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Commit
·
adac6eb
1
Parent(s):
f6221dd
syncs
Browse files- app.py +1 -2
- model.py +125 -2
- t_model.py +0 -152
app.py
CHANGED
@@ -6,8 +6,7 @@ import numpy as np
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import pandas as pd
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import gradio as gr
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import librosa.display
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from model import EvalNet
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from t_model import t_EvalNet
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from utils import get_modelist, find_files, embed, MODEL_DIR
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import pandas as pd
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import gradio as gr
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import librosa.display
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from model import EvalNet, t_EvalNet
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from utils import get_modelist, find_files, embed, MODEL_DIR
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model.py
CHANGED
@@ -1,8 +1,8 @@
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import
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import torchvision.models as models
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from modelscope.msdatasets import MsDataset
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@@ -181,3 +181,126 @@ class EvalNet:
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out.size(0), self.out_channel_before_classifier, self.H, self.H
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)
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return self.classifier(out).squeeze()
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models as models
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import numpy as np
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from modelscope.msdatasets import MsDataset
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out.size(0), self.out_channel_before_classifier, self.H, self.H
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)
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return self.classifier(out).squeeze()
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+
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+
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class t_EvalNet:
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def __init__(
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self,
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backbone: str,
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cls_num: int,
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ori_T: int,
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imgnet_ver="v1",
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weight_path="",
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):
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if not hasattr(models, backbone):
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raise ValueError(f"Unsupported model {backbone}.")
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self.imgnet_ver = imgnet_ver
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self.type, self.weight_url, self.input_size = self._model_info(backbone)
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self.model: torch.nn.Module = eval("models.%s()" % backbone)
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self.ori_T = ori_T
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if self.type == "vit":
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self.hidden_dim = self.model.hidden_dim
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self.class_token = nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
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elif self.type == "swin_transformer":
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self.hidden_dim = 768
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self.cls_num = cls_num
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self._set_classifier()
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checkpoint = (
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torch.load(weight_path)
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if torch.cuda.is_available()
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else torch.load(weight_path, map_location="cpu")
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)
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self.model.load_state_dict(checkpoint["model"], False)
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self.classifier.load_state_dict(checkpoint["classifier"], False)
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if torch.cuda.is_available():
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self.model = self.model.cuda()
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self.classifier = self.classifier.cuda()
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self.model.eval()
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def _get_backbone(self, backbone_ver, backbone_list):
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for backbone_info in backbone_list:
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if backbone_ver == backbone_info["ver"]:
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return backbone_info
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raise ValueError("[Backbone not found] Please check if --model is correct!")
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def _model_info(self, backbone: str):
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backbone_list = MsDataset.load(
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"monetjoe/cv_backbones",
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split=self.imgnet_ver,
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cache_dir="./__pycache__",
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trust_remote_code=True,
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)
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backbone_info = self._get_backbone(backbone, backbone_list)
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return (
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str(backbone_info["type"]),
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str(backbone_info["url"]),
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int(backbone_info["input_size"]),
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)
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def _create_classifier(self):
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original_T_size = self.ori_T
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self.avgpool = nn.AdaptiveAvgPool2d((1, None)) # F -> 1
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upsample_module = nn.Sequential( # nn.AdaptiveAvgPool2d((1, None)), # F -> 1
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nn.ConvTranspose2d(
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self.hidden_dim, 256, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(256),
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nn.ConvTranspose2d(
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256, 128, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(128),
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nn.ConvTranspose2d(
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128, 64, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(64),
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nn.ConvTranspose2d(
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64, 32, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(32), # input for Interp: [bsz, C, 1, T]
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Interpolate(
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size=(1, original_T_size), mode="bilinear", align_corners=False
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), # classifier
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nn.Conv2d(32, 32, kernel_size=(1, 1)),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(32),
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nn.Conv2d(32, self.cls_num, kernel_size=(1, 1)),
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)
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return upsample_module
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def _set_classifier(self): #### set custom classifier ####
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if self.type == "vit" or self.type == "swin_transformer":
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self.classifier = self._create_classifier()
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def get_input_size(self):
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return self.input_size
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def forward(self, x: torch.Tensor):
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if torch.cuda.is_available():
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x = x.cuda()
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if self.type == "vit":
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x = self.model._process_input(x)
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batch_class_token = self.class_token.expand(x.size(0), -1, -1).cuda()
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x = torch.cat([batch_class_token, x], dim=1)
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x = self.model.encoder(x)
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x = x[:, 1:].permute(0, 2, 1)
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x = x.unsqueeze(2)
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return self.classifier(x).squeeze()
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elif self.type == "swin_transformer":
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x = self.model.features(x) # [B, H, W, C]
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x = x.permute(0, 3, 1, 2)
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x = self.avgpool(x) # [B, C, 1, W]
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return self.classifier(x).squeeze()
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return None
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t_model.py
DELETED
@@ -1,152 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models as models
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from modelscope.msdatasets import MsDataset
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class Interpolate(nn.Module):
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def __init__(
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self,
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size=None,
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scale_factor=None,
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mode="bilinear",
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align_corners=False,
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):
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super(Interpolate, self).__init__()
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self.size = size
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self.scale_factor = scale_factor
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self.mode = mode
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self.align_corners = align_corners
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def forward(self, x):
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return F.interpolate(
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x,
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size=self.size,
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scale_factor=self.scale_factor,
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mode=self.mode,
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align_corners=self.align_corners,
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)
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class t_EvalNet:
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def __init__(
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self,
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backbone: str,
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cls_num: int,
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ori_T: int,
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imgnet_ver="v1",
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weight_path="",
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):
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if not hasattr(models, backbone):
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raise ValueError(f"Unsupported model {backbone}.")
