TENSOR_LIST_IO_ENTRY")
AI_DEPRECATED
#define AI_TENSOR_LIST_EMPTY \
AI_TENSOR_LIST_OBJ_EMPTY
AI_DEPRECATED
#define AI_TENSOR_LIST_ENTRY(...) \
AI_TENSOR_LIST_OBJ_INIT(AI_FLAG_NONE, AI_NUMARGS(__VA_ARGS__), __VA_ARGS__)
AI_DEPRECATED
#define AI_TENSOR_LIST_IO_ENTRY(flags_, size_, ...) \
AI_TENSOR_LIST_IO_OBJ_INIT(flags_, size_, __VA_ARGS__)
#endif /* AI_TOOLS_API_VERSION_1_3 */
#define AI_TENSOR_LIST_OBJ_INIT(flags_, size_, ...) \
{ .size = (size_), .flags = (flags_), \
.tensor = (ai_tensor*[]) { __VA_ARGS__ }, .info = NULL \
}
#define AI_TENSOR_LIST_OBJ_EMPTY \
{ .size = 0, .flags = AI_FLAG_NONE, \
.tensor = (ai_tensor*[]) { NULL }, .info = NULL \
}
#define AI_TENSOR_LIST_OBJ_DECLARE(name_, attr_, flags_, size_, ...) \
AI_ALIGNED(4) \
attr_ ai_tensor_list name_ = AI_TENSOR_LIST_OBJ_INIT( \
flags_, size_, __VA_ARGS__);
/********************************* TENSOR LIST I/O MACROS ********************/
#define AI_TENSOR_LIST_IO_OBJ_INIT(flags_, size_, ...) \
{ .size = (size_), .flags = (flags_), \
.tensor = (ai_tensor*[]) { __VA_ARGS__ }, \
.info = (ai_tensor_list_info[1]) { { \
.buffer = (ai_buffer[size_]){AI_STRUCT_INIT}, \
.state = (ai_tensor_state[size_]){AI_STRUCT_INIT}, \
.meta = (ai_buffer_meta_info[size_]){AI_STRUCT_INIT} \
} } \
}
/********************************* TENSOR CHAIN MACROS ***********************/
#define AI_TENSOR_CHAIN_OBJ_INIT(flags_, size_, ...) \
{ .size = (size_), .flags = (flags_), \
.chain = (ai_tensor_list[]){ __VA_ARGS__ } }
#define AI_TENSOR_CHAIN_OBJ_DECLARE(name_, attr_, size_, ...) \
AI_ALIGNED(4) \
attr_ ai_tensor_chain name_ = \
AI_TENSOR_CHAIN_OBJ_INIT(AI_FLAG_NONE, size_, __VA_ARGS__);
/********************************* TENSOR CHAIN I/O MACROS *******************/
#define AI_TENSOR_CHAIN_IO_OBJ_INIT(flags_, in_tensor_list_, out_tensor_list_) \
{ .chain = (ai_tensor_list[]){ in_tensor_list_, out_tensor_list_ }, \
.size = 2, .flags = (flags_) }
#define AI_TENSOR_CHAIN_IO_OBJ_DECLARE( \
name_, attr_, flags_, in_tensor_list_, out_tensor_list_) \
AI_ALIGNED(4) \
attr_ ai_tensor_chain_io name_ = \
AI_TENSOR_CHAIN_IO_OBJ_INIT(flags_, in_tensor_list_, out_tensor_list_);
/******************************* NETWORK SECTION ****************************/
#define AI_NETWORK_OBJ(obj_) \
((ai_network*)(obj_))
#if (AI_TOOLS_API_VERSION < AI_TOOLS_API_VERSION_1_5)
AI_DEPRECATED
#define AI_NETWORK_OBJ_INIT( \
weights_buffer_, activations_buffer_, \
in_tensor_list_ptr_, out_tensor_list_ptr_, \
in_node_ptr_, signature_, klass_obj_) { \
.magic = 0x0, \
.signature = signature_, \
.klass = AI_KLASS_OBJ(klass_obj_), \
.flags = AI_FLAG_NONE, \
.error = AI_ERROR_INIT(NONE, NONE), \
.n_batches = 0, \
.batch_id = 0, \
.buffers = AI_NETWORK_BUFFERS_INIT( \
AI_BUFFER_ARRAY_OBJ_INIT_STATIC(AI_FLAG_NONE, 1, AI_PACK(weights_buffer_)), \
AI_BUFFER_ARRAY_OBJ_INIT_STATIC(AI_FLAG_NONE, 1, AI_PACK(activations_buffer_))), \
.tensors = AI_TENSOR_CHAIN_IO_OBJ_INIT(AI_FLAG_NONE, \
AI_PACK(in_tensor_list_ptr_), \
AI_PACK(out_tensor_list_ptr_)), \
.input_node = AI_NODE_OBJ(in_node_ptr_), \
.current_node = AI_NODE_OBJ(NULL), \
.on_node_exec = NULL, \
.data_exec = NULL, \
.lite_cb = NULL, \
}
#else
#define AI_NETWORK_OBJ_INIT( \
weights_buffer_, activations_buffer_, \
in_tensor_list_ptr_, out_tensor_list_ptr_, \
in_node_ptr_, signature_, klass_obj_) { \
.magic = 0x0, \
.signature = signature_, \
.klass = AI_KLASS_OBJ(klass_obj_), \
.flags = AI_FLAG_NONE, \
.error = AI_ERROR_INIT(NONE, NONE), \
.n_batches = 0, \
.batch_id = 0, \
.buffers = AI_NETWORK_BUFFERS_INIT(AI_PACK(weights_buffer_), \
AI_PACK(activations_buffer_)), \
.tensors = AI_TENSOR_CHAIN_IO_OBJ_INIT(AI_FLAG_NONE, \
AI_PACK(in_tensor_list_ptr_), \
AI_PACK(out_tensor_list_ptr_)), \
.input_node = AI_NODE_OBJ(in_node_ptr_), \
.current_node = AI_NODE_OBJ(NULL), \
.on_node_exec = NULL, \
.data_exec = NULL, \
.lite_cb = NULL, \
}
#endif // AI_TOOLS_API_VERSION
#define AI_NETWORK_OBJ_DECLARE( \
name_, attr_, \
weights_buffer_, activations_buffer_, \
in_tensor_list_ptr_, out_tensor_list_ptr_, \
in_node_ptr_, signature_, klass_obj_) \
AI_ALIGNED(4) \
attr_ ai_network name_ = AI_NETWORK_OBJ_INIT( \
AI_PACK(weights_buffer_), \
AI_PACK(activations_buffer_), \
AI_PACK(in_tensor_list_ptr_), \
AI_PACK(out_tensor_list_ptr_), \
(in_node_ptr_), (signature_), (klass_obj_));
#define AI_NETWORK_ACQUIRE_CTX(handle_) \
AI_NETWORK_OBJ(ai_platform_context_acquire(handle_))
/******************************************************************************/
AI_API_DECLARE_BEGIN
/*!
* @typedef ai_version
* @ingroup ai_platform_interface
* @brief Packed representation for @ref ai_platform_version
*/
typedef uint32_t ai_version;
/*!
* @typedef ai_klass_obj
* @ingroup ai_platform_interface
* @brief handler to (private) generic subclass derivatives implementation
*/
typedef void* ai_klass_obj;
/*!
* @typedef ai_ptr
* @ingroup ai_platform_interface
* @brief Byte pointer data addressing
*/
typedef uint8_t* ai_ptr;
/*!
* @typedef ai_ptr_const
* @ingroup ai_platform_interface
* @brief Constant byte pointer data addressing
*/
typedef const uint8_t* ai_ptr_const;
/*!
* @typedef ai_ptr_offset
* @ingroup ai_platform_interface
* @brief byte offset for computing strides
*/
typedef int32_t ai_ptr_offset;
/*!
* @typedef ai_magic
* @ingroup ai_platform_interface
* @brief magic field to mark internal datatstructures
*/
typedef uint32_t ai_magic;
/*!
* @typedef ai_any_ptr
* @ingroup ai_platform_interface
* @brief union for defining any pointer
*/
typedef union {
ai_handle handle;
ai_ptr ptr;
ai_float* float32;
ai_double* float64;
ai_u8* u8;
ai_i8* s8;
ai_u16* u16;
ai_i16* s16;
ai_u32* u32;
ai_i32* s32;
ai_u64* u64;
ai_i64* s64;
} ai_any_ptr;
#define AI_ANY_PTR_INIT(ptr_) \
{ .handle = (ai_handle)(ptr_) }
#define AI_CONTEXT_FIELDS \
ai_magic magic; /*!< magic word to mark valid contexts datastructs*/ \
ai_signature signature; /*!< 32bit signature for network consistency checks */
#define AI_CONTEXT_OBJ(obj) ((ai_context*)(obj))
/*!
* @typedef ai_context
* @ingroup ai_platform_interface
* @brief Abstract internal context header exposed to codegen interface
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_context_ {
AI_CONTEXT_FIELDS
} ai_context;
AI_PACKED_STRUCT_END
/*!
* @enum ai_shape_2d_type
* @ingroup ai_platform_interface
* @brief Codes for the 2D tensor dimensions
*/
typedef enum {
AI_SHAPE_2D_MAX_DIMENSION = 0x2,
AI_SHAPE_2D_HEIGHT = 0x1,
AI_SHAPE_2D_WIDTH = 0x0,
} ai_shape_2d_type;
/*!
* @struct ai_shape_2d
* @ingroup ai_platform_interface
* @brief Dimensions for generic 2D tensors
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_shape_2d_s {
ai_shape_dimension data[AI_SHAPE_2D_MAX_DIMENSION]; /*!< 2D tensor dimensions */
} ai_shape_2d;
AI_PACKED_STRUCT_END
/*!
* @struct ai_array
* @ingroup ai_platform_interface
* @brief Generic flattened array with size
* and (byte) stride of each item
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_array_s {
ai_array_format format; /*!< array format (see @ref ai_array_format) */
ai_array_size size; /*!< number of elements in the array (NOT number
of bytes!). The size of the array could be
determine using @ref AI_ARRAY_GET_BYTE_SIZE
macro */
ai_ptr data; /*!< pointer to data */
ai_ptr data_start; /*!< pointer to parent's data start address */
} ai_array;
AI_PACKED_STRUCT_END
/*!
* @struct ai_tensor_info
* @ingroup ai_platform_interface
* @brief ai_tensor_info info structure for storing size of the array list,
* tensor dimensionality, etc.
*
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_info_s {
ai_u16 id;
ai_u8 flags;
ai_u8 data_size;
} ai_tensor_info;
AI_PACKED_STRUCT_END
/*!
* @struct ai_tensor
* @ingroup ai_platform_interface
* @brief Generic tensor structure for storing parameters and activations
*
* The data is stored in a flattened array with an implicit order given by the
* reverse order in @ref ai_shape_dimension:
* in_channels, channels, width, height.
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_s {
ai_klass_obj klass; /*!< opaque pointer to klass context */
ai_tensor_info info; /*!< tensor info metadata see @ref ai_tensor_info)*/
ai_shape shape; /*!< tensor shape see @ref ai_shape */
ai_stride stride; /*!< tensor stride see @ref ai_stride */
ai_array* data; /*!< flattened array pointer to tensor data */
} ai_tensor;
AI_PACKED_STRUCT_END
/*!
* @struct ai_tensor_state
* @ingroup ai_platform_interface
* @brief state context for tensor management (used for I/O network tensors)
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_state_s {
ai_ptr end_ptr; /*!< end address of the I/O tensor data buffer */
ai_ptr curr_ptr; /*!< current address of the I/O tensor data buffer (for batching) */
ai_ptr_offset stride; /*!< single batch buffer size (in bytes) */
ai_size size; /*!< total size in bytes of the I/O tensor buffer */
} ai_tensor_state;
AI_PACKED_STRUCT_END
/*!
* @struct ai_tensor_list_info
* @ingroup ai_platform_interface
* @brief info metadata for tensor list management (used for I/O network tensors)
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_list_info_s {
ai_tensor_state* state; /*!< I/O buffer internal pointers state */
ai_buffer* buffer; /*!< I/O buffer pointer */
ai_buffer_meta_info* meta; /*!< I/O buffer meta informations */
} ai_tensor_list_info;
AI_PACKED_STRUCT_END
/********************************* INTEGER QUANTIZATION DATATYPES ************/
#define AI_INTQ_INFO_OBJ_INIT(flags_, scale_ , zeropoint_) { \
.scale = (scale_), \
.zeropoint = (ai_handle)(zeropoint_), \
.flags = (flags_), \
}
#define AI_PACK_INTQ_INFO_LIST(...) \
(ai_intq_info_list[]) { AI_PACK(__VA_ARGS__) }
#define AI_PACK_INTQ_INFO(scale_, zp_) \
(INTQ_CONST ai_intq_info[1]) { { \
.scale = (INTQ_CONST ai_float*) AI_PACK(scale_), \
.zeropoint = (ai_handle) AI_PACK(zp_) \
} }
#define AI_PACK_INTQ_SCALE(...) \
(INTQ_CONST ai_float[]) { AI_PACK(__VA_ARGS__) }
#define AI_PACK_INTQ_ZP(...) \
(INTQ_CONST ai_i8[]) { AI_PACK(__VA_ARGS__) }
#define AI_PACK_UINTQ_ZP(...) \
(INTQ_CONST ai_u8[]) { AI_PACK(__VA_ARGS__) }
#define AI_PACK_INTQ_ZP16(...) \
(INTQ_CONST ai_i16[]) { AI_PACK(__VA_ARGS__) }
#define AI_PACK_UINTQ_ZP16(...) \
(INTQ_CONST ai_u16[]) { AI_PACK(__VA_ARGS__) }
#define AI_INTQ_INFO_LIST_OBJ_INIT(flags_, size_, info_) \
{ \
.flags = (flags_), \
.size = (size_), \
.info = (info_), \
}
#define AI_INTQ_INFO_LIST_OBJ_EMPTY { 0 }
#define AI_INTQ_INFO_LIST_OBJ_DECLARE(name_, attr_, flags_, size_, info_) \
AI_ALIGNED(4) \
attr_ ai_intq_info_list name_ = \
AI_INTQ_INFO_LIST_OBJ_INIT(flags_, size_, AI_PACK(info_));
#define AI_INTQ_INFO_LIST_OBJ_DECLARE_EMPTY(name_, attr_) \
AI_ALIGNED(4) \
attr_ ai_intq_info_list name_ = AI_INTQ_INFO_LIST_OBJ_EMPTY;
/********************************* TENSOR CHAINS DATATYPES *******************/
/*!
* @enum ai_tensor_chain_type
* @ingroup ai_platform_interface
* @brief Enum for the different tensor chains supported in the library
*/
typedef enum {
AI_TENSOR_CHAIN_INPUT = 0x0,
AI_TENSOR_CHAIN_OUTPUT = 0x1,
AI_TENSOR_CHAIN_WEIGHTS = 0x2,
AI_TENSOR_CHAIN_SCRATCH = 0x3,
AI_TENSOR_CHAIN_SIZE
} ai_tensor_chain_type;
/*!
* @struct ai_tensor_list
* @ingroup ai_platform_interface
* @brief list (in form of arrays) of internal nodes tensor pointers
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_list_s {
ai_u16 size; /*!< number of elements in the the tensor list */
ai_u16 flags; /*!< optional flags to store tensor list attributes */
ai_tensor** tensor; /*!< array of linked tensor pointer */
ai_tensor_list_info* info; /*!< pointer to an array of metainfo associated to the tensors */
} ai_tensor_list;
AI_PACKED_STRUCT_END
/*!
* @struct ai_tensor_chain
* @ingroup ai_platform_interface
* @brief tensor chain datastruct for internal network nodes
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_tensor_chain_s {
ai_u16 size;
ai_u16 flags;
ai_tensor_list* chain; /*!< pointer to a 4 sized array see @ref ai_tensor_chain_type */
} ai_tensor_chain;
AI_PACKED_STRUCT_END
/************************************** LAYER DATATYPES *******************/
/*!
* @struct ai_layer
* @ingroup ai_platform_interface
* @brief Structure encoding a generic opaque layer in the network
*
*/
typedef void ai_layer;
/************************************** OBSERVER DATATYPES *******************/
/* forward function */
struct ai_node_s;
/*!
* @struct ai_observer_node
* @ingroup ai_observer_interface
* @brief observer node data struct for internal network nodes
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_observer_node_s {
ai_u16 c_idx; /*!< node index (position in the execution list) */
ai_u16 type; /*!< node type info @see ai_node datastruct */
ai_u16 id; /*!< node id assigned by codegen tool to identify the model layer*/
ai_u16 unused; /*!< unused field for alignment */
const ai_tensor_chain* inner_tensors; /*!< pointer to the inner tensor if available */
const ai_tensor_chain* tensors; /*!< pointer to a 4 sized array see @ref ai_tensor_chain_type */
} ai_observer_node;
AI_PACKED_STRUCT_END
#define AI_OBSERVER_NONE_EVT (0) /*!< No event */
#define AI_OBSERVER_INIT_EVT (1 << 0) /*!< called at the end of the init function */
#define AI_OBSERVER_PRE_EVT (1 << 1) /*!< before c-node execution */
#define AI_OBSERVER_POST_EVT (1 << 2) /*!< after c-node execution */
#define AI_OBSERVER_FIRST_EVT (1 << 8) /*!< indicate the first c-node */
#define AI_OBSERVER_LAST_EVT (1 << 9) /*!< indicate the last c-node */
#define AI_OBSERVER_REGISTERED (1 << 24) /*!< internal flag */
#define AI_OBSERVER_MASK_EVT (0xFF) /*!< mask for requested user event */
/* Client callback definition */
typedef ai_u32 (*ai_observer_node_cb)(
const ai_handle cookie,
const ai_u32 flags,
const ai_observer_node *node);
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_observer_exec_ctx_s {
ai_observer_node_cb on_node; /*!< registered user observer call-back function */
ai_handle cookie; /*!< reference of the user context */
ai_u32 flags; /*!< flags definition */
ai_u16 c_idx; /*!< store/indicate the index of the current c_node */
ai_u16 n_nodes; /*!< total number of c_node */
struct ai_node_s *cur; /*!< pointer of the current node (pre or post) */
} ai_observer_exec_ctx;
AI_PACKED_STRUCT_END
typedef enum {
AI_NODE_EXEC_INIT = 0x0,
AI_NODE_EXEC_START = 0x1,
AI_NODE_EXEC_PRE = 0x2,
AI_NODE_EXEC_POST = 0x3,
} ai_node_exec_state;
/* Internal/private definition of node execution callback */
typedef ai_u32 (*ai_node_exec_cb)(
const ai_node_exec_state state,
struct ai_node_s *cur,
const ai_handle ctx);
/********************************* NETWORK DATATYPES *************************/
/*!
* @struct ai_network
* @ingroup layers
* @brief Structure encoding a sequential neural network
*/
AI_PACKED_STRUCT_START
typedef AI_ALIGNED_TYPE(struct, 4) AI_PACKED ai_network_s {
AI_CONTEXT_FIELDS
ai_klass_obj klass; /*!< opaque handler to specific network implementations */
ai_flags flags; /*!< bitflags mask to track some network state info */
ai_error error; /*!< track 1st error code in the network */
ai_u16 n_batches; /*!< number of batches to process */
ai_u16 batch_id; /*!< current batch to to process btw [0, n_batches)*/
// New 6.1 context storing explicitly network buffers. This allow also management of network persistent state now
ai_network_buffers buffers; /*!< network buffers datastruct */
ai_tensor_chain tensors; /*!< I/O tensor chain list see @ref ai_tensor_list */
struct ai_node_s* input_node; /*!< first node to execute */
struct ai_node_s* current_node; /*!< current node to execute */
ai_node_exec_cb on_node_exec; /*!< registered call-back function called when
a node/operator is scheduled */
ai_handle data_exec; /*!< private reference for the runtime context */
ai_handle lite_cb; /*!< registered opaque call-back handler for lite APIs */
ai_version tool_api_version; /*! Tools Codegen API version */
} ai_network;
AI_PACKED_STRUCT_END
/*!
* @brief Get platform runtime lib revision version as string.
* @ingroup ai_platform_interface
* @return a string containing the revision of the runtime library
*/
AI_INTERFACE_TYPE
const char* ai_platform_runtime_get_revision(void);
/*!
* @brief Get platform runtime lib version as datastruct.
* @ingroup ai_platform_interface
* @return a datastruct containing the version of the runtime library
*/
AI_INTERFACE_TYPE
ai_platform_version ai_platform_runtime_get_version(void);
/*!
* @brief Get platform public APIs version as datastruct.
* @ingroup ai_platform_interface
* @return a datastruct containing the version of the public APIs
*/
AI_INTERFACE_TYPE
ai_platform_version ai_platform_api_get_version(void);
/*!
* @brief Get platform interface private APIs version as datastruct.
* @ingroup ai_platform_interface
* @return a datastruct containing the version of the interface private APIs
*/
AI_INTERFACE_TYPE
ai_platform_version ai_platform_interface_api_get_version(void);
/****************************************************************************
** Context APIs
****************************************************************************/
/*!
* @brief Get platform context.
* @ingroup ai_platform_interface
* @return a valid context handle or NULL otherwise
*/
AI_INTERFACE_TYPE
ai_context* ai_platform_context_acquire(const ai_handle handle);
/*!
* @brief Release platform context.
* @ingroup ai_platform_interface
* @return an opaque handle to the released object
*/
AI_INTERFACE_TYPE
ai_handle ai_platform_context_release(ai_context* ctx);
/****************************************************************************
** Platform Network Params APIs
****************************************************************************/
/*!
* @brief get the weights map from user provided network params info
* @ingroup ai_platform_interface
* @param params a pointer to ai_network_params struct
* @param map table pointer to the table map to initialize
* @param map_size the number of entries of the table to initialize
* @return true if initialization succeeded, false otherwise
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_get_weights_map(
ai_ptr* map, const ai_size map_size, const ai_network_params* params);
/*!
* @brief get the activations map from user provided network params info
* @ingroup ai_platform_interface
* @param params a pointer to ai_network_params struct
* @param map table pointer to the table map to initialize
* @param map_size the number of entries of the table to initialize
* @return true if initialization succeeded, false otherwise
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_get_activations_map(
ai_ptr* map, const ai_size map_size, const ai_network_params* params);
/*!
* @brief bind code generated weights and activations map arrays to ai_netwoek_params
* @ingroup ai_platform_interface
* @param[out] params the network params struct reporting binded params
* @param[in] map_weights pointer to the codegened weights map array to be bound
* @param[in] map_activations pointer to the codegened activation map array to be bound
* @return true if network parameters binding succeed, false otherwise
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_bind_network_params(
ai_network_params* params,
const ai_buffer_array* map_weights, const ai_buffer_array* map_activations);
/****************************************************************************
** Platform Network APIs
****************************************************************************/
/*!
* @brief get **first** error tracked when using the network
* @ingroup ai_platform_interface
* @param network an opaque handler to the network context
* @return ai_error the FIRST error generated during network processing
*/
AI_INTERFACE_TYPE
ai_error ai_platform_network_get_error(ai_handle network);
/*!
* @brief Set specific error code of the network. if an error is already present
* keep it
* @ingroup ai_platform_interface
* @param net_ctx a pointer to the network context
* @param type error type as defined in @ref ai_error_type
* @param code error code as defined in @ref ai_error_code
* @return true if no previous errors where recorded, false if a previous error
* is present or context is invalid
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_network_set_error(
ai_network* net_ctx, const ai_error_type type, const ai_error_code code);
/*!
* @brief Finalize network report datastruct with I/O buffer infos
* @ingroup ai_platform_interface
* @return bool if the report has been finalized correctly. false otherwise
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_api_get_network_report(
ai_handle network, ai_network_report* r);
/*!
* @brief Get network inputs array pointer as a ai_buffer array pointer.
* @ingroup network
* @param network an opaque handler to the network context
* @param n_buffer optional parameter to return the number of inputs
* @return a ai_buffer pointer to the inputs arrays
*/
AI_INTERFACE_TYPE
ai_buffer* ai_platform_inputs_get(ai_handle network, ai_u16 *n_buffer);
/*!
* @brief Get network outputs array pointer as a ai_buffer array pointer.
* @ingroup network
* @param network an opaque handler to the network context
* @param n_buffer optional parameter to return the number of outputs
* @return a ai_buffer pointer to the inputs arrays
*/
AI_INTERFACE_TYPE
ai_buffer* ai_platform_outputs_get(ai_handle network, ai_u16 *n_buffer);
/*!
* @brief create a network context with some error check
* @ingroup ai_platform_interface
* @param a pointer to an opaque handle of the network context
* @param an (optional) pointer to the network config buffer info
* @param net_ctx a pointer to the network context structure to initialize
* @param tool_major major version id of the tool used to generate the network
* @param tool_minor minor version id of the tool used to generate the network
* @param tool_micro micro version id of the tool used to generate the network
* @return the error during network creation or error none if ok
*/
AI_INTERFACE_TYPE
ai_error ai_platform_network_create(
ai_handle* network, const ai_buffer* network_config,
ai_network* net_ctx,
const ai_u8 tool_major, const ai_u8 tool_minor, const ai_u8 tool_micro);
/*!
