"""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])