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# Copyright (c) Meta Platforms, Inc. and affiliates
from detectron2.utils.registry import Registry
from typing import Dict
from detectron2.layers import ShapeSpec
from torch import nn
import torch
import numpy as np
import fvcore.nn.weight_init as weight_init
from pytorch3d.transforms.rotation_conversions import _copysign
from pytorch3d.transforms import (
rotation_6d_to_matrix,
euler_angles_to_matrix,
quaternion_to_matrix
)
from ProposalNetwork.proposals.proposals import propose
from ProposalNetwork.utils.conversions import cube_to_box
from ProposalNetwork.utils.spaces import Cubes
from ProposalNetwork.utils.utils import iou_3d
ROI_CUBE_HEAD_REGISTRY = Registry("ROI_CUBE_HEAD")
@ROI_CUBE_HEAD_REGISTRY.register()
class CubeHead(nn.Module):
def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
#-------------------------------------------
# Settings
#-------------------------------------------
self.num_classes = cfg.MODEL.ROI_HEADS.NUM_CLASSES
self.use_conf = cfg.MODEL.ROI_CUBE_HEAD.USE_CONFIDENCE
self.z_type = cfg.MODEL.ROI_CUBE_HEAD.Z_TYPE
self.pose_type = cfg.MODEL.ROI_CUBE_HEAD.POSE_TYPE
self.cluster_bins = cfg.MODEL.ROI_CUBE_HEAD.CLUSTER_BINS
self.shared_fc = cfg.MODEL.ROI_CUBE_HEAD.SHARED_FC
#-------------------------------------------
# Feature generator
#-------------------------------------------
num_conv = cfg.MODEL.ROI_CUBE_HEAD.NUM_CONV
conv_dim = cfg.MODEL.ROI_CUBE_HEAD.CONV_DIM
num_fc = cfg.MODEL.ROI_CUBE_HEAD.NUM_FC
fc_dim = cfg.MODEL.ROI_CUBE_HEAD.FC_DIM
conv_dims = [conv_dim] * num_conv
fc_dims = [fc_dim] * num_fc
assert len(conv_dims) + len(fc_dims) > 0
self._output_size = (input_shape.channels, input_shape.height, input_shape.width)
if self.shared_fc:
self.feature_generator = nn.Sequential()
else:
self.feature_generator_XY = nn.Sequential()
self.feature_generator_dims = nn.Sequential()
self.feature_generator_pose = nn.Sequential()
self.feature_generator_Z = nn.Sequential()
if self.use_conf:
self.feature_generator_conf = nn.Sequential()
# create fully connected layers for Cube Head
for k, fc_dim in enumerate(fc_dims):
fc_dim_in = int(np.prod(self._output_size))
self._output_size = fc_dim
if self.shared_fc:
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator.add_module("fc{}".format(k + 1), fc)
self.feature_generator.add_module("fc_relu{}".format(k + 1), nn.ReLU())
else:
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator_dims.add_module("fc{}".format(k + 1), fc)
self.feature_generator_dims.add_module("fc_relu{}".format(k + 1), nn.ReLU())
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator_XY.add_module("fc{}".format(k + 1), fc)
self.feature_generator_XY.add_module("fc_relu{}".format(k + 1), nn.ReLU())
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator_pose.add_module("fc{}".format(k + 1), fc)
self.feature_generator_pose.add_module("fc_relu{}".format(k + 1), nn.ReLU())
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator_Z.add_module("fc{}".format(k + 1), fc)
self.feature_generator_Z.add_module("fc_relu{}".format(k + 1), nn.ReLU())
if self.use_conf:
fc = nn.Linear(fc_dim_in, fc_dim)
weight_init.c2_xavier_fill(fc)
self.feature_generator_conf.add_module("fc{}".format(k + 1), fc)
self.feature_generator_conf.add_module("fc_relu{}".format(k + 1), nn.ReLU())
#-------------------------------------------
# 3D outputs
#-------------------------------------------
# Dimensions in meters (width, height, length)
self.bbox_3D_dims = nn.Linear(self._output_size, self.num_classes*3)
nn.init.normal_(self.bbox_3D_dims.weight, std=0.001)
nn.init.constant_(self.bbox_3D_dims.bias, 0)
cluster_bins = self.cluster_bins if self.cluster_bins > 1 else 1
# XY
self.bbox_3D_center_deltas = nn.Linear(self._output_size, self.num_classes*2)
nn.init.normal_(self.bbox_3D_center_deltas.weight, std=0.001)
nn.init.constant_(self.bbox_3D_center_deltas.bias, 0)
# Pose
if self.pose_type == '6d':
