728 lines
26 KiB
Python
728 lines
26 KiB
Python
# ------------------------------------------------------------------------------
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# Copyright (c) Microsoft
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# Licensed under the MIT License.
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# Written by Bin Xiao (Bin.Xiao@microsoft.com)
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# Modified by Sen Yang (yangsenius@seu.edu.cn)
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# ------------------------------------------------------------------------------
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import copy
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import logging
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import os
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import torch
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import torch.nn.functional as F
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from torch import nn
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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(
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in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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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 = nn.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 = nn.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 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.conv2 = nn.Conv2d(
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
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)
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self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
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self.conv3 = nn.Conv2d(
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planes, planes * self.expansion, kernel_size=1, bias=False
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)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion, 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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def _get_clones(module, N):
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
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def _get_activation_fn(activation):
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"""Return an activation function given a string"""
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if activation == "relu":
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return F.relu
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if activation == "gelu":
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return F.gelu
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if activation == "glu":
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return F.glu
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raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
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class HighResolutionModule(nn.Module):
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def __init__(
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self,
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num_branches,
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blocks,
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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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multi_scale_output=True,
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):
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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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)
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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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)
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self.fuse_layers = self._make_fuse_layers()
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self.relu = nn.ReLU(True)
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def _check_branches(
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self, num_branches, blocks, num_blocks, num_inchannels, num_channels
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):
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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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)
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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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)
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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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)
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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, stride=1):
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downsample = None
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if (
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stride != 1
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or self.num_inchannels[branch_index]
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!= num_channels[branch_index] * block.expansion
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):
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downsample = nn.Sequential(
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nn.Conv2d(
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self.num_inchannels[branch_index],
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num_channels[branch_index] * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(
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num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM
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),
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)
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layers = []
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layers.append(
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block(
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self.num_inchannels[branch_index],
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num_channels[branch_index],
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stride,
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downsample,
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)
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)
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self.num_inchannels[branch_index] = 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(
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block(self.num_inchannels[branch_index], num_channels[branch_index])
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)
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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(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(
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nn.Sequential(
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nn.Conv2d(
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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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),
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nn.BatchNorm2d(num_inchannels[i]),
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nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"),
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)
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)
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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(
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nn.Sequential(
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nn.Conv2d(
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num_inchannels[j],
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num_outchannels_conv3x3,
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3,
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2,
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1,
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bias=False,
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),
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nn.BatchNorm2d(num_outchannels_conv3x3),
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)
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)
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else:
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num_outchannels_conv3x3 = num_inchannels[j]
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conv3x3s.append(
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nn.Sequential(
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nn.Conv2d(
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num_inchannels[j],
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num_outchannels_conv3x3,
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3,
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2,
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1,
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bias=False,
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),
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nn.BatchNorm2d(num_outchannels_conv3x3),
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nn.ReLU(True),
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)
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)
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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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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 = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck}
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class HRNET_base(nn.Module):
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def __init__(self, cfg, **kwargs):
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self.inplanes = 64
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extra = cfg["MODEL"]["EXTRA"]
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super(HRNET_base, self).__init__()
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# stem net
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
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self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(Bottleneck, 64, 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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]
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self.transition1 = self._make_transition_layer([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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)
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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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]
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self.transition2 = self._make_transition_layer(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, multi_scale_output=False
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)
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self.pretrained_layers = extra["PRETRAINED_LAYERS"]
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def _make_transition_layer(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(
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nn.Sequential(
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nn.Conv2d(
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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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),
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nn.BatchNorm2d(num_channels_cur_layer[i]),
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nn.ReLU(inplace=True),
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)
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)
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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 = (
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num_channels_cur_layer[i]
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if j == i - num_branches_pre
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else inchannels
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)
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conv3x3s.append(
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nn.Sequential(
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nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
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nn.BatchNorm2d(outchannels),
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nn.ReLU(inplace=True),
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)
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)
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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, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def _make_stage(self, layer_config, num_inchannels, 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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# multi_scale_output is only used last module
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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(
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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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)
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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 = 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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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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return y_list[0]
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def init_weights(self, pretrained="", print_load_info=False):
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logger.info("=> init weights from normal distribution")
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.normal_(m.weight, std=0.001)
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for name, _ in m.named_parameters():
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if name in ["bias"]:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.ConvTranspose2d):
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nn.init.normal_(m.weight, std=0.001)
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for name, _ in m.named_parameters():
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if name in ["bias"]:
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nn.init.constant_(m.bias, 0)
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if os.path.isfile(pretrained):
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pretrained_state_dict = torch.load(
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pretrained, map_location=torch.device("cpu")
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)
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logger.info("=> loading pretrained model {}".format(pretrained))
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existing_state_dict = {}
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for name, m in pretrained_state_dict.items():
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if (
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name.split(".")[0] in self.pretrained_layers
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and name in self.state_dict()
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or self.pretrained_layers[0] is "*"
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):
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existing_state_dict[name] = m
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if print_load_info:
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print(":: {} is loaded from {}".format(name, pretrained))
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self.load_state_dict(existing_state_dict, strict=False)
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elif pretrained:
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logger.error("=> please download pre-trained models first!")
