model.py
1,661 bytes
| 1 | import timm |
|---|---|
| 2 | import torch |
| 3 | import torch.nn as nn |
| 4 | |
| 5 | |
| 6 | class CountryClassifier(nn.Module): |
| 7 | def __init__(self, num_classes, backbone="vit_base_patch16_clip_224.openai", |
| 8 | dropout=0.2, pretrained=True): |
| 9 | super().__init__() |
| 10 | self.backbone = timm.create_model(backbone, pretrained=pretrained, num_classes=0) |
| 11 | self.head = nn.Sequential( |
| 12 | nn.Dropout(dropout), |
| 13 | nn.Linear(self.backbone.num_features, num_classes), |
| 14 | ) |
| 15 | |
| 16 | def forward(self, x): |
| 17 | return self.head(self.backbone(x)) |
| 18 | |
| 19 | def freeze_backbone(self): |
| 20 | for p in self.backbone.parameters(): |
| 21 | p.requires_grad = False |
| 22 | |
| 23 | def set_finetune_mode(self, unfreeze_blocks): |
| 24 | self.freeze_backbone() |
| 25 | if hasattr(self.backbone, "blocks"): |
| 26 | for block in list(self.backbone.blocks)[-unfreeze_blocks:]: |
| 27 | for p in block.parameters(): |
| 28 | p.requires_grad = True |
| 29 | if hasattr(self.backbone, "norm"): |
| 30 | for p in self.backbone.norm.parameters(): |
| 31 | p.requires_grad = True |
| 32 | else: |
| 33 | # Mitte-ViT backbone'il puudub plokkide loend, avame kõik. |
| 34 | for p in self.backbone.parameters(): |
| 35 | p.requires_grad = True |
| 36 | |
| 37 | |
| 38 | def load_checkpoint(path, device="cpu"): |
| 39 | ckpt = torch.load(path, map_location=device, weights_only=True) |
| 40 | model = CountryClassifier( |
| 41 | num_classes=len(ckpt["classes"]), |
| 42 | backbone=ckpt["backbone"], |
| 43 | dropout=ckpt.get("dropout", 0.0), |
| 44 | pretrained=False, |
| 45 | ) |
| 46 | model.load_state_dict(ckpt["model"]) |
| 47 | model.to(device).eval() |
| 48 | return model, ckpt["classes"] |
| 49 | |