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self.imgnet_ver = imgnet_ver
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self.type, self.weight_url, self.input_size = self._model_info(backbone)
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self.model: torch.nn.Module = eval("models.%s()" % backbone)
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self.ori_T = ori_T
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if self.type == "vit":
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self.hidden_dim = self.model.hidden_dim
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self.class_token = nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
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elif self.type == "swin_transformer":
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self.hidden_dim = 768
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self.cls_num = cls_num
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self._set_classifier()
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checkpoint = (
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torch.load(weight_path)
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if torch.cuda.is_available()
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else torch.load(weight_path, map_location="cpu")
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)
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self.model.load_state_dict(checkpoint["model"], False)
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self.classifier.load_state_dict(checkpoint["classifier"], False)
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if torch.cuda.is_available():
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self.model = self.model.cuda()
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self.classifier = self.classifier.cuda()
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self.model.eval()
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-
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def _get_backbone(self, backbone_ver, backbone_list):
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for backbone_info in backbone_list:
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if backbone_ver == backbone_info["ver"]:
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return backbone_info
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raise ValueError("[Backbone not found] Please check if --model is correct!")
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def _model_info(self, backbone: str):
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backbone_list = MsDataset.load(
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"monetjoe/cv_backbones",
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split=self.imgnet_ver,
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cache_dir="./__pycache__",
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trust_remote_code=True,
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)
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backbone_info = self._get_backbone(backbone, backbone_list)
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return (
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str(backbone_info["type"]),
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str(backbone_info["url"]),
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int(backbone_info["input_size"]),
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)
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def _create_classifier(self):
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original_T_size = self.ori_T
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self.avgpool = nn.AdaptiveAvgPool2d((1, None)) # F -> 1
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upsample_module = nn.Sequential( # nn.AdaptiveAvgPool2d((1, None)), # F -> 1
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nn.ConvTranspose2d(
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self.hidden_dim, 256, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(256),
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nn.ConvTranspose2d(
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256, 128, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(128),
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nn.ConvTranspose2d(
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128, 64, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(64),
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nn.ConvTranspose2d(
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64, 32, kernel_size=(1, 4), stride=(1, 2), padding=(0, 1)
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),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(32), # input for Interp: [bsz, C, 1, T]
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Interpolate(
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size=(1, original_T_size), mode="bilinear", align_corners=False
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), # classifier
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nn.Conv2d(32, 32, kernel_size=(1, 1)),
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(32),
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nn.Conv2d(32, self.cls_num, kernel_size=(1, 1)),
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)
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return upsample_module
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def _set_classifier(self): #### set custom classifier ####
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if self.type == "vit" or self.type == "swin_transformer":
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self.classifier = self._create_classifier()
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def get_input_size(self):
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return self.input_size
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def forward(self, x: torch.Tensor):
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if torch.cuda.is_available():
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x = x.cuda()
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if self.type == "vit":
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x = self.model._process_input(x)
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batch_class_token = self.class_token.expand(x.size(0), -1, -1).cuda()
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x = torch.cat([batch_class_token, x], dim=1)
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x = self.model.encoder(x)
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x = x[:, 1:].permute(0, 2, 1)
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x = x.unsqueeze(2)
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return self.classifier(x).squeeze()
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elif self.type == "swin_transformer":
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x = self.model.features(x) # [B, H, W, C]
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x = x.permute(0, 3, 1, 2)
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x = self.avgpool(x) # [B, C, 1, W]
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return self.classifier(x).squeeze()
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return None
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