* @brief destroy a network context
* @ingroup ai_platform_interface
* @param network a pointer to an opaque handle of the network context
* @return AI_HANDLE_NULL if deallocation OK, same network handle if failed
*/
AI_INTERFACE_TYPE
ai_handle ai_platform_network_destroy(ai_handle network);
/*!
* @brief initialize the network context
* @ingroup ai_platform_interface
* @param network a pointer to an opaque handle of the network context
* @return a valid network context, NULL if initialization failed
*/
AI_INTERFACE_TYPE
ai_network* ai_platform_network_init(
ai_handle network, const ai_network_params* params);
/*!
* @brief post-initialize of the network context.
* @ingroup ai_platform_interface
* @param network a pointer to an opaque handle of the network context
* @return a valid network context, NULL if initialization failed
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_network_post_init(ai_handle network);
/*!
* @brief main platform runtime execute of a network
* @ingroup ai_platform_interface
* @param network an opaque handler to the network context
* @param input a pointer to the input buffer data to process
* @param output a pointer to the output buffer
* @return the number of batches processed from the input. A result <=0 in case
* of error
*/
AI_INTERFACE_TYPE
ai_i32 ai_platform_network_process(
ai_handle network, const ai_buffer* input, ai_buffer* output);
/****************************************************************************
** Observer APIs
****************************************************************************/
/*!
* @brief Return the info of a requested c-node (defined by the
* c_idx field). Should be called after the initialization phase.
* @ingroup ai_platform_observer
* @param network a pointer to an opaque handle of the network context
* @param node_info a pointer to a reference of the node description
* @return true if the node_info->c_idx designates a valid index else
* false (network error is updated).
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_observer_node_info(
ai_handle network, ai_observer_node *node_info);
/*!
* @brief Register an observer context. Allows to register a client CB which
* will be called before or/and after the execution of a c-node with
* the references of the used tensors (see @ref ai_observer_node).
* @ingroup ai_platform_observer
* @param network a pointer to an opaque handle of the network context
* @param cb reference of the user callback function
* @param cookie reference of a user object/ctx
* @param flags indicate expected events (see AI_OBSERVER_XX_EVT flag definition)
* @return false if the registration has failed (network error is updated) else true
* of error.
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_observer_register(
ai_handle network,
ai_observer_node_cb cb,
ai_handle cookie,
ai_u32 flags);
AI_INTERFACE_TYPE
ai_bool ai_platform_observer_register_s(ai_handle network,
ai_observer_exec_ctx *ctx);
/*!
* @brief un-register the observer context.
* @ingroup ai_platform_observer
* @param network a pointer to an opaque handle of the network context
* @param ctx a pointer to a reference of the registered platform observer context
* @param cb reference of the registered user callback function
* @param cookie reference of the registered user object/ctx
* @return false if the un-registration has failed (network error is updated) else true
* of error.
*/
AI_INTERFACE_TYPE
ai_bool ai_platform_observer_unregister(ai_handle network,
ai_observer_node_cb cb, ai_handle cookie);
AI_INTERFACE_TYPE
ai_bool ai_platform_observer_unregister_s(ai_handle network,
ai_observer_exec_ctx *ctx);
AI_API_DECLARE_END
#endif /*AI_PLATFORM_INTERFACE_H*/
| 35,318 | C | 33.091699 | 115 | 0.615012 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_common.h | /**
******************************************************************************
* @file core_common.h
* @author AST Embedded Analytics Research Platform
* @brief header file of common core datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef CORE_COMMON_H
#define CORE_COMMON_H
#pragma once
#include "ai_platform.h"
#include "ai_platform_interface.h"
#include "core_datatypes.h"
// #include "core_log.h"
/*!
* @defgroup core_common Common Core Library Routines
* @brief Common macros, datatypes and routines of core common module
* @details This module contains the definitons and handling of the @ref ai_node
* datastructures. An ai_node is a generic abstraction for a network node that
* could be either a fixed function layer or an operator. Ideally the platform
* interface defined in api module should handle an process generic nodes in the
* network, not relying on the fact that they are layers or operators datastructs
* Specific implementative details should be kept inside layers and operators
* modules. The core module implements additionally common routines used in the
* layers and operators modules.
*/
/******************************************************************************/
#ifdef HAS_AI_ASSERT
#define ASSERT_ARRAY_SANITY(a_) \
AI_ASSERT((a_) && (a_)->size>0)
#define ASSERT_ARRAY_DATA_SANITY(a_) \
ASSERT_ARRAY_SANITY(a_) \
AI_ASSERT((a_)->data && (a_)->data_start)
#define ASSERT_TENSOR_SANITY(t_) \
AI_ASSERT((t_) && (t_)->data) \
AI_ASSERT(CORE_TENSOR_GET_SHAPE_SIZE(t_)>0) \
ASSERT_ARRAY_SANITY((t_)->data)
#define ASSERT_TENSOR_LIST_SANITY(tlist_) \
AI_ASSERT((tlist_) && (GET_TENSOR_LIST_SIZE(tlist_)>0)) \
#define ASSERT_TENSOR_DATA_SANITY(t_) \
ASSERT_TENSOR_SANITY(t_) \
ASSERT_ARRAY_DATA_SANITY((t_)->data)
#define ASSERT_NODE_SANITY(node_) \
do { \
AI_ASSERT(AI_NODE_OBJ(node_)->tensors && AI_NODE_OBJ(node_)->tensors->chain) \
ASSERT_TENSOR_SANITY(GET_TENSOR_IN(AI_NODE_OBJ(node_)->tensors, 0)) \
ASSERT_TENSOR_SANITY(GET_TENSOR_OUT(AI_NODE_OBJ(node_)->tensors, 0)) \
} while (0);
#else
#define ASSERT_ARRAY_SANITY(a_) /* ASSERT_ARRAY_SANITY */
#define ASSERT_ARRAY_DATA_SANITY(a_) /* ASSERT_ARRAY_DATA_SANITY */
#define ASSERT_TENSOR_SANITY(t_) /* ASSERT_TENSOR_SANITY */
#define ASSERT_TENSOR_LIST_SANITY(tlist_) /* ASSERT_TENSOR_LIST_SANITY */
#define ASSERT_TENSOR_DATA_SANITY(t_) /* ASSERT_TENSOR_DATA_SANITY */
#define ASSERT_NODE_SANITY(node_) /* ASSERT_NODE_SANITY */
#endif /*HAS_AI_ASSERT*/
#if defined(__GNUC__) || defined(__clang__)
/* Suppress unused function warnings */
#define AI_UNUSED_FUNCTION __attribute__((unused))
/* Manage false positives in address sanitizer */
#define AI_NO_SANITIZE_ADDRESS __attribute__((no_sanitize_address))
#else
#define AI_UNUSED_FUNCTION /* AI_UNUSED_FUNCTION */
#define AI_NO_SANITIZE_ADDRESS /* AI_NO_SANITIZE_ADDRESS */
#endif
/******************************************************************************/
#define AI_NODE_TYPE(type_) \
((ai_node_type)((ai_u32)(type_)&0xFFFF))
#define AI_NODE_OBJ(obj_) \
((ai_node*)(obj_))
#define AI_NODE_FUNC(func_) \
((node_func)(func_))
#define AI_NODE_COMMON_FIELDS_DECLARE \
ai_node_type type; /*!< node type id (see @ref ai_node_type) */ \
ai_id_obj id; /*!< node object instance id (see @ref ai_id_obj) */ \
ai_flags flags; /*!< node object flags */ \
ai_klass_obj klass; /*!< opaque handler to specific layer implementations */ \
struct ai_network_s* network; /*!< handle to global network context */ \
struct ai_node_s* next; /*!< the next node object in the sequence */ \
node_func forward; /*!< forward function for the node */ \
AI_CONST ai_tensor_chain* tensors; /*!< pointer to node tensor chain */
#define AI_NODE_STATEFUL_FIELDS_DECLARE \
AI_NODE_COMMON_FIELDS_DECLARE \
ai_handle state; \
node_func init; \
node_func update; \
node_func destroy;
#define AI_NODE_COMMON_INIT(type_, id_, flags_, klass_, network_, next_, forward_) \
.type = AI_NODE_TYPE(type_), \
.id = AI_ID_OBJ(id_), \
.flags = (flags_), \
.klass = AI_KLASS_OBJ(klass_), \
.network = AI_NETWORK_OBJ(network_), \
.next = AI_NODE_OBJ(next_), \
.forward = AI_NODE_FUNC(forward_)
/*****************************************************************************/
/** Network Tensors Chains / Lists Handlers **/
/*****************************************************************************/
#define AI_FOR_EACH_TENSOR_CHAIN_DO(tlist_ptr_, chain_) \
ai_tensor_list* tlist_ptr_ = (chain_)->chain; \
for (; tlist_ptr_<(((chain_)->chain)+((chain_)->size)); tlist_ptr_++)
#define AI_FOR_EACH_TENSOR_LIST_DO(idx_, t_ptr_, tlist_ptr_) \
ai_tensor* t_ptr_ = NULL; \
for (ai_size idx_ = 0; (idx_ < GET_TENSOR_LIST_SIZE(tlist_ptr_)) && \
((t_ptr_ = GET_TENSOR_LIST_ITEM(tlist_ptr_, idx_)) != NULL); ++idx_)
#define GET_TENSOR_LIST_INFO(list_) \
((list_)->info)
#define GET_TENSOR_LIST_META(list_, pos_) \
(&(GET_TENSOR_LIST_INFO(list_)->meta[pos_]))
#define GET_TENSOR_LIST_STATE(list_, pos_) \
(&(GET_TENSOR_LIST_INFO(list_)->state[pos_]))
#define GET_TENSOR_LIST_BUFFER(list_, pos_) \
(&(GET_TENSOR_LIST_INFO(list_)->buffer[pos_]))
#define GET_TENSOR_LIST_ITEM(list_, pos_) \
((NULL!=GET_TENSOR_LIST_ITEMS(list_)) \
? GET_TENSOR_LIST_ITEMS(list_)[(pos_)] : NULL)
#define GET_TENSOR_LIST_ITEMS(list_) \
((list_)->tensor)
#define GET_TENSOR_LIST_SIZE(list_) \
((NULL!=(list_)) ? (list_)->size : 0)
#define GET_TENSOR_CHAIN_SIZE(chain_) \
((NULL!=(chain_)) ? (chain_)->size : 0)
#define GET_TENSOR_LIST(chain_, type_) \
((AI_CONCAT(AI_TENSOR_CHAIN_, type_)<(chain_)->size) \
? &(chain_)->chain[AI_CONCAT(AI_TENSOR_CHAIN_, type_)] : NULL)
#define GET_TENSOR_LIST_IN(chain_) \
(GET_TENSOR_LIST(chain_, INPUT))
#define GET_TENSOR_LIST_OUT(chain_) \
(GET_TENSOR_LIST(chain_, OUTPUT))
#define GET_TENSOR_LIST_WEIGTHS(chain_) \
(GET_TENSOR_LIST(chain_, WEIGHTS))
#define GET_TENSOR_LIST_SCRATCH(chain_) \
(GET_TENSOR_LIST(chain_, SCRATCH))
#define GET_TENSOR_IN(chain_, pos_) \
(GET_TENSOR_LIST_ITEM(GET_TENSOR_LIST_IN(chain_), (pos_)))
#define GET_TENSOR_OUT(chain_, pos_) \
(GET_TENSOR_LIST_ITEM(GET_TENSOR_LIST_OUT(chain_), (pos_)))
#define GET_TENSOR_WEIGHTS(chain_, pos_) \
(GET_TENSOR_LIST_ITEM(GET_TENSOR_LIST_WEIGTHS(chain_), (pos_)))
#define GET_TENSOR_SCRATCH(chain_, pos_) \
(GET_TENSOR_LIST_ITEM(GET_TENSOR_LIST_SCRATCH(chain_), (pos_)))
/******************************************************************************/
#if 1
#define SECTION_SERIAL(expr) expr
#define SECTION_PARALLEL(expr)
#else
#define SECTION_SERIAL(expr)
#define SECTION_PARALLEL(expr) expr
#endif
AI_API_DECLARE_BEGIN
/*!
* @struct ai_node_type
* @ingroup core_common
* @brief generic network node numeric type ID
*
*/
typedef uint16_t ai_node_type;
/*!
* @typedef void (*node_func)(struct ai_node_s* node)
* @ingroup core_common
* @brief Callback signatures for all forward functions
*/
typedef void (*node_func)(struct ai_node_s* node);
/*!
* @typedef ai_float (*func_nl_el)(const ai_float x)
* @ingroup core_common
* @brief Fuction pointer for generic elementwise transforms
*
* This function pointer abstracts a generic nonlinear function applied to a
* single element. See @ref ai_math_sqrt in @ref math_helpers as examples.
*/
typedef ai_float (*func_nl_el)(const ai_float x);
/*!
* @struct ai_node
* @ingroup core_common
* @brief Structure encoding a generic node of the network
*
* The node struct includes information about the network it belong to, the
* next node in a sequential network and the forward function. The forward
* functions are implemented in the @ref layers module.
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_node_s {
AI_NODE_COMMON_FIELDS_DECLARE
} ai_node;
/*!
* @struct ai_node_stateful
* @ingroup core_common
* @brief Structure encoding a stateful node of the network
*
* The node struct includes information about the network it belong to, the
* next node in a sequential network and the init, update and forward functions.
* The node functions are implemented in the @ref layers module.
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_node_stateful_s {
AI_NODE_STATEFUL_FIELDS_DECLARE
} ai_node_stateful;
/*!
* @brief initialize core module
* @ingroup core_common
* @return false if initialization fails, false otherwise
*/
AI_INTERNAL_API
ai_bool core_init(void);
/*!
* @brief get 1st error raised during processing
* @ingroup core_common
* @param[out] error the @ref ai_error recorded during processing
* @return the 1st error generated during processing. If no errors AI_ERROR_NONE
*/
AI_INTERNAL_API
ai_error core_get_error(ai_error* error);
/*!
* @brief set error recorded during processing
* @ingroup core_common
* @param[out] error the @ref ai_error to set
* @param[in] type the specific error type to set
* @param[in] code the specific error code to set
* @return true if the error is set, false in case a precedent error was already
*/
AI_INTERNAL_API
ai_bool core_set_error(
ai_error* error, const ai_error_type type, const ai_error_code code);
AI_API_DECLARE_END
#endif /*CORE_COMMON_H*/
| 9,995 | C | 33.588235 | 92 | 0.615408 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers.h | /**
******************************************************************************
* @file layers.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_H
#define LAYERS_H
#pragma once
#include "layers_common.h"
#include "layers_conv2d.h"
#include "layers_custom.h"
#include "layers_dense.h"
#include "layers_formats_converters.h"
#include "layers_generic.h"
#include "layers_lite_graph.h"
#include "layers_nl.h"
#include "layers_norm.h"
#include "layers_pad_dqnn.h"
#include "layers_pad_generic.h"
#include "layers_pool.h"
#include "layers_rnn.h"
#include "layers_upsample_generic.h"
#include "layers_sm.h"
#include "layers_ml.h"
#include "layers_ml_iforest.h"
#include "layers_ml_svc.h"
#include "layers_ml.h"
#include "layers_ml_linearclassifier.h"
#include "layers_ml_treeensembleclassifier.h"
#include "layers_ml_treeensembleregressor.h"
#include "layers_ml_svmregressor.h"
#include "layers_conv2d_dqnn.h"
#include "layers_dense_dqnn.h"
#include "layers_pool_dqnn.h"
#include "layers_generic_dqnn.h"
#include "layers_upsample_generic.h"
// #include "layers_template.h"
AI_API_DECLARE_BEGIN
/*!
* @struct ai_any_layer_ptr
* @ingroup layers
* @brief Generic union for typed layers pointers
*/
typedef struct {
ai_layer_type type; /*!< layer type id (see @ref ai_layer_type) */
union {
#define LAYER_ENTRY(type_, id_, struct_, forward_func_, init_func_, destroy_func_) \
AI_CONCAT(ai_layer_, struct_)* struct_;
#include "layers_list.h"
};
} ai_any_layer_ptr;
AI_API_DECLARE_END
#endif /*LAYERS_H*/
| 2,190 | C | 27.089743 | 84 | 0.603653 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_net_inspect_interface.h | /**
******************************************************************************
* @file core_net_inspect_interface.h
* @author AST Embedded Analytics Research Platform
* @brief header file of core network inspection interface APIs
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef __CORE_NET_INSPECT_INTERFACE_H_
#define __CORE_NET_INSPECT_INTERFACE_H_
#pragma once
#include "ai_platform.h"
AI_API_DECLARE_BEGIN
/*!
* @defgroup core_validation Validation Core
* @brief Implementation of the validation network interface headers
*/
/*!
* @struct ai_inspect_node_info
* @brief network node inspection context: there is one of this datastruct
* for each node of the network
*/
typedef struct ai_inspect_node_info_s {
ai_u16 type; /*!< node type info @see ai_node datastruct */
ai_u16 id; /*!< node id assigned by codegen tool to identify
the specific node instance */
ai_u16 batch_id; /*!< current node batch processed */
ai_u16 n_batches; /*!< total number of node batches to process */
ai_float elapsed_ms; /*!< node performance analysys: time in
milliseconds to execute the node forward
function */
ai_u16 in_size; /*!< number of node's input activation buffers */
ai_u16 out_size; /*!< number of node's output activation buffers */
ai_buffer* in; /*!< input node activation buffer see @ref ai_buffer */
ai_buffer* out; /*!< output node activation buffer see @ref ai_buffer */
} ai_inspect_node_info;
/*!
* @struct ai_inspect_net_report
* @brief network inspection report context
*/
typedef struct ai_inspect_net_report_s {
ai_u32 id; /*!< id of the report */
ai_signature signature; /*!< network identification checksum */
ai_u32 num_inferences; /*!< total number of inferences processed
during the inspection */
ai_u32 n_nodes; /*!< number of nodes in the network */
ai_float elapsed_ms; /*!< network total time (in ms) for processing
num_inferences inferences */
ai_inspect_node_info* node; /*!< pointer to the array of size n_nodes where
a single node report is reported. see @ref
ai_inspect_node_info datastruct */
} ai_inspect_net_report;
/*!
* @enum net inspector inspection mode
* @brief configuration flags to set net inspection mode
*/
typedef enum {
VALIDATION_INSPECT = (0x1<<0), /**< Network validation inspection mode */
STORE_ALL_IO_ACTIVATIONS = (0x1<<7), /**< Store all I/O activations on snapshot datastruct */
} ai_inspect_mode;
typedef enum {
AI_NODE_EXEC_PRE_FORWARD_STAGE = 0x0,
AI_NODE_EXEC_POST_FORWARD_STAGE = 0x1,
} ai_node_exec_stage;
/*!
* @brief function pointer to callback report
*/
typedef void (*ai_inspect_report_cb_func)(
const ai_handle cookie,
const ai_inspect_net_report* report);
/*!
* @brief function pointer to node execute
*/
typedef void (*ai_inspect_exec_node_cb_func)(
const ai_handle cookie,
const ai_inspect_node_info* node_info,
const ai_node_exec_stage stage);
/*!
* @struct ai_inspect_config
* @brief inspection config datastruct
*/
typedef struct ai_inspect_config_s {
ai_u8 validation_mode; /*!< validation mode flags
see @ref ai_inspect_mode */
ai_u8 log_level; /*!< log class level see @ref LOG_SUDO */
ai_bool log_quiet; /*!< log class quiet mode */
ai_inspect_report_cb_func on_report_destroy; /*!< callback function
called when a report datastruct
is released from memory */
ai_inspect_exec_node_cb_func on_exec_node; /*!< callback function
called when a node is executed (pre & post) */
ai_handle cookie;
} ai_inspect_config;
AI_API_DECLARE_END
#endif /*__CORE_NET_INSPECT_INTERFACE_H_*/
| 4,734 | C | 37.495935 | 98 | 0.562315 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_rnn.h | /**
******************************************************************************
* @file layers_rnn.h
* @author AST Embedded Analytics Research Platform
* @brief header file of RNN layers
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_RNN_H
#define LAYERS_RNN_H
#pragma once
#include "layers_common.h"
#include "layers_nl.h"
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_lstm
* @ingroup layers
* @brief LSTM layer with generic nonlinearities and peephole connections
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_lstm_ {
AI_LAYER_STATEFUL_FIELDS_DECLARE
ai_size n_units; /**< size of the hidden RNN state */
func_nl activation_nl; /**< activation nonlinearity (input to cell) */
func_nl recurrent_nl; /**< recurrent nonlinearity (hidden to cell) */
func_nl out_nl; /**< output nonlinearity (cell to hidden) */
ai_bool go_backwards; /**< process reversed input */
ai_bool return_state; /**< return state */
ai_bool reverse_seq; /**< reverse output sequence */
ai_float cell_clip; /**< cell clip value */
} ai_layer_lstm;
/*!
* @struct ai_layer_gru
* @ingroup layers
* @brief Gated Recurrent Unit (GRU) layer with generic nonlinearities
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_gru_ {
AI_LAYER_STATEFUL_FIELDS_DECLARE
ai_size n_units; /**< size of the hidden RNN state */
func_nl activation_nl; /**< activation nonlinearity (input to cell) */
func_nl recurrent_nl; /**< recurrent nonlinearity (hidden to cell) */
ai_bool reset_after;
ai_bool return_state;
ai_bool go_backwards; /**< process reversed input */
ai_bool reverse_seq; /**< reverse output sequence */
} ai_layer_gru;
/*!
* @struct ai_layer_rnn
* @ingroup layers
* @brief Simple Recurrent Neural Network (RNN) layer
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_rnn_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_size n_units; /**< size of the hidden RNN state */
func_nl activation_nl; /**< activation nonlinearity (input to hidden) */
ai_bool go_backwards; /**< process reversed input */
ai_bool reverse_seq; /**< reverse output sequence */
ai_bool return_state;
} ai_layer_rnn;
/*!
* @brief Initialize a Long-Short Term Memory (LSTM) layer.
* @ingroup layers
*
* Function used to initialize lstm internal state
*/
AI_INTERNAL_API
void init_lstm(ai_layer * layer);
/*!
* @brief Destroy a Long-Short Term Memory (LSTM) layer state.
* @ingroup layers
*
* Function used to destroy lstm internal state
*/
AI_INTERNAL_API
void destroy_lstm(ai_layer * layer);
/*!
* @brief Computes the activations of a Long-Short Term Memory (LSTM) layer.
* @ingroup layers
*
* Implements a Long-Short Term Layer with peephole connections:
* \f{eqnarray*}{
* i_t &=& \sigma_a(x_t W_{xi} + h_{t-1} W_{hi}
* + w_{ci} \odot c_{t-1} + b_i)\\
* f_t &=& \sigma_a(x_t W_{xf} + h_{t-1} W_{hf}
* + w_{cf} \odot c_{t-1} + b_f)\\
* c_t &=& f_t \odot c_{t - 1}
* + i_t \odot \sigma_r(x_t W_{xc} + h_{t-1} W_{hc} + b_c)\\
* o_t &=& \sigma_a(x_t W_{xo} + h_{t-1} W_{ho} + w_{co} \odot c_t + b_o)\\
* h_t &=& o_t \odot \sigma_o(c_t)
* \f}
* where \f$\sigma_a\f$ is the activation nonlinearity, \f$\sigma_r\f$ is the
* recurrent nonlinearity and \f$\sigma_o\f$ is the out nonlinearity. The
* \f$W_x\f$, \f$W_h\f$ and \f$W_c\f$ weights are sliced from the kernel,
* recurrent and peephole weights.
*
* @param layer the LSTM layer
*/
AI_INTERNAL_API
void forward_lstm(ai_layer * layer);
/*!
* @brief Initialize a Gated Recurrent Unit (GRU) layer.
* @ingroup layers
*
* Function used to initialize gru internal state
*/
AI_INTERNAL_API
void init_gru(ai_layer * layer);
/*!
* @brief Destroy a Gated Recurrent Unit (GRU) layer state.
* @ingroup layers
*
* Function used to destroy gru internal state
*/
AI_INTERNAL_API
void destroy_gru(ai_layer * layer);
/*!
* @brief Computes the activations of a Gated Recurrent Unit (GRU) layer.
* @ingroup layers
*
* Implements a Gated Recurrent Unit with the formula:
* \f{eqnarray*}{
* r_t &=& \sigma_a(x_t W_{xr} + h_{t - 1} W_{hr} + b_r) \\
* z_t &=& \sigma_a(x_t W_{xz} + h_{t - 1} W_{hz} + b_z) \\
* c_t &=& \sigma_r(x_t W_{xc} + r_t \odot (h_{t - 1} W_{hc} + b_{hc}) + b_c)
* \qquad \textnormal{when reset after is true} \\
* c_t &=& \sigma_r(x_t W_{xc} + (r_t \odot h_{t - 1}) W_{hc} + b_{hc} + b_c)
* \qquad \textnormal{when reset after is false (default)} \\
* h_t &=& (1 - z_t) \odot h_{t - 1} + z_t \odot c_t
* \f}
* where \f$\sigma_a\f$ is the activation nonlinearity and \f$\sigma_r\f$ is
* the recurrent nonlinearity. The weights are sliced from the kernel and
* recurrent weights.
