Spaces:
Running
on
L40S
Running
on
L40S
File size: 7,543 Bytes
2252f3d |
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 |
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None,
dcn=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError(
'BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError(
"Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
norm_layer=nn.BatchNorm2d,
dcn=None):
super(Bottleneck, self).__init__()
self.dcn = dcn
self.with_dcn = dcn is not None
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = norm_layer(planes, momentum=0.1)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = norm_layer(planes, momentum=0.1)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes * 4, momentum=0.1)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = F.relu(self.bn1(self.conv1(x)), inplace=True)
if not self.with_dcn:
out = F.relu(self.bn2(self.conv2(out)), inplace=True)
elif self.with_modulated_dcn:
offset_mask = self.conv2_offset(out)
offset = offset_mask[:, :18 * self.deformable_groups, :, :]
mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
mask = mask.sigmoid()
out = F.relu(self.bn2(self.conv2(out, offset, mask)))
else:
offset = self.conv2_offset(out)
out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = F.relu(out)
return out
class ResNet(nn.Module):
""" ResNet """
def __init__(self,
architecture,
norm_layer=nn.BatchNorm2d,
dcn=None,
stage_with_dcn=(False, False, False, False)):
super(ResNet, self).__init__()
self._norm_layer = norm_layer
assert architecture in [
"resnet18", "resnet34", "resnet50", "resnet101", 'resnet152'
]
layers = {
'resnet18': [2, 2, 2, 2],
'resnet34': [3, 4, 6, 3],
'resnet50': [3, 4, 6, 3],
'resnet101': [3, 4, 23, 3],
'resnet152': [3, 8, 36, 3],
}
self.inplanes = 64
if architecture == "resnet18" or architecture == 'resnet34':
self.block = BasicBlock
else:
self.block = Bottleneck
self.layers = layers[architecture]
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn]
self.layer1 = self.make_layer(self.block,
64,
self.layers[0],
dcn=stage_dcn[0])
self.layer2 = self.make_layer(self.block,
128,
self.layers[1],
stride=2,
dcn=stage_dcn[1])
self.layer3 = self.make_layer(self.block,
256,
self.layers[2],
stride=2,
dcn=stage_dcn[2])
self.layer4 = self.make_layer(self.block,
512,
self.layers[3],
stride=2,
dcn=stage_dcn[3])
def forward(self, x):
x = self.maxpool(self.relu(self.bn1(self.conv1(x)))) # 64 * h/4 * w/4
x = self.layer1(x) # 256 * h/4 * w/4
x = self.layer2(x) # 512 * h/8 * w/8
x = self.layer3(x) # 1024 * h/16 * w/16
x = self.layer4(x) # 2048 * h/32 * w/32
return x
def stages(self):
return [self.layer1, self.layer2, self.layer3, self.layer4]
def make_layer(self, block, planes, blocks, stride=1, dcn=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
self._norm_layer(planes * block.expansion),
)
layers = []
layers.append(
block(self.inplanes,
planes,
stride,
downsample,
norm_layer=self._norm_layer,
dcn=dcn))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
norm_layer=self._norm_layer,
dcn=dcn))
return nn.Sequential(*layers)
|