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raise ValueError("{} is not exist!".format(pretrained))
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|
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class HRNET4_base(nn.Module):
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def __init__(self, cfg, **kwargs):
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self.inplanes = 64
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extra = cfg["MODEL"]["EXTRA"]
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super(HRNET4_base, self).__init__()
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# stem net
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
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self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
|
|
self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.layer1 = self._make_layer(Bottleneck, 64, 4)
|
|
|
|
# stage 2
|
|
self.stage2_cfg = extra["STAGE2"]
|
|
num_channels = self.stage2_cfg["NUM_CHANNELS"]
|
|
block = blocks_dict[self.stage2_cfg["BLOCK"]]
|
|
num_channels = [
|
|
num_channels[i] * block.expansion for i in range(len(num_channels))
|
|
]
|
|
self.transition1 = self._make_transition_layer([256], num_channels)
|
|
self.stage2, pre_stage_channels = self._make_stage(
|
|
self.stage2_cfg, num_channels
|
|
)
|
|
|
|
# stage 3
|
|
self.stage3_cfg = extra["STAGE3"]
|
|
num_channels = self.stage3_cfg["NUM_CHANNELS"]
|
|
block = blocks_dict[self.stage3_cfg["BLOCK"]]
|
|
num_channels = [
|
|
num_channels[i] * block.expansion for i in range(len(num_channels))
|
|
]
|
|
self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
|
|
self.stage3, pre_stage_channels = self._make_stage(
|
|
self.stage3_cfg, num_channels
|
|
)
|
|
|
|
# stage 4
|
|
self.stage4_cfg = extra["STAGE4"]
|
|
num_channels = self.stage4_cfg["NUM_CHANNELS"]
|
|
block = blocks_dict[self.stage4_cfg["BLOCK"]]
|
|
num_channels = [
|
|
num_channels[i] * block.expansion for i in range(len(num_channels))
|
|
]
|
|
self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
|
|
self.stage4, pre_stage_channels = self._make_stage(
|
|
self.stage4_cfg, num_channels, multi_scale_output=False
|
|
)
|
|
|
|
self.pretrained_layers = extra["PRETRAINED_LAYERS"]
|
|
|
|
def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
|
|
num_branches_cur = len(num_channels_cur_layer)
|
|
num_branches_pre = len(num_channels_pre_layer)
|
|
|
|
transition_layers = []
|
|
for i in range(num_branches_cur):
|
|
if i < num_branches_pre:
|
|
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
|
|
transition_layers.append(
|
|
nn.Sequential(
|
|
nn.Conv2d(
|
|
num_channels_pre_layer[i],
|
|
num_channels_cur_layer[i],
|
|
3,
|
|
1,
|
|
1,
|
|
bias=False,
|
|
),
|
|
nn.BatchNorm2d(num_channels_cur_layer[i]),
|
|
nn.ReLU(inplace=True),
|
|
)
|
|
)
|
|
else:
|
|
transition_layers.append(None)
|
|
else:
|
|
conv3x3s = []
|
|
for j in range(i + 1 - num_branches_pre):
|
|
inchannels = num_channels_pre_layer[-1]
|
|
outchannels = (
|
|
num_channels_cur_layer[i]
|
|
if j == i - num_branches_pre
|
|
else inchannels
|
|
)
|
|
conv3x3s.append(
|
|
nn.Sequential(
|
|
nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
|
|
nn.BatchNorm2d(outchannels),
|
|
nn.ReLU(inplace=True),
|
|
)
|
|
)
|
|
transition_layers.append(nn.Sequential(*conv3x3s))
|
|
|
|
return nn.ModuleList(transition_layers)
|
|
|
|
def _make_layer(self, block, planes, blocks, stride=1):
