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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
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 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
upsample_module = 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)),
)
return upsample_module
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()
if self.type == "vit":
x = self.model._process_input(x)
batch_class_token = self.class_token.expand(x.size(0), -1, -1).cuda()
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
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