identification_benchmark/big_model_inference/inference_resnet.py

import torch
from torchvision import transforms, models
from data_loading import LMDBImageDataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import argparse

torch.multiprocessing.set_sharing_strategy('file_system')

def main():
    # Parse command line arguments.
    parser = argparse.ArgumentParser(description="Compute ResNet embeddings")
    parser.add_argument('--resnet_type', type=str, default='resnet152',
                        help="Type of ResNet model to use (e.g., resnet18, resnet34, resnet50, resnet101, resnet152)")
    parser.add_argument('--lmdb_path', type=str, default='../lmdb_all_crops_pmfeed_4_3_16',
                        help="Path to the LMDB image dataset")
    args = parser.parse_args()

    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
    ])

    # Create the dataset and dataloader.
    dataset = LMDBImageDataset(
        lmdb_path=args.lmdb_path, 
        transform=transform, 
        limit=None
    )
    dataloader = DataLoader(
        dataset, 
        batch_size=128, 
        shuffle=False, 
        num_workers=8,
    )

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # Dynamically load the specified ResNet model.
    resnet_constructor = getattr(models, args.resnet_type)
    model = resnet_constructor(weights='IMAGENET1K_V1')
    # Remove the last fully-connected layer to obtain embeddings.
    model = list(model.children())[:-1]
    model = torch.nn.Sequential(*model)
    model.to(device)
    model.eval()

    all_embeddings = []
    all_cow_ids = []

    # Loop through the dataset and compute embeddings.
    with torch.no_grad():
        for images, cow_ids in tqdm(dataloader, unit='batch'):
            images = images.to(device)
            image_features = model(images)
            image_features = image_features.squeeze()
            all_embeddings.append(image_features.cpu())
            all_cow_ids.append(cow_ids)

    # Concatenate and save all embeddings.
    embeddings = torch.cat(all_embeddings, dim=0)
    torch.save(embeddings, f"{args.resnet_type}_embeddings.pt")
    all_cow_ids = torch.cat(all_cow_ids, dim=0)
    torch.save(all_cow_ids, f"all_cow_ids.pt")

if __name__ == '__main__':
    main()