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import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from openrec.modeling.common import Activation
class ConvBNLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
bias=False,
)
self.bn = nn.BatchNorm2d(out_channels)
self.act = Activation(act) if act else None
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.act is not None:
x = self.act(x)
return x
class LocalizationNetwork(nn.Module):
def __init__(self, in_channels, num_fiducial, loc_lr, model_name):
super(LocalizationNetwork, self).__init__()
self.F = num_fiducial
F = num_fiducial
if model_name == 'large':
num_filters_list = [64, 128, 256, 512]
fc_dim = 256
else:
num_filters_list = [16, 32, 64, 128]
fc_dim = 64
self.block_list = nn.ModuleList()
for fno in range(0, len(num_filters_list)):
num_filters = num_filters_list[fno]
conv = ConvBNLayer(
in_channels=in_channels,
out_channels=num_filters,
kernel_size=3,
act='relu',
)
self.block_list.append(conv)
if fno == len(num_filters_list) - 1:
pool = nn.AdaptiveAvgPool2d(1)
else:
pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
in_channels = num_filters
self.block_list.append(pool)
self.fc1 = nn.Linear(in_channels, fc_dim)
# Init fc2 in LocalizationNetwork
self.fc2 = nn.Linear(fc_dim, F * 2)
initial_bias = self.get_initial_fiducials()
initial_bias = initial_bias.reshape(-1)
self.fc2.bias.data = torch.tensor(initial_bias, dtype=torch.float32)
nn.init.zeros_(self.fc2.weight.data)
self.out_channels = F * 2
def forward(self, x):
"""
Estimating parameters of geometric transformation
Args:
image: input
Return:
batch_C_prime: the matrix of the geometric transformation
"""
for block in self.block_list:
x = block(x)
x = x.squeeze(dim=2).squeeze(dim=2)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = x.reshape(shape=[-1, self.F, 2])
return x
def get_initial_fiducials(self):
"""see RARE paper Fig.
6 (a)
"""
F = self.F
ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
return initial_bias
class GridGenerator(nn.Module):
def __init__(self, in_channels, num_fiducial):
super(GridGenerator, self).__init__()
self.eps = 1e-6
self.F = num_fiducial
self.fc = nn.Linear(in_channels, 6)
nn.init.constant_(self.fc.weight, 0)
nn.init.constant_(self.fc.bias, 0)
self.fc.weight.requires_grad = False
self.fc.bias.requires_grad = False
def forward(self, batch_C_prime, I_r_size):
"""Generate the grid for the grid_sampler.
Args:
batch_C_prime: the matrix of the geometric transformation
I_r_size: the shape of the input image
Return:
batch_P_prime: the grid for the grid_sampler
"""
C = self.build_C_paddle()
P = self.build_P_paddle(I_r_size)
inv_delta_C_tensor = self.build_inv_delta_C_paddle(C).float()
P_hat_tensor = self.build_P_hat_paddle(C, torch.tensor(P)).float()
batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
batch_C_prime_with_zeros = torch.cat(
[batch_C_prime, batch_C_ex_part_tensor], dim=1)
batch_T = torch.matmul(
inv_delta_C_tensor.to(batch_C_prime_with_zeros.device),
batch_C_prime_with_zeros,
)
batch_P_prime = torch.matmul(P_hat_tensor.to(batch_T.device), batch_T)
return batch_P_prime
def build_C_paddle(self):
"""Return coordinates of fiducial points in I_r; C."""
