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import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
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 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")
) # self.model.load_state_dict(checkpoint, False)
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__",
)
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
upsample_module = 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)),
)
return upsample_module
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)
out = self.classifier(out).squeeze()
return out
else:
out = self.model(x)
out = out.view(
out.size(0), self.out_channel_before_classifier, self.H, self.H
)
out = self.classifier(out).squeeze()
return out
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