Spaces:
Running
Running
File size: 10,583 Bytes
f1b22d5 adac6eb f1b22d5 adac6eb f1b22d5 6e9a4e4 f1b22d5 f6221dd f1b22d5 f6221dd f1b22d5 d613312 f1b22d5 f6221dd f1b22d5 f6221dd adac6eb d613312 adac6eb d613312 adac6eb d613312 adac6eb |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import numpy as np
from modelscope.msdatasets import MsDataset
class Interpolate(nn.Module):
def __init__(
self,
size=None,
scale_factor=None,
mode="bilinear",
align_corners=False,
):
super(Interpolate, self).__init__()
self.size = size
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
return F.interpolate(
x,
size=self.size,
scale_factor=self.scale_factor,
mode=self.mode,
align_corners=self.align_corners,
)
class EvalNet:
def __init__(
self,
backbone: str,
cls_num: int,
ori_T: int,
imgnet_ver="v1",
weight_path="",
):
if not hasattr(models, backbone):
raise ValueError(f"Unsupported model {backbone}.")
self.imgnet_ver = imgnet_ver
self.training = bool(weight_path == "")
self.type, self.weight_url, self.input_size = self._model_info(backbone)
self.model: torch.nn.Module = eval("models.%s()" % backbone)
self.ori_T = ori_T
self.out_channel_before_classifier = 0
self._set_channel_outsize() # set out channel size
self.cls_num = cls_num
self._set_classifier()
self._pseudo_foward()
checkpoint = (
torch.load(weight_path)
if torch.cuda.is_available()
else torch.load(weight_path, map_location="cpu")
)
if self.type == "squeezenet":
self.model.load_state_dict(checkpoint, False)
else:
self.model.load_state_dict(checkpoint["model"], False)
self.classifier.load_state_dict(checkpoint["classifier"], False)
if torch.cuda.is_available():
self.model = self.model.cuda()
self.classifier = self.classifier.cuda()
self.model.eval()
def _get_backbone(self, backbone_ver, backbone_list):
for backbone_info in backbone_list:
if backbone_ver == backbone_info["ver"]:
return backbone_info
raise ValueError("[Backbone not found] Please check if --model is correct!")
def _model_info(self, backbone: str):
backbone_list = MsDataset.load(
"monetjoe/cv_backbones",
split=self.imgnet_ver,
cache_dir="./__pycache__",
trust_remote_code=True,
)
backbone_info = self._get_backbone(backbone, backbone_list)
return (
str(backbone_info["type"]),
str(backbone_info["url"]),
int(backbone_info["input_size"]),
)
def _create_classifier(self):
original_T_size = self.ori_T
return nn.Sequential(
nn.AdaptiveAvgPool2d((1, None)), # F -> 1
nn.ConvTranspose2d(
self.out_channel_before_classifier,
256,
kernel_size=(1, 4),
stride=(1, 2),
padding=(0, 1),
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.ConvTranspose2d(
256, 128, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(128),
nn.ConvTranspose2d(
128, 64, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(64),
nn.ConvTranspose2d(
64, 32, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32), # input for Interp: [bsz, C, 1, T]
Interpolate(
size=(1, original_T_size), mode="bilinear", align_corners=False
), # classifier
nn.Conv2d(32, 32, kernel_size=(1, 1)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32),
nn.Conv2d(32, self.cls_num, kernel_size=(1, 1)),
)
def _set_channel_outsize(self): #### get the output size before classifier ####
conv2d_out_ch = []
for name, module in self.model.named_modules():
if isinstance(module, torch.nn.Conv2d):
conv2d_out_ch.append(module.out_channels)
if (
str(name).__contains__("classifier")
or str(name).__eq__("fc")
or str(name).__contains__("head")
):
if isinstance(module, torch.nn.Conv2d):
conv2d_out_ch.append(module.in_channels)
break
self.out_channel_before_classifier = conv2d_out_ch[-1]
def _set_classifier(self): #### set custom classifier ####
if self.type == "resnet":
self.model.avgpool = nn.Identity()
self.model.fc = nn.Identity()
self.classifier = self._create_classifier()
elif (
self.type == "vgg" or self.type == "efficientnet" or self.type == "convnext"
):
self.model.avgpool = nn.Identity()
self.model.classifier = nn.Identity()
self.classifier = self._create_classifier()
elif self.type == "squeezenet":
self.model.classifier = nn.Identity()
self.classifier = self._create_classifier()
def get_input_size(self):
return self.input_size
def _pseudo_foward(self):
temp = torch.randn(4, 3, self.input_size, self.input_size)
out = self.model(temp)
self.H = int(np.sqrt(out.size(1) / self.out_channel_before_classifier))
def forward(self, x):
if torch.cuda.is_available():
x = x.cuda()
if self.type == "convnext":
out = self.model(x)
return self.classifier(out).squeeze()
else:
out = self.model(x)
out = out.view(
out.size(0), self.out_channel_before_classifier, self.H, self.H
)
return self.classifier(out).squeeze()
class t_EvalNet:
def __init__(
self,
backbone: str,
cls_num: int,
ori_T: int,
imgnet_ver="v1",
weight_path="",
):
if not hasattr(models, backbone):
raise ValueError(f"Unsupported model {backbone}.")
