mirror of https://github.com/JDAI-CV/fast-reid.git
339 lines
14 KiB
Python
339 lines
14 KiB
Python
# encoding: utf-8
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# based on:
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# https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/resnest.py
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"""ResNeSt models"""
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import torch
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from torch import nn
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import math
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import logging
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from .resnet import ResNet, Bottleneck
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from .build import BACKBONE_REGISTRY
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_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'
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_model_sha256 = {name: checksum for checksum, name in [
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('528c19ca', '50'),
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('22405ba7', '101'),
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('75117900', '200'),
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('0cc87c48', '269'),
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]}
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def short_hash(name):
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if name not in _model_sha256:
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raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
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return _model_sha256[name][:8]
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resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
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name in _model_sha256.keys()
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}
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class Bottleneck(nn.Module):
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"""ResNet Bottleneck
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"""
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# pylint: disable=unused-argument
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None,
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radix=1, cardinality=1, bottleneck_width=64,
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avd=False, avd_first=False, dilation=1, is_first=False,
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rectified_conv=False, rectify_avg=False,
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norm_layer=None, dropblock_prob=0.0, last_gamma=False):
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super(Bottleneck, self).__init__()
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group_width = int(planes * (bottleneck_width / 64.)) * cardinality
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self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
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self.bn1 = norm_layer(group_width)
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self.dropblock_prob = dropblock_prob
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self.radix = radix
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self.avd = avd and (stride > 1 or is_first)
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self.avd_first = avd_first
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if self.avd:
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self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
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stride = 1
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if radix > 1:
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self.conv2 = SplAtConv2d(
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group_width, group_width, kernel_size=3,
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stride=stride, padding=dilation,
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dilation=dilation, groups=cardinality, bias=False,
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radix=radix, rectify=rectified_conv,
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rectify_avg=rectify_avg,
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norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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elif rectified_conv:
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from rfconv import RFConv2d
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self.conv2 = RFConv2d(
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group_width, group_width, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation,
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groups=cardinality, bias=False,
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average_mode=rectify_avg)
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self.bn2 = norm_layer(group_width)
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else:
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self.conv2 = nn.Conv2d(
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group_width, group_width, kernel_size=3, stride=stride,
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padding=dilation, dilation=dilation,
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groups=cardinality, bias=False)
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self.bn2 = norm_layer(group_width)
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self.conv3 = nn.Conv2d(
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group_width, planes * 4, kernel_size=1, bias=False)
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self.bn3 = norm_layer(planes * 4)
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if last_gamma:
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from torch.nn.init import zeros_
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zeros_(self.bn3.weight)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.dilation = dilation
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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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if self.dropblock_prob > 0.0:
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out = self.dropblock1(out)
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out = self.relu(out)
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if self.avd and self.avd_first:
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out = self.avd_layer(out)
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out = self.conv2(out)
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if self.radix == 1:
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out = self.bn2(out)
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if self.dropblock_prob > 0.0:
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out = self.dropblock2(out)
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out = self.relu(out)
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if self.avd and not self.avd_first:
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out = self.avd_layer(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.dropblock_prob > 0.0:
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out = self.dropblock3(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 ResNest(nn.Module):
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"""ResNet Variants ResNest
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Parameters
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----------
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block : Block
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Class for the residual block. Options are BasicBlockV1, BottleneckV1.
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layers : list of int
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Numbers of layers in each block
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classes : int, default 1000
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Number of classification classes.
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dilated : bool, default False
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Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
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typically used in Semantic Segmentation.
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norm_layer : object
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Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
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for Synchronized Cross-GPU BachNormalization).
