| import os | |
| import torch | |
| from torchvision import models, transforms | |
| from torch.utils.data import Dataset, DataLoader | |
| from PIL import Image | |
| import pandas as pd | |
| class InferenceDataset(Dataset): | |
| def __init__(self, folder, transform): | |
| self.paths = [os.path.join(folder, f) for f in os.listdir(folder) | |
| if f.lower().endswith(("png", "jpg", "jpeg"))] | |
| self.transform = transform | |
| def __len__(self): | |
| return len(self.paths) | |
| def __getitem__(self, idx): | |
| img = Image.open(self.paths[idx]).convert("RGB") | |
| return self.transform(img), self.paths[idx] | |
| def run_inference(image_folder, output_csv="predictions.csv"): | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = models.resnet18(pretrained=True) | |
| for p in model.parameters(): | |
| p.requires_grad = False | |
| model.fc = torch.nn.Linear(model.fc.in_features, 1) | |
| model = model.to(device) | |
| model.eval() | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], | |
| [0.229, 0.224, 0.225]) | |
| ]) | |
| dataset = InferenceDataset(image_folder, transform) | |
| loader = DataLoader(dataset, batch_size=1, shuffle=False) | |
| results = [] | |
| with torch.no_grad(): | |
| for img, path in loader: | |
| img = img.to(device) | |
| pred = torch.sigmoid(model(img)).item() | |
| label = "Dog" if pred >= 0.5 else "Cat" | |
| results.append({"image_path": path[0], "prediction": label, "score": pred}) | |
| pd.DataFrame(results).to_csv(output_csv, index=False) | |