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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()
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