Spaces:
Runtime error
Runtime error
File size: 10,804 Bytes
a6dac9a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
""" Pytorch Inception-V4 implementation
Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg
from .layers import create_classifier
from .registry import register_model
__all__ = ['InceptionV4']
default_cfgs = {
'inception_v4': {
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth',
'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
'crop_pct': 0.875, 'interpolation': 'bicubic',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'features.0.conv', 'classifier': 'last_linear',
'label_offset': 1, # 1001 classes in pretrained weights
}
}
class BasicConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(
in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
self.bn = nn.BatchNorm2d(out_planes, eps=0.001)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Mixed3a(nn.Module):
def __init__(self):
super(Mixed3a, self).__init__()
self.maxpool = nn.MaxPool2d(3, stride=2)
self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
def forward(self, x):
x0 = self.maxpool(x)
x1 = self.conv(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed4a(nn.Module):
def __init__(self):
super(Mixed4a, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1)
)
self.branch1 = nn.Sequential(
BasicConv2d(160, 64, kernel_size=1, stride=1),
BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
out = torch.cat((x0, x1), 1)
return out
class Mixed5a(nn.Module):
def __init__(self):
super(Mixed5a, self).__init__()
self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
self.maxpool = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.conv(x)
x1 = self.maxpool(x)
out = torch.cat((x0, x1), 1)
return out
class InceptionA(nn.Module):
def __init__(self):
super(InceptionA, self).__init__()
self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
BasicConv2d(384, 64, kernel_size=1, stride=1),
BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(384, 96, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class ReductionA(nn.Module):
def __init__(self):
super(ReductionA, self).__init__()
self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
self.branch1 = nn.Sequential(
BasicConv2d(384, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
BasicConv2d(224, 256, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class InceptionB(nn.Module):
def __init__(self):
super(InceptionB, self).__init__()
self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0))
)
self.branch2 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3))
)
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1024, 128, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class ReductionB(nn.Module):
def __init__(self):
super(ReductionB, self).__init__()
self.branch0 = nn.Sequential(
BasicConv2d(1024, 192, kernel_size=1, stride=1),
BasicConv2d(192, 192, kernel_size=3, stride=2)
)
self.branch1 = nn.Sequential(
BasicConv2d(1024, 256, kernel_size=1, stride=1),
BasicConv2d(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
BasicConv2d(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
BasicConv2d(320, 320, kernel_size=3, stride=2)
)
self.branch2 = nn.MaxPool2d(3, stride=2)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
out = torch.cat((x0, x1, x2), 1)
return out
class InceptionC(nn.Module):
def __init__(self):
super(InceptionC, self).__init__()
self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
self.branch3 = nn.Sequential(
nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
BasicConv2d(1536, 256, kernel_size=1, stride=1)
)
def forward(self, x):
x0 = self.branch0(x)
x1_0 = self.branch1_0(x)
x1_1a = self.branch1_1a(x1_0)
x1_1b = self.branch1_1b(x1_0)
x1 = torch.cat((x1_1a, x1_1b), 1)
x2_0 = self.branch2_0(x)
x2_1 = self.branch2_1(x2_0)
x2_2 = self.branch2_2(x2_1)
x2_3a = self.branch2_3a(x2_2)
x2_3b = self.branch2_3b(x2_2)
x2 = torch.cat((x2_3a, x2_3b), 1)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class InceptionV4(nn.Module):
def __init__(self, num_classes=1000, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg'):
super(InceptionV4, self).__init__()
assert output_stride == 32
self.drop_rate = drop_rate
self.num_classes = num_classes
self.num_features = 1536
self.features = nn.Sequential(
BasicConv2d(in_chans, 32, kernel_size=3, stride=2),
BasicConv2d(32, 32, kernel_size=3, stride=1),
BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
Mixed3a(),
Mixed4a(),
Mixed5a(),
InceptionA(),
InceptionA(),
InceptionA(),
InceptionA(),
ReductionA(), # Mixed6a
InceptionB(),
InceptionB(),
InceptionB(),
InceptionB(),
InceptionB(),
InceptionB(),
InceptionB(),
ReductionB(), # Mixed7a
InceptionC(),
InceptionC(),
InceptionC(),
)
self.feature_info = [
dict(num_chs=64, reduction=2, module='features.2'),
dict(num_chs=160, reduction=4, module='features.3'),
dict(num_chs=384, reduction=8, module='features.9'),
dict(num_chs=1024, reduction=16, module='features.17'),
dict(num_chs=1536, reduction=32, module='features.21'),
]
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def get_classifier(self):
return self.last_linear
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.last_linear = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
return self.features(x)
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x)
if self.drop_rate > 0:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.last_linear(x)
return x
def _create_inception_v4(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
InceptionV4, variant, pretrained,
default_cfg=default_cfgs[variant],
feature_cfg=dict(flatten_sequential=True),
**kwargs)
@register_model
def inception_v4(pretrained=False, **kwargs):
return _create_inception_v4('inception_v4', pretrained, **kwargs)
|