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import torch
import argparse
import re
from transformers import AutoProcessor, AutoModel
from torchvision import transforms
from data_loading import LMDBImageDataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import time
torch.multiprocessing.set_sharing_strategy('file_system')
def infer_image_size(model_name):
"""
Infer image size from the model name.
Looks for a trailing hyphen followed by digits (e.g., "-336").
Defaults to 224 if not found.
"""
match = re.search(r'-([0-9]+)$', model_name)
if match:
return int(match.group(1))
else:
return 224
def collate_fn(batch):
images, labels = zip(*batch)
return list(images), list(labels)
def main():
parser = argparse.ArgumentParser(description="Compute embeddings for a Hugging Face model")
parser.add_argument('--model_name', type=str, default="facebook/vit-mae-base",
help="Hugging Face model name, e.g., facebook/vit-mae-base or openai/clip-vit-base-patch14-336")
parser.add_argument('--lmdb_path', type=str, default='../lmdb_all_crops_pmfeed_4_3_16', help="Path to the LMDB image dataset")
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--num_workers', type=int, default=8)
args = parser.parse_args()
# Infer image size from the model name
image_size = infer_image_size(args.model_name)
print(f"Inferred image size: {image_size}")
transform = transforms.Compose([
transforms.Resize((image_size, image_size)),
])
# Create the dataset and dataloader.
dataset = LMDBImageDataset(
lmdb_path=args.lmdb_path,
transform=transform,
limit=None
)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn
)
# Load the model and processor.
model_name = args.model_name
processor = AutoProcessor.from_pretrained(model_name, do_normalize=False)
model = AutoModel.from_pretrained(model_name)
print(f"Using model: {model_name}")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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'):
inputs = processor(images=images, return_tensors="pt")
inputs = inputs.to(device)
# Get the mean of the last hidden state as the image embedding.
if "clip-vit" in model_name:
image_features = model.get_image_features(**inputs)
elif "vit-mae" in model_name:
image_features = model(**inputs).last_hidden_state.mean(dim=1)
else:
image_features = model(**inputs).pooler_output
# image_features = model(**inputs).last_hidden_state.mean(dim=1) # mae model
# image_features = model.get_image_features(**inputs) # clip model
# image_features = model(**inputs).pooler_output # everything else
all_embeddings.append(image_features.cpu())
all_cow_ids.extend(cow_ids)
# Concatenate and save the embeddings.
embeddings = torch.cat(all_embeddings, dim=0)
output_file = f"{model_name.replace('/', '_')}_embeddings.pt"
torch.save(embeddings, output_file)
print(f"Embeddings saved to {output_file}")
if __name__ == '__main__':
main() |