*
* @param layer the GRU layer
*/
AI_INTERNAL_API
void forward_gru(ai_layer * layer);
/*!
* @brief Computes the activations of a Recurrent Neural Network (RNN) layer.
* @ingroup layers
*
* Implements a recurrent layer with the formula:
* \f{eqnarray*}{
* h_t &=& \sigma_a(x_t W_{xr} + h_{t - 1} W_{hr} + b_r)
* \f}
* where \f$\sigma_a\f$ is the activation nonlinearity. The weights are sliced
* from the kernel and recurrent weights.
*
* @param layer the RNN layer
*/
AI_INTERNAL_API
void forward_rnn(ai_layer * layer);
AI_API_DECLARE_END
#endif /* LAYERS_RNN_H */
| 5,866 | C | 30.713513 | 80 | 0.596147 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_lite_graph.h | /**
******************************************************************************
* @file layers_lite_graph.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform lite graph layers wrapper interface
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_LITE_GRAPH_H
#define LAYERS_LITE_GRAPH_H
#pragma once
#include "core_common.h"
/*!
* @defgroup layers_lite_graph Lite Graph Wrapper Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_lite_graph
* @ingroup layers_lite_graph
* @brief Generic Lite Graph Layer Wrapper
*
* The type of lite graph is handled by the specific forward lite graph function.
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_lite_graph_ {
AI_NODE_COMMON_FIELDS_DECLARE
ai_handle* activations_map; /*!< array of pointers to shared activations memory pools */
ai_handle* weights_map; /*!< array of pointers to shared weights memory pools */
} ai_layer_lite_graph;
AI_API_DECLARE_END
#endif /*LAYERS_LITE_GRAPH_H*/
| 1,598 | C | 29.169811 | 98 | 0.558824 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_datatypes.h | /**
******************************************************************************
* @file core_datatypes.h
* @author AST Embedded Analytics Research Platform
* @brief header file of core module private defines and datatypes
* to public nor codegen tool
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_CORE_DATATYPES_H
#define AI_CORE_DATATYPES_H
#pragma once
#include <stdint.h>
/*!
* @defgroup Core Module Datatypes
* @brief Data structures and defines used by core module
*/
/*!
* @brief platform runtime core library version
*/
#define AI_PLATFORM_RUNTIME_MAJOR 8
#define AI_PLATFORM_RUNTIME_MINOR 1
#define AI_PLATFORM_RUNTIME_MICRO 0
#define AI_PLATFORM_RUNTIME_BUILD A1-SNAPSHOT
#define AI_MAGIC_CONTEXT_TOKEN (0xA1C00100) /*!< AI Cool! Magic Token */
#define AI_MAGIC_INSPECTOR_TOKEN (0xA1C00101) /*!< AI Cool! Magic Token */
#define AI_ID_OBJ(id) \
((ai_id_obj)(id))
#define AI_C_ARRAY_COUNT(array_) \
( sizeof(array_) / sizeof((array_)[0]) )
#define AI_C_ARRAY_BYTE_SIZE(array_) \
( sizeof(array_) )
/*!
* @typedef ai_id_obj
* @ingroup core_datatypes
* @brief numeric identifier for generic object instances (e.g. layers,
* operators, etc.) It is used by codegen tool to keep tracks of specific
* instances created
*/
typedef uint16_t ai_id_obj;
#endif /*AI_CORE_DATATYPES_H*/
| 1,901 | C | 27.818181 | 80 | 0.570752 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_ml_svmregressor.h | /**
******************************************************************************
* @file layers_svmregressor.h
* @author AIS
* @brief header file of AI platform SVM Regressor datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_SVMREGRESSOR_H
#define LAYERS_SVMREGRESSOR_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_svmreg Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/* SVM regressor kernel types */
typedef enum ai_svm_kernel_e_ {
AI_SVMREG_KERNEL_LINEAR = 0,
AI_SVMREG_KERNEL_POLYNOMIAL,
AI_SVMREG_KERNEL_RBF,
AI_SVMREG_KERNEL_SIGMOID,
AI_SVMREG_KERNEL_UNSUPPORTED,
} ai_svm_kernel_e;
/*!
* @struct ai_layer_svmreg
* @ingroup layers_svmreg
* @brief SVM Regressor layer
*
* The type of svmreg function is handled by the specific forward function
* @ref forward_svm_regressor
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_svmreg_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_float intercept; /*!< constant used in the decision function */
ai_float gamma; /*!< kernel coefficient for rbf, polynomial and sigmoid functions */
ai_float coef0; /*!< term in polynomial and sigmoid functions */
ai_u32 degree; /*!< polynomial function degree */
ai_svm_kernel_e kernel_type; /*!< kernel type : see ai_svm_kernel_e */
} ai_layer_svmreg;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Decodes the SVM Regressor ML operator.
* @ingroup layers_svmreg
* @param layer svm regressor layer
*/
AI_INTERNAL_API
void forward_svm_regressor(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_SVMREGRESSOR_H*/
| 2,397 | C | 27.891566 | 96 | 0.53567 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_bn_integer.h | #ifndef LITE_BN_INTEGER_H
#define LITE_BN_INTEGER_H
#pragma once
#include "ai_lite_interface.h"
/**
* @brief Batch Normalization with 16-bit input, 16-bit threshold and binary output.
* It is implemented using a threshold, and this is possible because the output is binary.
*
* @param[in] pIn Input data pointer
* @param[out] pOut_32 Output data pointer
* @param[in] pThreshold Thresholds pointer (one per channel)
* @param[in] dim_x X dimension
* @param[in] dim_y Y dimension
* @param[in] channels_num Channels number
*/
LITE_API_ENTRY
void forward_lite_bn_is16os1ws16(const ai_i16 *pIn,
ai_u32 *pOut_32,
const ai_i16 *pThreshold,
const ai_i16 dim_x,
const ai_i16 dim_y,
const ai_i16 channels_num);
#endif /* LITE_BN_INTEGER_H */
| 913 | C | 34.153845 | 90 | 0.591457 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_datatypes_internal.h | /**
******************************************************************************
* @file ai_datatypes_internal.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform private APIs types
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_DATATYPES_INTERNAL_H
#define AI_DATATYPES_INTERNAL_H
#pragma once
#include "ai_datatypes.h"
#include "ai_datatypes_defines.h"
/*!
* @defgroup datatypes_internal Internal Datatypes
* @brief Data structures used internally to implement neural networks
*
* The layers are defined as structs; a generic layer type defines the basic
* layer parameters and type-specific parameters are handled by specializations
* implemented as a C union. The layers keep also a pointer to the parent
* network and the next layer in the network.
* The input, output and parameters are tensor with an hard-coded maximum
* dimension of 4. Tensors are floating point arrays with a notion of size.
* The network is a linked list of layers, and thus it stores only the pointer
* to the first layer.
*/
/*!
* @section Offsets
* @ingroup datatypes_internal
* Macros to handle (byte) stride addressing on tensors. The `AI_PTR` macro
* is used to always cast a pointer to byte array. The macros `AI_OFFSET_X` are
* used to compute (byte) offsets of respectively adjacents row elements, col
* elements, channel elements and `channel_in` elements.
* @{
*/
/*! AI_STORAGE_KLASS SECTION ************************************/
#define AI_STORAGE_KLASS_TYPE(s_) \
( (s_)->type )
#define AI_STORAGE_KLASS_SIZE(s_) \
( (s_)->size )
#define AI_STORAGE_KLASS_DATA(s_, type_) \
( (type_*)((s_)->data) )
#define AI_STORAGE_KLASS_COPY(dst_, dst_type_, src_, src_type_) \
{ \
AI_ASSERT(AI_STORAGE_KLASS_SIZE(src_)>=AI_STORAGE_KLASS_SIZE(dst_)) \
AI_STORAGE_KLASS_SIZE(dst_) = AI_STORAGE_KLASS_SIZE(src_); \
for (ai_size i=0; i<AI_STORAGE_KLASS_SIZE(dst_); i++ ) { \
AI_STORAGE_KLASS_DATA(dst_, dst_type_)[i] = \
AI_STORAGE_KLASS_DATA(src_, src_type_)[i]; \
} \
}
#define AI_STORAGE_KLASS_DUMP(s_, pfx_, post_, fmt_, type_) \
{ \
AI_ASSERT(s_) \
AI_DEBUG_PRINT(pfx_, AI_STORAGE_KLASS_SIZE(s_)) \
for ( ai_u32 i=0; i<AI_STORAGE_KLASS_SIZE(s_); i++ ) { \
if ( (i % 8)==0 ) { AI_DEBUG_PRINT("\n ") } \
AI_DEBUG_PRINT(fmt_, AI_STORAGE_KLASS_DATA(s_, type_)[i]) \
} \
AI_DEBUG_PRINT(post_) \
}
/*! AI_SHAPES SECTION ************************************/
#define AI_SHAPE_2D_H(shape_) \
AI_SHAPE_ELEM(shape_, AI_SHAPE_2D_HEIGHT)
#define AI_SHAPE_2D_W(shape_) \
AI_SHAPE_ELEM(shape_, AI_SHAPE_2D_WIDTH)
#define AI_SHAPE_ELEM(shape_, pos_) \
AI_STORAGE_KLASS_DATA(shape_, ai_shape_dimension)[pos_]
#define AI_SHAPE_GET_ELEM(shape_, pos_) \
(((pos_) < AI_SHAPE_SIZE(shape_)) ? AI_SHAPE_ELEM(shape_, pos_) : 1)
#define AI_SHAPE_SET_ELEM(shape_, pos_, val_) \
if ((pos_) < AI_SHAPE_SIZE(shape_)) { AI_SHAPE_ELEM(shape_, pos_) = (val_); }
#define AI_SHAPE_TYPE(shape_) \
AI_STORAGE_KLASS_TYPE(shape_)
#define AI_SHAPE_SIZE(shape_) \
AI_STORAGE_KLASS_SIZE(shape_)
#define AI_SHAPE_CLONE(dst_, src_) \
AI_STORAGE_KLASS_COPY(dst_, ai_shape_dimension, src_, ai_shape_dimension)
#define AI_SHAPE_BCAST_CLONE(dst_, src_) \
{ \
for (ai_size i = 0; i < AI_SHAPE_SIZE(dst_); i++) { \
AI_SHAPE_SET_ELEM(dst_, i, AI_SHAPE_GET_ELEM(src_, i)); \
} \
}
//#define AI_SHAPE_BATCH(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_BATCH_CHANNEL)
#define AI_SHAPE_H(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_HEIGHT)
#define AI_SHAPE_W(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_WIDTH)
#define AI_SHAPE_CH(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_CHANNEL)
#define AI_SHAPE_IN_CH(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_IN_CHANNEL)
#define AI_SHAPE_D(shape_) ((AI_SHAPE_SIZE((shape_)) > AI_SHAPE_DEPTH) \
? AI_SHAPE_ELEM((shape_), AI_SHAPE_DEPTH) : 1)
#define AI_SHAPE_E(shape_) ((AI_SHAPE_SIZE((shape_)) > AI_SHAPE_EXTENSION) \
? AI_SHAPE_ELEM((shape_), AI_SHAPE_EXTENSION) : 1)
#define AI_SHAPE_T(shape_) AI_SHAPE_ELEM((shape_), AI_SHAPE_TIME)
#define AI_CONV_SHAPE_H AI_SHAPE_W
#define AI_CONV_SHAPE_W AI_SHAPE_CH
#define AI_CONV_SHAPE_CH AI_SHAPE_H
#define AI_CONV_SHAPE_IN_CH AI_SHAPE_IN_CH
/*! AI_STRIDES SECTION ***********************************/
#define AI_STRIDE_2D_H(stride_) \
AI_STRIDE_ELEM((stride_), AI_SHAPE_2D_HEIGHT)
#define AI_STRIDE_2D_W(stride_) \
AI_STRIDE_ELEM((stride_), AI_SHAPE_2D_WIDTH)
#define AI_STRIDE_ELEM(stride_, pos_) \
AI_STORAGE_KLASS_DATA(stride_, ai_stride_dimension)[pos_]
#define AI_STRIDE_GET_ELEM(stride_, pos_) \
(((pos_) < AI_STRIDE_SIZE(stride_)) ? AI_STRIDE_ELEM(stride_, pos_) : 0)
#define AI_STRIDE_SET_ELEM(stride_, pos_, val_) \
if ((pos_) < AI_STRIDE_SIZE(stride_)) AI_STRIDE_ELEM(stride_, pos_) = (val_);
#define AI_STRIDE_TYPE(stride_) \
AI_STORAGE_KLASS_TYPE(stride_)
#define AI_STRIDE_SIZE(stride_) \
AI_STORAGE_KLASS_SIZE(stride_)
#define AI_STRIDE_CLONE(dst_, src_) \
AI_STORAGE_KLASS_COPY(dst_, ai_stride_dimension, src_, ai_stride_dimension)
#define AI_STRIDE_BCAST_CLONE(dst_, src_) \
{ \
for (ai_size i=0; i<AI_STRIDE_SIZE(dst_); i++) { \
AI_STRIDE_SET_ELEM(dst_, i, AI_STRIDE_GET_ELEM(src_, i)); \
} \
}
//#define AI_STRIDE_BATCH(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_BATCH_CHANNEL)
#define AI_STRIDE_H(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_HEIGHT)
#define AI_STRIDE_W(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_WIDTH)
#define AI_STRIDE_CH(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_CHANNEL)
#define AI_STRIDE_IN_CH(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_IN_CHANNEL)
#define AI_STRIDE_D(stride) ((AI_STRIDE_SIZE((stride)) >= 5) ? AI_STRIDE_ELEM((stride), AI_SHAPE_DEPTH) : 0)
#define AI_STRIDE_E(stride) ((AI_STRIDE_SIZE((stride)) == 6) ? AI_STRIDE_ELEM((stride), AI_SHAPE_EXTENSION) : 0)
#define AI_STRIDE_T(stride) AI_STRIDE_ELEM((stride), AI_SHAPE_TIME)
#define AI_STRIDE_SET_H(stride, val) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_HEIGHT, val)
#define AI_STRIDE_SET_W(stride, val) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_WIDTH, val)
#define AI_STRIDE_SET_CH(stride, val) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_CHANNEL, val)
#define AI_STRIDE_SET_IN_CH(stride, val) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_IN_CHANNEL, val)
#define AI_STRIDE_SET_D(stride, val) if (AI_STRIDE_SIZE((stride)) >= 5) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_DEPTH, val)
#define AI_STRIDE_SET_E(stride, val) if (AI_STRIDE_SIZE((stride)) == 6) AI_STRIDE_SET_ELEM((stride), AI_SHAPE_EXTENSION, val)
/*! AI_TENSORS SECTION ***********************************/
#define AI_TENSOR_KLASS(tensor_) \
((tensor_) ? (tensor_)->klass : NULL)
#define AI_TENSOR_SHAPE(tensor_) \
(&((tensor_)->shape))
#define AI_TENSOR_STRIDE(tensor_) \
(&((tensor_)->stride))
#define AI_TENSOR_INFO(tensor_) \
(&((tensor_)->info))
#define AI_TENSOR_ARRAY(tensor_) \
((tensor_) ? (tensor_)->data : NULL)
#define AI_TENSOR_ID(tensor_) \
((tensor_) ? AI_TENSOR_INFO(tensor_)->id : 0)
#define AI_TENSOR_FLAGS(tensor_) \
((tensor_) ? AI_TENSOR_INFO(tensor_)->flags : 0)
#define AI_TENSOR_DATA_SIZE(tensor_) \
((tensor_) ? AI_TENSOR_INFO(tensor_)->data_size : 0)
/*! AI_OFFSETS SECTION ***********************************/
//#define AI_OFFSET_BATCH(b, stride) ((ai_ptr_offset)(b) * AI_STRIDE_BATCH(stride))
#define AI_OFFSET_H(y, stride) ((ai_ptr_offset)(y) * AI_STRIDE_H(stride))
#define AI_OFFSET_W(x, stride) ((ai_ptr_offset)(x) * AI_STRIDE_W(stride))
#define AI_OFFSET_CH(ch, stride) ((ai_ptr_offset)(ch) * AI_STRIDE_CH(stride))
#define AI_OFFSET_IN_CH(in_ch, stride) ((ai_ptr_offset)(in_ch) * \
AI_STRIDE_IN_CH(stride))
#define AI_OFFSET_D(d, stride) ((ai_ptr_offset)(d) * AI_STRIDE_D(stride))
#define AI_OFFSET_E(e, stride) ((ai_ptr_offset)(e) * AI_STRIDE_E(stride))
#define AI_OFFSET_5D(y, x, d, e, ch, stride) ( \
AI_OFFSET_H((y), (stride)) + AI_OFFSET_W((x), (stride)) + \
AI_OFFSET_D((d), (stride)) + AI_OFFSET_E((e), (stride)) + \
AI_OFFSET_CH((ch), (stride)) )
#define AI_OFFSET(y, x, ch, z, stride) ( \
AI_OFFSET_H((y), (stride)) + AI_OFFSET_W((x), (stride)) + \
AI_OFFSET_CH((ch), (stride)) + \
((AI_STRIDE_SIZE((stride)) == 4) ? AI_OFFSET_IN_CH((z), (stride)) : AI_OFFSET_D((z), (stride))) )
/*! @} */
#define AI_GET_CONV_OUT_SIZE(in_size, filt_size, pad_l, pad_r, filt_stride) \
((((in_size) - (filt_size) + (pad_l) + (pad_r)) / (filt_stride)) + 1)
/** Tensors datatypes defines handlers ****************************************/
#define AI_TENSOR_SIZE(tensor_) \
get_tensor_size(tensor_, true)
#define AI_TENSOR_SIZE_UNPAD(tensor_) \
get_tensor_size(tensor_, false)
#define AI_TENSOR_BYTE_SIZE(tensor_) \
get_tensor_byte_size(tensor_)
/******************************************************************************/
#define AI_PLATFORM_VERSION_INIT(major_, minor_, micro_) \
{ .major = (major_), .minor = (minor_), .micro = (micro_), .reserved = 0x0 }
/** Integer tensor info extraction ********************************************/
#define AI_INTQ_INFO_LIST_SCALE_ARRAY(list_, type_) \
( ((list_) && (list_)->info) \
? ((type_*)((list_)->info->scale)) : NULL )
#define AI_INTQ_INFO_LIST_ZEROPOINT_ARRAY(list_, type_) \
( ((list_) && (list_)->info) \
? ((type_*)((list_)->info->zeropoint)) : NULL )
#define AI_KLASS_GET_INTQ_INFO_LIST(tensor_) \
((ai_intq_info_list*)((tensor_)->klass))
AI_API_DECLARE_BEGIN
/*!
* @brief Check whether 2 shapes have identical dimensions.
* @ingroup datatypes_internal
* @param shape0 the 1st tensor shape to compare
* @param shape1 the 2nd tensor shape to compare
* @return true if shape0 and shape1 have same dimensions. false otherwise
*/
AI_DECLARE_STATIC
ai_bool ai_shape_is_same(
const ai_shape* shape0, const ai_shape* shape1)
{
AI_ASSERT(shape0 && shape1)
if (AI_SHAPE_SIZE(shape0) != AI_SHAPE_SIZE(shape1))
return false;
ai_size dim = AI_SHAPE_SIZE(shape0);
while ( dim>0 ) {
dim--;
if ( AI_SHAPE_ELEM(shape0, dim)!=AI_SHAPE_ELEM(shape1, dim) )
return false;
}
return true;
}
/*!
* @brief Check whether the shapes is 1*1*1... for a scalar value content.
* @ingroup datatypes_internal
* @param shape the tensor shape to evaluate
* @return true if shape0 is scalar false otherwise
*/
AI_DECLARE_STATIC
ai_bool ai_shape_is_scalar(
const ai_shape* shape0)
{
ai_size dim = AI_SHAPE_SIZE(shape0);
while (dim>0) {
dim--;
if (AI_SHAPE_ELEM(shape0, dim) != 1)
return false;
}
return true;
}
/*!
* @brief Check if shape0 is a subshape of shape1
* @ingroup datatypes_internal
* @param shape0 the 1st tensor shape to compare
* @param shape1 the 2nd tensor shape to compare
* @return true if shape0 is a subshape of shape1 (all shape0 dimensions are
* smallers or equal of the shape1 ones). false otherwise
*/
AI_DECLARE_STATIC
ai_bool ai_shape_is_subshape(
const ai_shape* shape0, const ai_shape* shape1)
{
AI_ASSERT(shape0 && shape1)
AI_ASSERT(AI_SHAPE_SIZE(shape0)==AI_SHAPE_SIZE(shape1))
ai_size dim = AI_SHAPE_SIZE(shape0);
while (dim) {
dim--;
if ( AI_SHAPE_ELEM(shape0, dim)>AI_SHAPE_ELEM(shape1, dim) )
return false;
}
return true;
}
/*!
* @brief Computes the total size of a tensor given its dimensions.
* @ingroup datatypes_internal
* @param shape the tensor shape
*/
AI_DECLARE_STATIC
ai_size ai_shape_get_size(const ai_shape* shape)
{
AI_ASSERT(shape)
ai_size dim = AI_SHAPE_SIZE(shape);
ai_size size = 1;
while (dim>0) {
dim--;
size *= AI_SHAPE_ELEM(shape, dim);
}
return size;
}
/*!
* @brief Computes the size of the input image discarding the channels.
* @ingroup datatypes_internal
* @param shape the tensor shape
*/
AI_DECLARE_STATIC
ai_size ai_shape_get_npixels(const ai_shape* shape)
{
AI_ASSERT(shape)
const ai_size npixels = AI_SHAPE_W(shape) * AI_SHAPE_H(shape);
return npixels;
}
/** APIs Section *************************************************************/
/*!
* @brief Get packed version from major, minor, micro representaion.
* @ingroup datatypes_internal
* @param major major version value
* @param minor minor version value
* @param micro micro version value
* @return a packed version info obtained serializing input values
*/
AI_INTERNAL_API
ai_version ai_version_get(const ai_u8 major, const ai_u8 minor, const ai_u8 micro);
/*!
* @brief Get un-packed version from packed version representaion.
* @ingroup datatypes_internal
* @param version a packed varsion info
* @return struct with de-serialized major, minor, micro values
*/
AI_INTERNAL_API
ai_platform_version ai_platform_version_get(const ai_version version);
/*!
* @brief Map from ai_buffer data struct to ai_array data struct.
* @ingroup datatypes_internal
* @param buf a pointer to the ai_buffer to be mapped to ai_array
* @return an initialized @ref ai_array struct representing same data
*/
AI_INTERNAL_API
ai_array ai_from_buffer_to_array(const ai_buffer* buf);
/*!
* @brief Map from ai_array data struct to ai_buffer data struct.
* @ingroup datatypes_internal
* @param array a pointer to the ai_array to be mapped to ai_buffer
* @return an initialized @ref ai_buffer struct representing same data
*/
AI_INTERNAL_API
ai_buffer ai_from_array_to_buffer(const ai_array* array);
/*!
* @brief get the total number of elements of a n-dimensional tensor.
* @ingroup datatypes_internal
* @param t a pointer to an @ref ai_tensor
* @param with_padding when true it considers also padded elements
* @return the number of elements of the tensor (with/without padded ones)
*/
AI_INTERNAL_API
ai_size get_tensor_size(const ai_tensor* t, const ai_bool with_padding);
/*!
* @brief get the total size in bytes of elements of a n-dimensional tensor (excluding padded ones).
* @ingroup datatypes_internal
* @param t a pointer to an @ref ai_tensor
* @return the total size in bytes of elements of the tensor (excluding padded ones)
*/
AI_INTERNAL_API
ai_size get_tensor_byte_size(const ai_tensor* t);
AI_API_DECLARE_END
#endif /*AI_DATATYPES_INTERNAL_H*/
| 14,911 | C | 34.336493 | 133 | 0.62303 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dw_dqnn.h | /**
******************************************************************************
* @file lite_dw_dqnn.h
* @author AIS
* @brief header file of AI platform lite dw kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_DW_DQNN_H
#define LITE_DW_DQNN_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles 2D DW convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_dw_is1os1ws1_bn_pad0(const ai_u32 *pDataIn_init,
ai_u32 * pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold);
/*!
