import os os.system("cd open_flamingo && pip install .") os.system("cd transformers && pip install .") import numpy as np import torch from PIL import Image from open_flamingo.train.distributed import init_distributed_device, world_info_from_env import string import cv2 import gradio as gr import torch from PIL import Image from huggingface_hub import hf_hub_download, login from open_flamingo.src.factory import create_model_and_transforms flamingo, image_processor, tokenizer, vis_embed_size = create_model_and_transforms( "ViT-L-14", "datacomp_xl_s13b_b90k", "facebook/opt-350m", "facebook/opt-350m", add_visual_grounding=True, location_token_num=1000, add_visual_token = True, use_format_v2 = True, ) checkpoint_path = hf_hub_download("chendl/mm", "checkpoint_opt350m_v2.pt") checkpoint = torch.load(checkpoint_path, map_location="cpu") model_state_dict = {} for key in checkpoint.keys(): model_state_dict[key.replace("module.", "")] = checkpoint[key] if "vision_encoder.logit_scale"in model_state_dict: # previous checkpoint has some unnecessary weights del model_state_dict["vision_encoder.logit_scale"] del model_state_dict["vision_encoder.visual.proj"] del model_state_dict["vision_encoder.visual.ln_post.weight"] del model_state_dict["vision_encoder.visual.ln_post.bias"] flamingo.load_state_dict(model_state_dict, strict=True) def get_outputs( model, batch_images, attention_mask, max_generation_length, min_generation_length, num_beams, length_penalty, input_ids, image_start_index_list=None, image_nums=None, bad_words_ids=None, ): # and torch.cuda.amp.autocast(dtype=torch.float16) with torch.inference_mode(): outputs = model.generate( batch_images, input_ids, attention_mask=attention_mask, max_new_tokens=max_generation_length, min_length=min_generation_length, num_beams=num_beams, length_penalty=length_penalty, image_start_index_list=image_start_index_list, image_nums=image_nums, bad_words_ids=bad_words_ids, ) outputs = outputs[:, len(input_ids[0]) :] return outputs def generate( idx, image, text, vis_embed_size=256, rank=0, world_size=1, ): if image is None: raise gr.Error("Please upload an image.") flamingo.eval() loc_token_ids = [] for i in range(1000): loc_token_ids.append(int(tokenizer(f"", add_special_tokens=False)["input_ids"][-1])) media_token_id = tokenizer("<|#image#|>", add_special_tokens=False)["input_ids"][-1] endofchunk_token_id = tokenizer("<|endofchunk|>", add_special_tokens=False)["input_ids"][-1] endofmedia_token_id = tokenizer("<|#endofimage#|>", add_special_tokens=False)["input_ids"][-1] pad_token_id = tokenizer(tokenizer.pad_token, add_special_tokens=False)["input_ids"][-1] bos_token_id = tokenizer(tokenizer.bos_token, add_special_tokens=False)["input_ids"][-1] all_ids = set(range(flamingo.lang_encoder.lm_head.out_features)) bad_words_ids = list(all_ids - set(loc_token_ids)) bad_words_ids = [[b] for b in bad_words_ids] loc_word_ids = list(set(loc_token_ids)) loc_word_ids = [[b] for b in loc_word_ids] min_loc_token_id = min(loc_token_ids) max_loc_token_id = max(loc_token_ids) image_ori = image image = image.convert("RGB") width = image.width height = image.height image = image.resize((224, 224)) batch_images = image_processor(image).unsqueeze(0).unsqueeze(1).unsqueeze(0) if idx == 1: prompt = [f"<|#image#|>{tokenizer.pad_token*vis_embed_size}<|#endofimage#|><|#obj#|>{text.rstrip('.')}<|#loc#|>"] bad_words_ids = None max_generation_length = 5 else: prompt = [f"<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|>{text.rstrip('.')