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| import os | |
| import gradio as gr | |
| import torch | |
| import PIL | |
| from open_flamingo import create_model_and_transforms | |
| model, image_processor, tokenizer = create_model_and_transforms( | |
| clip_vision_encoder_path="ViT-L-14", | |
| clip_vision_encoder_pretrained="openai", | |
| lang_encoder_path="anas-awadalla/mpt-1b-redpajama-200b", | |
| tokenizer_path="anas-awadalla/mpt-1b-redpajama-200b", | |
| cross_attn_every_n_layers=1 | |
| ) | |
| # grab model checkpoint from huggingface hub | |
| from huggingface_hub import hf_hub_download | |
| import torch | |
| checkpoint_path = hf_hub_download("openflamingo/OpenFlamingo-3B-vitl-mpt1b", "checkpoint.pt") | |
| model.load_state_dict(torch.load(checkpoint_path), strict=False) | |
| from PIL import Image | |
| import requests | |
| import torch | |
| """ | |
| Step 1: Load images | |
| """ | |
| demo_image_one = Image.open( | |
| requests.get( | |
| "http://images.cocodataset.org/val2017/000000039769.jpg", stream=True | |
| ).raw | |
| ) | |
| demo_image_two = Image.open( | |
| requests.get( | |
| "http://images.cocodataset.org/test-stuff2017/000000028137.jpg", | |
| stream=True | |
| ).raw | |
| ) | |
| query_image = Image.open( | |
| requests.get( | |
| "http://images.cocodataset.org/test-stuff2017/000000028352.jpg", | |
| stream=True | |
| ).raw | |
| ) | |
| """ | |
| Step 2: Preprocessing images | |
| Details: For OpenFlamingo, we expect the image to be a torch tensor of shape | |
| batch_size x num_media x num_frames x channels x height x width. | |
| In this case batch_size = 1, num_media = 3, num_frames = 1, | |
| channels = 3, height = 224, width = 224. | |
| Step 3: Preprocessing text | |
| Details: In the text we expect an <image> special token to indicate where an image is. | |
| We also expect an <|endofchunk|> special token to indicate the end of the text | |
| portion associated with an image. | |
| tokenizer.padding_side = "left" # For generation padding tokens should be on the left | |
| lang_x = tokenizer( | |
| ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"], | |
| return_tensors="pt", | |
| ) | |
| """ | |
| """ | |
| Step 4: Generate text | |
| """ | |
| #print("Generated text: ", tokenizer.decode(generated_text[0])) | |
| def predict_caption(image, prompt): | |
| assert isinstance(prompt, str) | |
| vision_x = [image_processor(demo_image_one).unsqueeze(0), image_processor(demo_image_two).unsqueeze(0), image_processor(query_image).unsqueeze(0)] | |
| vision_x = torch.cat(vision_x, dim=0) | |
| vision_x = vision_x.unsqueeze(1).unsqueeze(0) | |
| tokenizer.padding_side = "left" # For generation padding tokens should be on the left | |
| lang_x = tokenizer( | |
| ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"], | |
| return_tensors="pt", | |
| ) | |
| tokenizer.padding_side = "left" # For generation padding tokens should be on the left | |
| lang_x = tokenizer( | |
| ["<image>An image of two cats.<|endofchunk|><image>An image of a bathroom sink.<|endofchunk|><image>An image of"], | |
| return_tensors="pt", | |
| ) | |
| caption = tokenizer.decode(generated_text[0]) | |
| return caption | |
| iface = gr.Interface(fn=predict_caption, | |
| inputs=[gr.Image(type="pil"), gr.Textbox(value=DEFAULT_PROMPT, label="Prompt")], | |
| examples=examples, | |
| outputs="text") | |
| iface.launch() | |