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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)
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