Spaces:
Runtime error
Runtime error
File size: 13,800 Bytes
2d5f249 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 |
# code brought in part from https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/models/pose_resnet.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import os
from lib.pymaf.core.cfgs import cfg
import logging
logger = logging.getLogger(__name__)
BN_MOMENTUM = 0.1
def conv3x3(in_planes, out_planes, stride=1, bias=False, groups=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes * groups,
out_planes * groups,
kernel_size=3,
stride=stride,
padding=1,
bias=bias,
groups=groups)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):
super().__init__()
self.conv1 = conv3x3(inplanes, planes, stride, groups=groups)
self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, groups=groups)
self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = 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:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):
super().__init__()
self.conv1 = nn.Conv2d(inplanes * groups,
planes * groups,
kernel_size=1,
bias=False,
groups=groups)
self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
self.conv2 = nn.Conv2d(planes * groups,
planes * groups,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
groups=groups)
self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)
self.conv3 = nn.Conv2d(planes * groups,
planes * self.expansion * groups,
kernel_size=1,
bias=False,
groups=groups)
self.bn3 = nn.BatchNorm2d(planes * self.expansion * groups,
momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
resnet_spec = {
18: (BasicBlock, [2, 2, 2, 2]),
34: (BasicBlock, [3, 4, 6, 3]),
50: (Bottleneck, [3, 4, 6, 3]),
101: (Bottleneck, [3, 4, 23, 3]),
152: (Bottleneck, [3, 8, 36, 3])
}
class IUV_predict_layer(nn.Module):
def __init__(self,
feat_dim=256,
final_cov_k=3,
part_out_dim=25,
with_uv=True):
super().__init__()
self.with_uv = with_uv
if self.with_uv:
self.predict_u = nn.Conv2d(in_channels=feat_dim,
out_channels=25,
kernel_size=final_cov_k,
stride=1,
padding=1 if final_cov_k == 3 else 0)
self.predict_v = nn.Conv2d(in_channels=feat_dim,
out_channels=25,
kernel_size=final_cov_k,
stride=1,
padding=1 if final_cov_k == 3 else 0)
self.predict_ann_index = nn.Conv2d(
in_channels=feat_dim,
out_channels=15,
kernel_size=final_cov_k,
stride=1,
padding=1 if final_cov_k == 3 else 0)
self.predict_uv_index = nn.Conv2d(in_channels=feat_dim,
out_channels=25,
kernel_size=final_cov_k,
stride=1,
padding=1 if final_cov_k == 3 else 0)
self.inplanes = feat_dim
def _make_layer(self, block, planes, blocks, stride=1):
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),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
return_dict = {}
predict_uv_index = self.predict_uv_index(x)
predict_ann_index = self.predict_ann_index(x)
return_dict['predict_uv_index'] = predict_uv_index
return_dict['predict_ann_index'] = predict_ann_index
if self.with_uv:
predict_u = self.predict_u(x)
predict_v = self.predict_v(x)
return_dict['predict_u'] = predict_u
return_dict['predict_v'] = predict_v
else:
return_dict['predict_u'] = None
return_dict['predict_v'] = None
# return_dict['predict_u'] = torch.zeros(predict_uv_index.shape).to(predict_uv_index.device)
# return_dict['predict_v'] = torch.zeros(predict_uv_index.shape).to(predict_uv_index.device)
return return_dict
class SmplResNet(nn.Module):
def __init__(self,
resnet_nums,
in_channels=3,
num_classes=229,
last_stride=2,
n_extra_feat=0,
truncate=0,
**kwargs):
super().__init__()
self.inplanes = 64
self.truncate = truncate
# extra = cfg.MODEL.EXTRA
# self.deconv_with_bias = extra.DECONV_WITH_BIAS
block, layers = resnet_spec[resnet_nums]
self.conv1 = nn.Conv2d(in_channels,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2],
stride=2) if truncate < 2 else None
self.layer4 = self._make_layer(
block, 512, layers[3],
stride=last_stride) if truncate < 1 else None
self.avg_pooling = nn.AdaptiveAvgPool2d(1)
self.num_classes = num_classes
if num_classes > 0:
self.final_layer = nn.Linear(512 * block.expansion, num_classes)
nn.init.xavier_uniform_(self.final_layer.weight, gain=0.01)
self.n_extra_feat = n_extra_feat
if n_extra_feat > 0:
self.trans_conv = nn.Sequential(
nn.Conv2d(n_extra_feat + 512 * block.expansion,
512 * block.expansion,
kernel_size=1,
bias=False),
nn.BatchNorm2d(512 * block.expansion, momentum=BN_MOMENTUM),
nn.ReLU(True))
def _make_layer(self, block, planes, blocks, stride=1):
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),
nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x, infeat=None):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2) if self.truncate < 2 else x2
x4 = self.layer4(x3) if self.truncate < 1 else x3
if infeat is not None:
x4 = self.trans_conv(torch.cat([infeat, x4], 1))
if self.num_classes > 0:
xp = self.avg_pooling(x4)
cls = self.final_layer(xp.view(xp.size(0), -1))
if not cfg.DANET.USE_MEAN_PARA:
# for non-negative scale
scale = F.relu(cls[:, 0]).unsqueeze(1)
cls = torch.cat((scale, cls[:, 1:]), dim=1)
else:
cls = None
return cls, {'x4': x4}
def init_weights(self, pretrained=''):
if os.path.isfile(pretrained):
logger.info('=> loading pretrained model {}'.format(pretrained))
# self.load_state_dict(pretrained_state_dict, strict=False)
checkpoint = torch.load(pretrained)
if isinstance(checkpoint, OrderedDict):
# state_dict = checkpoint
state_dict_old = self.state_dict()
for key in state_dict_old.keys():
if key in checkpoint.keys():
if state_dict_old[key].shape != checkpoint[key].shape:
del checkpoint[key]
state_dict = checkpoint
elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict_old = checkpoint['state_dict']
state_dict = OrderedDict()
# delete 'module.' because it is saved from DataParallel module
for key in state_dict_old.keys():
if key.startswith('module.'):
# state_dict[key[7:]] = state_dict[key]
# state_dict.pop(key)
state_dict[key[7:]] = state_dict_old[key]
else:
state_dict[key] = state_dict_old[key]
else:
raise RuntimeError(
'No state_dict found in checkpoint file {}'.format(
pretrained))
self.load_state_dict(state_dict, strict=False)
else:
logger.error('=> imagenet pretrained model dose not exist')
logger.error('=> please download it first')
raise ValueError('imagenet pretrained model does not exist')
class LimbResLayers(nn.Module):
def __init__(self,
resnet_nums,
inplanes,
outplanes=None,
groups=1,
**kwargs):
super().__init__()
self.inplanes = inplanes
block, layers = resnet_spec[resnet_nums]
self.outplanes = 512 if outplanes == None else outplanes
self.layer4 = self._make_layer(block,
self.outplanes,
layers[3],
stride=2,
groups=groups)
self.avg_pooling = nn.AdaptiveAvgPool2d(1)
def _make_layer(self, block, planes, blocks, stride=1, groups=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes * groups,
planes * block.expansion * groups,
kernel_size=1,
stride=stride,
bias=False,
groups=groups),
nn.BatchNorm2d(planes * block.expansion * groups,
momentum=BN_MOMENTUM),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, groups=groups))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=groups))
return nn.Sequential(*layers)
def forward(self, x):
x = self.layer4(x)
x = self.avg_pooling(x)
return x
|