import os import sys from pathlib import Path # os.system("cd transformers && pip install .") os.system("cd multimodal && pip install .") os.system("cd multimodal/YOLOX && pip install .") import numpy as np import torch from PIL import Image import tempfile 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 from open_flamingo.chat.conversation import Chat, CONV_VISION sys.path.append(str(Path(__file__).parent.parent.parent)) TEMP_FILE_DIR = Path(__file__).parent / 'temp' TEMP_FILE_DIR.mkdir(parents=True, exist_ok=True) SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue. You can duplicate and use it with a paid private GPU. Duplicate Space Alternatively, you can also use the demo on our [project page](https://compositionalvlm.github.io/). ''' flamingo, image_processor, tokenizer, vis_embed_size = create_model_and_transforms( "ViT-L-14", "datacomp_xl_s13b_b90k", "EleutherAI/pythia-1.4b", "EleutherAI/pythia-1.4b", location_token_num=1000, lora=False, lora_r=16, use_sam=None, add_visual_token=True, use_format_v2=True, add_box=True, add_pe=False, add_relation=False, enhance_data=False, ) checkpoint_path = "/home/aimos/huggingface/space/demo.pt" # hf_hub_download("chendl/compositional_test", "pythiaS.pt") checkpoint = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"] 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) chat = Chat(flamingo, image_processor, tokenizer, vis_embed_size ) 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( vision_x=batch_images, lang_x=input_ids, attention_mask=attention_mask, labels=None, image_nums=image_nums, image_start_index_list=image_start_index_list, added_bbox_list=None, add_box=False, ) # 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, # ) 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] 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] prebox_token_id = tokenizer("<|#prebox#|>", add_special_tokens=False)["input_ids"][-1] 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"{tokenizer.bos_token}<|#image#|>{tokenizer.pad_token * vis_embed_size}<|#endofimage#|><|#object#|> {text.rstrip('.').strip()}<|#endofobject#|><|#visual#|>"] 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, ) boxes = outputs["boxes"] scores = outputs["scores"] if len(scores) > 0: box = boxes[scores.argmax()]/224 print(f"{box}") if idx == 1: open_cv_image = np.array(image_ori) # Convert RGB to BGR open_cv_image = open_cv_image[:, :, ::-1].copy() box = box*[width,height,width,height] # for box in boxes: open_cv_image = cv2.rectangle(open_cv_image, box[:2].astype(int), box[2:].astype(int), (255, 0, 0), 2) out_image = Image.fromarray(cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2RGB)) return f"Output:{box}", out_image elif idx == 2: gen_text = tokenizer.batch_decode(outputs) return (f"Question: {text.strip()} Answer: {gen_text}") else: gen_text = tokenizer.batch_decode(outputs) return (f"Output:{gen_text}") title = """

Demo of Compositional-VLM

""" description = """

This is the demo of Compositional-VLM. Upload your images and start chatting!

""" article = """
""" # TODO show examples below # ======================================== # Gradio Setting # ======================================== def gradio_reset(chat_state, img_list): if chat_state is not None: chat_state = [] if img_list is not None: img_list = [] return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False), gr.update( value="Upload & Start Chat", interactive=True), chat_state, img_list def build_image(image): if image is None: return None # res = draw_bounding_boxes(image=image, boxes=boxes_to_draw, colors=color_to_draw, width=8) from torchvision.transforms import ToPILImage # res = ToPILImage()(res) _, path = tempfile.mkstemp(suffix='.jpg', dir=TEMP_FILE_DIR) image.save(path) return path def upload_img(gr_img, text_input, chat_state,chatbot): if gr_img is None: return None, None, gr.update(interactive=True), chat_state, None chat_state = [] img_list = [] path = build_image(gr_img) chatbot = chatbot + [[(path,), None]] llm_message = chat.upload_img(gr_img, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update( value="Start Chatting", interactive=False), chat_state, img_list,chatbot def gradio_ask(user_message, chatbot, chat_state,radio): if len(user_message) == 0: return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state chat.ask(user_message, chat_state,radio) chatbot = chatbot + [[user_message, None]] return chatbot, chat_state def gradio_answer(chatbot, chat_state, img_list, radio, text,num_beams, temperature): image = None llm_message,image = \ chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000,radio = radio,text_input = text) chatbot[-1][1] = llm_message if chat_state[-1]["from"]=="gpt": chat_state[-1]["value"] = llm_message if image==None: return "", chatbot, chat_state, img_list else: path = build_image(image) chatbot = chatbot + [[None,(path,)]] return "", chatbot, chat_state, img_list task_template = { "Cap": "Summarize the content of the photo .", "VQA": "For this image , I want a simple and direct answer to my question: ", "REC": "Can you point out in the image and provide the coordinates of its location?", "GC": "Can you give me a description of the region in image ?", "Advanced": "", } with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown(SHARED_UI_WARNING) gr.Markdown(description) gr.Markdown(article) with gr.Row(): with gr.Column(scale=0.5): image = gr.Image(type="pil") upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") clear = gr.Button("Restart") radio = gr.Radio( ["Cap", "VQA", "REC", "Advanced"], label="Task Template", value='Cap', ) num_beams = gr.Slider( minimum=1, maximum=5, value=1, step=1, interactive=True, label="beam search numbers)", ) temperature = gr.Slider( minimum=0.1, maximum=2.0, value=1.0, step=0.1, interactive=True, label="Temperature", ) with gr.Column(): chat_state = gr.State() img_list = gr.State() chatbot = gr.Chatbot(label='Compositional-VLM') # template = gr.Textbox(label='Template', show_label=True, lines=1, interactive=False, # value='Provide a comprehensive description of the image and specify the positions of any mentioned objects in square brackets.') # text_input = gr.Textbox(label='', show_label=True, placeholder="Please upload your image first， then input...", lines=3, # value=None, visible=False, interactive=False) text_input = gr.Textbox(label='User', placeholder='Please upload your image first, then input...', interactive=False) upload_button.click(upload_img, [image, text_input, chat_state,chatbot], [image, text_input, upload_button, chat_state, img_list,chatbot]) text_input.submit(gradio_ask, [text_input, chatbot, chat_state,radio], [chatbot, chat_state]).then( gradio_answer, [chatbot, chat_state, img_list, radio, text_input,num_beams, temperature], [text_input,chatbot, chat_state, img_list] ) clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False) demo.launch(enable_queue=True,share=True) # # 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("πŸ“· 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("πŸ¦“ 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("πŸ”’ 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("πŸ•΅οΈ 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()