DL-Art-School/codes/models/classifiers/cifar_resnet_branched.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.distributed as dist

from models.switched_conv.switched_conv_hard_routing import SwitchNorm, RouteTop1
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 ResNetTail(nn.Module):
    def __init__(self, block, num_block, num_classes=100):
        super().__init__()

        self.in_channels = 64
        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.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        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.conv4_x(x)
        output = self.conv5_x(output)
        output = self.avg_pool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output


class DropoutNorm(SwitchNorm):
    def __init__(self, group_size, dropout_rate, accumulator_size=256, eps=1e-6):
        super().__init__(group_size, accumulator_size)
        self.accumulator_desired_size = accumulator_size
        self.group_size = group_size
        self.dropout_rate = dropout_rate
        self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
        self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
        self.register_buffer("accumulator", torch.zeros(accumulator_size, group_size))
        self.eps = eps

    def add_norm_to_buffer(self, x):
        flatten_dims = [0] + [k+2 for k in range(len(x.shape)-2)]
        flat = x.mean(dim=flatten_dims)

        self.accumulator[self.accumulator_index] = flat.detach().clone()
        self.accumulator_index += 1
        if self.accumulator_index >= self.accumulator_desired_size:
            self.accumulator_index *= 0
            if self.accumulator_filled <= 0:
                self.accumulator_filled += 1

    # Input into forward is a switching tensor of shape (batch,groups,<misc>)
    def forward(self, x: torch.Tensor):
        assert len(x.shape) >= 2

        if not self.training:
            return x

        # Only accumulate the "winning" switch slots.
        mask = torch.nn.functional.one_hot(x.argmax(dim=1), num_classes=x.shape[1])
        if len(x.shape) > 2:
            mask = mask.permute(0, 3, 1, 2)  # TODO: Make this more extensible.
        xtop = torch.ones_like(x)
        xtop[mask != 1] = 0

        # Push the accumulator to the right device on the first iteration.
        if self.accumulator.device != xtop.device:
            self.accumulator = self.accumulator.to(xtop.device)
        self.add_norm_to_buffer(xtop)

        # Reduce across all distributed entities, if needed
        if dist.is_available() and dist.is_initialized():
            dist.all_reduce(self.accumulator, op=dist.ReduceOp.SUM)
            self.accumulator /= dist.get_world_size()

        # Compute the dropout probabilities. This module is a no-op before the accumulator is initialized.
        if self.accumulator_filled > 0:
            with torch.no_grad():
                probs = torch.mean(self.accumulator, dim=0) * self.dropout_rate
                bs, br = x.shape[:2]
                drop = torch.rand((bs, br), device=x.device) > probs.unsqueeze(0)
                # Ensure that there is always at least one switch left un-dropped out
                fix_blank = (drop.sum(dim=1, keepdim=True) == 0).repeat(1, br)
                drop = drop.logical_or(fix_blank)
            x_dropped = drop * x + ~drop * -1e20
            x = x_dropped

        return x


class HardRoutingGate(nn.Module):
    def __init__(self, breadth, dropout_rate=.8):
        super().__init__()
        self.norm = DropoutNorm(breadth, dropout_rate, accumulator_size=128)

    def forward(self, x):
        soft = nn.functional.softmax(self.norm(x), dim=1)
        return RouteTop1.apply(soft)
        return soft


class ResNet(nn.Module):

    def __init__(self, block, num_block, num_classes=100, num_tails=8):
        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))

        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.tails = nn.ModuleList([ResNetTail(block, num_block, 256) for _ in range(num_tails)])
        self.selector = ResNetTail(block, num_block, num_tails)
        self.selector_gate = nn.Linear(256, 1)
        self.gate = HardRoutingGate(num_tails, dropout_rate=2)
        self.final_linear = nn.Linear(256, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        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 get_debug_values(self, step, __):
        logs = {'histogram_switch_usage': self.latest_masks}
        return logs

    def forward(self, x, coarse_label, return_selector=False):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)

        keys = []
        for t in self.tails:
            keys.append(t(output))
        keys = torch.stack(keys, dim=1)

        query = self.selector(output).unsqueeze(2)
        selector = self.selector_gate(query * keys).squeeze(-1)
        selector = self.gate(selector)
        self.latest_masks = (selector.max(dim=1, keepdim=True)[0].repeat(1,8) == selector).float().argmax(dim=1)
        values = self.final_linear(selector.unsqueeze(-1) * keys)

        if return_selector:
            return values.sum(dim=1), selector
        else:
            return values.sum(dim=1)

        #bs = output.shape[0]
        #return (tailouts[coarse_label] * torch.eye(n=bs, device=x.device).view(bs,bs,1)).sum(dim=1)

@register_model
def register_cifar_resnet18_branched(opt_net, opt):
    """ return a ResNet 18 object
    """
    return ResNet(BasicBlock, [2, 2, 2, 2])

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])


if __name__ == '__main__':
    model = ResNet(BasicBlock, [2,2,2,2])
    for j in range(10):
        v = model(torch.randn(256,3,32,32), None)
        print(model.get_debug_values(0, None))
    print(v.shape)
    l = nn.MSELoss()(v, torch.randn_like(v))
    l.backward()