* @brief Handles 2D DW convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* - Optimized thanks to Optim3 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_dw_is1os1ws1_bn_pad0_optim3(const ai_u32 *pDataIn_init,
ai_u32 * pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_dw_is1os1ws1_bn_pad1(const ai_u32 *pDataIn_init,
ai_u32 * pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i32 pad_value);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with +1/-1 padding (Larq like) - Lite I/F
* - Optimized thanks to Optim3 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_dw_is1os1ws1_bn_pad1_optim3(const ai_u32 *pDataIn_init,
ai_u32 * pDataOut_init,
const ai_u32 *pWeights_init,
ai_float *pScratch_32,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_in,
const ai_i32 height_in,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 filt_width,
const ai_i32 filt_height,
const ai_i32 filt_pad_x,
const ai_i32 filt_pad_y,
const ai_i32 filt_stride_x,
const ai_i32 filt_stride_y,
const ai_i32 *pThreshold,
const ai_i32 pad_value);
#endif /*LITE_DW_DQNN_H*/
| 6,834 | C | 49.629629 | 80 | 0.362891 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_upsample_generic.h | /**
******************************************************************************
* @file lite_upsample.h
* @author AIS
* @brief header file of AI platform lite pw kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_UPSAMPLE_GENERIC_H
#define LITE_UPSAMPLE_GENERIC_H
#pragma once
#include "ai_lite_interface.h"
void forward_lite_upsample_generic_nearest(const ai_u8* in_data,
ai_u8* out_data,
const ai_size width_in,
const ai_size width_out,
const ai_float width_scale,
const ai_size height_out,
const ai_float height_scale,
const ai_u32 output_tensor_w_stride,
const ai_float offset_round_coeff);
void forward_lite_upsample_nearest(ai_ptr in_data,
ai_ptr out_data,
const ai_size width_in,
const ai_float width_scale,
const ai_float height_scale,
const ai_size width_out,
const ai_size height_out,
const ai_ptr_offset stride_w,
const ai_float offset_round_coeff);
void forward_lite_upsample_zeros( ai_ptr in_data,
ai_ptr out_data,
const ai_size width_in,
const ai_size height_in,
const ai_float width_scale,
const ai_float height_scale,
const ai_size width_out,
const ai_size height_out,
const ai_ptr_offset stride_ch,
const ai_ptr_offset stride_w,
const ai_handle p_zero_value);
#endif /*LITE_UPSAMPLE_GENERIC_H*/
| 2,756 | C | 44.196721 | 80 | 0.394049 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_lite_interface.h | /**
******************************************************************************
* @file ai_lite_interface.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions and implementations of runtime-lite codegen APIs
******************************************************************************
* @attention
*
* Copyright (c) 2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_LITE_INTERFACE_H
#define AI_LITE_INTERFACE_H
#pragma once
#include "ai_platform.h"
#include "ai_lite.h"
/*****************************************************************************/
/* Generic Codegen Section */
// #ifdef HAS_LOG
#if 0
#include "core_log.h"
#define LITE_GRAPH_START(_graph_name) \
AI_LOG_DEBUG("[LITE GRAPH START] : " _graph_name)
#define LITE_GRAPH_END(_graph_name) \
AI_LOG_DEBUG("[LITE GRAPH END] : " _graph_name)
#else
#define LITE_GRAPH_START(_graph_name) \
/* LITE_GRAPH_START() */
#define LITE_GRAPH_END(_graph_name) \
/* LITE_GRAPH_END() */
#endif /* HAS_LOG */
#ifdef HAS_AI_ASSERT
#include <assert.h>
#define LITE_ASSERT(_cond) \
{ assert(_cond); }
#else
#define LITE_ASSERT(_cond) \
do { /* LITE_ASSERT() */ } while (0);
#endif /*HAS_AI_ASSERT*/
/*****************************************************************************/
#if defined(_MSC_VER)
#define LITE_DECLARE_STATIC static __inline
#define LITE_HINT_INLINE static __inline
#define LITE_FORCE_INLINE static __inline
#elif defined(__ICCARM__) || defined (__IAR_SYSTEMS_ICC__)
#define LITE_DECLARE_STATIC static inline
#define LITE_HINT_INLINE static inline
#define LITE_FORCE_INLINE static inline
#elif defined(__GNUC__)
#define LITE_DECLARE_STATIC static __inline
#define LITE_HINT_INLINE static __inline
#define LITE_FORCE_INLINE static __inline
#else
#define LITE_DECLARE_STATIC static __inline
#define LITE_HINT_INLINE static __inline
#define LITE_FORCE_INLINE static __inline
#endif /* _MSC_VER */
#define LITE_API_ENTRY /* LITE_API_ENTRY */
#define LITE_PACK(...) \
__VA_ARGS__
#define LITE_UNUSED(_elem) \
((void)(_elem));
#define LITE_KERNEL_SECTION(_code_block) \
{ LITE_PACK(_code_block) }
/*****************************************************************************/
/* Arrays Section */
#define LITE_ARRAY_VALUES(...) \
{ LITE_PACK(__VA_ARGS__) }
#define LITE_ARRAY_DATA(_array, _type) \
((_type*)(_array)->data)
#define LITE_ARRAY_DATA_START(_array, _type) \
((_type*)(_array)->data_start)
/*****************************************************************************/
/* Tensors Section */
#define LITE_TENSOR_ARRAY(_tensor, _pos) \
(((_tensor)->data) + (_pos))
/*****************************************************************************/
/* Tensors List Section */
#define LITE_TENSOR_LIST(_chain, _pos) \
(&(_chain)->chain[_pos])
#define LITE_TENSOR_IN(_chain, _pos) \
(LITE_TENSOR_LIST(_chain, 0)->tensor[_pos])
#define LITE_TENSOR_OUT(_chain, _pos) \
(LITE_TENSOR_LIST(_chain, 1)->tensor[_pos])
#define LITE_TENSOR_WEIGHTS(_chain, _pos) \
(LITE_TENSOR_LIST(_chain, 2)->tensor[_pos])
#define LITE_TENSOR_SCRATCHS(_chain, _pos) \
(LITE_TENSOR_LIST(_chain, 3)->tensor[_pos])
/*****************************************************************************/
#define LITE_LAYER_ACQUIRE(name_, cast_type_, ptr_) \
LITE_ASSERT(ptr_) \
AI_CONCAT(ai_layer_, cast_type_)* name_ = \
(AI_CONCAT(ai_layer_, cast_type_)*)(ptr_);
#define LITE_LAYER_RELEASE(name_, cast_type_) \
/* LITE_LAYER_RELEASE() */
/*****************************************************************************/
AI_API_DECLARE_BEGIN
AI_API_DECLARE_END
#endif /* AI_LITE_INTERFACE_H */
| 4,226 | C | 28.767605 | 80 | 0.503076 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_sm.h | /**
******************************************************************************
* @file layers_sm.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform non softmax layer datatype
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_SM_H
#define LAYERS_SM_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers SoftMax Layer Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @brief Softmax normalization computed on an array of fixed point channels
* @ingroup layers_sm
* @param out opaque handler to output channel array
* @param in opaque handler to input channel array
* @param in_size total size (number of elements) to process on the input
* @param channel_size number of elements of the input channel
* @param in_channel_step number of elements to move to next input element
* @param out_channel_step number of elements to move to next output element
*/
AI_INTERNAL_API
void sm_func_sm_array_fixed(ai_handle out, const ai_handle in,
const ai_size in_size,
const ai_size channel_size,
const ai_size in_channel_step,
const ai_size out_channel_step);
/*!
* @brief Computes the activations of a fixed point softmax nonlinear layer.
* @ingroup layers_sm
* @param layer the softmax (sm) layer
*/
AI_INTERNAL_API
void forward_sm_fixed(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_SM_H*/
| 2,051 | C | 30.56923 | 80 | 0.570453 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/datatypes_network.h | /**
******************************************************************************
* @file datatypes_network.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of code generated network types
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef DATATYPES_NETWORK_H
#define DATATYPES_NETWORK_H
#pragma once
/*
* Header to be overriden by the generated version
* by including with <> the include directories are searched in the order
* specified in the compiler
* To enable the override, put the generated path before the API path
*/
#include "ai_platform.h"
AI_API_DECLARE_BEGIN
#ifdef AI_OVERRIDE_CUSTOM_TYPES
#warning "Warning: Custom Types have been already defined!\n"
#endif
#define AI_CUSTOM_TYPES_COUNT (3)
#define AI_CUSTOM_TYPES_SIGNATURE_DECLARE(name) \
const ai_custom_type_signature name[AI_CUSTOM_TYPES_COUNT+1] = { \
AI_CUSTOM_TYPES_COUNT, \
AI_CUSTOM_SIZE(ai_shape_dimension), \
AI_CUSTOM_SIZE(ai_stride_dimension), \
AI_CUSTOM_SIZE(ai_array_size), \
};
typedef ai_i32 ai_stride_dimension;
typedef ai_u32 ai_array_size;
AI_API_DECLARE_END
#endif /*DATATYPES_NETWORK_H*/
| 1,694 | C | 27.728813 | 80 | 0.579103 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_layer_custom_interface.h | /**
******************************************************************************
* @file ai_layer_custom_interface.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform custom layers interface APIs
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_LAYER_CUSTOM_INTERFACE_H
#define AI_LAYER_CUSTOM_INTERFACE_H
#pragma once
#include "ai_platform.h"
#include "ai_platform_interface.h"
#include "layers_custom.h"
#define INTQ_SCALE_FLOAT (AI_BUFFER_META_FLAG_SCALE_FLOAT)
#define INTQ_ZEROPOINT_U8 (AI_BUFFER_META_FLAG_ZEROPOINT_U8)
#define INTQ_ZEROPOINT_S8 (AI_BUFFER_META_FLAG_ZEROPOINT_S8)
#define INTQ_ZEROPOINT_U16 (AI_BUFFER_META_FLAG_ZEROPOINT_U16)
#define INTQ_ZEROPOINT_S16 (AI_BUFFER_META_FLAG_ZEROPOINT_S16)
#define AI_TENSOR_HEIGHT (3)
#define AI_TENSOR_WIDTH (2)
#define AI_TENSOR_CHANNEL (1)
#define AI_TENSOR_IN_CHANNEL (0)
AI_API_DECLARE_BEGIN
typedef enum {
TYPE_NONE = 0x0,
TYPE_FLOAT,
TYPE_BOOL,
TYPE_INTEGER,
TYPE_SIGNED,
TYPE_UNSIGNED,
} ai_tensor_type;
typedef struct {
ai_tensor_type type;
ai_i8 bits;
ai_i8 fbits;
} ai_tensor_format;
typedef struct {
ai_u16 flags; /*!< optional flags to store intq info attributes */
ai_u16 size; /*!< number of elements in the the intq_info list */
ai_float* scale; /*!< array of scales factors */
union {
ai_u8* zeropoint_u8; /*!< array of zeropoints as unsigned */
ai_i8* zeropoint_s8; /*!< array of zeropoints as signed */
};
} ai_tensor_intq_info;
/****************************************************************************
** Layer Custom Interface APIs
****************************************************************************/
/*!
* @brief acquire the custom layer from its handle
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the custom layer
* @return a pointer to ai_layer_custom if found and valid, else NULL
*/
AI_INTERFACE_TYPE
ai_layer_custom* ai_layer_custom_get(
ai_layer* layer);
/*!
* @brief release the custom layer provided its handle
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the custom layer to release
*/
AI_INTERFACE_TYPE
void ai_layer_custom_release(
ai_layer* layer);
/*!
* @brief get the number of inputs tensors of a custom layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the custom layer
* @return the number of input tensors of the layer. 0 if no input tensors or error
*/
AI_INTERFACE_TYPE
ai_size ai_layer_get_tensor_in_size(
const ai_layer* layer);
/*!
* @brief get the number of outputs tensors of a custom layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the custom layer
* @return the number of outputs tensors of the layer. 0 if no outputs tensors or error
*/
AI_INTERFACE_TYPE
ai_size ai_layer_get_tensor_out_size(
const ai_layer* layer);
/*!
* @brief get the number of weights tensors of a custom layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the custom layer
* @return the number of weights tensors of the layer. 0 if no weights tensors or error
*/
AI_INTERFACE_TYPE
ai_size ai_layer_get_tensor_weights_size(
const ai_layer* layer);
/*!
* @brief get the n-th (at index pos) input tensor pointer from a layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the layer
* @param pos the index position in the tensor list
* @return a pointer to a tensor if found, else, if invalid or out-of-range NULL
*/
AI_INTERFACE_TYPE
ai_tensor* ai_layer_get_tensor_in(
const ai_layer* layer, const ai_u16 pos);
/*!
* @brief get the n-th (at index pos) output tensor pointer from a layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the layer
* @param pos the index position in the tensor list
* @return a pointer to a tensor if found, else, if invalid or out-of-range NULL
*/
AI_INTERFACE_TYPE
ai_tensor* ai_layer_get_tensor_out(
const ai_layer* layer, const ai_u16 pos);
/*!
* @brief get the n-th (at index pos) weight tensor pointer from a layer
* @ingroup ai_layer_custom_interface
* @param layer an opaque handler to the layer
* @param pos the index position in the tensor list
* @return a pointer to a tensor if found, else, if invalid or out-of-range NULL
*/
AI_INTERFACE_TYPE
ai_tensor* ai_layer_get_tensor_weights(
const ai_layer* layer, const ai_u16 pos);
/**** Layer Tensors APIs ***************************************************/
/*!
* @brief check if the tensor has integer quantization informations @ref ai_tensor_intq_info
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return true if tensot has integer quantization informations, false otherwise
*/
AI_INTERFACE_TYPE
ai_bool ai_tensor_has_intq(
const ai_tensor* t);
/*!
* @brief get the tensor integer quantization informations @ref ai_tensor_intq_info
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the integer quantization informations as a struct @ref ai_tensor_intq_info
*/
AI_INTERFACE_TYPE
ai_tensor_intq_info ai_tensor_get_intq(
const ai_tensor* t);
/*!
* @brief get the format of the tensor see @ref ai_tensor_format
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the tensor format
*/
AI_INTERFACE_TYPE
ai_tensor_format ai_tensor_get_format(
const ai_tensor* t);
/**** Shapes Getters ****/
/*!
* @brief get the dimensionality of the tensor shapes
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the dimensionality of the tensor shape
*/
AI_INTERFACE_TYPE
ai_size ai_tensor_get_shape_size(
const ai_tensor* t);
/*!
* @brief get the value of the shape dimensionality pos
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the value of the shape dimensionality at pos of the tensor
*/
AI_INTERFACE_TYPE
ai_shape_dimension ai_tensor_get_shape(
const ai_tensor* t, const ai_u16 pos);
/**** Strides Getters ****/
/*!
* @brief get the dimensionality of the tensor strides
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the dimensionality of the tensor strides @ref ai_stride
*/
AI_INTERFACE_TYPE
ai_size ai_tensor_get_stride_size(
const ai_tensor* t);
/*!
* @brief get the value of the stride dimensionality pos
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the value of the stride dimensionality at pos of the tensor
*/
AI_INTERFACE_TYPE
ai_stride_dimension ai_tensor_get_stride(
const ai_tensor* t, const ai_u16 pos);
/**** Data Storage Getters ****/
/*!
* @brief get tensor storage data buffer pointer
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return a pointer to the tensor data buffer, set to NULL if error
*/
AI_INTERFACE_TYPE
ai_any_ptr ai_tensor_get_data(
const ai_tensor* t);
/*!
* @brief get number of tensor elements
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the number of tensor elements or 0 if error
*/
AI_INTERFACE_TYPE
ai_size ai_tensor_get_data_size(
const ai_tensor* t);
/*!
* @brief get the size in bytes of the tensor data buffer
* @ingroup ai_layer_custom_interface
* @param tensor a pointer to the tensor
* @return the size in bytes of the tensor data buffer. 0 if error
*/
AI_INTERFACE_TYPE
ai_size ai_tensor_get_data_byte_size(
const ai_tensor* t);
AI_API_DECLARE_END
#endif /*AI_LAYER_CUSTOM_INTERFACE_H*/
| 8,272 | C | 29.985019 | 92 | 0.66308 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_pool_dqnn.h | /**
******************************************************************************
* @file layers_conv2d_dqnn.h
* @author AIS
* @brief header file of AI platform DQNN pool datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_POOL_DQNN_H
#define LAYERS_POOL_DQNN_H
#pragma once
#include "layers_common.h"
#include "layers_pool.h"
/*!
* @defgroup layers_pool_dqnn Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_pool_dqnn
* @ingroup layers_pool_dqnn
* @brief pool_dqnn layer
*
* @ref forward_maxpool_is1os1
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_pool_dqnn_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_shape_2d pool_size; /*!< pooling size */
ai_shape_2d pool_stride; /*!< pooling stride */
ai_shape pool_pad; /*!< pooling pad, y,x border sizes */
// ai_u32 pad_value; /*!< pooling pad value */
} ai_layer_pool_dqnn;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles max pooling with binary input and binary output
* @ingroup layers_pool_dqnn
* @param layer conv2d_pool layer
*/
AI_INTERNAL_API
void forward_maxpool_is1os1(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_POOL_DQNN_H*/
| 1,996 | C | 26.356164 | 80 | 0.482966 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dw.h | /**
******************************************************************************
* @file lite_dw.h
* @author AIS
* @brief header file of AI platform lite dw kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_DW_H
#define LITE_DW_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles dw convolutions generic case (supports depth multiplier >= 1)
* @ingroup lite_dw
*/
LITE_API_ENTRY
void
forward_lite_dw_dm_sssa8_ch(const ai_i8 *Im_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_i8 *wt,
const ai_u16 ch_im_out,
const ai_u16 dim_kernel_x,
const ai_u16 dim_kernel_y,
const ai_u16 padding_x,
const ai_u16 padding_y,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_i32 *bias,
const ai_i8 In_ZeroPoint,
const ai_i8 Out_ZeroPoint,
ai_i8 *Im_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
const ai_i32 nl_pool_fused,
ai_i16 *bufferA);
/*!
* @brief Handles dw convolutions with depth multiplier = 1 only
* @ingroup lite_dw
*/
LITE_API_ENTRY
void
forward_lite_dw_sssa8_ch(const ai_i8 *Im_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_i8 *wt,
const ai_u16 dim_kernel_x,
const ai_u16 dim_kernel_y,
const ai_u16 padding_x,
const ai_u16 padding_y,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_i32 *bias,
const ai_i8 In_ZeroPoint,
const ai_i8 Out_ZeroPoint,
ai_i8 *Im_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
const ai_i32 nl_pool_fused,
ai_i16 *bufferA);
/*!
* @brief Handles dw convolutions with depth multiplier = 1, valid padding
* and 3*3 kernel size
* @ingroup lite_dw
*/
LITE_API_ENTRY
void
forward_lite_dw_3x3_sssa8_ch(const ai_i8 *Im_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_i8 *wt,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_i32 *bias,
const ai_i8 In_ZeroPoint,
const ai_i8 Out_ZeroPoint,
ai_i8 *Im_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
const ai_i32 nl_pool_fused,
ai_i16 *bufferA);
/*!
* @brief Handles dw convolutions with depth multiplier = 1, valid padding,
* 3*3 kernel size, stride_x = 1 and weights/input are channel first
* @ingroup lite_dw
*/
LITE_API_ENTRY
void
forward_lite_dw_3x3_ch1st_sssa8_ch(const ai_i8 *Im_in,
const ai_u16 dim_im_in_x,
const ai_u16 dim_im_in_y,
const ai_u16 ch_im_in,
const ai_i8 *wt,
const ai_u16 stride_x,
const ai_u16 stride_y,
const ai_i32 *bias,
const ai_i8 In_ZeroPoint,
const ai_i8 Out_ZeroPoint,
ai_i8 *Im_out,
const ai_u16 dim_im_out_x,
const ai_u16 dim_im_out_y,
const ai_i32 nl_pool_fused,
ai_i16 *bufferA);
#endif /*LITE_DW_H*/
| 5,348 | C | 39.218045 | 80 | 0.382199 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_ml.h | /**
******************************************************************************
* @file layers_ml.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform ml layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_ML_H
#define LAYERS_ML_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_generic ML Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_ArrayFeatureExtractor
* @ingroup layers_ml
* @brief ai_layer_ArrayFeatureExtractor layer definition
*
* This layer select elements of the input tensor based on the indices passed. It is intended to be used
* by his associated forward function @ref forward_arrayfeatureextractor
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_arrayfeatureextractor_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_tensor* indices; /*!< Indices of corrisponding axis in axes*/
} ai_layer_arrayfeatureextractor;
/*!
* @struct ai_layer_ZipMap
* @ingroup layers_ml
* @brief ai_layer_ZipMap layer definition
*
* This layer creates a map from the input and the attributes.
* The values are provided by the input tensor, while the keys are specified by the attributes.
* The user must provide keys in either classlabels_strings or classlabels_int64s (but not both).
* The columns of the tensor correspond one-by-one to the keys specified by the attributes.
* There must be as many columns as keys.
* It is intended to be used by his associated forward function @ref forward_zipmap.
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_zipmap_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_bool has_classlabels_int;
} ai_layer_zipmap;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief select elements of the input tensor based on the indices passed.
* @ingroup layers_ml
* @param layer array feture extractor
*/
AI_INTERNAL_API
void forward_arrayfeatureextractor(ai_layer* layer);
/*!
* @brief creates a map from the inputs and the attributes
* @ingroup layers_ml
* @param layer zipmap
*/
AI_INTERNAL_API
void forward_zipmap(ai_layer* layer);
AI_API_DECLARE_END
#endif /*LAYERS_ML_H*/
| 2,899 | C | 28.896907 | 104 | 0.595378 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dense_is8os1ws1.h | /**
******************************************************************************
* @file lite_dense_is8os1ws1.h
* @author Marco Forleo
* @brief header file of AI platform lite dense kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2023 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_DENSE_IS8OS1WS1_H
#define LITE_DENSE_IS8OS1WS1_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Forward function for a dense layer with signed 8 bits input,
* binary weights and binary output.
* @ingroup lite_dense_is8os1ws1
* @param out_ptr The pointer to output buffer.
*@param data_in_init_ptr The pointer to input buffer.
* @param weights_ptr The pointer to weights.
* @param scratch_ptr The pointer to scratch buffer.
* @param scratch_size The value of scratch tensor size.
* @param n_channel_out The number of channels of the output, i.e.,
* the number of dense hidden neurons.
* @param n_channel_in The number of channels of the input.
* @param scale_ptr The pointer to scale buffer of BN.
* @param offset_ptr The pointer to offset buffer of BN.
