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377dccd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | # Copyright 2022-present, Lorenzo Bonicelli, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara.
# 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
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 ResNet(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(ResNet, 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)
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.projector = nn.Linear(nf * 8 , nf * 8)
self._features = nn.Sequential(self.conv1,
self.bn1,
nn.ReLU(),
self.layer1,
self.layer2,
self.layer3,
self.layer4
)
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, 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
out = self.layer3(out) # -> 256, 8, 8
out = self.layer4(out) # -> 512, 4, 4
out = avg_pool2d(out, out.shape[2]) # -> 512, 1, 1
feature = out.view(out.size(0), -1) # 512
if returnt == 'features':
return feature
out = self.classifier(feature)
if returnt == 'out':
return out
elif returnt == 'all':
return (out, feature)
raise NotImplementedError("Unknown return type")
def forward_all_layers(self, x: torch.Tensor) -> torch.Tensor:
feats = []
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
feats.append(out)
out = self.layer2(out) # -> 128, 16, 16
feats.append(out)
out = self.layer3(out) # -> 256, 8, 8
feats.append(out)
out = self.layer4(out) # -> 512, 4, 4
feats.append(out)
out = avg_pool2d(out, out.shape[2]) # -> 512, 1, 1
feature = out.view(out.size(0), -1) # 512
out = self.classifier(feature)
return out, feats
def resnet18(nclasses: int, nf: int=64) -> 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)
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