241 lines
8.4 KiB
Python
241 lines
8.4 KiB
Python
"""resnet in pytorch
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
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Deep Residual Learning for Image Recognition
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https://arxiv.org/abs/1512.03385v1
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch_intermediary as ml
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from trainer.networks import register_model
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class BasicBlock(nn.Module):
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"""Basic Block for resnet 18 and resnet 34
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"""
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#BasicBlock and BottleNeck block
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#have different output size
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#we use class attribute expansion
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#to distinct
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expansion = 1
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def __init__(self, in_channels, out_channels, stride=1):
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super().__init__()
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#residual function
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self.residual_function = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels * BasicBlock.expansion)
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)
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#shortcut
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self.shortcut = nn.Sequential()
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#the shortcut output dimension is not the same with residual function
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#use 1*1 convolution to match the dimension
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if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_channels * BasicBlock.expansion)
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)
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def forward(self, x):
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return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
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class BottleNeck(nn.Module):
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"""Residual block for resnet over 50 layers
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"""
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expansion = 4
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def __init__(self, in_channels, out_channels, stride=1):
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super().__init__()
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self.residual_function = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels * BottleNeck.expansion),
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)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
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nn.BatchNorm2d(out_channels * BottleNeck.expansion)
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)
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def forward(self, x):
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return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
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class ResNet(nn.Module):
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def __init__(self, block, num_block, num_classes=100):
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super().__init__()
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self.in_channels = 32
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(inplace=True))
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#we use a different inputsize than the original paper
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#so conv2_x's stride is 1
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self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
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self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
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self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
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self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = ml.Linear(256 * block.expansion, num_classes)
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def _make_layer(self, block, out_channels, num_blocks, stride):
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"""make resnet layers(by layer i didnt mean this 'layer' was the
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same as a neuron netowork layer, ex. conv layer), one layer may
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contain more than one residual block
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Args:
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block: block type, basic block or bottle neck block
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out_channels: output depth channel number of this layer
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num_blocks: how many blocks per layer
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stride: the stride of the first block of this layer
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Return:
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return a resnet layer
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"""
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# we have num_block blocks per layer, the first block
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# could be 1 or 2, other blocks would always be 1
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_channels, out_channels, stride))
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self.in_channels = out_channels * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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output = self.conv1(x)
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output = self.conv2_x(output)
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output = self.conv3_x(output)
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output = self.conv4_x(output)
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output = self.conv5_x(output)
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output = self.avgpool(output)
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output = output.view(output.size(0), -1)
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output = self.fc(output)
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return output
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class SymbolicLoss:
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def __init__(self, category_depths=[3,5,5,3], convergence_weighting=[1,.6,.3,.1], divergence_weighting=[.1,.3,.6,1]):
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self.depths = category_depths
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self.total_classes = 1
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for c in category_depths:
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self.total_classes *= c
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self.elements_per_level = []
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m = 1
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for c in category_depths[1:]:
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m *= c
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self.elements_per_level.append(self.total_classes // m)
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self.elements_per_level = self.elements_per_level + [1]
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self.convergence_weighting = convergence_weighting
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self.divergence_weighting = divergence_weighting
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# TODO: improve the above logic, I'm sure it can be done better.
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def __call__(self, logits, collaboratorLabels):
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"""
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Computes the symbolic loss.
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:param logits: Nested level scores for the network under training.
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:param collaboratorLabels: level labels from the collaborator network.
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:return: Convergence loss & divergence loss.
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"""
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b, l = logits.shape
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assert l == self.total_classes, f"Expected {self.total_classes} predictions, got {l}"
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convergence_loss = 0
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divergence_loss = 0
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for epc, cw, dw in zip(self.elements_per_level, self.convergence_weighting, self.divergence_weighting):
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level_logits = logits.view(b, l//epc, epc)
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level_logits = level_logits.sum(dim=-1)
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level_labels = collaboratorLabels.div(epc, rounding_mode='trunc')
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# Convergence
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convergence_loss = convergence_loss + F.cross_entropy(level_logits, level_labels) * cw
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# Divergence
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div_label_indices = level_logits.argmax(dim=-1)
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# TODO: find the torch-y way of doing this.
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dp = []
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for bi, i in enumerate(div_label_indices):
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dp.append(level_logits[:, i])
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div_preds = torch.stack(dp, dim=0)
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div_labels = torch.arange(0, b, device=logits.device)
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divergence_loss = divergence_loss + F.cross_entropy(div_preds, div_labels)
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return convergence_loss, divergence_loss
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if __name__ == '__main__':
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sl = SymbolicLoss()
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logits = torch.randn(5, sl.total_classes)
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labels = torch.randint(0, sl.total_classes, (5,))
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sl(logits, labels)
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class TwinnedCifar(nn.Module):
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def __init__(self):
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super().__init__()
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self.loss = SymbolicLoss()
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self.netA = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=self.loss.total_classes)
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self.netB = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=self.loss.total_classes)
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def forward(self, x):
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y1 = self.netA(x)
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y2 = self.netB(x)
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b = x.shape[0]
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convergenceA, divergenceA = self.loss(y1[:b//2], y2.argmax(dim=-1)[:b//2])
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convergenceB, divergenceB = self.loss(y2[b//2:], y1.argmax(dim=-1)[b//2:])
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return convergenceA + convergenceB, divergenceA + divergenceB
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@register_model
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def register_twin_cifar(opt_net, opt):
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""" return a ResNet 18 object
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"""
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return TwinnedCifar()
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def resnet34():
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""" return a ResNet 34 object
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"""
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return ResNet(BasicBlock, [3, 4, 6, 3])
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def resnet50():
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""" return a ResNet 50 object
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"""
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return ResNet(BottleNeck, [3, 4, 6, 3])
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def resnet101():
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""" return a ResNet 101 object
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"""
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return ResNet(BottleNeck, [3, 4, 23, 3])
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def resnet152():
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""" return a ResNet 152 object
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"""
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return ResNet(BottleNeck, [3, 8, 36, 3])
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