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378 lines
14 KiB
378 lines
14 KiB
import torch
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from torch import nn
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import torch.nn.functional as F
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from torch.hub import load_state_dict_from_url
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# This code is borrow from torchvision.
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
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'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
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}
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, 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, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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if groups != 1 or base_width != 64:
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raise ValueError(
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'BasicBlock only supports groups=1 and base_width=64')
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# if dilation > 1:
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# raise NotImplementedError(
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# "Dilation > 1 not supported in BasicBlock")
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv3x3(inplanes, planes, stride, dilation=dilation)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes, dilation=dilation)
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self.bn2 = norm_layer(planes)
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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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identity = 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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identity = self.downsample(x)
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out += identity
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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, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(planes * (base_width / 64.)) * groups
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, width)
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self.bn1 = norm_layer(width)
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self.conv2 = conv3x3(width, width, stride, groups, dilation)
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self.bn2 = norm_layer(width)
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self.conv3 = conv1x1(width, planes * self.expansion)
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self.bn3 = norm_layer(planes * self.expansion)
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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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identity = 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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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class ResNetWrapper(nn.Module):
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def __init__(self, cfg):
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super(ResNetWrapper, self).__init__()
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self.cfg = cfg
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self.in_channels = [64, 128, 256, 512]
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if 'in_channels' in cfg.backbone:
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self.in_channels = cfg.backbone.in_channels
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self.model = eval(cfg.backbone.resnet)(
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pretrained=cfg.backbone.pretrained,
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replace_stride_with_dilation=cfg.backbone.replace_stride_with_dilation, in_channels=self.in_channels)
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self.out = None
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if cfg.backbone.out_conv:
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out_channel = 512
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for chan in reversed(self.in_channels):
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if chan < 0: continue
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out_channel = chan
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break
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self.out = conv1x1(
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out_channel * self.model.expansion, 128)
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def forward(self, x):
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x = self.model(x)
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if self.out:
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x = self.out(x)
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return x
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class ResNet(nn.Module):
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def __init__(self, block, layers, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None,
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norm_layer=None, in_channels=None):
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
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bias=False)
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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.in_channels = in_channels
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self.layer1 = self._make_layer(block, in_channels[0], layers[0])
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self.layer2 = self._make_layer(block, in_channels[1], layers[1], stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, in_channels[2], layers[2], stride=2,
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dilate=replace_stride_with_dilation[1])
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if in_channels[3] > 0:
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self.layer4 = self._make_layer(block, in_channels[3], layers[3], stride=2,
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dilate=replace_stride_with_dilation[2])
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self.expansion = block.expansion
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# self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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# self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(
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m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0)
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer))
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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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if self.in_channels[3] > 0:
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x = self.layer4(x)
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# x = self.avgpool(x)
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# x = torch.flatten(x, 1)
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# x = self.fc(x)
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return x
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def _resnet(arch, block, layers, pretrained, progress, **kwargs):
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model = ResNet(block, layers, **kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls[arch],
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progress=progress)
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model.load_state_dict(state_dict, strict=False)
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return model
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def resnet18(pretrained=False, progress=True, **kwargs):
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r"""ResNet-18 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
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**kwargs)
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def resnet34(pretrained=False, progress=True, **kwargs):
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r"""ResNet-34 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet50(pretrained=False, progress=True, **kwargs):
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r"""ResNet-50 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet101(pretrained=False, progress=True, **kwargs):
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r"""ResNet-101 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
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**kwargs)
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def resnet152(pretrained=False, progress=True, **kwargs):
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r"""ResNet-152 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
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**kwargs)
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def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
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r"""ResNeXt-50 32x4d model from
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`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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kwargs['groups'] = 32
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kwargs['width_per_group'] = 4
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return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
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pretrained, progress, **kwargs)
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def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
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r"""ResNeXt-101 32x8d model from
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`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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kwargs['groups'] = 32
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kwargs['width_per_group'] = 8
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return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
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pretrained, progress, **kwargs)
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def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
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r"""Wide ResNet-50-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
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The model is the same as ResNet except for the bottleneck number of channels
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which is twice larger in every block. The number of channels in outer 1x1
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convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
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channels, and in Wide ResNet-50-2 has 2048-1024-2048.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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kwargs['width_per_group'] = 64 * 2
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return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
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pretrained, progress, **kwargs)
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def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
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r"""Wide ResNet-101-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
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The model is the same as ResNet except for the bottleneck number of channels
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which is twice larger in every block. The number of channels in outer 1x1
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convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
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channels, and in Wide ResNet-50-2 has 2048-1024-2048.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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kwargs['width_per_group'] = 64 * 2
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return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
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pretrained, progress, **kwargs)
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