*/
LITE_API_ENTRY
void forward_lite_dense_is8os1ws1_bn_fxp(ai_pbits *out_ptr,
const ai_i8 *data_in_init_ptr,
const ai_pbits *weights_ptr,
ai_i32 *scratch_ptr,
const ai_u32 scratch_size,
const ai_u32 n_channel_out,
const ai_u32 n_channel_in,
const ai_i32 *threshold_ptr);
#endif /*LITE_DENSE_IS8OS1WS1_H*/
| 2,452 | C | 41.293103 | 80 | 0.472268 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_list.h | /**
******************************************************************************
* @file layers_list.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
/* No sentry. This is deliberate!! */
/* Template: LAYER_ENTRY(type_, id_, struct_, forward_func_, init_func_, destroy_func_)
* Where:
* - type_ is the (enum) type name of the layer. to have the complete enum
* value you should use the macro @ref AI_LAYER_TYPE_ENTRY(type_) that adds
* the specific prefix and postfix tokens to the type_
* - id_ is the numeric id of the layer
* - struct_ is the name of the datastruct of the layer without the ai_layer_
* prefix
* - forward_func_ is the forward function name of the routine implementing
* actual layer processing
* - init_func_ is the init function name of the routine implementing
* actual layer initialization
* - destroy_func_ is the destroy function name of the routine implementing
* actual layer de-initialization
*/
/* Layer IDs for stateless layers (bit 8 set) */
#define LAYER_ID(id_) \
(0x100 + (id_))
/* Layer IDs for stateful layers (bits 7 and 8 set) */
#define LAYER_STATEFUL_ID(id_) \
(0x180 + (id_))
/*!< Base layer */
LAYER_ENTRY(BASE, LAYER_ID(0), base, NULL, NULL, NULL)
/*!< Elementwise addition layer */
LAYER_ENTRY(ADD, LAYER_ID(1), add, forward_add, NULL, NULL)
/*!< Batch normalization layer */
LAYER_ENTRY(BN, LAYER_ID(2), bn, forward_bn, NULL, NULL)
/*!< 2D Convolutional layer */
LAYER_ENTRY(CONV2D, LAYER_ID(3), conv2d, forward_conv2d, NULL, NULL)
/*!< Dense layer */
LAYER_ENTRY(DENSE, LAYER_ID(4), dense, forward_dense, NULL, NULL)
/*!< Local Response Normalization layer */
LAYER_ENTRY(LRN, LAYER_ID(6), lrn, forward_lrn, NULL, NULL)
/*!< Nonlinearity layer */
LAYER_ENTRY(NL, LAYER_ID(7), nl, NULL, NULL, NULL)
/*!< Normalization layer */
LAYER_ENTRY(NORM, LAYER_ID(8), norm, forward_norm, NULL, NULL)
/*!< Merged Conv2d / Pool layer */
LAYER_ENTRY(OPTIMIZED_CONV2D, LAYER_ID(9), conv2d_nl_pool, forward_conv2d_nl_pool, NULL, NULL)
/*!< Transpose Tensor layer */
LAYER_ENTRY(TRANSPOSE, LAYER_ID(10), transpose, forward_transpose, NULL, NULL)
/*!< Pooling layer */
LAYER_ENTRY(POOL, LAYER_ID(11), pool, forward_pool, NULL, NULL)
/*!< Softmax layer */
LAYER_ENTRY(SM, LAYER_ID(12), sm, forward_sm, NULL, NULL)
/*!< Split layer */
LAYER_ENTRY(SPLIT, LAYER_ID(13), split, forward_split, NULL, NULL)
/*!< TimeDelay layer */
LAYER_ENTRY(TIME_DELAY, LAYER_ID(14), time_delay, forward_time_delay, NULL, NULL)
/*!< TimeDistributed layer */
LAYER_ENTRY(TIME_DISTRIBUTED, LAYER_ID(15), time_distributed, forward_time_distributed, NULL, NULL)
/*!< Concat Tensor layer */
LAYER_ENTRY(CONCAT, LAYER_ID(16), concat, forward_concat, NULL, NULL)
/*!< GEMM layer */
LAYER_ENTRY(GEMM, LAYER_ID(17), gemm, forward_gemm, NULL, NULL)
/*!< Upsample layer */
LAYER_ENTRY(UPSAMPLE, LAYER_ID(18), upsample, forward_upsample, NULL, NULL)
/*!< Container layer for eltwise operations */
LAYER_ENTRY(ELTWISE, LAYER_ID(19), eltwise, forward_eltwise, NULL, NULL)
/*!< Container layer for eltwise integer operations */
LAYER_ENTRY(ELTWISE_INTEGER, LAYER_ID(20), eltwise_integer, NULL, NULL, NULL)
/*!< InstanceNormalization layer */
LAYER_ENTRY(INSTANCENORMALIZATION, LAYER_ID(21), instanceNormalization, forward_instanceNormalization, NULL, NULL)
/*!< Pad layer */
LAYER_ENTRY(PAD, LAYER_ID(22), pad, forward_pad, NULL, NULL)
/*!< Slice layer */
LAYER_ENTRY(SLICE, LAYER_ID(23), slice, forward_slice, NULL, NULL)
/*!< Tile layer */
LAYER_ENTRY(TILE, LAYER_ID(24), tile, forward_tile, NULL, NULL)
/*!< Container layer for reduce operations */
LAYER_ENTRY(REDUCE, LAYER_ID(25), reduce, forward_reduce, NULL, NULL)
/*!< Recurrent Neural Network layer */
LAYER_ENTRY(RNN, LAYER_ID(26), rnn, forward_rnn, NULL, NULL)
/*!< Resize layer */
LAYER_ENTRY(RESIZE, LAYER_ID(27), resize, forward_resize, NULL, NULL)
/*!< Gather layer */
LAYER_ENTRY(GATHER, LAYER_ID(28), gather, forward_gather, NULL, NULL)
/*!< Pack layer */
LAYER_ENTRY(PACK, LAYER_ID(29), pack, forward_pack, NULL, NULL)
/*!< Unpack layer */
LAYER_ENTRY(UNPACK, LAYER_ID(30), unpack, forward_unpack, NULL, NULL)
/*!< ArgMax layer */
LAYER_ENTRY(ARGMAX, LAYER_ID(31), argmax, forward_argmax, NULL, NULL)
/*!< ArgMin layer */
LAYER_ENTRY(ARGMIN, LAYER_ID(32), argmin, forward_argmin, NULL, NULL)
/*!< Cast Neural Network Layer */
LAYER_ENTRY(CAST, LAYER_ID(33), cast, forward_cast, NULL, NULL)
/*!< iForest layer */
LAYER_ENTRY(IFOREST, LAYER_ID(34), iforest, forward_iforest, NULL, NULL)
/*!< SVM Regressor layer */
LAYER_ENTRY(SVMREG, LAYER_ID(35), svmreg, forward_svm_regressor, NULL, NULL)
/*!< ArrayFeatureExtractor layer */
LAYER_ENTRY(ARRAYFEATUREEXTRACTOR, LAYER_ID(36), arrayfeatureextractor, forward_arrayfeatureextractor, NULL, NULL)
/*!< SVM Classifier (SVC) layer */
LAYER_ENTRY(SVC, LAYER_ID(37), svc, forward_svc, NULL, NULL)
/*!< ZipMap layer */
LAYER_ENTRY(ZIPMAP, LAYER_ID(38), zipmap, forward_zipmap, NULL, NULL)
/*!< Where layer */
LAYER_ENTRY(WHERE, LAYER_ID(39), where, forward_where, NULL, NULL)
/*!< LinearClassifier layer */
LAYER_ENTRY(LINEARCLASSIFIER, LAYER_ID(42), linearclassifier, forward_linearclassifier, NULL, NULL)
/*!< TreeEnsembleClassifier layer */
LAYER_ENTRY(TREE_ENSEMBLE_CLASSIFIER, LAYER_ID(43), tree_ensemble_classifier, forward_tree_ensemble_classifier, NULL, NULL)
/*!< TopK layer */
LAYER_ENTRY(TOPK, LAYER_ID(45), topK, forward_topK, NULL, NULL)
/*!< ReduceLogSumExp layer */
LAYER_ENTRY(REDUCE_LOG_SUM_EXP, LAYER_ID(51), reduce_log_sum_exp, forward_reduce_log_sum_exp, NULL, NULL)
/*!< ReduceL1 layer */
LAYER_ENTRY(REDUCE_L1, LAYER_ID(52), reduce_l1, forward_reduce_l1, NULL, NULL)
/*!< Runtime Lite Graph Wrapper layer */
LAYER_ENTRY(LITE_GRAPH, LAYER_ID(63), lite_graph, NULL, NULL, NULL)
/*!< TreeEnsembleRegressor layer */
LAYER_ENTRY(TREE_ENSEMBLE_REGRESSOR, LAYER_ID(66), tree_ensemble_regressor, forward_tree_ensemble_regressor, NULL, NULL)
/*!< Deeply Quantized Dense Layers */
LAYER_ENTRY(CONV2D_DQNN, LAYER_ID(40), conv2d_dqnn, forward_pw_is1os1ws1_bn, NULL, NULL)
LAYER_ENTRY(POOL_DQNN, LAYER_ID(41), pool_dqnn, forward_maxpool_is1os1, NULL, NULL)
LAYER_ENTRY(DENSE_DQNN, LAYER_ID(44), dense_dqnn, forward_dense_is1os1ws1, NULL, NULL)
/*!< Reverse layer */
LAYER_ENTRY(REVERSE, LAYER_ID(50), reverse, forward_reverse, NULL, NULL)
/*****************************************************************************/
/*!< Base Stateful Layer type */
LAYER_ENTRY(STATEFUL, LAYER_STATEFUL_ID(0), stateful, NULL, NULL, NULL)
/*!< Long Short Time Memory layer */
LAYER_ENTRY(LSTM, LAYER_STATEFUL_ID(1), lstm, forward_lstm, init_lstm, destroy_lstm)
/*!< Custom layer */
LAYER_ENTRY(CUSTOM, LAYER_STATEFUL_ID(2), custom, NULL, NULL, NULL)
/*!< Gated Recurrent Unit layer */
LAYER_ENTRY(GRU, LAYER_STATEFUL_ID(3), gru, forward_gru, init_gru, destroy_gru)
/*!< Stateless Template layer declaration */
/* LAYER_ENTRY(TEMPLATE, LAYER_ID(XX), template, forward_template, NULL, NULL) */
/*!< Stateful Template layer declaration */
/* LAYER_ENTRY(TEMPLATE, LAYER_STATEFUL_ID(XX), template, forward_template, init_template, destroy_template) */
#undef LAYER_ENTRY
#undef LAYER_ID
#undef LAYER_STATEFUL_ID
| 7,821 | C | 45.838323 | 123 | 0.671781 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_pad_generic.h | /**
******************************************************************************
* @file lite_pad_generic.h
* @author AIS
* @brief header file of AI platform lite padding kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2022 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_PADDING_DQNN_H
#define LITE_PADDING_DQNN_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles padding with 8 bits input/output in constant mode - Lite I/F
* Channel 1st Format Input and Output
* @ingroup lite_padding_dqnn
*/
LITE_API_ENTRY
void forward_lite_pad_8bit_ch1st_3x3_constant(ai_ptr_const in_data_tensor,
ai_ptr out_data_tensor,
const ai_handle fill_value,
const ai_i32 height_in,
const ai_i32 channel_in,
const ai_ptr_offset ch_stride_in,
const ai_ptr_offset h_stride_in,
const ai_ptr_offset h_stride_pad);
/*!
* @brief Handles padding with 8 bits input/output in constant mode - Lite I/F
* @ingroup lite_padding_dqnn
*/
LITE_API_ENTRY
void forward_lite_pad_constant(ai_ptr_const in_data,
ai_ptr out_data,
const ai_handle fill_value,
const ai_i16 in_bits,
const ai_i32 height_in,
const ai_ptr_offset ch_stride_in,
const ai_ptr_offset h_stride_in,
const ai_ptr_offset h_stride_pad,
const ai_ptr_offset h_stride_pad_b,
const ai_ptr_offset w_stride_pad,
const ai_ptr_offset w_stride_pad_r);
/*!
* @brief Handles padding with 8 bits input/output in edge mode - Lite I/F
* @ingroup lite_padding_dqnn
*/
void forward_lite_pad_edge(ai_ptr_const in_data_tensor,
ai_ptr out_data,
const ai_i32 height_in,
const ai_i16 pads_y,
const ai_i16 pads_x_r,
const ai_ptr_offset h_stride_in,
const ai_ptr_offset w_stride_in,
const ai_ptr_offset h_stride_out,
const ai_ptr_offset h_stride_pad,
const ai_ptr_offset w_stride_pad,
const ai_ptr_offset h_stride_pad_b);
/*!
* @brief Handles padding with 8 bits input/output in reflect mode - Lite I/F
* @ingroup lite_padding_dqnn
*/
void forward_lite_pad_reflect(ai_ptr_const in_data,
ai_ptr out_data,
const ai_i32 depth,
const ai_i32 height_in,
const ai_i32 width_in,
const ai_i32 height_out,
const ai_i32 width_out,
const ai_ptr_offset h_stride_in,
const ai_ptr_offset w_stride_in,
const ai_ptr_offset h_stride_out,
const ai_ptr_offset w_stride_out,
const ai_i16 pads_x,
const ai_i16 pads_y,
const ai_i16 pads_y_b,
const ai_ptr_offset h_stride_pad,
const ai_ptr_offset w_stride_pad,
const ai_ptr_offset w_stride_pad_r);
#endif /*LITE_PADDING_GENERIC_H*/
| 4,546 | C | 43.145631 | 80 | 0.421909 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_datatypes_format.h | /**
******************************************************************************
* @file ai_datatypes_format.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform private format handling routines
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_DATATYPES_FORMAT_H
#define AI_DATATYPES_FORMAT_H
#pragma once
#include "ai_platform.h"
#include "ai_datatypes_defines.h"
// #include "core_datatypes.h"
/*!
* @defgroup ai_datatypes_format Definiton and Macro of array and buffer formats
* @brief Type definition and implementation of internal @ref ai_array and
* @ref ai_buffer formats.
* @details The library handles 2 different kind of formats: an internal format
* that is part of the @ref ai_array struct that is a packed 32bit representation
* of the format attributes, and a public format (used in public APIs) associated
* with @ref ai_buffer struct , defined as enum in @ref ai_platform.h,
* that is just an enum type. Converters are provided in this header file to
* convert from one format representation to another.
* Some MSB bits are reserved in both formats to code some bit flag useful to
* declare some special attribute. Three flags are actually implemented in both
* formats: the @ref AI_BUFFER_FMT_FLAG_CONST and @ref AI_FMT_FLAG_CONST used
* to tag read-only memory buffers, @ref AI_BUFFER_FMT_FLAG_STATIC and
* @ref AI_FMT_FLAG_STATIC to mark statically allocated memory buffers and
* @ref AI_FMT_FLAG_SCRATCH_BUFFER to tag temporary scratch buffers.
* All the formats are declared in a proper tuple organize table header named
* @ref format_lists.h that enumerates all the formats available for the library.
* A new format could be added easily by adding a new FMY_ENTRY() as required.
* The preprocessor automatically generates the code for the handling of the
* format according to this tuples entry. A rational for the methodology could
* be found here:
* - https://codecraft.co/2012/10/29/how-enums-spread-disease-and-how-to-cure-it/
*
* The 32bits internal format fields are organized as follows:
*
* MSB LSB
* 31 25 24 23 21 17 14 7 0
* /---------------------------------------------------------------------------/
* / ATTR. FLAGS | FLOAT | SIGN | LDIV | TYPE | PMASK | BITS | FBITS /
* /---------------------------------------------------------------------------/
* Where:
* - FLAGS: is the reserved bits to store additional format attributes (e.g.
* I/O / STATIC flags. etc.)
* - FLOAT: 1 bit mark the format as floating point type
* - SIGN : 1 bit mark the format as signed type
* - LDIV : 2 bits is a log2 value that is used to compute elements size
* with some special format such as the compressed ones. It is a shift
* factor usually set to zero
* - TYPE : 4 bits mark the format "family" type. Actually 5 families are coded,
* @ref AI_FMT_FLOAT (float types)
* @ref AI_FMT_Q (fixed-point types in Qm.n format)
* @ref AI_FMT_BOOL (boolean type)
* @ref AI_FMT_LUT4 (compressed lookup 16 formats)
* @ref AI_FMT_LUT8 (compressed lookup 256 formats)
* - PMASK 3 bits padding mask used to set the optional dimension for padding
* to handle special aligned formats/ E.g. a 1 bit format
* Usually this is set to 0x0
* - BITS 7 bits set the total number of bits of the element, padding bits
* excluded. The bits are thus = sign bit + fractional bits + integer bits
* The number of integer bits could thus be known using the @ref
* AI_FMT_GET_IBITS() macro.
* - FBITS 7 bits set the number of fractional bits in the format
*
*
* A reference code snippet for usage is the test unit that uses this header:
*
* \include test/test_lcut_formats.cpp
*
*/
/*!
* Format bitfields definition. NOTE: 7 MSB are masked off
* for (optional) atributes setting using flags. see @ref AI_FMT_FLAG_CONST that
* is used for marking a data as constant readonly
*/
/* 1 bit field to identify floating point values*/
#define _FMT_FLOAT_MASK (0x1)
#define _FMT_FLOAT_BITS (24)
/*! 1 bit sign info */
#define _FMT_SIGN_MASK (0x1)
#define _FMT_SIGN_BITS (23)
/*! fractional bits field (i.e. for Q formats see @ref AI_FMT_Q) */
#define _FMT_FBITS_MASK (0x7F)
#define _FMT_FBITS_BITS (0)
#define _FMT_FBITS_BIAS ((_FMT_FBITS_MASK+1) >> 1)
/*! TOTAL number of bits (fractional+integer+sign) (excluded padding ones) */
#define _FMT_BITS_MASK (0x7F)
#define _FMT_BITS_BITS (7)
#define _FMT_BITS_BIAS (0)
/*! Padding bits for handling formats not aligned to multiples of 8 bits */
#define _FMT_PMASK_MASK (0x7)
#define _FMT_PMASK_BITS (14)
/*! bits reserved for identifying the family format, e.g. float, fixed-point..*/
#define _FMT_TYPE_MASK (0xF)
#define _FMT_TYPE_BITS (17)
#define _FMT_LDIV_MASK (0x3)
#define _FMT_LDIV_BITS (21)
/******************************************************************************/
#define AI_FMT_OBJ(fmt_) ((ai_array_format)(fmt_))
/*!
* Only 25 LSB bits are used for storing actual format bits. 7 bits are reserved
* for format attributes, see @ref AI_FMT_FLAG_CONST flag
*/
#define AI_FMT_FLAG_BITS (25)
#define AI_FMT_MASK ((0x1<<AI_FMT_FLAG_BITS)-1)
#define AI_FMT_FLAG_CONST (0x1<<30)
#define AI_FMT_FLAG_STATIC (0x1<<29)
#define AI_FMT_FLAG_SCRATCH_BUFFER (0x1<<28)
#define AI_FMT_FLAG_IS_IO (0x1<<27)
#define AI_FMT_FLAG_VISITED (0x1<<26)
/******************************************************************************/
/*!
* Format "Class" type : this identify the family of the format:
* float, integer, fixed point (i.e. Q format), compressed via lookup table
*/
#define AI_FMT_NONE (0x0)
#define AI_FMT_FLOAT (0x1)
#define AI_FMT_Q (0x2)
#define AI_FMT_BOOL (0x3)
#define AI_FMT_LUT4 (0x4)
#define AI_FMT_LUT8 (0x8)
#define AI_FMT_QMASK \
( (_FMT_FBITS_MASK<<_FMT_FBITS_BITS) | \
(_FMT_BITS_MASK<<_FMT_BITS_BITS) | \
(_FMT_PMASK_MASK<<_FMT_PMASK_BITS) )
#define AI_FMT_BINARY_MASK \
(AI_FMT_MASK & (~(_FMT_SIGN_MASK<<_FMT_SIGN_BITS)))
#define AI_FMT_IS_BINARY(val_) \
(((val_) & AI_FMT_BINARY_MASK) == AI_ARRAY_FORMAT_U1)
#define AI_FMT_GET(val_) \
( (AI_FMT_OBJ(val_)) & AI_FMT_MASK )
#define AI_FMT_MASK_Q(val_) \
( AI_FMT_OBJ(val_) & (~(AI_FMT_QMASK)) )
#define AI_FMT_GET_Q(val_) \
( AI_FMT_MASK_Q(val_) | AI_FMT_SET_BITS(0) | AI_FMT_SET_FBITS(0) )
#define AI_FMT_GET_FLAGS(val_) \
( ((AI_FMT_OBJ(val_)) & (~AI_FMT_MASK)) >> AI_FMT_FLAG_BITS )
#define AI_FMT_SAME(fmt1_, fmt2_) \
( AI_FMT_GET(fmt1_) == AI_FMT_GET(fmt2_) )
#define _FMT_SET(val, mask, bits) AI_FMT_OBJ(((val)&(mask))<<(bits))
#define _FMT_GET(fmt, mask, bits) ((AI_FMT_OBJ(fmt)>>(bits))&(mask))
#define AI_FMT_SET_FLOAT(val) _FMT_SET(val, _FMT_FLOAT_MASK, _FMT_FLOAT_BITS)
#define AI_FMT_GET_FLOAT(fmt) _FMT_GET(fmt, _FMT_FLOAT_MASK, _FMT_FLOAT_BITS)
#define AI_FMT_SET_SIGN(val) _FMT_SET(val, _FMT_SIGN_MASK, _FMT_SIGN_BITS)
#define AI_FMT_GET_SIGN(fmt) _FMT_GET(fmt, _FMT_SIGN_MASK, _FMT_SIGN_BITS)
#define AI_FMT_SET_PMASK(val) _FMT_SET(val, _FMT_PMASK_MASK, _FMT_PMASK_BITS)
#define AI_FMT_GET_PMASK(fmt) _FMT_GET(fmt, _FMT_PMASK_MASK, _FMT_PMASK_BITS)
#define AI_FMT_SET_TYPE(val) _FMT_SET(val, _FMT_TYPE_MASK, _FMT_TYPE_BITS)
#define AI_FMT_GET_TYPE(fmt) _FMT_GET(fmt, _FMT_TYPE_MASK, _FMT_TYPE_BITS)
#define AI_FMT_SET_LDIV(val) _FMT_SET(val, _FMT_LDIV_MASK, _FMT_LDIV_BITS)
#define AI_FMT_GET_LDIV(fmt) _FMT_GET(fmt, _FMT_LDIV_MASK, _FMT_LDIV_BITS)
#define AI_FMT_SET_BITS(val) \
_FMT_SET((val) + _FMT_BITS_BIAS, _FMT_BITS_MASK, _FMT_BITS_BITS)
#define AI_FMT_GET_BITS(fmt) \
((ai_i8)_FMT_GET(fmt, _FMT_BITS_MASK, _FMT_BITS_BITS) - _FMT_BITS_BIAS)
#define AI_FMT_SET_FBITS(val) \
_FMT_SET((val) + _FMT_FBITS_BIAS, _FMT_FBITS_MASK, _FMT_FBITS_BITS)
#define AI_FMT_GET_FBITS(fmt) \
((ai_i8)_FMT_GET(fmt, _FMT_FBITS_MASK, _FMT_FBITS_BITS) - _FMT_FBITS_BIAS)
/*!
* The total number of bits for a given format is supposed to be the sum of the
* bits + padding bits. This means that the number of integer bits is derived
* as follow: int_bits = bits - fbits (fractional bits) - 1 (for the sign)
*/
#define AI_FMT_GET_BITS_SIZE(fmt_) \
AI_FMT_GET_BITS(fmt_)
/*! Macro used to compute the integer bits for a format */
#define AI_FMT_GET_IBITS(fmt_) \
((ai_i16)AI_FMT_GET_BITS(fmt_)-AI_FMT_GET_FBITS(fmt_)-AI_FMT_GET_SIGN(fmt_))
/*! ai_buffer format handlers section *****************************************/
#define AI_BUFFER_FMT_MASK_Q(fmt_) \
( AI_BUFFER_FMT_OBJ(fmt_) & 0xFFFFC000 )
#define AI_BUFFER_FMT_GET_Q(fmt_) \
( AI_BUFFER_FMT_MASK_Q(fmt_) | AI_BUFFER_FMT_SET_FBITS(0) | \
AI_BUFFER_FMT_SET_FBITS(0) )
#define AI_BUFFER_FMT_SET_Q(bits_, fbits_) \
AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, bits_, fbits_)
#define AI_BUFFER_FMT_IS_Q(fmt_) \
( (AI_BUFFER_FMT_TYPE_Q==AI_BUFFER_FMT_GET_TYPE(fmt_)) && \
(1==AI_BUFFER_FMT_GET_SIGN(fmt_)) )
#define AI_BUFFER_FMT_SET_UQ(bits_, fbits_) \
AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, bits_, fbits_)
#define AI_BUFFER_FMT_IS_UQ(fmt_) \
( (AI_BUFFER_FMT_TYPE_Q==AI_BUFFER_FMT_GET_TYPE(fmt_)) && \
(0==AI_BUFFER_FMT_GET_SIGN(fmt_)) )
/*! Q ai_array format handlers ************************************************/
#define AI_ARRAY_FMT_Q(bits_, fbits_) \
( AI_FMT_MASK_Q(AI_ARRAY_FORMAT_Q) | AI_FMT_SET_BITS(bits_) | AI_FMT_SET_FBITS(fbits_) )
#define AI_ARRAY_FMT_SET_Q(bits_, fbits_) \
AI_ARRAY_FMT_Q(bits_, fbits_)
#define AI_ARRAY_FMT_IS_Q(fmt_) \
( AI_FMT_GET(AI_FMT_MASK_Q(AI_ARRAY_FORMAT_Q))==AI_FMT_GET(AI_FMT_MASK_Q(fmt_)) )
#define AI_ARRAY_FMT_UQ(bits_, fbits_) \
( AI_FMT_MASK_Q(AI_ARRAY_FORMAT_UQ) | AI_FMT_SET_BITS(bits_) | AI_FMT_SET_FBITS(fbits_) )
#define AI_ARRAY_FMT_SET_UQ(bits_, fbits_) \
AI_ARRAY_FMT_UQ(bits_, fbits_)
#define AI_ARRAY_FMT_IS_UQ(fmt_) \
( AI_FMT_GET(AI_FMT_MASK_Q(AI_ARRAY_FORMAT_UQ))==AI_FMT_GET(AI_FMT_MASK_Q(fmt_)) )
AI_DEPRECATED
/* Alias for AI_ARRAY_FMT_SET_Q */
#define AI_ARRAY_FMT_SET_SQ(bits_, fbits_) \
AI_ARRAY_FMT_SET_Q(bits_, fbits_)
AI_DEPRECATED
/* Alias for AI_ARRAY_FMT_IS_Q */
#define AI_ARRAY_FMT_IS_SQ(fmt_) \
AI_ARRAY_FMT_IS_Q(fmt_)
/*! ai_array section **********************************************************/
#define AI_ARRAY_FMT_ENTRY(name_) \
AI_CONCAT(AI_ARRAY_FORMAT_, name_)
#define AI_ARRAY_FMT_NAME(fmt_) \
ai_array_fmt_name(fmt_)
#define AI_ARRAY_FMT_VALID(fmt_) \
ai_array_fmt_valid(fmt_)
#define AI_ARRAY_FMT_EXPORTED(fmt_) \
ai_array_fmt_exported(fmt_)
#define AI_ARRAY_FMT_GET_FORMATS(formats_) \
ai_array_fmt_get_formats(formats_)
#define AI_ARRAY_TO_BUFFER_FMT(fmt_) \
ai_array_to_buffer_fmt(fmt_)
#define AI_ARRAY_GET_BYTE_SIZE(fmt_, count_) \
ai_array_get_byte_size(fmt_, count_)
#define AI_ARRAY_GET_DATA_BYTE_SIZE(fmt_, count_) \
ai_array_get_data_byte_size(fmt_, count_)
#define AI_ARRAY_GET_ELEMS_FROM_SIZE(fmt_, size_) \
ai_array_get_elems_from_size(fmt_, size_)
AI_API_DECLARE_BEGIN
/*!
* @typedef ai_array_format
* @ingroup ai_datatypes_format
* @brief Generic Data Format Specifier for @ref ai_array (32bits packed info)
*/
typedef int32_t ai_array_format;
/*!
