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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import logging
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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BatchNorm2d = nn.BatchNorm2d
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BN_MOMENTUM = 0.1
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logger = logging.getLogger(__name__)
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3,
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stride=stride, padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
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bias=False)
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self.bn3 = BatchNorm2d(planes * self.expansion,
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momentum=BN_MOMENTUM)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class HighResolutionModule(nn.Module):
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def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
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num_channels, fuse_method, multi_scale_output=True):
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super(HighResolutionModule, self).__init__()
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self._check_branches(
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num_branches, blocks, num_blocks, num_inchannels, num_channels)
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self.num_inchannels = num_inchannels
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self.fuse_method = fuse_method
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self.num_branches = num_branches
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self.multi_scale_output = multi_scale_output
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self.branches = self._make_branches(
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num_branches, blocks, num_blocks, num_channels)
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self.fuse_layers = self._make_fuse_layers()
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self.relu = nn.ReLU(inplace=True)
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def _check_branches(self, num_branches, blocks, num_blocks,
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num_inchannels, num_channels):
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if num_branches != len(num_blocks):
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error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
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num_branches, len(num_blocks))
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logger.error(error_msg)
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raise ValueError(error_msg)
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if num_branches != len(num_channels):
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error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
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num_branches, len(num_channels))
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logger.error(error_msg)
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raise ValueError(error_msg)
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if num_branches != len(num_inchannels):
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error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
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num_branches, len(num_inchannels))
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logger.error(error_msg)
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raise ValueError(error_msg)
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def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
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stride=1):
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downsample = None
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if stride != 1 or \
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self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.num_inchannels[branch_index],
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num_channels[branch_index] * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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BatchNorm2d(num_channels[branch_index] * block.expansion,
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momentum=BN_MOMENTUM),
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)
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layers = []
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layers.append(block(self.num_inchannels[branch_index],
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num_channels[branch_index], stride, downsample))
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self.num_inchannels[branch_index] = \
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num_channels[branch_index] * block.expansion
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for i in range(1, num_blocks[branch_index]):
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layers.append(block(self.num_inchannels[branch_index],
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num_channels[branch_index]))
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return nn.Sequential(*layers)
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def _make_branches(self, num_branches, block, num_blocks, num_channels):
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branches = []
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for i in range(num_branches):
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branches.append(
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self._make_one_branch(i, block, num_blocks, num_channels))
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return nn.ModuleList(branches)
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def _make_fuse_layers(self):
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if self.num_branches == 1:
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return None
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num_branches = self.num_branches
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num_inchannels = self.num_inchannels
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fuse_layers = []
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for i in range(num_branches if self.multi_scale_output else 1):
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fuse_layer = []
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for j in range(num_branches):
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if j > i:
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fuse_layer.append(nn.Sequential(
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nn.Conv2d(num_inchannels[j],
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num_inchannels[i],
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1,
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1,
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0,
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bias=False),
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BatchNorm2d(num_inchannels[i], momentum=BN_MOMENTUM)))
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elif j == i:
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fuse_layer.append(None)
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else:
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conv3x3s = []
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for k in range(i - j):
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if k == i - j - 1:
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num_outchannels_conv3x3 = num_inchannels[i]
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conv3x3s.append(nn.Sequential(
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nn.Conv2d(num_inchannels[j],
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num_outchannels_conv3x3,
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3, 2, 1, bias=False),
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BatchNorm2d(num_outchannels_conv3x3, momentum=BN_MOMENTUM)))
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else:
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num_outchannels_conv3x3 = num_inchannels[j]
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conv3x3s.append(nn.Sequential(
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nn.Conv2d(num_inchannels[j],
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num_outchannels_conv3x3,
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3, 2, 1, bias=False),
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BatchNorm2d(num_outchannels_conv3x3,
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momentum=BN_MOMENTUM),