self.bbox_3D_pose = nn.Linear(self._output_size, self.num_classes*6)
elif self.pose_type == 'quaternion':
self.bbox_3D_pose = nn.Linear(self._output_size, self.num_classes*4)
elif self.pose_type == 'euler':
self.bbox_3D_pose = nn.Linear(self._output_size, self.num_classes*3)
else:
raise ValueError('Cuboid pose type {} is not recognized'.format(self.pose_type))
nn.init.normal_(self.bbox_3D_pose.weight, std=0.001)
nn.init.constant_(self.bbox_3D_pose.bias, 0)
# Z
self.bbox_3D_center_depth = nn.Linear(self._output_size, self.num_classes*cluster_bins)
nn.init.normal_(self.bbox_3D_center_depth.weight, std=0.001)
nn.init.constant_(self.bbox_3D_center_depth.bias, 1) # NOTE Changed second input from 0 to 1
# Optionally, box confidence
if self.use_conf:
self.bbox_3D_uncertainty = nn.Linear(self._output_size, self.num_classes*1)
nn.init.normal_(self.bbox_3D_uncertainty.weight, std=0.001)
nn.init.constant_(self.bbox_3D_uncertainty.bias, 5)
def forward(self, x):
n = x.shape[0]
box_z = None
box_uncert = None
box_2d_deltas = None
if self.shared_fc:
features = self.feature_generator(x)
box_2d_deltas = self.bbox_3D_center_deltas(features)
box_dims = self.bbox_3D_dims(features)
box_pose = self.bbox_3D_pose(features)
box_z = self.bbox_3D_center_depth(features)
if self.use_conf:
box_uncert = self.bbox_3D_uncertainty(features).clip(0.01)
else:
box_2d_deltas = self.bbox_3D_center_deltas(self.feature_generator_XY(x))
box_dims = self.bbox_3D_dims(self.feature_generator_dims(x))
box_pose = self.bbox_3D_pose(self.feature_generator_pose(x))
box_z = self.bbox_3D_center_depth(self.feature_generator_Z(x))
if self.use_conf:
box_uncert = self.bbox_3D_uncertainty(self.feature_generator_conf(x)).clip(0.01)
# Pose
if self.pose_type == '6d':
box_pose = rotation_6d_to_matrix(box_pose.view(-1, 6))
elif self.pose_type == 'quaternion':
quats = box_pose.view(-1, 4)
quats_scales = (quats * quats).sum(1)
quats = quats / _copysign(torch.sqrt(quats_scales), quats[:, 0])[:, None]
box_pose = quaternion_to_matrix(quats)
elif self.pose_type == 'euler':
box_pose = euler_angles_to_matrix(box_pose.view(-1, 3), 'XYZ')
box_2d_deltas = box_2d_deltas.view(n, self.num_classes, 2)
box_dims = box_dims.view(n, self.num_classes, 3)
box_pose = box_pose.view(n, self.num_classes, 3, 3)
if self.cluster_bins > 1:
box_z = box_z.view(n, self.cluster_bins, self.num_classes, -1)
else:
box_z = box_z.view(n, self.num_classes, -1)
return box_2d_deltas, box_z, box_dims, box_pose, box_uncert
@ROI_CUBE_HEAD_REGISTRY.register()
class ScoreHead(nn.Module):
'''This is called a multi-task learning problem as it involves performing two tasks —
1) regression to find the score for a cube,
2) regression to find the Cube coordinates
The cube head in the cube-rcnn model has 2 fc layers and then 1 extra layer for each type of output (z, rotation etc.). Therefore, we have chose to do the same'''
def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
super().__init__()
in_features = input_shape.height * input_shape.width * input_shape.channels
base_out = 64
self.mlp = nn.Sequential(
nn.Linear(in_features, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.BatchNorm1d(128), # I think the model could perhaps be better if this was a Dropout layer
nn.ReLU(),
nn.Linear(128, base_out),
nn.ReLU(),
)
self.fc_cube_centers, self.fc_dims = nn.Linear(base_out, 3), nn.Linear(base_out, 3) # center
# following the Cube-RCNN method we also predict 6d rotation.
self.rotation_6d = nn.Linear(base_out, 6)
def forward(self, x):
x = self.mlp(x)
centers, dims = self.fc_cube_centers(x), self.fc_dims(x)
centers_tmp = torch.exp(centers[:,2].clip(max=5))
centers = torch.cat((centers[:,:2],centers_tmp.unsqueeze(1)),axis=1)
dims = torch.exp(dims.clip(max=5))
x_cubes = Cubes(torch.cat((centers, dims, rotation_6d_to_matrix(self.rotation_6d(x)).view(-1,9)), 1))
return x_cubes
def build_cube_head(cfg, input_shape: Dict[str, ShapeSpec]):
name = cfg.MODEL.ROI_CUBE_HEAD.NAME
return ROI_CUBE_HEAD_REGISTRY.get(name)(cfg, input_shape) |