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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, MaxPool2d, 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 HourglassAEModule(nn.Module):
"""Modified Hourglass Module for HourglassNet_AE backbone.
Generate module recursively and use BasicBlock as the base unit.
Args:
depth (int): Depth of current HourglassModule.
stage_channels (list[int]): Feature channels of sub-modules in current
and follow-up HourglassModule.
norm_cfg (dict): Dictionary to construct and config norm layer.
"""
def __init__(self,
depth,
stage_channels,
norm_cfg=dict(type='BN', requires_grad=True)):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.depth = depth
cur_channel = stage_channels[0]
next_channel = stage_channels[1]
self.up1 = ConvModule(
cur_channel, cur_channel, 3, padding=1, norm_cfg=norm_cfg)
self.pool1 = MaxPool2d(2, 2)
self.low1 = ConvModule(
cur_channel, next_channel, 3, padding=1, norm_cfg=norm_cfg)
if self.depth > 1:
self.low2 = HourglassAEModule(depth - 1, stage_channels[1:])
else:
self.low2 = ConvModule(
next_channel, next_channel, 3, padding=1, norm_cfg=norm_cfg)
self.low3 = ConvModule(
next_channel, cur_channel, 3, padding=1, norm_cfg=norm_cfg)
self.up2 = nn.UpsamplingNearest2d(scale_factor=2)
def forward(self, x):
"""Model forward function."""
up1 = self.up1(x)
pool1 = self.pool1(x)
low1 = self.low1(pool1)
low2 = self.low2(low1)
low3 = self.low3(low2)
up2 = self.up2(low3)
return up1 + up2
@BACKBONES.register_module()
class HourglassAENet(BaseBackbone):
"""Hourglass-AE Network proposed by Newell et al.
Associative Embedding: End-to-End Learning for Joint
Detection and Grouping.
More details can be found in the `paper
<https://arxiv.org/abs/1611.05424>`__ .
Args:
downsample_times (int): Downsample times in a HourglassModule.
num_stacks (int): Number of HourglassModule modules stacked,
1 for Hourglass-52, 2 for Hourglass-104.
stage_channels (list[int]): Feature channel of each sub-module in a
HourglassModule.
stage_blocks (list[int]): Number of sub-modules stacked in a
HourglassModule.
feat_channels (int): Feature channel of conv after a HourglassModule.
norm_cfg (dict): Dictionary to construct and config norm layer.
Example:
>>> from mmpose.models import HourglassAENet
>>> import torch
>>> self = HourglassAENet()
>>> self.eval()
>>> inputs = torch.rand(1, 3, 512, 512)
>>> level_outputs = self.forward(inputs)
>>> for level_output in level_outputs:
... print(tuple(level_output.shape))
(1, 34, 128, 128)
"""
def __init__(self,
downsample_times=4,
num_stacks=1,
out_channels=34,
stage_channels=(256, 384, 512, 640, 768),
feat_channels=256,
norm_cfg=dict(type='BN', requires_grad=True)):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.num_stacks = num_stacks
assert self.num_stacks >= 1
assert len(stage_channels) > downsample_times
cur_channels = stage_channels[0]
self.stem = nn.Sequential(
ConvModule(3, 64, 7, padding=3, stride=2, norm_cfg=norm_cfg),
ConvModule(64, 128, 3, padding=1, norm_cfg=norm_cfg),
MaxPool2d(2, 2),
ConvModule(128, 128, 3, padding=1, norm_cfg=norm_cfg),
ConvModule(128, feat_channels, 3, padding=1, norm_cfg=norm_cfg),
)
self.hourglass_modules = nn.ModuleList([
nn.Sequential(
HourglassAEModule(
downsample_times, stage_channels, norm_cfg=norm_cfg),
ConvModule(
feat_channels,
feat_channels,
3,
padding=1,
norm_cfg=norm_cfg),
ConvModule(
feat_channels,
feat_channels,
3,
padding=1,
norm_cfg=norm_cfg)) for _ in range(num_stacks)
])
self.out_convs = nn.ModuleList([
ConvModule(
cur_channels,
out_channels,
1,
padding=0,
norm_cfg=None,
act_cfg=None) for _ in range(num_stacks)
])
self.remap_out_convs = nn.ModuleList([
ConvModule(
out_channels,
feat_channels,
1,
norm_cfg=norm_cfg,
act_cfg=None) for _ in range(num_stacks - 1)
])
self.remap_feature_convs = nn.ModuleList([
ConvModule(
feat_channels,
feat_channels,
1,
norm_cfg=norm_cfg,
act_cfg=None) for _ in range(num_stacks - 1)
])
self.relu = nn.ReLU(inplace=True)
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."""
inter_feat = self.stem(x)
out_feats = []
for ind in range(self.num_stacks):
single_hourglass = self.hourglass_modules[ind]
out_conv = self.out_convs[ind]
hourglass_feat = single_hourglass(inter_feat)
out_feat = out_conv(hourglass_feat)
out_feats.append(out_feat)
if ind < self.num_stacks - 1:
inter_feat = inter_feat + self.remap_out_convs[ind](
out_feat) + self.remap_feature_convs[ind](
hourglass_feat)
return out_feats
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