* @enum internal data format enums
* @ingroup ai_datatypes_format
* @brief Generic Data Format Specifier (32bits packed info)
*/
typedef enum {
#define FMT_ENTRY(exp_, name_, type_id_, sign_bit_, float_bit_, \
pmask_, bits_, fbits_, ldiv_bits_) \
AI_ARRAY_FMT_ENTRY(name_) = (AI_FMT_SET_FLOAT(float_bit_) | \
AI_FMT_SET_SIGN(sign_bit_) | \
AI_FMT_SET_BITS(bits_) | \
AI_FMT_SET_FBITS(fbits_) | \
AI_FMT_SET_PMASK(pmask_) | \
AI_FMT_SET_TYPE(type_id_) | \
AI_FMT_SET_LDIV(ldiv_bits_)),
#include "formats_list.h"
} ai_array_format_entry;
/*!
* @brief Get a human readable string from the format ID value
* @ingroup ai_datatypes_format
* @param[in] type the @ref ai_array_format to print out
* @return a string with a human readable name of the format
*/
AI_INTERNAL_API
const char* ai_array_fmt_name(const ai_array_format type);
/*!
* @brief Check if @ref ai_array_format is a exportable to an @ref ai_buffer_format
* @ingroup ai_datatypes_format
* @param[in] type the ai_array_format to check
* @return true if the format is exported, false otherwise
*/
AI_INTERNAL_API
ai_bool ai_array_fmt_exported(const ai_array_format type);
/*!
* @brief Check if @ref ai_array_format is a valid format present in the list of
* supported formats
* @ingroup ai_datatypes_format
* @param[in] type the ai_array_format to check
* @return true if the format is valid, false otherwise
*/
AI_INTERNAL_API
ai_bool ai_array_fmt_valid(const ai_array_format type);
/*!
* @brief Get the complete list of supported @ref ai_array_format formats
* @ingroup ai_datatypes_format
* @param[out] formats a pointer to an array withj all supported formats listed
* @return the number of supported formats
*/
AI_INTERNAL_API
ai_size ai_array_fmt_get_formats(const ai_array_format** formats);
/*! ai_buffer section *********************************************************
* Only 25 LSB bits are used for storing actual format bits. 7 bits are reserved
* for format atrtributes, see @ref AI_FMT_FLAG_CONST flag
*/
#define AI_BUFFER_FMT_ENTRY(name_) \
AI_CONCAT(AI_BUFFER_FORMAT_, name_)
#define AI_BUFFER_FMT_NAME(type_) \
ai_buffer_fmt_name(type_)
#define AI_BUFFER_FMT_VALID(type_) \
ai_buffer_fmt_valid(type_)
#define AI_BUFFER_FMT_GET_FORMATS(formats_) \
ai_buffer_fmt_get_formats(formats_)
#define AI_BUFFER_TO_ARRAY_FMT(fmt_) \
ai_buffer_to_array_fmt(fmt_)
#define AI_BUFFER_GET_BITS_SIZE(fmt) \
AI_ARRAY_GET_BITS_SIZE(AI_BUFFER_TO_ARRAY_FMT(fmt))
/*!
* @brief Get a human readable string from the format ID value
* @ingroup ai_datatypes_format
* @param[in] type the @ref ai_buffer_format to print out
* @return a string with a human readable name of the format
*/
AI_INTERNAL_API
const char* ai_buffer_fmt_name(
const ai_buffer_format type);
/*!
* @brief Check if @ref ai_buffer_format is a valid format present in the list
* of supported formats
* @ingroup ai_datatypes_format
* @param[in] type the @ref ai_buffer_format to check
* @return true if the format is valid, false otherwise
*/
AI_INTERNAL_API
ai_bool ai_buffer_fmt_valid(
const ai_buffer_format type);
/*!
* @brief Get the complete list of supported @ref ai_buffer_format formats
* @ingroup ai_datatypes_format
* @param[out] formats a pointer to an array with all supported formats listed
* @return the number of supported formats
*/
AI_INTERNAL_API
ai_size ai_buffer_fmt_get_formats(
const ai_buffer_format** formats);
/*! Conversions section *******************************************************/
/*!
* @brief Convert from ai_array_format to ai_buffer_format.
* @ingroup ai_datatypes_format
* @param fmt the input ai_array_format to convert
* @return the converted format as a ai_buffer_format
*/
AI_INTERNAL_API
ai_buffer_format ai_array_to_buffer_fmt(
const ai_array_format fmt);
/*!
* @brief Convert from ai_buffer_format to ai_array_format.
* @ingroup ai_datatypes_format
* @param fmt the input ai_buffer_format to convert
* @return the converted format as a ai_array_format
*/
AI_INTERNAL_API
ai_array_format ai_buffer_to_array_fmt(
const ai_buffer_format fmt);
/** helpers section ***********************************************************/
/*!
* @brief Computes the size in bytes given an ai_array_format and number of
* array elements.
* @details This routine computes from the number of elements of the array its
* size in bytes. If the array is referred by a tensor structure, it is the task
* of the latter to handle per-dimension padding (e.g. to align odd rows in a
* 4-bit matrix. At array level the padding elements MUST be included in the
* number of elements.
* @ingroup ai_datatypes_format
* @param[in] fmt the input array format as an ai_array_format
* @param[in] count the number of elements stored in the data array
* @return the size in bytes of the array given the specific format and number
* of elements (including padding elements)
*/
AI_INTERNAL_API
ai_size ai_array_get_byte_size(
const ai_array_format fmt, const ai_size count);
/*!
* @brief Computes the size in bytes given an ai_array_format and number of
* array elements of the data fields (e.g. LUT table size excluded).
* @details This routine computes from the number of elements of the array its
* size in bytes. If the array is referred by a tensor structure, it is the task
* of the latter to handle per-dimension padding (e.g. to align odd rows in a
* 4-bit matrix. At array level the padding elements MUST be included in the
* number of elements.
* @ingroup ai_datatypes_format
* @param[in] fmt the input array format as an ai_array_format
* @param[in] count the number of elements stored in the data array
* @return the size in bytes of the array given the specific format and number
* of elements (including padding elements)
*/
AI_INTERNAL_API
ai_size ai_array_get_data_byte_size(
const ai_array_format fmt, const ai_size count);
/*!
* @brief Computes the number of elements from ai_array_format and
* the size in byte of the array.
* @ingroup ai_datatypes_format
* @param fmt the input array format as an ai_array_format
* @param size the size in bytes of the array
* @return the number of elements that could be stored given the format
*/
AI_INTERNAL_API
ai_size ai_array_get_elems_from_size(
const ai_array_format fmt, const ai_size byte_size);
AI_API_DECLARE_END
#endif /*AI_DATATYPES_FORMAT_H*/
| 18,640 | C | 36.965377 | 91 | 0.635569 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_net_inspect.h | /**
******************************************************************************
* @file core_net_inspect.h
* @author AST Embedded Analytics Research Platform
* @brief header file of core network inspection APIs
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef __CORE_NET_INSPECT_H_
#define __CORE_NET_INSPECT_H_
#pragma once
#include "core_net_inspect_interface.h"
#include "core_common.h"
#include "layers_common.h"
/*!
* @defgroup core_net_inspect Core Network Inspection routines
* @brief Implementation of core network inspection routines that allows to
* inspect on a node basis a generated network model
* @details A network context @ref ai_network basically contains a chained list
* of nodes @ref ai_node that have an associated forward function.
* Each ai)network context and ai_node datastructs have as a required member
* field an opaque handler (i.e. a void pointer) to a klass object.
* This handler is intended to be used as a platform specific node context
* that implements specific target platform routines.
* The inspector module basically acts as a plugin that exploiting these features
* by temporary creating an hidden inspection context (see
* @ref ai_core_inspect_net_klass) associated to the network and
* linking it by re-routing the klass field to this inspection context. The
* inspection context saves as part of its state (by a stack push operation), the
* internal state of the network (all node / network klass pointers and actual
* forward functions).
* Thus, for each node it re-routes all node's forward functions to a dedicated
* inspection forward function (see @ref _forward_inspect_validate() routine)
* This routine is the core of the mechanism and it allows to inspect a network
* node by node. Some additional inspection could thus be done inside the
* _forward_inspect_validate() routine before and after the actual node
* forward function is called;
*
*/
AI_API_DECLARE_BEGIN
/*!
* @defgroup core_net_inspect Network Inspection Core
* @brief Implementation of the validation network routines
*/
/*!
* @brief Initialize the network inspection context on a given network
* @ingroup core net inspect
* @param network opaque handler to the network instance
* @param cfg a pointer to the inspector configuration we want to use
* @return true if execution of the API is fine, false otherwise
*/
AI_API_ENTRY
ai_bool ai_network_inspect_init(
ai_handle network, const ai_inspect_config* cfg);
/*!
* @brief Get a summary report from the inspected network
* @ingroup core net inspect
* @param network opaque handler to the network instance
* @param report a pointer to the report provided back by the inspection
* @return true if execution of the API is fine, false otherwise
*/
AI_API_ENTRY
ai_bool ai_network_inspect_get_report(
ai_handle network, ai_inspect_net_report* report);
/*!
* @brief Destroy the network inspection context on a given network
* @ingroup core net inspect
* @param network opaque handler to the network instance
* @return true if execution of the API is fine, false otherwise
*/
AI_API_ENTRY
ai_bool ai_network_inspect_destroy(ai_handle network);
AI_API_DECLARE_END
#endif /*__CORE_NET_INSPECT_H_*/
| 3,780 | C | 37.581632 | 81 | 0.682804 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_nl_generic_integer.h | #ifndef LITE_NL_GENERIC_INTEGER_H
#define LITE_NL_GENERIC_INTEGER_H
#pragma once
#include "ai_lite_interface.h"
/**
* @brief forward lite function for a s8 softmax non-linearity where the softmax is applied per channel.
* @ingroup lite_nl_generic_integer
* @param output The pointer to output buffer (s8).
* @param input The pointer to input buffer (s8).
* @param in_size. The size of the input (including channels).
* @param ch_size The nsize of each channel.
* @param in_ch_step The step between consecutive elements (inputs)
* @param out_ch_step The step between consecutive elements (outputs)
* @param mult
* @param shift
* @param min_diff
*/
LITE_API_ENTRY
void forward_lite_nl_softmax_is8os8(
ai_i8* out_ptr, const ai_i8* in_ptr,
const ai_size in_size, const ai_size ch_size,
const ai_i32 in_ch_step, const ai_i32 out_ch_step,
const ai_i32 mult, const ai_i32 shift, const ai_i32 min_diff,
ai_i32* scratch);
/**
* @brief forward lite function for a u8 softmax non-linearity where the softmax is applied per channel.
* @ingroup lite_nl_generic_integer
* @param output The pointer to output buffer (s8).
* @param input The pointer to input buffer (s8).
* @param in_size. The size of the input (including channels).
* @param ch_size The nsize of each channel.
* @param in_ch_step The step between consecutive elements (inputs)
* @param out_ch_step The step between consecutive elements (outputs)
* @param mult
* @param shift
* @param min_diff
*/
LITE_API_ENTRY
void forward_lite_nl_softmax_iu8ou8(
ai_u8* out_ptr, const ai_u8* in_ptr,
const ai_size in_size, const ai_size ch_size,
const ai_i32 in_ch_step, const ai_i32 out_ch_step,
const ai_i32 mult, const ai_i32 shift, const ai_i32 min_diff,
ai_i32* scratch);
#endif /* LITE_NL_GENERIC_INTEGER_H */
| 1,815 | C | 34.607842 | 104 | 0.713499 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_datatypes.h | /**
******************************************************************************
* @file ai_datatypes.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform private APIs types
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_DATATYPES_H
#define AI_DATATYPES_H
#pragma once
#include <string.h>
#include "ai_platform.h"
#include "ai_platform_interface.h"
/*!
* @defgroup datatypes Platform Interface Datatypes
* @brief Data structures used by AI platform to implement neural networks
*
*/
/** Count Variable Number of Arguments (up to 64 elements) *******************/
#define AI_NUMARGS(...) \
PP_NARG_(__VA_ARGS__,PP_RSEQ_N())
#define PP_NARG_(...) \
PP_ARG_N(__VA_ARGS__)
#define PP_ARG_N( \
_1, _2, _3, _4, _5, _6, _7, _8, _9,_10, \
_11,_12,_13,_14,_15,_16,_17,_18,_19,_20, \
_21,_22,_23,_24,_25,_26,_27,_28,_29,_30, \
_31,_32,_33,_34,_35,_36,_37,_38,_39,_40, \
_41,_42,_43,_44,_45,_46,_47,_48,_49,_50, \
_51,_52,_53,_54,_55,_56,_57,_58,_59,_60, \
_61,_62,_63,N,...) N
#define PP_RSEQ_N() \
63,62,61,60, \
59,58,57,56,55,54,53,52,51,50, \
49,48,47,46,45,44,43,42,41,40, \
39,38,37,36,35,34,33,32,31,30, \
29,28,27,26,25,24,23,22,21,20, \
19,18,17,16,15,14,13,12,11,10, \
9,8,7,6,5,4,3,2,1,0
/*****************************************************************************/
#define AI_PTR_ALIGN(ptr, alignment) \
((((ai_uptr)(ptr))+((ai_uptr)(alignment)-1))&(~((ai_uptr)(alignment)-1)))
/*!
* @typedef ai_offset
* @ingroup ai_datatypes_internal
* @brief Generic index offset type
*/
typedef int32_t ai_offset;
AI_API_DECLARE_BEGIN
AI_API_DECLARE_END
#endif /* AI_DATATYPES_H */
| 2,349 | C | 29.51948 | 80 | 0.473819 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_common_config.h | /**
******************************************************************************
* @file ai_common_config.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform common compile configuration defines
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_COMMON_CONFIG_H
#define AI_COMMON_CONFIG_H
#pragma once
/*!
* @defgroup layers Layers Compilation Config Definitions
* @brief definition
*
*/
#define HAS_PROFILE_FLOAT
#define HAS_PROFILE_FIXED
#endif /*AI_COMMON_CONFIG_H*/
| 1,070 | C | 28.749999 | 80 | 0.495327 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_pw_dqnn.h | /**
******************************************************************************
* @file lite_pw_dqnn.h
* @author AIS
* @brief header file of AI platform lite pw kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_PW_DQNN_H
#define LITE_PW_DQNN_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles point wise convolution with binary input, binary output and
* binary weights - Lite API version
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1os1ws1_bn(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 *pThreshold);
/*!
* @brief Handles point wise convolution with binary input, binary output and
* binary weights - Lite API version - Optimized thanks to Optim2
* assumptions
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1os1ws1_bn_optim2(const ai_u32 *pDataIn_init,
ai_u32 *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_i32 *pThreshold);
/*!
* @brief Handles point wise convolution with binary input, 8-bits output and
* binary weights - Lite API version
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1os8ws1_bn(const ai_u32 *pDataIn_init,
ai_i8 *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_float *pScale,
const ai_float *pOffset);
/*!
* @brief Handles point wise convolution with binary input, 8-bits output and
* binary weights - Lite API version - Optimized thanks to Optim1
* assumptions
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1os8ws1_bn_optim1(const ai_u32 *pDataIn_init,
ai_i8 *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_float *pScale,
const ai_float *pOffset);
/*!
* @brief Handles point-wise convolution with binary input, float32 output
* and binary weights - Lite API version
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1of32ws1_bn(const ai_u32 *pDataIn_init,
ai_float *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_float *pScale,
const ai_float *pOffset);
/*!
* @brief Handles point-wise convolution with binary input, float32 output
* and binary weights - Lite API version - Optimized thanks to Optim1
* assumptions
* @ingroup lite_pw_dqnn
*/
LITE_API_ENTRY
void forward_lite_pw_is1of32ws1_bn_optim1(const ai_u32 *pDataIn_init,
ai_float *pDataOut_init,
const ai_u32 *pWeights_init,
const ai_u32 n_channel_in,
const ai_u32 n_channel_out,
const ai_i32 width_out,
const ai_i32 height_out,
const ai_float *pScale,
const ai_float *pOffset);
#endif /*LITE_PW_DQNN_H*/
| 5,632 | C | 42 | 80 | 0.430753 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/ai_platform.h | /**
******************************************************************************
* @file ai_platform.h
* @author AST Embedded Analytics Research Platform
* @brief Definitions of AI platform public APIs types
******************************************************************************
* @attention
*
* Copyright (c) 2017 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef AI_PLATFORM_H
#define AI_PLATFORM_H
#pragma once
#include <stdint.h>
#include <stddef.h>
#include <inttypes.h>
#ifndef AI_PLATFORM_API_MAJOR
#define AI_PLATFORM_API_MAJOR (1)
#endif
#ifndef AI_PLATFORM_API_MINOR
#define AI_PLATFORM_API_MINOR (2)
#endif
#ifndef AI_PLATFORM_API_MICRO
#define AI_PLATFORM_API_MICRO (0)
#endif
#define AI_PLATFORM_API_VERSION \
AI_VERSION(AI_PLATFORM_API_MAJOR, \
AI_PLATFORM_API_MINOR, \
AI_PLATFORM_API_MICRO)
#ifndef AI_TOOLS_API_VERSION_MAJOR
#define AI_TOOLS_API_VERSION_MAJOR (1)
#endif
#ifndef AI_TOOLS_API_VERSION_MINOR
#define AI_TOOLS_API_VERSION_MINOR (5)
#endif
#ifndef AI_TOOLS_API_VERSION_MICRO
#define AI_TOOLS_API_VERSION_MICRO (0)
#endif
/*****************************************************************************/
#define AI_TOOLS_API_VERSION \
AI_VERSION(AI_TOOLS_API_VERSION_MAJOR, \
AI_TOOLS_API_VERSION_MINOR, \
AI_TOOLS_API_VERSION_MICRO)
#define AI_TOOLS_API_VERSION_1_3 \
AI_VERSION(1, 3, 0)
#define AI_TOOLS_API_VERSION_1_4 \
AI_VERSION(1, 4, 0)
#define AI_TOOLS_API_VERSION_1_5 \
AI_VERSION(1, 5, 0)
/*****************************************************************************/
#ifdef __cplusplus
#define AI_API_DECLARE_BEGIN extern "C" {
#define AI_API_DECLARE_END }
#else
#include <stdbool.h>
#define AI_API_DECLARE_BEGIN /* AI_API_DECLARE_BEGIN */
#define AI_API_DECLARE_END /* AI_API_DECLARE_END */
#endif
/*****************************************************************************/
#define AI_FLAG_NONE (0x0)
/*****************************************************************************/
AI_API_DECLARE_BEGIN
/*!
* @typedef ai_flags
* @ingroup ai_platform
* @brief bitmask for flags management
*/
typedef uint32_t ai_flags;
/*****************************************************************************/
#define AI_CONCAT_ARG(a, b) a ## b
#define AI_CONCAT(a, b) AI_CONCAT_ARG(a, b)
/*! AI_CAST SECTION ***********************************/
#define AI_CAST(type_, expr_) ((type_)(expr_))
/*****************************************************************************/
#define AI_MAGIC_SIGNATURE \
(0xa1facade)
#define AI_PACK(...) \
__VA_ARGS__
/*****************************************************************************/
#define AI_SHAPE_BCWH (0x01u)
/*!
* @typedef ai_shape_dimension
* @ingroup ai_platform
* @brief shape dimension type to be used in shape related structs @ref ai_buffer_shape
*/
typedef uint32_t ai_shape_dimension;
/*****************************************************************************/
#if defined(_MSC_VER)
#define AI_API_ENTRY __declspec(dllexport)
#define AI_ALIGNED(x) /* AI_ALIGNED(x) */
#elif defined(__ICCARM__) || defined (__IAR_SYSTEMS_ICC__)
#define AI_API_ENTRY /* AI_API_ENTRY */
#define AI_ALIGNED(x) AI_CONCAT(AI_ALIGNED_,x)
#define AI_ALIGNED_1 _Pragma("data_alignment = 1")
#define AI_ALIGNED_2 _Pragma("data_alignment = 2")
#define AI_ALIGNED_4 _Pragma("data_alignment = 4")
#define AI_ALIGNED_8 _Pragma("data_alignment = 8")
#define AI_ALIGNED_16 _Pragma("data_alignment = 16")
#define AI_ALIGNED_32 _Pragma("data_alignment = 32")
#elif defined(__CC_ARM)
#define AI_API_ENTRY __attribute__((visibility("default")))
#define AI_ALIGNED(x) __attribute__((aligned (x)))
/* Keil disallows anonymous union initialization by default */
#pragma anon_unions
#elif defined(__GNUC__)
//#define AI_API_ENTRY __attribute__((visibility("default")))
#define AI_API_ENTRY /* AI_API_ENTRY */
#define AI_ALIGNED(x) __attribute__((aligned(x)))
#else
/* Dynamic libraries are not supported by the compiler */
#define AI_API_ENTRY /* AI_API_ENTRY */
#define AI_ALIGNED(x) /* AI_ALIGNED(x) */
#endif
#define AI_HANDLE_PTR(ptr_) ((ai_handle)(ptr_))
#define AI_HANDLE_NULL AI_HANDLE_PTR(NULL)
#define AI_HANDLE_FUNC_PTR(func) ((ai_handle_func)(func))
#define AI_UNUSED(x) (void)(x);
#define AI_DEPRECATED /* AI_DEPRECATED */
#define AI_LEGACY /* AI_LEGACY */
#define AI_MAGIC_MARKER (0xA1FACADE)
#if defined(__cplusplus)
#define AI_STRUCT_INIT {}
#define AI_C_ARRAY_INIT {}
#else
#define AI_STRUCT_INIT {0}
#define AI_C_ARRAY_INIT {0}
#endif
#define AI_ERROR_FMT AIU32_FMT
#define AI_IS_UNSIGNED(type) \
((((type)0) - 1) > 0)
#define AI_CUSTOM_SIZE(type) \
(ai_custom_type_signature)((AI_IS_UNSIGNED(type)) \
? (0x80|(sizeof(type)&0x7f)) : (sizeof(type)&0x7f))
/*! network buffers struct handlers *******************************************/
#ifdef __cplusplus
#define AI_NETWORK_PARAMS_INIT(params_, activations_) \
{ \
{{ params_, activations_ }} \
}
#define AI_NETWORK_BUFFERS_INIT(weights_buffers_, activations_buffers_) \
{ \
AI_MAGIC_SIGNATURE, AI_PACK(weights_buffers_), AI_PACK(activations_buffers_) \
}
#else
#define AI_NETWORK_PARAMS_INIT(params_, activations_) \
{ \
.params = params_, \
.activations = activations_ \
}
#define AI_NETWORK_BUFFERS_INIT(weights_buffers_, activations_buffers_) \
{ \
.map_signature = AI_MAGIC_SIGNATURE, \
.map_weights = AI_PACK(weights_buffers_), \
.map_activations = AI_PACK(activations_buffers_) \
}
#endif // __cplusplus
/*! binary padded bits macro helpers *****************************************/
#define AI_PBITS_MASK \
(0x1F)
#define AI_PBITS_SHIFTS \
(5)
#define AI_PBITS_PADDED_BYTES_COUNT(bits_) \
(((ai_u32)(bits_) + 7) >> 3)
#define AI_PBITS_PADDED_WORDS_COUNT(bits_) \
(((ai_size)(bits_) + AI_PBITS_MASK) >> AI_PBITS_SHIFTS)
#define AI_PBITS_GET_WORD(word_ptr_, bits_) \
(((ai_pbits*)(word_ptr_)) + ((bits_) >> AI_PBITS_SHIFTS))
#define AI_PAD_CHANNELS(format_, channels_) \
((AI_BUFFER_FMT_GET_BITS(format_)==1) ? (AI_PBITS_PADDED_WORDS_COUNT(channels_) << AI_PBITS_SHIFTS) : (channels_))
/*! ai_intq_info struct handlers *********************************************/
#define INTQ_CONST const
// #define INTQ_CONST
#define AI_INTQ_INFO_LIST(list_) \
((list_)->info)
#define AI_INTQ_INFO_LIST_FLAGS(list_) \
((list_) ? (list_)->flags : 0)
#define AI_INTQ_INFO_LIST_SIZE(list_) \
((list_) ? (list_)->size : 0)
#define AI_HAS_INTQ_INFO_LIST(list_) \
((list_) ? (((list_)->info) && ((list_)->size>0)) : false)
#define AI_INTQ_INFO_LIST_SCALE(list_, type_, pos_) \
(((list_) && (list_)->info && ((pos_)<(list_)->size)) \
? ((type_*)((list_)->info->scale))[(pos_)] : 0)
#define AI_INTQ_INFO_LIST_ZEROPOINT(list_, type_, pos_) \
(((list_) && (list_)->info && ((pos_)<(list_)->size)) \
? ((type_*)((list_)->info->zeropoint))[(pos_)] : 0)
/*! ai_buffer format handlers ************************************************/
/*!
* @enum buffer format definition
* @ingroup ai_platform
*
* 32 bit signed format list.