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nn.ReLU(inplace=True)))
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fuse_layer.append(nn.Sequential(*conv3x3s))
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fuse_layers.append(nn.ModuleList(fuse_layer))
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return nn.ModuleList(fuse_layers)
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def get_num_inchannels(self):
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return self.num_inchannels
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def forward(self, x):
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if self.num_branches == 1:
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return [self.branches[0](x[0])]
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for i in range(self.num_branches):
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x[i] = self.branches[i](x[i])
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x_fuse = []
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for i in range(len(self.fuse_layers)):
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y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
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for j in range(1, self.num_branches):
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if i == j:
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y = y + x[j]
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elif j > i:
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y = y + F.interpolate(
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self.fuse_layers[i][j](x[j]),
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size=[x[i].shape[2], x[i].shape[3]],
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mode='bilinear')
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else:
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y = y + self.fuse_layers[i][j](x[j])
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x_fuse.append(self.relu(y))
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return x_fuse
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blocks_dict = {
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'BASIC': BasicBlock,
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'BOTTLENECK': Bottleneck
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}
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class HighResolutionNet(nn.Module):
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def __init__(self, config, **kwargs):
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self.inplanes = 64
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extra = config['MODEL']['EXTRA']
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super(HighResolutionNet, self).__init__()
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=1,
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bias=False)
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self.bn1 = BatchNorm2d(self.inplanes, momentum=BN_MOMENTUM)
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self.conv2 = nn.Conv2d(self.inplanes, self.inplanes, kernel_size=3, stride=2, padding=1,
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bias=False)
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self.bn2 = BatchNorm2d(self.inplanes, momentum=BN_MOMENTUM)
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self.relu = nn.ReLU(inplace=True)
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self.sf = nn.Softmax(dim=1)
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self.layer1 = self._make_layer(Bottleneck, self.inplanes, self.inplanes, 4)
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self.stage2_cfg = extra['STAGE2']
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num_channels = self.stage2_cfg['NUM_CHANNELS']
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block = blocks_dict[self.stage2_cfg['BLOCK']]
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num_channels = [
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num_channels[i] * block.expansion for i in range(len(num_channels))]
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self.transition1 = self._make_transition_layer(
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[256], num_channels)
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self.stage2, pre_stage_channels = self._make_stage(
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self.stage2_cfg, num_channels)
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self.stage3_cfg = extra['STAGE3']
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num_channels = self.stage3_cfg['NUM_CHANNELS']
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block = blocks_dict[self.stage3_cfg['BLOCK']]
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num_channels = [
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num_channels[i] * block.expansion for i in range(len(num_channels))]
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self.transition2 = self._make_transition_layer(
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pre_stage_channels, num_channels)
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self.stage3, pre_stage_channels = self._make_stage(
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self.stage3_cfg, num_channels)
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self.stage4_cfg = extra['STAGE4']
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num_channels = self.stage4_cfg['NUM_CHANNELS']
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block = blocks_dict[self.stage4_cfg['BLOCK']]
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num_channels = [
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num_channels[i] * block.expansion for i in range(len(num_channels))]
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self.transition3 = self._make_transition_layer(
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pre_stage_channels, num_channels)
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self.stage4, pre_stage_channels = self._make_stage(
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self.stage4_cfg, num_channels, multi_scale_output=True)
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self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
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final_inp_channels = sum(pre_stage_channels) + self.inplanes
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self.head = nn.Sequential(nn.Sequential(
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nn.Conv2d(
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in_channels=final_inp_channels,
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out_channels=final_inp_channels,
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kernel_size=1),
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BatchNorm2d(final_inp_channels, momentum=BN_MOMENTUM),
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nn.ReLU(inplace=True),
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nn.Conv2d(
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in_channels=final_inp_channels,
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out_channels=config['MODEL']['NUM_JOINTS'],
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kernel_size=extra['FINAL_CONV_KERNEL']),
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nn.Sigmoid()))
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def _make_head(self, x, x_skip):
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x = self.upsample(x)
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x = torch.cat([x, x_skip], dim=1)
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x = self.head(x)
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return x
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def _make_transition_layer(
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self, num_channels_pre_layer, num_channels_cur_layer):
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num_branches_cur = len(num_channels_cur_layer)
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num_branches_pre = len(num_channels_pre_layer)
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transition_layers = []
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for i in range(num_branches_cur):
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if i < num_branches_pre:
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if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
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transition_layers.append(nn.Sequential(
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nn.Conv2d(num_channels_pre_layer[i],
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num_channels_cur_layer[i],
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3,
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1,