}"] bad_words_ids = loc_word_ids max_generation_length = 30 encodings = tokenizer( prompt, padding="longest", truncation=True, return_tensors="pt", max_length=2000, ) input_ids = encodings["input_ids"] attention_mask = encodings["attention_mask"] image_start_index_list = ((input_ids == media_token_id).nonzero(as_tuple=True)[-1] + 1).tolist() image_start_index_list = [[x] for x in image_start_index_list] image_nums = [1] * len(input_ids) outputs = get_outputs( model=flamingo, batch_images=batch_images, attention_mask=attention_mask, max_generation_length=max_generation_length, min_generation_length=4, num_beams=1, length_penalty=1.0, input_ids=input_ids, bad_words_ids=bad_words_ids, image_start_index_list=image_start_index_list, image_nums=image_nums, ) box = [] out_image = image_ori for o in outputs[0]: if o >= min_loc_token_id and o <= max_loc_token_id: box.append(o.item() - min_loc_token_id) if len(box) == 4: break # else: # tqdm.write(f"output: {tokenizer.batch_decode(outputs)}") # tqdm.write(f"prompt: {prompt}") if len(box) == 4: img = cv2.cvtColor(np.array(image_ori), cv2.COLOR_RGB2BGR) out = cv2.rectangle(img, (int(box[0] * width / 1000), int(box[1] * height / 1000)), (int(box[2] * width / 1000), int(box[3] * height / 1000)), color=(255, 0, 255), thickness=2) out = cv2.cvtColor(out, cv2.COLOR_BGR2RGB) out_image = Image.fromarray(out) # else: # tqdm.write(f"output: {tokenizer.batch_decode(outputs)}") # tqdm.write(f"prompt: {prompt}") gen_text = tokenizer.batch_decode(outputs) if idx == 1: return f"Output:{gen_text}", out_image elif idx == 2: return (f"Question: {text.strip()} Answer: {gen_text}") else: return (f"Output:{gen_text}") with gr.Blocks() as demo: gr.Markdown( """ 🍜 Object Centric Pretraining Demo In this demo we showcase the in-context learning and grounding capabilities of the Object-Centric Pretrained model, a large multimodal model. Note that we add two additional demonstrations to the ones presented to improve the demo experience. The model is trained on an interleaved mixture of text, images and bounding box and is able to generate text conditioned on sequences of images/text. """ ) with gr.Accordion("See terms and conditions"): gr.Markdown( """**Please read the following information carefully before proceeding.**This demo does NOT store any personal information on its users, and it does NOT store user queries.""") with gr.Tab("📷 Image Captioning"): with gr.Row(): query_image = gr.Image(type="pil") with gr.Row(): chat_input = gr.Textbox(lines=1, label="Chat Input") text_output = gr.Textbox(value="Output:", label="Model output") run_btn = gr.Button("Run model") def on_click_fn(img,text): return generate(0, img, text) run_btn.click(on_click_fn, inputs=[query_image,chat_input], outputs=[text_output]) with gr.Tab("🦓 Grounding"): with gr.Row(): with gr.Column(scale=1): query_image = gr.Image(type="pil") with gr.Column(scale=1): out_image = gr.Image(type="pil") with gr.Row(): chat_input = gr.Textbox(lines=1, label="Chat Input") text_output = gr.Textbox(value="Output:", label="Model output") run_btn = gr.Button("Run model") def on_click_fn(img, text): return generate(1, img, text) run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output, out_image]) with gr.Tab("🔢 Counting objects"): with gr.Row(): query_image = gr.Image(type="pil") with gr.Row(): chat_input = gr.Textbox(lines=1, label="Chat Input") text_output = gr.Textbox(value="Output:", label="Model output") run_btn = gr.Button("Run model") def on_click_fn(img,text): return generate(0, img, text) run_btn.click(on_click_fn, inputs=[query_image, chat_input], outputs=[text_output]) with gr.Tab("🕵️ Visual Question Answering"): with gr.Row(): query_image = gr.Image(type="pil") with gr.Row(): question = gr.Textbox(lines=1, label="Question") text_output = gr.Textbox(value="Output:", label="Model output") run_btn = gr.Button("Run model") def on_click_fn(img, txt): return generate(2, img, txt) run_btn.click( on_click_fn, inputs=[query_image, question], outputs=[text_output] ) with gr.Tab("🌎 Custom"): gr.Markdown( """### Customize the demonstration by uploading your own images and text samples. ### **Note: Any text prompt you use will be prepended with an 'Output:', so you don't need to include it in your prompt.**""" ) with gr.Row(): query_image = gr.Image(type="pil") with gr.Row(): question = gr.Textbox(lines=1, label="Question") text_output = gr.Textbox(value="Output:", label="Model output") run_btn = gr.Button("Run model") def on_click_fn(img, txt): return generate(2, img, txt) run_btn.click( on_click_fn, inputs=[query_image, question], outputs=[text_output] ) demo.queue(concurrency_count=1) demo.launch()