Move to xautodl
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D-X-Y
217
xautodl/models/ImageNet_ResNet.py
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217
xautodl/models/ImageNet_ResNet.py
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# Deep Residual Learning for Image Recognition, CVPR 2016
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import torch.nn as nn
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from .initialization import initialize_resnet
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def conv3x3(in_planes, out_planes, stride=1, groups=1):
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return nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=groups,
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bias=False,
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)
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def conv1x1(in_planes, out_planes, stride=1):
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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__(
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self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
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):
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super(BasicBlock, self).__init__()
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if groups != 1 or base_width != 64:
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raise ValueError("BasicBlock only supports groups=1 and base_width=64")
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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)
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self.bn1 = nn.BatchNorm2d(planes)
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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)
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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__(
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self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
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):
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super(Bottleneck, self).__init__()
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width = int(planes * (base_width / 64.0)) * 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 = nn.BatchNorm2d(width)
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self.conv2 = conv3x3(width, width, stride, groups)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = conv1x1(width, planes * self.expansion)
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self.bn3 = nn.BatchNorm2d(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 ResNet(nn.Module):
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def __init__(
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self,
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block_name,
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layers,
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deep_stem,
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num_classes,
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zero_init_residual,
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groups,
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width_per_group,
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):
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super(ResNet, self).__init__()
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# planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
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if block_name == "BasicBlock":
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block = BasicBlock
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elif block_name == "Bottleneck":
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block = Bottleneck
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else:
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raise ValueError("invalid block-name : {:}".format(block_name))
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if not deep_stem:
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self.conv = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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)
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else:
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self.conv = nn.Sequential(
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nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True),
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nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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)
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self.inplanes = 64
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(
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block, 64, layers[0], stride=1, groups=groups, base_width=width_per_group
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)
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self.layer2 = self._make_layer(
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block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group
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)
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self.layer3 = self._make_layer(
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block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group
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)
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self.layer4 = self._make_layer(
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block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group
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)
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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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self.message = (
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"block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}".format(
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block, layers, deep_stem, num_classes
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)
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)
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self.apply(initialize_resnet)
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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, groups, base_width):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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if stride == 2:
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downsample = nn.Sequential(
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nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
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conv1x1(self.inplanes, planes * block.expansion, 1),
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nn.BatchNorm2d(planes * block.expansion),
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)
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elif stride == 1:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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nn.BatchNorm2d(planes * block.expansion),
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)
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else:
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raise ValueError("invalid stride [{:}] for downsample".format(stride))
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layers = []
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layers.append(
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block(self.inplanes, planes, stride, downsample, groups, base_width)
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)
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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, 1, None, groups, base_width))
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return nn.Sequential(*layers)
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def get_message(self):
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return self.message
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def forward(self, x):
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x = self.conv(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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features = self.avgpool(x)
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features = features.view(features.size(0), -1)
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logits = self.fc(features)
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return features, logits
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