|
|
downsample = None
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
nn.Conv2d(
|
|
self.inplanes,
|
|
planes * block.expansion,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
bias=False,
|
|
),
|
|
nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample))
|
|
self.inplanes = planes * block.expansion
|
|
for i in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True):
|
|
num_modules = layer_config["NUM_MODULES"]
|
|
num_branches = layer_config["NUM_BRANCHES"]
|
|
num_blocks = layer_config["NUM_BLOCKS"]
|
|
num_channels = layer_config["NUM_CHANNELS"]
|
|
block = blocks_dict[layer_config["BLOCK"]]
|
|
fuse_method = layer_config["FUSE_METHOD"]
|
|
|
|
modules = []
|
|
for i in range(num_modules):
|
|
# multi_scale_output is only used last module
|
|
if not multi_scale_output and i == num_modules - 1:
|
|
reset_multi_scale_output = False
|
|
else:
|
|
reset_multi_scale_output = True
|
|
|
|
modules.append(
|
|
HighResolutionModule(
|
|
num_branches,
|
|
block,
|
|
num_blocks,
|
|
num_inchannels,
|
|
num_channels,
|
|
fuse_method,
|
|
reset_multi_scale_output,
|
|
)
|
|
)
|
|
num_inchannels = modules[-1].get_num_inchannels()
|
|
|
|
return nn.Sequential(*modules), num_inchannels
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.conv2(x)
|
|
x = self.bn2(x)
|
|
x = self.relu(x)
|
|
x = self.layer1(x)
|
|
|
|
# stage 2
|
|
x_list = []
|
|
for i in range(self.stage2_cfg["NUM_BRANCHES"]):
|
|
if self.transition1[i] is not None:
|
|
x_list.append(self.transition1[i](x))
|
|
else:
|
|
x_list.append(x)
|
|
y_list = self.stage2(x_list)
|
|
|
|
# stage 3
|
|
x_list = []
|
|
for i in range(self.stage3_cfg["NUM_BRANCHES"]):
|
|
if self.transition2[i] is not None:
|
|
x_list.append(self.transition2[i](y_list[-1]))
|
|
else:
|
|
x_list.append(y_list[i])
|
|
y_list = self.stage3(x_list)
|
|
|
|
# stage 4
|
|
x_list = []
|
|
for i in range(self.stage4_cfg["NUM_BRANCHES"]):
|
|
if self.transition3[i] is not None:
|
|
x_list.append(self.transition3[i](y_list[-1]))
|
|
else:
|
|
x_list.append(y_list[i])
|
|
y_list = self.stage4(x_list)
|
|
|
|
return y_list[0]
|
|
|
|
def init_weights(self, pretrained="", print_load_info=False):
|
|
logger.info("=> init weights from normal distribution")
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.normal_(m.weight, std=0.001)
|
|
for name, _ in m.named_parameters():
|
|
if name in ["bias"]:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.BatchNorm2d):
|
|
nn.init.constant_(m.weight, 1)
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.ConvTranspose2d):
|
|
nn.init.normal_(m.weight, std=0.001)
|
|
for name, _ in m.named_parameters():
|
|
if name in ["bias"]:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
if os.path.isfile(pretrained):
|
|
pretrained_state_dict = torch.load(pretrained)
|
|
logger.info("=> loading pretrained model {}".format(pretrained))
|
|
|
|
existing_state_dict = {}
|
|
for name, m in pretrained_state_dict.items():
|
|
if (
|
|
name.split(".")[0] in self.pretrained_layers
|
|
and name in self.state_dict()
|
|
or self.pretrained_layers[0] is "*"
|
|
):
|
|
existing_state_dict[name] = m
|
|
if print_load_info:
|
|
print(":: {} is loaded from {}".format(name, pretrained))
|
|
self.load_state_dict(existing_state_dict, strict=False)
|
|
elif pretrained:
|
|
logger.error("=> please download pre-trained models first!")
|
|
raise ValueError("{} is not exist!".format(pretrained))
|