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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, normal_init
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.utils import get_root_logger
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
from .utils import load_checkpoint
class CpmBlock(nn.Module):
"""CpmBlock for Convolutional Pose Machine.
Args:
in_channels (int): Input channels of this block.
channels (list): Output channels of each conv module.
kernels (list): Kernel sizes of each conv module.
"""
def __init__(self,
in_channels,
channels=(128, 128, 128),
kernels=(11, 11, 11),
norm_cfg=None):
super().__init__()
assert len(channels) == len(kernels)
layers = []
for i in range(len(channels)):
if i == 0:
input_channels = in_channels
else:
input_channels = channels[i - 1]
layers.append(
ConvModule(
input_channels,
channels[i],
kernels[i],
padding=(kernels[i] - 1) // 2,
norm_cfg=norm_cfg))
self.model = nn.Sequential(*layers)
def forward(self, x):
"""Model forward function."""
out = self.model(x)
return out
@BACKBONES.register_module()
class CPM(BaseBackbone):
"""CPM backbone.
Convolutional Pose Machines.
More details can be found in the `paper
<https://arxiv.org/abs/1602.00134>`__ .
Args:
in_channels (int): The input channels of the CPM.
out_channels (int): The output channels of the CPM.
feat_channels (int): Feature channel of each CPM stage.
middle_channels (int): Feature channel of conv after the middle stage.
num_stages (int): Number of stages.
norm_cfg (dict): Dictionary to construct and config norm layer.
Example:
>>> from mmpose.models import CPM
>>> import torch
>>> self = CPM(3, 17)
>>> self.eval()
>>> inputs = torch.rand(1, 3, 368, 368)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
... print(tuple(level_output.shape))
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
(1, 17, 46, 46)
"""
def __init__(self,
in_channels,
out_channels,
feat_channels=128,
middle_channels=32,
num_stages=6,
norm_cfg=dict(type='BN', requires_grad=True)):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
assert in_channels == 3
self.num_stages = num_stages
assert self.num_stages >= 1
self.stem = nn.Sequential(
ConvModule(in_channels, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
ConvModule(128, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
ConvModule(128, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
ConvModule(128, 32, 5, padding=2, norm_cfg=norm_cfg),
ConvModule(32, 512, 9, padding=4, norm_cfg=norm_cfg),
ConvModule(512, 512, 1, padding=0, norm_cfg=norm_cfg),
ConvModule(512, out_channels, 1, padding=0, act_cfg=None))
self.middle = nn.Sequential(
ConvModule(in_channels, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
ConvModule(128, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
ConvModule(128, 128, 9, padding=4, norm_cfg=norm_cfg),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
self.cpm_stages = nn.ModuleList([
CpmBlock(
middle_channels + out_channels,
channels=[feat_channels, feat_channels, feat_channels],
kernels=[11, 11, 11],
norm_cfg=norm_cfg) for _ in range(num_stages - 1)
])
self.middle_conv = nn.ModuleList([
nn.Sequential(
ConvModule(
128, middle_channels, 5, padding=2, norm_cfg=norm_cfg))
for _ in range(num_stages - 1)
])
self.out_convs = nn.ModuleList([
nn.Sequential(
ConvModule(
feat_channels,
feat_channels,
1,
padding=0,
norm_cfg=norm_cfg),
ConvModule(feat_channels, out_channels, 1, act_cfg=None))
for _ in range(num_stages - 1)
])
def init_weights(self, pretrained=None):
"""Initialize the weights in backbone.
Args:
pretrained (str, optional): Path to pre-trained weights.
Defaults to None.
"""
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
normal_init(m, std=0.001)
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
constant_init(m, 1)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
"""Model forward function."""
stage1_out = self.stem(x)
middle_out = self.middle(x)
out_feats = []
out_feats.append(stage1_out)
for ind in range(self.num_stages - 1):
single_stage = self.cpm_stages[ind]
out_conv = self.out_convs[ind]
inp_feat = torch.cat(
[out_feats[-1], self.middle_conv[ind](middle_out)], 1)
cpm_feat = single_stage(inp_feat)
out_feat = out_conv(cpm_feat)
out_feats.append(out_feat)
return out_feats
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