*/
typedef int32_t ai_buffer_format;
/*! ai_buffer_meta flags & macros ********************************************/
#define AI_BUFFER_META_HAS_INTQ_INFO (0x1U << 0)
#define AI_BUFFER_META_FLAG_SCALE_FLOAT (0x1U << 0)
#define AI_BUFFER_META_FLAG_ZEROPOINT_U8 (0x1U << 1)
#define AI_BUFFER_META_FLAG_ZEROPOINT_S8 (0x1U << 2)
#define AI_BUFFER_META_FLAG_ZEROPOINT_U16 (0x1U << 3)
#define AI_BUFFER_META_FLAG_ZEROPOINT_S16 (0x1U << 4)
/*! ai_buffer format variable flags & macros *********************************/
#define AI_BUFFER_FMT_TYPE_NONE (0x0)
#define AI_BUFFER_FMT_TYPE_FLOAT (0x1)
#define AI_BUFFER_FMT_TYPE_Q (0x2)
#define AI_BUFFER_FMT_TYPE_BOOL (0x3)
#define AI_BUFFER_FMT_FLAG_CONST (0x1U<<30)
#define AI_BUFFER_FMT_FLAG_STATIC (0x1U<<29)
#define AI_BUFFER_FMT_FLAG_IS_IO (0x1U<<27)
#define AI_BUFFER_FMT_FLAG_PERSISTENT (0x1U<<29)
#define AI_BUFFER_FMT_PACK(value_, mask_, bits_) \
( ((value_) & (mask_)) << (bits_) )
#define AI_BUFFER_FMT_UNPACK(fmt_, mask_, bits_) \
( (AI_BUFFER_FMT_OBJ(fmt_) >> (bits_)) & (mask_) )
#define AI_BUFFER_FMT_OBJ(fmt_) \
((ai_buffer_format)(fmt_))
#define AI_BUFFER_FMT_GET_FLOAT(fmt_) \
AI_BUFFER_FMT_UNPACK(fmt_, 0x1, 24)
#define AI_BUFFER_FMT_GET_SIGN(fmt_) \
AI_BUFFER_FMT_UNPACK(fmt_, 0x1, 23)
#define AI_BUFFER_FMT_GET_TYPE(fmt_) \
AI_BUFFER_FMT_UNPACK(fmt_, 0xF, 17)
#define AI_BUFFER_FMT_GET_BITS(fmt_) \
AI_BUFFER_FMT_UNPACK(fmt_, 0x7F, 7)
#define AI_BUFFER_FMT_SET_BITS(bits_) \
AI_BUFFER_FMT_PACK((bits_), 0x7F, 7)
#define AI_BUFFER_FMT_GET_FBITS(fmt_) \
( (ai_i8)AI_BUFFER_FMT_UNPACK(fmt_, 0x7F, 0) - 64 )
#define AI_BUFFER_FMT_SET_FBITS(fbits_) \
AI_BUFFER_FMT_PACK((fbits_)+64, 0x7F, 0)
#define AI_BUFFER_FMT_SET(type_id_, sign_bit_, float_bit_, bits_, fbits_) \
AI_BUFFER_FMT_OBJ( \
AI_BUFFER_FMT_PACK(float_bit_, 0x1, 24) | \
AI_BUFFER_FMT_PACK(sign_bit_, 0x1, 23) | \
AI_BUFFER_FMT_PACK(0, 0x3, 21) | \
AI_BUFFER_FMT_PACK(type_id_, 0xF, 17) | \
AI_BUFFER_FMT_PACK(0, 0x7, 14) | \
AI_BUFFER_FMT_SET_BITS(bits_) | \
AI_BUFFER_FMT_SET_FBITS(fbits_) \
)
#define AI_BUFFER_FMT_SAME(fmt1_, fmt2_) \
( AI_BUFFER_FMT_GET(fmt1_) == AI_BUFFER_FMT_GET(fmt2_) )
#define AI_BUFFER_FMT_GET(fmt_) \
(AI_BUFFER_FMT_OBJ(fmt_) & 0x01FFFFFF)
#define AI_BUFFER_FORMAT(buf_) \
AI_BUFFER_FMT_GET((buf_)->format)
/*!
* @define shape type index
* @ingroup ai_platform
* @brief positional ID for generic shapes C structs
*/
#define AI_SHAPE_EXTENSION (0x5)
#define AI_SHAPE_DEPTH (0x4)
#define AI_SHAPE_HEIGHT (0x3)
#define AI_SHAPE_WIDTH (0x2)
#define AI_SHAPE_CHANNEL (0x1)
#define AI_SHAPE_IN_CHANNEL (0x0)
#define AI_SHAPE_BATCH (0x0)
#define AI_SHAPE_TIME (0x0)
AI_DEPRECATED
#define AI_BUFFER_WIDTH(buf_) \
((buf_)->shape.data[AI_SHAPE_WIDTH])
AI_DEPRECATED
#define AI_BUFFER_HEIGHT(buf_) \
((buf_)->shape.data[AI_SHAPE_HEIGHT])
AI_DEPRECATED
#define AI_BUFFER_CHANNELS(buf_) \
((buf_)->shape.data[AI_SHAPE_CHANNEL])
AI_DEPRECATED
#define AI_BUFFER_N_BATCHES(buf_) \
((buf_)->shape.data[AI_SHAPE_BATCH])
#define AI_BUFFER_DATA(buf_, type_) \
((type_*)((buf_)->data))
#define AI_BUFFER_META_INFO(buf_) \
((buf_)->meta_info)
#define AI_BUFFER_META_INFO_INTQ(meta_) \
((meta_) && ((meta_)->flags & AI_BUFFER_META_HAS_INTQ_INFO)) \
? ((meta_)->intq_info) : NULL
#define AI_BUFFER_META_INFO_INTQ_GET_SIZE(meta_) \
( (AI_BUFFER_META_INFO_INTQ(meta_)) \
? AI_INTQ_INFO_LIST_SIZE(AI_BUFFER_META_INFO_INTQ(meta_)) \
: 0 )
#define AI_BUFFER_META_INFO_INTQ_GET_SCALE(meta_, pos_) \
( (AI_BUFFER_META_INFO_INTQ(meta_)) \
? AI_INTQ_INFO_LIST_SCALE(AI_BUFFER_META_INFO_INTQ(meta_), ai_float, pos_) \
: 0 )
#define AI_BUFFER_META_INFO_INTQ_GET_ZEROPOINT(meta_, pos_) \
( (AI_BUFFER_META_INFO_INTQ(meta_)) \
? ((AI_INTQ_INFO_LIST_FLAGS(AI_BUFFER_META_INFO_INTQ(meta_))&AI_BUFFER_META_FLAG_ZEROPOINT_U8) \
? AI_INTQ_INFO_LIST_ZEROPOINT(AI_BUFFER_META_INFO_INTQ(meta_), ai_u8, pos_) \
: AI_INTQ_INFO_LIST_ZEROPOINT(AI_BUFFER_META_INFO_INTQ(meta_), ai_i8, pos_) ) \
: 0 )
#define AI_BUFFER_META_INFO_INIT(flags_, intq_info_) { \
.flags = (flags_), \
.intq_info = AI_PACK(intq_info_) \
}
#define AI_BUFFER_SIZE(buf_) \
ai_buffer_get_size(buf_, true)
#define AI_BUFFER_SIZE_UNPAD(buf_) \
ai_buffer_get_size(buf_, false)
#define AI_BUFFER_BYTE_SIZE(count_, fmt_) \
ai_buffer_get_byte_size(count_, fmt_)
#define AI_BUFFER_FLAGS(buf_) \
((buf_) ? (buf_)->flags : 0x0)
#define AI_BUFFER_SHAPE_INIT(type_, size_, ...) \
{ \
.type = (type_), \
.size = (size_), \
.data = (ai_shape_dimension[]){ __VA_ARGS__ } \
}
#define AI_BUFFER_SHAPE_INIT_FROM_ARRAY(type_, size_, array_ptr_) \
{ \
.type = (type_), \
.size = (size_), \
.data = (ai_shape_dimension*)(array_ptr_) \
}
#define AI_BUFFER_SHAPE_SIZE(buf_) \
((buf_) ? (buf_)->shape.size : 0)
#define AI_BUFFER_SHAPE_TYPE(buf_) \
((buf_) ? (buf_)->shape.type : 0)
#if defined(HAS_AI_ASSERT) && defined(AI_ASSERT)
#define AI_BUFFER_SET_SHAPE_ELEM(buf_, pos_, value_) { \
AI_ASSERT(buf_) \
(buf_)->shape.data[pos_] = (value_); \
}
#define AI_BUFFER_SHAPE_ELEM(buf_, pos_) \
(((pos_)<AI_BUFFER_SHAPE_SIZE(buf_)) ? (buf_)->shape.data[pos_] : 0)
#else
#define AI_BUFFER_SET_SHAPE_ELEM(buf_, pos_, value_) { \
(buf_)->shape.data[pos_] = (value_); \
}
#define AI_BUFFER_SHAPE_ELEM(buf_, pos_) \
(buf_)->shape.data[pos_]
#endif
AI_DEPRECATED
#define AI_BUFFER_OBJ_INIT(format_, h_, w_, ch_, n_batches_, data_) \
{ .format = (ai_buffer_format)(format_), \
.data = (ai_handle)(data_), \
.meta_info = NULL, \
.flags = AI_FLAG_NONE, \
.size = (h_) * (w_) * AI_PAD_CHANNELS(format_, ch_), \
.shape = AI_BUFFER_SHAPE_INIT(AI_SHAPE_BCWH, 4, (n_batches_), (ch_), (w_), (h_)), \
}
AI_DEPRECATED
#define AI_BUFFER_OBJ_INIT_STATIC(type_, format_, h_, w_, ch_, n_batches_, ...) \
{ .format = (ai_buffer_format)(format_), \
.data = (ai_handle)((type_[]){__VA_ARGS__}), \
.meta_info = NULL, \
.flags = AI_FLAG_NONE, \
.size = (h_) * (w_) * AI_PAD_CHANNELS(format_, ch_), \
.shape = AI_BUFFER_SHAPE_INIT(AI_SHAPE_BCWH, 4, (n_batches_), (ch_), (w_), (h_)) \
}
/* 7.1 new macro API */
#define AI_BUFFER_INIT(flags_, format_, shape_, size_, meta_info_, data_) \
{ .format = (ai_buffer_format)(format_), \
.data = (ai_handle)(data_), \
.meta_info = (meta_info_), \
.flags = (flags_), \
.size = (size_), \
.shape = AI_PACK(shape_) \
}
/* 7.1 new macro API */
#define AI_BUFFER_INIT_STATIC(type_, flags_, format_, shape_, size_, meta_info_, ...) \
{ .format = (ai_buffer_format)(format_), \
.data = (ai_handle)((type_[]){__VA_ARGS__}), \
.meta_info = (meta_info_), \
.flags = (flags_), \
.size = (size_), \
.shape = AI_PACK(shape_) \
}
/*****************************************************************************/
#define AI_NETWORK_BUFFERS_FIELD_DECLARE \
ai_signature map_signature; /*! structure signature (required!) */ \
ai_buffer_array map_weights; /*! info about weights array buffers (required!) */ \
ai_buffer_array map_activations; /*! info about activations array buffers (required!) */
#define AI_NETWORK_PARAMS_FIELDS_DECLARE \
union { \
struct { \
ai_buffer params; /*! info about params buffer(required!) */ \
ai_buffer activations; /*! info about activations buffer (required!) */ \
}; \
struct { \
AI_NETWORK_BUFFERS_FIELD_DECLARE \
}; \
};
/*****************************************************************************/
#define AI_BUFFER_ARRAY_OBJ_INIT(flags_, size_, buffer_array_) \
{ \
.flags = (ai_u16)(flags_), \
.size = (ai_u16)(size_), \
.buffer = (ai_buffer*)(buffer_array_) \
}
#define AI_BUFFER_ARRAY_OBJ_INIT_STATIC(flags_, size_, ...) \
{ \
.flags = (ai_u16)(flags_), \
.size = (ai_u16)(size_), \
.buffer = (ai_buffer*)((ai_buffer[]){__VA_ARGS__}) \
}
#define AI_BUFFER_ARRAY_SANE(buf_array_) \
ai_buffer_array_sane(buf_array_)
#define AI_BUFFER_ARRAY_FLAGS(buf_array_) \
((AI_BUFFER_ARRAY_SANE(buf_array_)) ? (buf_array_)->flags : AI_FLAG_NONE)
#define AI_BUFFER_ARRAY_SIZE(buf_array_) \
((AI_BUFFER_ARRAY_SANE(buf_array_)) ? (buf_array_)->size : 0)
#define AI_BUFFER_ARRAY_ITEM(buf_array_, pos_) \
((AI_BUFFER_ARRAY_SANE(buf_array_)) ? ((buf_array_)->buffer + (pos_)) : NULL)
#define AI_BUFFER_ARRAY_ITEM_SET_ADDRESS(buf_array_, pos_, address_) \
ai_buffer_array_item_set_address(buf_array_, pos_, address_)
/*!
* @enum buffer formats enum list
* @ingroup ai_platform
*
* List of supported ai_buffer format types.
*/
enum {
AI_BUFFER_FORMAT_NONE = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_NONE, 0, 0, 0, 0),
AI_BUFFER_FORMAT_FLOAT = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_FLOAT, 1, 1, 32, 0),
AI_BUFFER_FORMAT_U1 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 1, 0),
AI_BUFFER_FORMAT_U8 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 8, 0),
AI_BUFFER_FORMAT_U16 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 16, 0),
AI_BUFFER_FORMAT_U32 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 32, 0),
AI_BUFFER_FORMAT_S1 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 1, 0),
AI_BUFFER_FORMAT_S8 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 8, 0),
AI_BUFFER_FORMAT_S16 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 16, 0),
AI_BUFFER_FORMAT_S32 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 32, 0),
AI_BUFFER_FORMAT_Q = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 0, 0),
AI_BUFFER_FORMAT_Q7 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 8, 7),
AI_BUFFER_FORMAT_Q15 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 1, 0, 16, 15),
AI_BUFFER_FORMAT_UQ = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 0, 0),
AI_BUFFER_FORMAT_UQ7 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 8, 7),
AI_BUFFER_FORMAT_UQ15 = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_Q, 0, 0, 16, 15),
AI_BUFFER_FORMAT_BOOL = AI_BUFFER_FMT_SET(AI_BUFFER_FMT_TYPE_BOOL, 0, 0, 8, 0),
};
/*****************************************************************************/
#define AI_ERROR_INIT(type_, code_) { \
.type = AI_ERROR_##type_, \
.code = AI_ERROR_CODE_##code_ \
}
/* printf formats */
#define SSIZET_FMT "%" PRIu32
#define AII32_FMT "%" PRId32
#define AIU32_FMT "%" PRIu32
#define AII64_FMT "%" PRId64
#define AIU64_FMT "%" PRIu64
#define AI_VERSION(major_, minor_, micro_) \
(((major_)<<24) | ((minor_)<<16) | ((micro_)<<8))
typedef uint8_t ai_custom_type_signature;
typedef void* ai_handle;
typedef const void* ai_handle_const;
typedef float ai_float;
typedef double ai_double;
typedef bool ai_bool;
typedef char ai_char;
typedef uint32_t ai_size;
typedef int16_t ai_short_size;
typedef uintptr_t ai_uptr;
typedef unsigned int ai_uint;
typedef uint8_t ai_u8;
typedef uint16_t ai_u16;
typedef uint32_t ai_u32;
typedef uint64_t ai_u64;
typedef int ai_int;
typedef int8_t ai_i8;
typedef int16_t ai_i16;
typedef int32_t ai_i32;
typedef int64_t ai_i64;
typedef uint64_t ai_macc;
typedef int32_t ai_pbits;
typedef uint32_t ai_signature;
typedef void (*ai_handle_func)(ai_handle);
/*****************************************************************************/
/*!
* @struct ai_error
* @ingroup ai_platform
* @brief Structure encoding details about the last error.
*/
typedef struct ai_error_ {
ai_u32 type : 8; /*!< Error type represented by @ref ai_error_type */
ai_u32 code : 24; /*!< Error code represented by @ref ai_error_code */
} ai_error;
/*****************************************************************************/
/*!
* @struct ai_intq_info
* @ingroup ai_platform
* @brief an element of the ai_intq_info_list entry. It reports an array for the
* scale and zeropoint values for each buffer. Optional flags are also present
*/
typedef struct ai_intq_info_ {
INTQ_CONST ai_float* scale;
INTQ_CONST ai_handle zeropoint;
} ai_intq_info;
/*!
* @struct ai_intq_info_list
* @ingroup ai_platform
* @brief list reporting meta info for quantized networks integer support
* when size > 1 it means a per channel out quantization
*/
typedef struct ai_intq_info_list_ {
ai_u16 flags; /*!< optional flags to store intq info attributes */
ai_u16 size; /*!< number of elements in the the intq_info list */
INTQ_CONST ai_intq_info* info; /*!< pointer to an array of metainfo
* associated to the intq_info list */
} ai_intq_info_list;
/*****************************************************************************/
/*!
* @struct ai_buffer_meta_info
* @ingroup ai_platform
* @brief Optional meta attributes associated with the I/O buffer.
* This datastruct is used also for network querying, where the data field may
* may be NULL.
*/
typedef struct ai_buffer_meta_info_ {
ai_u32 flags; /*!< meta info flags */
ai_intq_info_list* intq_info; /*!< meta info related to integer format */
} ai_buffer_meta_info;
/*!
* @struct ai_buffer_shape
* @ingroup ai_platform
* @brief Memory buffer shape datatype definition.
*/
typedef struct ai_buffer_shape_ {
ai_u32 type : 8; /*!< shape type: reserved for compatibility */
ai_u32 size : 24; /*!< size: shape cardinality */
ai_shape_dimension* data; /*!< pointer to shape tuple array */
} ai_buffer_shape;
/*!
* @struct ai_buffer
* @ingroup ai_platform
* @brief Memory buffer storing data (optional) with a shape, size and type.
* This datastruct is used also for network querying, where the data field may
* may be NULL.
*/
typedef struct ai_buffer_ {
ai_buffer_format format; /*!< buffer format */
ai_handle data; /*!< pointer to buffer data */
ai_buffer_meta_info* meta_info; /*!< pointer to buffer metadata info */
/* New 7.1 fields */
ai_flags flags; /*!< shape optional flags */
ai_size size; /*!< number of elements of the buffer (including optional padding) */
ai_buffer_shape shape; /*!< n-dimensional shape info */
} ai_buffer;
/*!
* @struct ai_buffer_array
* @ingroup ai_platform
* @brief Array of @ref ai_buffer.
*/
typedef struct ai_buffer_array_ {
ai_u16 flags; /*!< buffer array flags */
ai_u16 size; /*!< buffer array size */
ai_buffer* buffer; /*!< buffer array buffers pointer */
} ai_buffer_array;
/* enums section */
/*!
* @enum ai_error_type
* @ingroup ai_platform
*
* Generic enum to list network error types.
*/
typedef enum {
AI_ERROR_NONE = 0x00, /*!< No error */
AI_ERROR_TOOL_PLATFORM_API_MISMATCH = 0x01,
AI_ERROR_TYPES_MISMATCH = 0x02,
AI_ERROR_INVALID_HANDLE = 0x10,
AI_ERROR_INVALID_STATE = 0x11,
AI_ERROR_INVALID_INPUT = 0x12,
AI_ERROR_INVALID_OUTPUT = 0x13,
AI_ERROR_INVALID_PARAM = 0x14,
AI_ERROR_INVALID_SIGNATURE = 0x15,
AI_ERROR_INVALID_SIZE = 0x16,
AI_ERROR_INVALID_VALUE = 0x17,
AI_ERROR_INIT_FAILED = 0x30,
AI_ERROR_ALLOCATION_FAILED = 0x31,
AI_ERROR_DEALLOCATION_FAILED = 0x32,
AI_ERROR_CREATE_FAILED = 0x33,
} ai_error_type;
/*!
* @enum ai_error_code
* @ingroup ai_platform
*
* Generic enum to list network error codes.
*/
typedef enum {
AI_ERROR_CODE_NONE = 0x0000, /*!< No error */
AI_ERROR_CODE_NETWORK = 0x0010,
AI_ERROR_CODE_NETWORK_PARAMS = 0x0011,
AI_ERROR_CODE_NETWORK_WEIGHTS = 0x0012,
AI_ERROR_CODE_NETWORK_ACTIVATIONS = 0x0013,
AI_ERROR_CODE_LAYER = 0x0014,
AI_ERROR_CODE_TENSOR = 0x0015,
AI_ERROR_CODE_ARRAY = 0x0016,
AI_ERROR_CODE_INVALID_PTR = 0x0017,
AI_ERROR_CODE_INVALID_SIZE = 0x0018,
AI_ERROR_CODE_INVALID_FORMAT = 0x0019,
AI_ERROR_CODE_OUT_OF_RANGE = 0x0020,
AI_ERROR_CODE_INVALID_BATCH = 0x0021,
AI_ERROR_CODE_MISSED_INIT = 0x0030,
AI_ERROR_CODE_IN_USE = 0x0040,
AI_ERROR_CODE_LOCK = 0x0041,
} ai_error_code;
/*!
* @struct ai_platform_version
* @ingroup ai_platform
* @brief Datastruct storing platform version info
*/
typedef struct ai_platform_version_ {
ai_u8 major;
ai_u8 minor;
ai_u8 micro;
ai_u8 reserved;
} ai_platform_version;
/*!
* @struct ai_network_params
* @ingroup ai_platform
*
* Datastructure to pass parameters during network initialization.
*/
typedef struct ai_network_params_ {
AI_NETWORK_PARAMS_FIELDS_DECLARE
} ai_network_params;
/*!
* @struct ai_network_buffers
* @ingroup ai_platform
*
* Datastructure to pass network buffers during network initialization.
*/
typedef struct ai_network_buffers_ {
AI_NETWORK_BUFFERS_FIELD_DECLARE
} ai_network_buffers;
/*!
* @struct ai_network_report
* @ingroup ai_platform
*
* Datastructure to query a network report with some relevant network detail.
*/
typedef struct ai_network_report_ {
const char* model_name;
const char* model_signature;
const char* model_datetime;
const char* compile_datetime;
const char* runtime_revision;
ai_platform_version runtime_version;
const char* tool_revision;
ai_platform_version tool_version;
ai_platform_version tool_api_version;
ai_platform_version api_version;
ai_platform_version interface_api_version;
ai_macc n_macc;
ai_u16 n_inputs;
ai_u16 n_outputs;
ai_buffer* inputs;
ai_buffer* outputs;
AI_NETWORK_PARAMS_FIELDS_DECLARE
ai_u32 n_nodes;
ai_signature signature;
} ai_network_report;
/*!
* @enum ai_upsample_mode
* @ingroup ai_platform
* @brief allowed mode in upsample layer
*/
typedef enum {
AI_UPSAMPLE_ZEROS = 0x0,
AI_UPSAMPLE_NEAREST,
AI_UPSAMPLE_BILINEAR,
AI_UPSAMPLE_TRILINEAR
} ai_upsample_mode;
/*!
* @enum ai_resize_mode
* @ingroup ai_platform
* @brief allowed mode in resize layer
*/
typedef enum {
AI_RESIZE_ZEROS = 0x0,
AI_RESIZE_NEAREST,
AI_RESIZE_LINEAR,
AI_RESIZE_CUBIC
} ai_resize_mode;
/*!
* @enum ai_coord_transf_mode
* @ingroup ai_platform
* @brief coordinate_transformation_mode in resize layer
*/
typedef enum {
AI_HALF_PIXEL = 0x0,
AI_PYTORCH_HALF_PIXEL,
AI_ALIGN_CORNERS,
AI_ASYMMETRIC,
AI_TF_HALF_PIXEL_FOR_NN,
AI_TF_CROP_AND_RESIZE
} ai_coord_transf_mode;
typedef enum {
AI_ROUND_PREFER_FLOOR = 0x0,
AI_ROUND_PREFER_CEIL,
AI_ROUND_FLOOR,
AI_ROUND_CEIL
} ai_nearest_mode;
typedef enum {
AI_PAD_CONSTANT = 0x0,
AI_PAD_REFLECT,
AI_PAD_EDGE,
AI_PAD_8BIT_CH1ST_CONSTANT,
} ai_pad_mode;
#define OUTPUT_PADDING_FLAG (1 << 0)
#define CHANNEL_FIRST_FLAG (1 << 1)
/* Carefull when changing those definitions
bit0 shall always select output padding (Valid vs Same)
bit1 shall always select Channel first /channel lst format
*/
typedef enum {
AI_LAYER_FORMAT_CHANNEL_LAST_VALID = 0x0,
AI_LAYER_FORMAT_CHANNEL_LAST_SAME = 0x1,
AI_LAYER_FORMAT_CHANNEL_FIRST_VALID = 0x2,
AI_LAYER_FORMAT_CHANNEL_FIRST_SAME = 0x3,
} ai_layer_format_type;
/*! ai_platform public APIs **************************************************/
/*!
* @brief get the total number of elements of an ai_buffer.
* @ingroup ai_platform
* @param buffer a pointer to an @ref ai_buffer
* @param with_padding when true it considers also padded elements
* @return the number of elements of the buffer (with/without padded ones)
*/
AI_API_ENTRY
ai_size ai_buffer_get_size(const ai_buffer* buffer, const ai_bool with_padding);
/*!
* @brief get the size in bytes of an ai_buffer (given the number of elements and format).