F = self.F
ctrl_pts_x = torch.linspace(-1.0, 1.0, int(F / 2), dtype=torch.float64)
ctrl_pts_y_top = -1 * torch.ones([int(F / 2)], dtype=torch.float64)
ctrl_pts_y_bottom = torch.ones([int(F / 2)], dtype=torch.float64)
ctrl_pts_top = torch.stack([ctrl_pts_x, ctrl_pts_y_top], dim=1)
ctrl_pts_bottom = torch.stack([ctrl_pts_x, ctrl_pts_y_bottom], dim=1)
C = torch.cat([ctrl_pts_top, ctrl_pts_bottom], dim=0)
return C # F x 2
def build_P_paddle(self, I_r_size):
I_r_height, I_r_width = I_r_size
I_r_grid_x = (torch.arange(-I_r_width, I_r_width, 2) +
1.0) / torch.tensor(np.array([I_r_width]))
I_r_grid_y = (torch.arange(-I_r_height, I_r_height, 2) +
1.0) / torch.tensor(np.array([I_r_height]))
# P: self.I_r_width x self.I_r_height x 2
P = torch.stack(torch.meshgrid(I_r_grid_x, I_r_grid_y), dim=2)
P = torch.permute(P, [1, 0, 2])
# n (= self.I_r_width x self.I_r_height) x 2
return P.reshape([-1, 2])
def build_inv_delta_C_paddle(self, C):
"""Return inv_delta_C which is needed to calculate T."""
F = self.F
hat_eye = torch.eye(F) # F x F
hat_C = torch.norm(C.reshape([1, F, 2]) - C.reshape([F, 1, 2]),
dim=2) + hat_eye
hat_C = (hat_C**2) * torch.log(hat_C)
delta_C = torch.cat( # F+3 x F+3
[
torch.cat([torch.ones((F, 1)), C, hat_C], dim=1), # F x F+3
torch.concat([torch.zeros(
(2, 3)), C.transpose(0, 1)], dim=1), # 2 x F+3
torch.concat([torch.zeros(
(1, 3)), torch.ones((1, F))], dim=1), # 1 x F+3
],
axis=0,
)
inv_delta_C = torch.inverse(delta_C)
return inv_delta_C # F+3 x F+3
def build_P_hat_paddle(self, C, P):
F = self.F
eps = self.eps
n = P.shape[0] # n (= self.I_r_width x self.I_r_height)
# P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
P_tile = torch.tile(torch.unsqueeze(P, dim=1), (1, F, 1))
C_tile = torch.unsqueeze(C, dim=0) # 1 x F x 2
P_diff = P_tile - C_tile # n x F x 2
# rbf_norm: n x F
rbf_norm = torch.norm(P_diff, p=2, dim=2, keepdim=False)
# rbf: n x F
rbf = torch.multiply(torch.square(rbf_norm), torch.log(rbf_norm + eps))
P_hat = torch.cat([torch.ones((n, 1)), P, rbf], dim=1)
return P_hat # n x F+3
def get_expand_tensor(self, batch_C_prime):
B, H, C = batch_C_prime.shape
batch_C_prime = batch_C_prime.reshape([B, H * C])
batch_C_ex_part_tensor = self.fc(batch_C_prime)
batch_C_ex_part_tensor = batch_C_ex_part_tensor.reshape([-1, 3, 2])
return batch_C_ex_part_tensor
class TPS(nn.Module):
def __init__(self, in_channels, num_fiducial, loc_lr, model_name):
super(TPS, self).__init__()
self.loc_net = LocalizationNetwork(in_channels, num_fiducial, loc_lr,
model_name)
self.grid_generator = GridGenerator(self.loc_net.out_channels,
num_fiducial)
self.out_channels = in_channels
def forward(self, image):
image.stop_gradient = False
batch_C_prime = self.loc_net(image)
batch_P_prime = self.grid_generator(batch_C_prime, image.shape[2:])
batch_P_prime = batch_P_prime.reshape(
[-1, image.shape[2], image.shape[3], 2])
is_fp16 = False
if batch_P_prime.dtype != torch.float32:
data_type = batch_P_prime.dtype
image = image.float()
batch_P_prime = batch_P_prime.float()
is_fp16 = True
batch_I_r = F.grid_sample(image, grid=batch_P_prime)
if is_fp16:
batch_I_r = batch_I_r.astype(data_type)
return batch_I_r
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