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# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import avg_pool2d, relu
from backbone import MammothBackbone
class Classifier(torch.nn.Module):
def __init__(self,
feat_dim,
nb_cls,
cos_temp):
super(Classifier, self).__init__()
fc = torch.nn.Linear(feat_dim, nb_cls)
self.weight = torch.nn.Parameter(fc.weight.t(), requires_grad=True)
self.bias = torch.nn.Parameter(fc.bias, requires_grad=True)
self.cos_temp = torch.nn.Parameter(torch.FloatTensor(1).fill_(cos_temp), requires_grad=False)
self.apply = self.apply_cosine
def get_weight(self):
return self.weight, self.bias
def apply_cosine(self, feature, weight, bias):
feature = F.normalize(feature, p=2, dim=1, eps=1e-12) ## Attention: normalized along 2nd dimension!!!
weight = F.normalize(weight, p=2, dim=0, eps=1e-12)## Attention: normalized along 1st dimension!!!
cls_score = self.cos_temp * (torch.mm(feature, weight))
return cls_score
def forward(self, feature):
weight, bias = self.get_weight()
cls_score = self.apply(feature, weight, bias)
return cls_score
def conv3x3(in_planes: int, out_planes: int, stride: int=1) -> F.conv2d:
"""
Instantiates a 3x3 convolutional layer with no bias.
:param in_planes: number of input channels
:param out_planes: number of output channels
:param stride: stride of the convolution
:return: convolutional layer
"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
"""
The basic block of ResNet.
"""
expansion = 1
def __init__(self, in_planes: int, planes: int, stride: int=1) -> None:
"""
Instantiates the basic block of the network.
:param in_planes: the number of input channels
:param planes: the number of channels (to be possibly expanded)
"""
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Compute a forward pass.
:param x: input tensor (batch_size, input_size)
:return: output tensor (10)
"""
out = relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = relu(out)
return out
class ResNet1(MammothBackbone):
"""
ResNet network architecture. Designed for complex datasets.
"""
def __init__(self, block: BasicBlock, num_blocks: List[int],
num_classes: int, nf: int) -> None:
"""
Instantiates the layers of the network.
:param block: the basic ResNet block
:param num_blocks: the number of blocks per layer
:param num_classes: the number of output classes
:param nf: the number of filters
"""
super(ResNet1, self).__init__()
self.in_planes = nf
self.block = block
self.num_classes = num_classes
self.nf = nf
self.final_d = nf * 8
self.conv1 = conv3x3(3, nf * 1)
self.bn1 = nn.BatchNorm2d(nf * 1)
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
def _make_layer(self, block: BasicBlock, planes: int,
num_blocks: int, stride: int) -> nn.Module:
"""
Instantiates a ResNet layer.
:param block: ResNet basic block
:param planes: channels across the network
:param num_blocks: number of blocks
:param stride: stride
:return: ResNet layer
"""
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x: torch.Tensor, returnt='out') -> torch.Tensor:
"""
Compute a forward pass.
:param x: input tensor (batch_size, *input_shape)
:param returnt: return type (a string among 'out', 'features', 'all')
:return: output tensor (output_classes)
"""
out = relu(self.bn1(self.conv1(x))) # 64, 32, 32
if hasattr(self, 'maxpool'):
out = self.maxpool(out)
out = self.layer1(out) # -> 64, 32, 32
out = self.layer2(out) # -> 128, 16, 16
return out
class ResNet2(MammothBackbone):
"""
ResNet network architecture. Designed for complex datasets.
"""
def __init__(self, block: BasicBlock, num_blocks: List[int],
num_classes: int, nf: int,use_cos=False) -> None:
"""
Instantiates the layers of the network.
:param block: the basic ResNet block
:param num_blocks: the number of blocks per layer
:param num_classes: the number of output classes
:param nf: the number of filters
"""
super(ResNet2, self).__init__()
self.in_planes = nf
self.block = block
self.num_classes = num_classes
self.nf = nf
self.final_d = nf * 8
self.layer1 = self._make_layer(block, nf * 1, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, nf * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, nf * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, nf * 8, num_blocks[3], stride=2)
self.linear = nn.Linear(nf * 8 * block.expansion, num_classes)
self.net_channels = [nf * 1, nf * 2, nf * 4, nf * 8]
self.y_hat_fc = nn.Sequential(
nn.Linear(num_classes, 128),
nn.LeakyReLU()
)
if use_cos:
self.classifier = Classifier(512*block.expansion, num_classes, 12)
print("use cos!")
else:
self.classifier = self.linear
def _make_layer(self, block: BasicBlock, planes: int,
num_blocks: int, stride: int) -> nn.Module:
"""
Instantiates a ResNet layer.
:param block: ResNet basic block
:param planes: channels across the network
:param num_blocks: number of blocks
:param stride: stride
:return: ResNet layer
"""
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x: torch.Tensor, y: torch.Tensor, returnt2='out') -> torch.Tensor:
"""
Compute a forward pass.
:param x: input tensor (batch_size, *input_shape)
:param returnt: return type (a string among 'out', 'features', 'all')
:return: output tensor (output_classes)
"""
out = x + self.y_hat_fc(y)[..., None, None]
out = self.layer3(out) # -> 256, 8, 8
out = self.layer4(out) # -> 512, 4, 4
feat = out
out = avg_pool2d(out, out.shape[2]) # -> 512, 1, 1
feature = out.view(out.size(0), -1) # 512
out = self.classifier(feature)
if returnt2=="tsne":
return feature
else:
return out[:, :self.num_classes], feat
class ResNet(MammothBackbone):
"""
ResNet network architecture. Designed for complex datasets.
"""
def __init__(self, block: BasicBlock, num_blocks: List[int],
num_classes: int, nf: int,use_cos=False) -> None:
"""
Instantiates the layers of the network.
:param block: the basic ResNet block
:param num_blocks: the number of blocks per layer
:param num_classes: the number of output classes
:param nf: the number of filters
"""
super(ResNet, self).__init__()
self.f1 = ResNet1(BasicBlock, [2, 2, 2, 2], num_classes, nf)
self.f2 = ResNet2(BasicBlock, [2, 2, 2, 2], num_classes, nf,use_cos)
self.in_planes = nf
self.block = block
self.num_classes = num_classes
self.nf = nf
self.final_d = nf * 8
def forward(self, x: torch.Tensor, y: torch.Tensor, returnt='features') -> torch.Tensor:
"""
Compute a forward pass.
:param x: input tensor (batch_size, *input_shape)
:param returnt: return type (a string among 'out', 'features', 'all')
:return: output tensor (output_classes)
"""
z = self.f1(x)
if returnt=='out':
y_pred, z_pred = self.f2(z, y,returnt2=returnt)
return y_pred, z_pred
if returnt == 'tsne':
feature = self.f2(z, y,returnt2=returnt)
return feature
def resnet18_id2(nclasses: int, nf: int=64,use_cos=False) -> ResNet:
"""
Instantiates a ResNet18 network.
:param nclasses: number of output classes
:param nf: number of filters
:return: ResNet network
"""
return ResNet(BasicBlock, [2, 2, 2, 2], nclasses, nf,use_cos)
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