* @ingroup ai_platform
* @param count the number of elements composing the buffer
* @param fmt the format of the ai_buffer
* @return the size in bytes of the buffer
*/
AI_API_ENTRY
ai_size ai_buffer_get_byte_size(const ai_size count, const ai_buffer_format fmt);
/*!
* @brief get total size in bytes of a buffer array.
* @ingroup ai_platform
* @param barray a pointer to the buffer array
* @return the total size in bytes of all the buffer arrays
*/
AI_API_ENTRY
ai_bool ai_buffer_array_is_empty(const ai_buffer_array* barray);
/*!
* @brief get total size in bytes of a buffer array.
* @ingroup ai_platform
* @param barray a pointer to the buffer array
* @return the total size in bytes of all the buffer arrays
*/
AI_API_ENTRY
ai_bool ai_buffer_array_is_valid(const ai_buffer_array* barray);
/*!
* @brief check if a buffer array is valid - i.e. not empty.
* @ingroup ai_platform
* @param barray a pointer to the buffer array
* @return true if the array is consistent and not empty, false otherwise
*/
AI_API_ENTRY
ai_bool ai_buffer_array_sane(const ai_buffer_array* barray);
/*!
* @brief get total size in bytes of a buffer array.
* @ingroup ai_platform
* @param barray a pointer to the buffer array
* @return the total size in bytes of all the buffer arrays
*/
AI_API_ENTRY
ai_size ai_buffer_array_get_byte_size(const ai_buffer_array* barray);
/*!
* @brief set the address of buffer array item @pos
* @ingroup ai_platform
* @param barray a pointer to the buffer array
* @param pos the index of the element in the array
* @param address the address to set
* @return true if successful, false otherwise
*/
AI_API_ENTRY
ai_bool ai_buffer_array_item_set_address(
ai_buffer_array* barray, const ai_u32 pos, ai_handle address);
AI_API_DECLARE_END
#endif /*AI_PLATFORM_H*/
| 30,193 | C | 30.159959 | 116 | 0.58189 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_ml_iforest.h | /**
******************************************************************************
* @file layers_iforest.h
* @author AIS
* @brief header file of AI platform iForest layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_IFOREST_H
#define LAYERS_IFOREST_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_ml Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/* Allowed tests branch in the iTrees */
typedef enum
{
AI_IFOREST_BRANCH_LT_IDX = 0,
AI_IFOREST_BRANCH_LEQ_IDX,
AI_IFOREST_BRANCH_EQ_IDX,
AI_IFOREST_BRANCH_END,
} ai_iforest_branch_e;
/*!
* @struct ai_layer_iforest
* @ingroup layers_iforest
* @brief iForest layer
*
* The type of iforest function is handled by the specific forward function
* @ref forward_iforest
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_iforest_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_float global_average_path_length; /*!< global average path length used to normalized average path length*/
ai_float score_threshold; /*!< score threshold used to center the score around 0 */
} ai_layer_iforest;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Decodes the iforest ML algorithm.
* @ingroup layers_iforest
* @param layer iforest layer
*/
AI_INTERNAL_API
void forward_iforest(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_IFOREST_H*/
| 2,134 | C | 25.6875 | 112 | 0.524367 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_ml_svc.h | /**
******************************************************************************
* @file layers_svc.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform SVM Classifier (SVC) datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_SVC_H
#define LAYERS_SVC_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_svc Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/* SVM classifier (SVC) kernel types */
typedef enum ai_svc_kernel_e_ {
AI_SVC_KERNEL_LINEAR = 0,
AI_SVC_KERNEL_POLYNOMIAL,
AI_SVC_KERNEL_RBF,
AI_SVC_KERNEL_SIGMOID,
AI_SVC_KERNEL_UNSUPPORTED
} ai_svc_kernel_e;
/*!
* @struct ai_layer_svc
* @ingroup layers_svc
* @brief SVM Classifier (SVC) layer
*
* The type of svc function is handled by the specific forward function
* @ref forward_svc
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_svc_ {
AI_LAYER_COMMON_FIELDS_DECLARE
ai_float gamma; /*!< kernel coefficient for rbf, polynomial and sigmoid functions */
ai_float coef0; /*!< term in polynomial and sigmoid functions */
ai_u32 degree; /*!< polynomial function degree */
ai_svc_kernel_e kernel_type; /*!< kernel type : see ai_svm_kernel_e */
ai_bool proba_support; /*!< whether or not use the parameters learned in Platt scaling */
ai_bool has_classlabels_int; /*!< if True, SVC returns classlabels int, else classlabels string */
} ai_layer_svc;
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Decodes the SVM Classifier ML operator.
* @ingroup layers_svc
* @param layer svm classifier layer
*/
AI_INTERNAL_API
void forward_svc(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_SVC_H*/
| 2,548 | C | 30.085365 | 110 | 0.523155 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/core_log.h | /**
******************************************************************************
* @file core_log.h
* @author AST Embedded Analytics Research Platform
* @brief header file of core log interfaces
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef CORE_LOG_H
#define CORE_LOG_H
#pragma once
#include "ai_platform.h"
#include "ai_datatypes_defines.h"
/*!
* @defgroup core_log Logger core routines wrapper interface
* @brief Common macros, datatypes and routines of ai logger module
* @details This header defines the wrapping macros interfaces to handle the
* global logger module. These macro are defined when the macro HAS_LOG is
* defined, otherwise they are all set to NOP routines and no logger code is
* compiled at all. When the macro HAS_LOG is defined, only the log messages
* having an enum id >= the value of the macro are compiled. Thus to include in
* compilation only log messages up to the error level the value of HAS_LOG must
* be equal the the enum value of LOG_ERROR macro (i.e. 3). a value of 6 means
* to include all log messages up to the lower LOG_TRACE level.
*/
#if defined HAS_LOG && (HAS_LOG>=0)
#include "ai_log.h"
#define AI_LOG_SECTION(...) \
{ __VA_ARGS__ }
#define AI_LOG_ACQUIRE() \
ai_log_acquire()
#define AI_LOG_SET_LEVEL(level_) \
AI_WRAP_FUNC(ai_log_set_level(level_);)
#define AI_LOG_SET_QUIET(onoff_) \
AI_WRAP_FUNC(ai_log_set_quiet(onoff_);)
#define AI_LOG_SET_LOCK_FN(fn_, udata_) \
AI_WRAP_FUNC(ai_log_set_lock(fn_, udata_);)
#define AI_LOG_CHANNEL_PUSH(level_, fn_, udata_) \
AI_WRAP_FUNC(ai_log_channel_push(level_, fn_, udata_);)
#define AI_LOG_CHANNEL_POP(fn_, udata_) \
AI_WRAP_FUNC(ai_log_channel_pop(fn_, udata_);)
#ifdef LOG_USE_FILE
#define AI_LOG_SET_FILE_POINTER(fp_) \
AI_WRAP_FUNC(ai_log_set_fp(fp_);)
#else
#define AI_LOG_SET_FILE_POINTER(fp_) \
AI_WRAP_FUNC(/*AI_LOG_SET_FILE_POINTER()*/)
#endif
#else
#define AI_LOG_SECTION(...) AI_WRAP_FUNC(/*AI_LOG_SECTION()*/)
#define AI_LOG_ACQUIRE() (NULL)
#define AI_LOG_SET_LEVEL(level_) AI_WRAP_FUNC(/*AI_LOG_SET_LEVEL()*/)
#define AI_LOG_SET_QUIET(onoff_) AI_WRAP_FUNC(/*AI_LOG_SET_QUIET()*/)
#define AI_LOG_SET_LOCK_FN(fn_, udata_) AI_WRAP_FUNC(/*AI_LOG_SET_LOCK_FN()*/)
#define AI_LOG_CHANNEL_PUSH(level_, fn_, udata_) AI_WRAP_FUNC(/*AI_LOG_CHANNEL_PUSH()*/)
#define AI_LOG_CHANNEL_POP(fn_, udata_) AI_WRAP_FUNC(/*AI_LOG_CHANNEL_POP()*/)
#define AI_LOG_SET_FILE_POINTER(fp_) AI_WRAP_FUNC(/*AI_LOG_SET_FILE_POINTER()*/)
#endif
#if defined HAS_LOG
#define AI_LOG_PRINT(level, fmt, ...) \
AI_WRAP_FUNC(ai_log_print(level, fmt, ##__VA_ARGS__);)
#else
#define AI_LOG_PRINT(level, fmt, ...) \
AI_WRAP_FUNC(/*AI_LOG_PRINT(...)*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_SUDO)
#define AI_LOG_SUDO(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_SUDO, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_SUDO(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_SUDO()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_TRACE)
#define AI_LOG_TRACE(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_TRACE, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_TRACE(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_TRACE()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_DEBUG)
#define AI_LOG_DEBUG(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_DEBUG, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_DEBUG(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_DEBUG()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_INFO)
#define AI_LOG_INFO(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_INFO, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_INFO(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_INFO()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_WARN)
#define AI_LOG_WARN(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_WARN, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_WARN(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_WARN()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_ERROR)
#define AI_LOG_ERROR(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_ERROR, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_ERROR(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_ERROR()*/)
#endif
#if defined HAS_LOG && (HAS_LOG>=LOG_FATAL)
#define AI_LOG_FATAL(fmt, ...) \
AI_WRAP_FUNC(ai_log_log(LOG_FATAL, __FILE__, __LINE__, fmt LOG_CR, ##__VA_ARGS__);)
#else
#define AI_LOG_FATAL(fmt, ...) AI_WRAP_FUNC(/*AI_LOG_FATAL()*/)
#endif
#endif /*CORE_LOG_H*/
| 5,222 | C | 37.404411 | 97 | 0.572769 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_dense_if32.h | #ifndef _LITE_DENSE_IF32_H
#define _LITE_DENSE_IF32_H
#pragma once
#include "ai_lite_interface.h"
/*!
* @brief Forward function for a dense layer with signed float input,
* signed float output, and float weights.
* @ingroup lite_dense_if32
* @param output The pointer to output buffer.
* @param input The pointer to input buffer.
* @param weights The pointer to weights.
* @param bias The pointer to bias (NULL if not available).
* @param n_channel_in The number of channels of the input.
* @param n_channel_out The number of channels of the output, i.e.,
* the number of dense hidden neurons.
*/
LITE_API_ENTRY
void forward_lite_dense_if32of32wf32(
ai_float* output, const ai_float* input,
const ai_float* weights, const ai_float* bias,
const ai_u32 n_channel_in, const ai_u32 n_channel_out);
#endif /*_LITE_DENSE_IF32_H*/
| 855 | C | 30.703703 | 69 | 0.71462 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_dense.h | /**
******************************************************************************
* @file layers_dense.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform dense layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_DENSE_H
#define LAYERS_DENSE_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers Normalization Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @brief Computes the activations of a fixed point dense (fully connected) layer.
* @ingroup layers_dense
* @param layer the dense layer
*/
AI_INTERNAL_API
void forward_dense_fixed(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_DENSE_H*/
| 1,264 | C | 24.3 | 82 | 0.524525 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_generic_dqnn.h | /**
******************************************************************************
* @file layers_generic_dqnn.h
* @author AIS
* @brief header file of AI platform DQNN generic datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_GENERIC_DQNN_H
#define LAYERS_GENERIC_DQNN_H
#pragma once
#include "layers_common.h"
#include "layers_generic.h"
/*!
* @defgroup layers_generic_dqnn Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles concat with binary input, binary output and
* binary weights
* @ingroup layers_generic_dqnn
* @param layer concat layer
*/
AI_INTERNAL_API
void forward_concat_is1os1(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_GENERIC_DQNN_H*/
| 1,537 | C | 26.464285 | 80 | 0.454782 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/lite_generic_float.h | /**
******************************************************************************
* @file lite_conv2d_dqnn.h
* @author AIS
* @brief header file of AI platform lite conv kernel datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2021 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LITE_GENERIC_FLOAT_H
#define LITE_GENERIC_FLOAT_H
#pragma once
#include "ai_lite_interface.h"
/******************************************************************************/
/* Forward Functions Section */
/******************************************************************************/
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_topK_axis_0_if32of32(const ai_float *pDataIn_init,
ai_float *pDataOut_values_init,
ai_i32 *pDataOut_index_init,
const ai_size height_in,
const ai_size width_in,
const ai_size n_channel_in,
const ai_size k, ai_i16 largest,
void (*f)(const ai_float* inputs, ai_float* values, ai_i32* indices, ai_size k, ai_size n_elements, ai_i32 stride, ai_i16 largest)
);
/*!
* @brief Handles 2D convolution with binary input, binary output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* - Optimized thanks to Optim0 assumptions
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_topK_axis_1_if32of32(const ai_float *pDataIn_init,
ai_float *pDataOut_values_init,
ai_i32 *pDataOut_index_init,
const ai_size height_in,
const ai_size width_in,
const ai_size n_channel_in,
const ai_size k, ai_i16 largest,
void (*f)(const ai_float* inputs, ai_float* values, ai_i32* indices, ai_size k, ai_size n_elements, ai_i32 stride, ai_i16 largest)
);
/*!
* @brief Handles 2D convolution with binary input, 8-bits output and
* binary weights - with 0 padding (QKeras like) - Lite I/F
* @ingroup lite_conv2d_dqnn
*/
LITE_API_ENTRY
void forward_lite_topK_axis_2_if32of32(const ai_float *pDataIn_init,
ai_float *pDataOut_values_init,
ai_i32 *pDataOut_index_init,
const ai_size height_in,
const ai_size width_in,
const ai_size n_channel_in,
const ai_size k, ai_i16 largest,
void (*f)(const ai_float* inputs, ai_float* values, ai_i32* indices, ai_size k, ai_size n_elements, ai_i32 stride, ai_i16 largest)
);
LITE_API_ENTRY
void forward_lite_func_reduce_l1_if32of32(
ai_float* out_ptr, const ai_float* in_ptr,
const ai_size out_size, const ai_size in_step,
const ai_size axis_size, const ai_size axis_step);
LITE_API_ENTRY
void forward_lite_func_reduce_l2_if32of32(
ai_float* out_ptr, const ai_float* in_ptr,
const ai_size out_size, const ai_size in_step,
const ai_size axis_size, const ai_size axis_step);
#endif /*LITE_GENERIC_FLOAT_H*/
| 4,287 | C | 42.755102 | 148 | 0.463028 |
Tbarkin121/GuardDog/stm32/TorquePoleNet/Middlewares/ST/AI/Inc/layers_nl.h | /**
******************************************************************************
* @file layers_nl.h
* @author AST Embedded Analytics Research Platform
* @brief header file of AI platform nonlinearity layers datatypes
******************************************************************************
* @attention
*
* Copyright (c) 2018 STMicroelectronics.
* All rights reserved.
*
* This software is licensed under terms that can be found in the LICENSE file
* in the root directory of this software component.
* If no LICENSE file comes with this software, it is provided AS-IS.
*
******************************************************************************
@verbatim
@endverbatim
******************************************************************************
*/
#ifndef LAYERS_NL_H
#define LAYERS_NL_H
#pragma once
#include "layers_common.h"
/*!
* @defgroup layers_nl Normalization Layers Definitions
* @brief definition
*
*/
AI_API_DECLARE_BEGIN
/*!
* @struct ai_layer_nl
* @ingroup layers_nl
* @brief Generic Nonlinearity layer
*
* The type of nonlinearity is handled by the specific forward function.
* It is a sequential layer. see @ref ai_layer
*/
typedef AI_ALIGNED_TYPE(struct, 4) ai_layer_nl_ {
AI_LAYER_COMMON_FIELDS_DECLARE
AI_CONST ai_array* nl_params; /*!< associated parameters array */
} ai_layer_nl;
/*!
* @struct ai_layer_sm
* @ingroup layers_nl
* @brief Softmax Nonlinearity layer
*
* It is a sequential layer. see @ref ai_layer
*/
typedef ai_layer_nl ai_layer_sm;
/*!
* @typedef (*func_nl)
* @ingroup layers_nl
* @brief Fuction pointer for generic non linear transform
* this function pointer abstracts a generic non linear layer.
* see @ref nl_func_tanh_array_f32 and similar as examples.
*/
//typedef void (*func_nl)(ai_array *out, const ai_array *in,
// const ai_size size, const ai_handle params);
typedef void (*func_nl)(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Softmax pooling computed on a single float channel
* @ingroup layers_nl
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param channel_size number of elements of the input channel
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sm_channel_f32(ai_tensor *out, const ai_tensor *in,
const ai_size channel_size, const ai_handle params);
/*!
* @brief Softmax normalization computed on an array of float channels
* @ingroup layers_nl
* @param out opaque handler to float output channel array
* @param in opaque handler to float input channel array
* @param in_size total size (number of elements) to process on the input
* @param channel_size number of elements of the input channel
* @param in_channel_step number of elements to move to next input element
* @param out_channel_step number of elements to move to next output element
*/
AI_INTERNAL_API
void nl_func_sm_array_f32(ai_tensor *out, ai_tensor *in,
const ai_size in_size,
const ai_size channel_size,
const ai_size in_channel_step,
const ai_size out_channel_step);
/*!
* @brief Softmax zero pooling computed on a single float channel
* @ingroup layers_nl
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param channel_size number of elements of the input channel
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sm_zero_channel_f32(ai_tensor *out, const ai_tensor *in,
const ai_size channel_size, const ai_handle params);
/*!
* @brief Probit non linearity
* @ingroup layers_nl
* @param out opaque handler to float output channel
* @param in opaque handler to float input channel
* @param channel_size number of elements of the input channel
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_probit_f32(ai_tensor *out, const ai_tensor *in,
const ai_size channel_size, const ai_handle params);
/*!
* @brief Computes the tanh function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_tanh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the tanh function on a fixed point data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_tanh_array_fixed(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sigmoid function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sigmoid_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sigmoid function on a fixed point data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sigmoid_array_fixed(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the hard sigmoid function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_hard_sigmoid_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the logistic function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_logistic_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the swish function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_swish_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the hard swish function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_hard_swish_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the absolute value function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_abs_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the cosine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_cos_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse cosine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_acos_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the hyperbolic cosine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_cosh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse hyperbolic cosine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_acosh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sin_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse sine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_asin_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the hyperbolic sine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sinh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse hyperbolic sine function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_asinh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the tangent function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_tan_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse tangent function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_atan_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the inverse hyperbolic tangent function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_atanh_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the error function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_erf_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the natural logarithm function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_log_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the reciprocal square root function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_rsqrt_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the squarefunction on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_square_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the floor function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_floor_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the ceil function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_ceil_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the rounding function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_round_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the exponential function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_exp_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sign negation function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_neg_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sign negation function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_not_array_bool(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the reciprocal function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_reciprocal_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the square root function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_sqrt_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the soft plus function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
*/
AI_INTERNAL_API
void nl_func_soft_plus_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the soft sign function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_soft_sign_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the sign function on a single float element.
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
*/
AI_INTERNAL_API
void nl_func_sign_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the clip function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_clip_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the hardmax function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param axis direction of the max index to be searched
*/
AI_INTERNAL_API
void nl_func_hardmax_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_shape *shape, const ai_handle params);
/*!
* @brief Computes the generic relu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_relu_generic_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the thresholded relu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_relu_thresholded_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the relu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_relu_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the relu function on a fixed point data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_relu_array_fixed(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the relu function on an integer-quantized data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
void nl_func_relu_array_integer(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the clip function on an integer-quantized data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
void nl_func_clip_array_integer(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the activation function on an integer-quantized data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to generated and used LUT
*/
void nl_func_array_integer(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the elu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_elu_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the max relu function on a fixed point data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_relu_max_array_fixed(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the selu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size number of elements in the input buffer
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_selu_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the prelu function on a float data array
* @ingroup layers_nl
* @param in opaque handler to float, size should be 1
* @param slope opaque handler to float, size should be 1
* @param out opaque handler to float output elem
* @param size size of the input data in bytes
* @param params opaque handler to optional nl parameters
*/
AI_INTERNAL_API
void nl_func_prelu_array_f32(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/*!
* @brief Computes the prelu function on an integer-quantized data array
* @ingroup layers_nl
* @param in opaque handler to input elements to process
* @param out opaque handler to output elements
* @param size total size (number of elements) to process on the input
* @param params opaque handler to optional nl parameters
*/
void nl_func_prelu_array_integer(ai_tensor *out, const ai_tensor *in,
const ai_size size, const ai_handle params);
/******************************************************************************/
/** Forward Functions Section **/
/******************************************************************************/
/*!
* @brief Computes the activations of a ReLU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_relu(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point ReLU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_relu_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a integer-quantized ReLU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_relu_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of a clip integer-quantized nonlinear layer.
* @ingroup layers_nl
* @param pLayer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_clip_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of a ReLU6 nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_relu_thresholded(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point max ReLU layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_relu_max_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a ELU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_elu(ai_layer* layer);
/*!
* @brief Computes the activations of a SELU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_selu(ai_layer* layer);
/*!
* @brief Computes the activations of a PRELU nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_prelu(ai_layer* layer);
/*!
* @brief Computes the activations of a binary tanh (sign) nonlinear layer.
* @ingroup layers
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sign(ai_layer* layer);
/*!
* @brief Computes the activations of a clip nonlinear layer.
* @ingroup layers
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_clip(ai_layer* layer);
/*!
* @brief Computes the activations of a sigmoid nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sigmoid(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point sigmoid nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sigmoid_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a hard sigmoid nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_hard_sigmoid(ai_layer* layer);
/*!
* @brief Computes the activations of a swish nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_swish(ai_layer* layer);
/*!
* @brief Computes the activations of a hard swish nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_hard_swish(ai_layer* layer);
/*!
* @brief Computes the activations of an exponential nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_exp(ai_layer* layer);
/*!
* @brief Computes the activations of an square root nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sqrt(ai_layer* layer);
/*!
* @brief Computes the activations of a soft plus nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_soft_plus(ai_layer* layer);
/*!
* @brief Computes the activations of a soft sign nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_soft_sign(ai_layer* layer);
/*!
* @brief Computes the activations of a cosine (cos) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_cos(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse cosine (acos) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_acos(ai_layer* layer);
/*!
* @brief Computes the activations of a hyperbolic cosine (cosh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_cosh(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse hyperbolic cosine (acosh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_acosh(ai_layer* layer);
/*!
* @brief Computes the activations of a sine (sin) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sin(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse sine (asin) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_asin(ai_layer* layer);
/*!
* @brief Computes the activations of a hyperbolic sine (sinh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_sinh(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse hyperbolic sine (asinh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_asinh(ai_layer* layer);
/*!
* @brief Computes the activations of a tangent (tan) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_tan(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse tangent (atan) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_atan(ai_layer* layer);
/*!
* @brief Computes the activations of a hyperbolic tangent (tanh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_tanh(ai_layer* layer);
/*!
* @brief Computes the activations of a inverse hyperbolic tangent (atanh) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_atanh(ai_layer* layer);
/*!
* @brief Computes the activations of a fixed point tanh nonlinear layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_tanh_fixed(ai_layer *pLayer);
/*!
* @brief Computes the activations of a error function (erf) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_erf(ai_layer* layer);
/*!
* @brief Computes the activations of a natural logarithm (log) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_log(ai_layer* layer);
/*!
* @brief Computes the activations of a reciprocal square root (rsqrt) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_rsqrt(ai_layer* layer);
/*!
* @brief Computes the activations of a square layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_square(ai_layer* layer);
/*!
* @brief Computes the activations of an absolute value (abs) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_abs(ai_layer* layer);
/*!
* @brief Computes the activations of a ceil layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_ceil(ai_layer* layer);
/*!
* @brief Computes the activations of a floor layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_floor(ai_layer* layer);
/*!
* @brief Computes the activations of a rounding layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_round(ai_layer* layer);
/*!
* @brief Computes the activations of a sign negation (neg) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_neg(ai_layer* layer);
/*!
* @brief Computes the activations of a sign negation (not) layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_not(ai_layer* layer);
/*!
* @brief Computes the activations of a reciprocal layer.
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_reciprocal(ai_layer* layer);
/*!
* @brief Hardmax on an input tensors
* @ingroup layers_generic
* @param layer the hardmax layer
*/
AI_INTERNAL_API
void forward_hardmax(ai_layer* layer);
/*!
* @brief Computes the activations of a softmax nonlinear layer.
* @ingroup layers_nl
* @param layer the softmax (sm) layer
*/
AI_INTERNAL_API
void forward_sm(ai_layer* layer);
/*!
* @brief Computes the activations of a softmax nonlinear layer (integer version).
* @ingroup layers_nl
* @param layer the softmax (sm) layer
*/
AI_INTERNAL_API
void forward_sm_integer(ai_layer* layer);
/*!
* @brief Computes the activations of an integer quantized nonlinear layer.
* Non linear operation is function of used LUT defined through
* (pLayer->nl_params->data)
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_nl_integer(ai_layer *pLayer);
/*!
* @brief Computes the activations of an integer quantized PReLu.
* Slope params are located like weights, not params because they are
* quantized
* @ingroup layers_nl
* @param layer the nonlinear (nl) layer
*/
AI_INTERNAL_API
void forward_prelu_integer(ai_layer *pLayer);
AI_API_DECLARE_END
#endif /*LAYERS_NL_H*/
| 37,339 | C | 32.63964 | 84 | 0.688663 |
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