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1,
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bias=False),
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BatchNorm2d(
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num_channels_cur_layer[i], momentum=BN_MOMENTUM),
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nn.ReLU(inplace=True)))
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else:
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transition_layers.append(None)
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else:
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conv3x3s = []
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for j in range(i + 1 - num_branches_pre):
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inchannels = num_channels_pre_layer[-1]
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outchannels = num_channels_cur_layer[i] \
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if j == i - num_branches_pre else inchannels
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conv3x3s.append(nn.Sequential(
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nn.Conv2d(
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inchannels, outchannels, 3, 2, 1, bias=False),
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BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
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nn.ReLU(inplace=True)))
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transition_layers.append(nn.Sequential(*conv3x3s))
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return nn.ModuleList(transition_layers)
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def _make_layer(self, block, inplanes, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
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)
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layers = []
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layers.append(block(inplanes, planes, stride, downsample))
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inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(inplanes, planes))
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return nn.Sequential(*layers)
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def _make_stage(self, layer_config, num_inchannels,
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multi_scale_output=True):
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num_modules = layer_config['NUM_MODULES']
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num_branches = layer_config['NUM_BRANCHES']
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num_blocks = layer_config['NUM_BLOCKS']
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num_channels = layer_config['NUM_CHANNELS']
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block = blocks_dict[layer_config['BLOCK']]
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fuse_method = layer_config['FUSE_METHOD']
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modules = []
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for i in range(num_modules):
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if not multi_scale_output and i == num_modules - 1:
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reset_multi_scale_output = False
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else:
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reset_multi_scale_output = True
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modules.append(
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HighResolutionModule(num_branches,
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block,
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num_blocks,
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num_inchannels,
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num_channels,
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fuse_method,
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reset_multi_scale_output)
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)
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num_inchannels = modules[-1].get_num_inchannels()
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return nn.Sequential(*modules), num_inchannels
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def forward(self, x):
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x = self.conv1(x)
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x_skip = x.clone()
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x = self.bn1(x)
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x = self.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = self.relu(x)
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x = self.layer1(x)
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x_list = []
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for i in range(self.stage2_cfg['NUM_BRANCHES']):
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if self.transition1[i] is not None:
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x_list.append(self.transition1[i](x))
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else:
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x_list.append(x)
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y_list = self.stage2(x_list)
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|
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x_list = []
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for i in range(self.stage3_cfg['NUM_BRANCHES']):
|
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if self.transition2[i] is not None:
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x_list.append(self.transition2[i](y_list[-1]))
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else:
|
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x_list.append(y_list[i])
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y_list = self.stage3(x_list)
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|
|
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x_list = []
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for i in range(self.stage4_cfg['NUM_BRANCHES']):
|
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if self.transition3[i] is not None:
|
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|
x_list.append(self.transition3[i](y_list[-1]))
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else:
|
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x_list.append(y_list[i])
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x = self.stage4(x_list)
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|
|
|
|
|
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height, width = x[0].size(2), x[0].size(3)
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x1 = F.interpolate(x[1], size=(height, width), mode='bilinear', align_corners=False)
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x2 = F.interpolate(x[2], size=(height, width), mode='bilinear', align_corners=False)
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x3 = F.interpolate(x[3], size=(height, width), mode='bilinear', align_corners=False)
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x = torch.cat([x[0], x1, x2, x3], 1)
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x = self._make_head(x, x_skip)
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return x
|
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|
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def init_weights(self, pretrained=''):
|
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|
logger.info('=> init weights from normal distribution')
|
|
|
for m in self.modules():
|
|
|
if isinstance(m, nn.Conv2d):
|
|
|
|
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nn.init.normal_(m.weight, std=0.001)
|
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|
|
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
|
nn.init.constant_(m.weight, 1)
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|
|
nn.init.constant_(m.bias, 0)
|
|
|
if os.path.isfile(pretrained):
|
|
|
pretrained_dict = torch.load(pretrained)
|
|
|
logger.info('=> loading pretrained model {}'.format(pretrained))
|
|
|
print('=> loading pretrained model {}'.format(pretrained))
|
|
|
model_dict = self.state_dict()
|
|
|
pretrained_dict = {k: v for k, v in pretrained_dict.items()
|
|
|
if k in model_dict.keys()}
|
|
|
for k, _ in pretrained_dict.items():
|
|
|
logger.info(
|
|
|
'=> loading {} pretrained model {}'.format(k, pretrained))
|
|
|
|
|
|
model_dict.update(pretrained_dict)
|
|
|
self.load_state_dict(model_dict)
|
|
|
|
|
|
|
|
|
def get_cls_net(config, pretrained='', **kwargs):
|
|
|
model = HighResolutionNet(config, **kwargs)
|
|
|
model.init_weights(pretrained)
|
|
|
return model
|
|
|
|
|
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|