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Reference:
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- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
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"""
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# pylint: disable=unused-variable
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def __init__(self, block, layers, radix=1, groups=1, bottleneck_width=64,
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num_classes=1000, dilated=False, dilation=1,
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deep_stem=False, stem_width=64, avg_down=False,
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rectified_conv=False, rectify_avg=False,
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avd=False, avd_first=False,
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final_drop=0.0, dropblock_prob=0,
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last_gamma=False, norm_layer=nn.BatchNorm2d):
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self.cardinality = groups
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self.bottleneck_width = bottleneck_width
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# ResNet-D params
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self.inplanes = stem_width * 2 if deep_stem else 64
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self.avg_down = avg_down
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self.last_gamma = last_gamma
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# ResNeSt params
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self.radix = radix
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self.avd = avd
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self.avd_first = avd_first
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super(ResNet, self).__init__()
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self.rectified_conv = rectified_conv
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self.rectify_avg = rectify_avg
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if rectified_conv:
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from rfconv import RFConv2d
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conv_layer = RFConv2d
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else:
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conv_layer = nn.Conv2d
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conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
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if deep_stem:
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self.conv1 = nn.Sequential(
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conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),
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norm_layer(stem_width),
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nn.ReLU(inplace=True),
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conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
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norm_layer(stem_width),
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nn.ReLU(inplace=True),
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conv_layer(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
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)
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else:
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self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False, **conv_kwargs)
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self.bn1 = norm_layer(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
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if dilated or dilation == 4:
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self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
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dilation=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
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dilation=4, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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elif dilation == 2:
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
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dilation=1, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
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dilation=2, norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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else:
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
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norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
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norm_layer=norm_layer,
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dropblock_prob=dropblock_prob)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, norm_layer):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,
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dropblock_prob=0.0, is_first=True):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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down_layers = []
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if self.avg_down:
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if dilation == 1:
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down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
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ceil_mode=True, count_include_pad=False))
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else:
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down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
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ceil_mode=True, count_include_pad=False))
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down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=1, bias=False))
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else:
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down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False))
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down_layers.append(norm_layer(planes * block.expansion))
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downsample = nn.Sequential(*down_layers)
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layers = []
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if dilation == 1 or dilation == 2:
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layers.append(block(self.inplanes, planes, stride, downsample=downsample,
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radix=self.radix, cardinality=self.cardinality,
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bottleneck_width=self.bottleneck_width,
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avd=self.avd, avd_first=self.avd_first,
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dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
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rectify_avg=self.rectify_avg,
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norm_layer=norm_layer, dropblock_prob=dropblock_prob,
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last_gamma=self.last_gamma))
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elif dilation == 4:
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layers.append(block(self.inplanes, planes, stride, downsample=downsample,
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radix=self.radix, cardinality=self.cardinality,
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bottleneck_width=self.bottleneck_width,
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avd=self.avd, avd_first=self.avd_first,
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dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
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rectify_avg=self.rectify_avg,
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norm_layer=norm_layer, dropblock_prob=dropblock_prob,
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last_gamma=self.last_gamma))
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else:
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raise RuntimeError("=> unknown dilation size: {}".format(dilation))
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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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radix=self.radix, cardinality=self.cardinality,
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bottleneck_width=self.bottleneck_width,
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avd=self.avd, avd_first=self.avd_first,
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dilation=dilation, rectified_conv=self.rectified_conv,
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rectify_avg=self.rectify_avg,
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norm_layer=norm_layer, dropblock_prob=dropblock_prob,
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last_gamma=self.last_gamma))
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return nn.Sequential(*layers)
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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.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x
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@BACKBONE_REGISTRY.register()
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def build_resnest_backbone(cfg):
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"""
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Create a ResNest instance from config.
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Returns:
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ResNet: a :class:`ResNet` instance.
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"""
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# fmt: off
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pretrain = cfg.MODEL.BACKBONE.PRETRAIN
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pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
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last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
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with_ibn = cfg.MODEL.BACKBONE.WITH_IBN
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with_se = cfg.MODEL.BACKBONE.WITH_SE
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depth = cfg.MODEL.BACKBONE.DEPTH
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num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 200: [3, 24, 36, 3], 269: [3, 30, 48, 8]}[depth]
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# model = ResNet(last_stride, with_ibn, with_se, Bottleneck, num_blocks_per_stage)
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model = ResNest(Bottleneck, [3, 4, 6, 3],
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radix=2, groups=1, bottleneck_width=64,
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deep_stem=True, stem_width=32, avg_down=True,
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avd=True, avd_first=False)
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if pretrain:
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if not with_ibn:
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# original resnet
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state_dict = torch.hub.load_state_dict_from_url(
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resnest_model_urls[depth], progress=True, check_hash=True)
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else:
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raise KeyError('Not implementation ibn in resnest')
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# # ibn resnet
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# state_dict = torch.load(pretrain_path)['state_dict']
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# # remove module in name
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# new_state_dict = {}
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# for k in state_dict:
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# new_k = '.'.join(k.split('.')[1:])
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# if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
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# new_state_dict[new_k] = state_dict[k]
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# state_dict = new_state_dict
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res = model.load_state_dict(state_dict, strict=False)
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logger = logging.getLogger(__name__)
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logger.info('missing keys is {}'.format(res.missing_keys))
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logger.info('unexpected keys is {}'.format(res.unexpected_keys))
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return model
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