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6b2e578 | 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 | import torch
import torch.nn as nn
from torchvision import models
from huggingface_hub import hf_hub_download
class Car_Classifier_Resnet(nn.Module):
def __init__(self, num_classes):
super().__init__()
self.model = models.resnet18(weights="DEFAULT")
for param in self.model.parameters():
param.requires_grad = False
for param in self.model.layer3.parameters():
param.requires_grad = True
for param in self.model.layer4.parameters():
param.requires_grad = True
self.model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(self.model.fc.in_features, 256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, num_classes)
)
def forward(self, x):
return self.model(x)
def load_resnet_model_from_hf(
repo_id: str,
filename: str = "car-damage-classifier.pt",
num_classes: int = 6,
device: str = "cpu",
hf_token: str = None,
) -> torch.nn.Module:
"""
Downloads and loads the state_dict for Car_Classifier_Resnet from Hugging Face Hub.
"""
print(f"Downloading checkpoint '{filename}' from Hugging Face repo '{repo_id}'...")
checkpoint_path = hf_hub_download(
repo_id=repo_id,
filename=filename,
token=hf_token
)
model = Car_Classifier_Resnet(num_classes=num_classes)
state_dict = torch.load(checkpoint_path, map_location=device)
if isinstance(state_dict, dict) and "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
model.load_state_dict(state_dict)
model.to(device)
model.eval()
print("✅ ResNet-18 model loaded successfully.")
return model |