Closed Raychanan closed 6 months ago
Poked around, and this commit contribution resolves my questions:
https://github.com/mlfoundations/open_clip/pull/498
https://github.com/mlfoundations/open_clip/pull/498/files
My code after updating the open_clip package source code for the generate
function coca_model.py
:
import open_clip
import torch
from PIL import Image
import os
print(open_clip.__file__)
import time
# Create model and transforms
model, _, transform = open_clip.create_model_and_transforms(
model_name="coca_ViT-L-14",
pretrained="mscoco_finetuned_laion2B-s13B-b90k",
device='cuda',
cache_dir="/global/scratch/users/USERNAME/huggingface_cache/"
)
# help(model.generate)
folder_path = './'
# List all files in the directory
files = os.listdir(folder_path)
# Assuming 'files' and 'folder_path' are defined
BATCH_SIZE = 4 # You can adjust this size
batch = []
start_time = time.time()
for file in files:
if file.lower().endswith(('.png', '.jpg', '.jpeg')):
image_path = os.path.join(folder_path, file)
# Load and transform the image
im = Image.open(image_path).convert("RGB")
im = transform(im).unsqueeze(0)
batch.append(im)
# Check if batch size is reached
if len(batch) == BATCH_SIZE:
# Process the batch
batch_tensor = torch.cat(batch, dim=0)
with torch.no_grad(), torch.cuda.amp.autocast():
generated = model.generate(batch_tensor, device='cuda', batch_size=BATCH_SIZE)
for idx, gen in enumerate(generated):
# Print the generated text for each image
print(f"Text for {files[idx]}:")
print(open_clip.decode(gen).split("<end_of_text>")[0].replace("<start_of_text>", ""))
# Clear the batch
batch = []
# Process any remaining images in the batch
if batch:
batch_tensor = torch.cat(batch, dim=0)
with torch.no_grad(), torch.cuda.amp.autocast():
generated = model.generate(batch_tensor, device='cuda')
for idx, gen in enumerate(generated):
print(f"Text for {files[idx]}:")
print(open_clip.decode(gen).split("<end_of_text>")[0].replace("<start_of_text>", ""))
end_time = time.time()
elapsed_time = end_time - start_time
print(f"Total time for processing: {elapsed_time} seconds")
Hi, thanks for your amazing work!
I'm currently following the official tutorial available on the project's homepage through Colab, and it's working out great for me.
I now have a GPU with ample memory. As I have numerous images to process, I'm wondering if there's a way to handle all the images in a folder in several batches for accelerating the processing? I've only used batching with text before and am quite new to doing this with images. If it's possible, could you provide some guidance or resources on this? Thanks!
Here is my existing code: