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c734473 78bc721 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | import torch
import torch.nn as nn
import torch.nn.functional as F
import antialiased_cnns
def drop_path(x, drop_prob=0.0, training=False):
"""Drop paths (Stochastic Depth) per sample."""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_()
output = x.div(keep_prob) * random_tensor
return output
class BasicBlock(nn.Module):
"""Original ResNet Basic Block with Stochastic Depth"""
expansion = 1
def __init__(self, in_channels, out_channels, stride=1, downsample=None, drop_prob=0.0, use_blurpool=False):
super().__init__()
self.use_blurpool = use_blurpool
self.stride = stride
# Modify conv1 based on stride and use_blurpool
if self.use_blurpool and self.stride == 2:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.blurpool = antialiased_cnns.BlurPool(out_channels, stride=2)
else:
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
self.drop_prob = drop_prob
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = F.relu(out, inplace=True)
# Apply blurpool after conv1 if downsampling with blurpool
if self.use_blurpool and self.stride == 2:
out = self.blurpool(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out = drop_path(out, self.drop_prob, self.training)
out += identity
out = F.relu(out, inplace=True)
return out
class BottleneckBlock(nn.Module):
"""Original ResNet Bottleneck Block with Stochastic Depth"""
expansion = 4
def __init__(self, in_channels, out_channels, stride=1, downsample=None, drop_prob=0.0, use_blurpool=False):
super().__init__()
self.use_blurpool = use_blurpool
self.stride = stride
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
# Modify conv2 based on stride and use_blurpool
if self.use_blurpool and self.stride == 2:
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.blurpool = antialiased_cnns.BlurPool(out_channels, stride=2)
else:
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.downsample = downsample
self.drop_prob = drop_prob
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = F.relu(out, inplace=True)
out = self.conv2(out)
out = self.bn2(out)
out = F.relu(out, inplace=True)
# Apply blurpool after conv2 if downsampling with blurpool
if self.use_blurpool and self.stride == 2:
out = self.blurpool(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out = drop_path(out, self.drop_prob, self.training)
out += identity
out = F.relu(out, inplace=True)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, drop_path_rate=0.2, use_blurpool=False):
super().__init__()
self.in_channels = 64
self.use_blurpool = use_blurpool
# Initial conv layer
# Apply blurpool if use_blurpool is True and stride is 2
if self.use_blurpool:
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
antialiased_cnns.BlurPool(64, stride=2)
)
else:
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
# Initial pooling layer (always MaxPool2d stride 2) replace maxpool by MaxBlurPool if use_blurpool is True
if self.use_blurpool:
self.maxpool_or_blurpool = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=1), antialiased_cnns.BlurPool(64, stride=2))
else:
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Calculate total number of blocks
total_blocks = sum(layers)
# Linear drop path rate schedule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_blocks)]
# Track current block index
block_idx = 0
self.layer1 = self._make_layer(block, 64, layers[0], stride=1,
drop_probs=dpr[block_idx:block_idx+layers[0]], use_blurpool=use_blurpool)
block_idx += layers[0]
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
drop_probs=dpr[block_idx:block_idx+layers[1]], use_blurpool=use_blurpool)
block_idx += layers[1]
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
drop_probs=dpr[block_idx:block_idx+layers[2]], use_blurpool=use_blurpool)
block_idx += layers[2]
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
drop_probs=dpr[block_idx:block_idx+layers[3]], use_blurpool=use_blurpool)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Conv2d(512 * block.expansion, num_classes, kernel_size=1)
def _make_layer(self, block, out_channels, blocks, stride, drop_probs, use_blurpool):
downsample = None
if stride != 1 or self.in_channels != out_channels * block.expansion:
# Downsample path
# If use_blurpool is True and stride is 2, replace strided conv with conv stride 1 + blurpool stride 2
if use_blurpool and stride == 2:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * block.expansion,
kernel_size=1, stride=1, bias=False), # Conv stride 1
nn.BatchNorm2d(out_channels * block.expansion),
antialiased_cnns.BlurPool(out_channels * block.expansion, stride=2) # BlurPool stride 2
)
else:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * block.expansion)
)
layers = []
# First block in the layer handles downsampling
layers.append(block(self.in_channels, out_channels, stride, downsample, drop_probs[0], use_blurpool=use_blurpool))
self.in_channels = out_channels * block.expansion
# Subsequent blocks have stride 1
for i in range(1, blocks):
layers.append(block(self.in_channels, out_channels, stride=1, drop_prob=drop_probs[i], use_blurpool=use_blurpool))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
# The original ResNet has maxpool after conv1 replace maxpool by MaxBlurPool if use_blurpool is True
if self.use_blurpool:
x = self.maxpool_or_blurpool(x)
else:
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = self.fc(x)
x = torch.flatten(x, 1)
return x
def resnet50(num_classes=1000, drop_path_rate=0.2, use_blurpool=False):
return ResNet(BottleneckBlock, [3, 4, 6, 3], num_classes, drop_path_rate, use_blurpool) |