self.imgnet_ver = imgnet_ver
self.type, self.weight_url, self.input_size = self._model_info(backbone)
self.model: torch.nn.Module = eval("models.%s()" % backbone)
self.ori_T = ori_T
if self.type == "vit":
self.hidden_dim = self.model.hidden_dim
self.class_token = nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
elif self.type == "swin_transformer":
self.hidden_dim = 768
self.cls_num = cls_num
self._set_classifier()
checkpoint = (
torch.load(weight_path)
if torch.cuda.is_available()
else torch.load(weight_path, map_location="cpu")
)
self.model.load_state_dict(checkpoint["model"], False)
self.classifier.load_state_dict(checkpoint["classifier"], False)
if torch.cuda.is_available():
self.model = self.model.cuda()
self.classifier = self.classifier.cuda()
self.model.eval()
def _get_backbone(self, backbone_ver, backbone_list):
for backbone_info in backbone_list:
if backbone_ver == backbone_info["ver"]:
return backbone_info
raise ValueError("[Backbone not found] Please check if --model is correct!")
def _model_info(self, backbone: str):
backbone_list = MsDataset.load(
"monetjoe/cv_backbones",
split=self.imgnet_ver,
cache_dir="./__pycache__",
trust_remote_code=True,
)
backbone_info = self._get_backbone(backbone, backbone_list)
return (
str(backbone_info["type"]),
str(backbone_info["url"]),
int(backbone_info["input_size"]),
)
def _create_classifier(self):
original_T_size = self.ori_T
self.avgpool = nn.AdaptiveAvgPool2d((1, None)) # F -> 1
return nn.Sequential( # nn.AdaptiveAvgPool2d((1, None)), # F -> 1
nn.ConvTranspose2d(
self.hidden_dim, 256, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(256),
nn.ConvTranspose2d(
256, 128, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(128),
nn.ConvTranspose2d(
128, 64, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(64),
nn.ConvTranspose2d(
64, 32, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32), # input for Interp: [bsz, C, 1, T]
Interpolate(
size=(1, original_T_size), mode="bilinear", align_corners=False
), # classifier
nn.Conv2d(32, 32, kernel_size=(1, 1)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32),
nn.Conv2d(32, self.cls_num, kernel_size=(1, 1)),
)
def _set_classifier(self): #### set custom classifier ####
if self.type == "vit" or self.type == "swin_transformer":
self.classifier = self._create_classifier()
def get_input_size(self):
return self.input_size
def forward(self, x: torch.Tensor):
if torch.cuda.is_available():
x = x.cuda()
self.class_token = self.class_token.cuda()
if self.type == "vit":
x = self.model._process_input(x)
batch_class_token = self.class_token.expand(x.size(0), -1, -1)
x = torch.cat([batch_class_token, x], dim=1)
x = self.model.encoder(x)
x = x[:, 1:].permute(0, 2, 1)
x = x.unsqueeze(2)
return self.classifier(x).squeeze()
elif self.type == "swin_transformer":
x = self.model.features(x) # [B, H, W, C]
x = x.permute(0, 3, 1, 2)
x = self.avgpool(x) # [B, C, 1, W]
return self.classifier(x).squeeze()
return None
|