DL-Art-School/codes/models/classifiers/twin_cifar_resnet.py

"""resnet in pytorch



[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.

    Deep Residual Learning for Image Recognition
    https://arxiv.org/abs/1512.03385v1
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from trainer.networks import register_model


class BasicBlock(nn.Module):
    """Basic Block for resnet 18 and resnet 34

    """

    #BasicBlock and BottleNeck block
    #have different output size
    #we use class attribute expansion
    #to distinct
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        #residual function
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
        )

        #shortcut
        self.shortcut = nn.Sequential()

        #the shortcut output dimension is not the same with residual function
        #use 1*1 convolution to match the dimension
        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class BottleNeck(nn.Module):
    """Residual block for resnet over 50 layers

    """
    expansion = 4
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
        )

        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class ResNet(nn.Module):

    def __init__(self, block, num_block, num_classes=100):
        super().__init__()

        self.in_channels = 32

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True))
        #we use a different inputsize than the original paper
        #so conv2_x's stride is 1
        self.conv2_x = self._make_layer(block, 32, num_block[0], 1)
        self.conv3_x = self._make_layer(block, 64, num_block[1], 2)
        self.conv4_x = self._make_layer(block, 128, num_block[2], 2)
        self.conv5_x = self._make_layer(block, 256, num_block[3], 2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        """make resnet layers(by layer i didnt mean this 'layer' was the
        same as a neuron netowork layer, ex. conv layer), one layer may
        contain more than one residual block

        Args:
            block: block type, basic block or bottle neck block
            out_channels: output depth channel number of this layer
            num_blocks: how many blocks per layer
            stride: the stride of the first block of this layer

        Return:
            return a resnet layer
        """

        # we have num_block blocks per layer, the first block
        # could be 1 or 2, other blocks would always be 1
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)
        output = self.conv4_x(output)
        output = self.conv5_x(output)
        output = self.avgpool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output


class SymbolicLoss:
    def __init__(self, category_depths=[3,5,5,3], convergence_weighting=[1,.6,.3,.1], divergence_weighting=[.1,.3,.6,1]):
        self.depths = category_depths
        self.total_classes = 1
        for c in category_depths:
            self.total_classes *= c
        self.elements_per_level = []
        m = 1
        for c in category_depths[1:]:
            m *= c
            self.elements_per_level.append(self.total_classes // m)
        self.elements_per_level = self.elements_per_level + [1]
        self.convergence_weighting = convergence_weighting
        self.divergence_weighting = divergence_weighting
        # TODO: improve the above logic, I'm sure it can be done better.

    def __call__(self, logits, collaboratorLabels):
        """
        Computes the symbolic loss.
        :param logits: Nested level scores for the network under training.
        :param collaboratorLabels: level labels from the collaborator network.
        :return: Convergence loss & divergence loss.
        """
        b, l = logits.shape
        assert l == self.total_classes, f"Expected {self.total_classes} predictions, got {l}"

        convergence_loss = 0
        divergence_loss = 0
        for epc, cw, dw in zip(self.elements_per_level, self.convergence_weighting, self.divergence_weighting):
            level_logits = logits.view(b, l//epc, epc)
            level_logits = level_logits.sum(dim=-1)
            level_labels = collaboratorLabels.div(epc, rounding_mode='trunc')
            # Convergence
            convergence_loss = convergence_loss + F.cross_entropy(level_logits, level_labels) * cw
            # Divergence
            div_label_indices = level_logits.argmax(dim=-1)
            # TODO: find the torch-y way of doing this.
            dp = []
            for bi, i in enumerate(div_label_indices):
                dp.append(level_logits[:, i])
            div_preds = torch.stack(dp, dim=0)
            div_labels = torch.arange(0, b, device=logits.device)
            divergence_loss = divergence_loss + F.cross_entropy(div_preds, div_labels)
        return convergence_loss, divergence_loss


if __name__ == '__main__':
    sl = SymbolicLoss()
    logits = torch.randn(5, sl.total_classes)
    labels = torch.randint(0, sl.total_classes, (5,))
    sl(logits, labels)


class TwinnedCifar(nn.Module):
    def __init__(self):
        super().__init__()
        self.loss = SymbolicLoss()
        self.netA = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=self.loss.total_classes)
        self.netB = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=self.loss.total_classes)

    def forward(self, x):
        y1 = self.netA(x)
        y2 = self.netB(x)
        b = x.shape[0]
        convergenceA, divergenceA = self.loss(y1[:b//2], y2.argmax(dim=-1)[:b//2])
        convergenceB, divergenceB = self.loss(y2[b//2:], y1.argmax(dim=-1)[b//2:])
        return convergenceA + convergenceB, divergenceA + divergenceB


@register_model
def register_twin_cifar(opt_net, opt):
    """ return a ResNet 18 object
    """
    return TwinnedCifar()

def resnet34():
    """ return a ResNet 34 object
    """
    return ResNet(BasicBlock, [3, 4, 6, 3])

def resnet50():
    """ return a ResNet 50 object
    """
    return ResNet(BottleNeck, [3, 4, 6, 3])

def resnet101():
    """ return a ResNet 101 object
    """
    return ResNet(BottleNeck, [3, 4, 23, 3])

def resnet152():
    """ return a ResNet 152 object
    """
    return ResNet(BottleNeck, [3, 8, 36, 3])