test v1

video_llama/app.py

@@ -1,192 +0,0 @@
-"""
-Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py
-"""
-import argparse
-import os
-import random
-
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-import gradio as gr
-
-from video_llama.common.config import Config
-from video_llama.common.dist_utils import get_rank
-from video_llama.common.registry import registry
-from video_llama.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle
-import decord
-decord.bridge.set_bridge('torch')
-
-#%%
-# imports modules for registration
-from video_llama.datasets.builders import *
-from video_llama.models import *
-from video_llama.processors import *
-from video_llama.runners import *
-from video_llama.tasks import *
-
-#%%
-def parse_args():
-    parser = argparse.ArgumentParser(description="Demo")
-    parser.add_argument("--cfg-path", default='eval_configs/video_llama_eval.yaml', help="path to configuration file.")
-    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
-    parser.add_argument(
-        "--options",
-        nargs="+",
-        help="override some settings in the used config, the key-value pair "
-        "in xxx=yyy format will be merged into config file (deprecate), "
-        "change to --cfg-options instead.",
-    )
-    args = parser.parse_args()
-    return args
-
-
-def setup_seeds(config):
-    seed = config.run_cfg.seed + get_rank()
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    cudnn.benchmark = False
-    cudnn.deterministic = True
-
-
-# ========================================
-#             Model Initialization
-# ========================================
-
-print('Initializing Chat')
-args = parse_args()
-cfg = Config(args)
-
-model_config = cfg.model_cfg
-model_config.device_8bit = args.gpu_id
-model_cls = registry.get_model_class(model_config.arch)
-model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
-
-vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train
-vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
-print('Initialization Finished')
-
-# ========================================
-#             Gradio Setting
-# ========================================
-
-def gradio_reset(chat_state, img_list):
-    if chat_state is not None:
-        chat_state.messages = []
-    if img_list is not None:
-        img_list = []
-    return None, gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
-
-def upload_imgorvideo(gr_video, gr_img, text_input, chat_state):
-    if gr_img is None and gr_video is None:
-        return None, None, None, gr.update(interactive=True), chat_state, None
-    elif gr_img is not None and gr_video is None:
-        print(gr_img)
-        chat_state = Conversation(
-            system= "You are able to understand the visual content that the user provides."
-           "Follow the instructions carefully and explain your answers in detail.",
-            roles=("Human", "Assistant"),
-            messages=[],
-            offset=0,
-            sep_style=SeparatorStyle.SINGLE,
-            sep="###",
-        )
-        img_list = []
-        llm_message = chat.upload_img(gr_img, chat_state, img_list)
-        return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
-    elif gr_video is not None and gr_img is None:
-        print(gr_video)
-        chat_state = default_conversation.copy()
-        chat_state = Conversation(
-            system= "You are able to understand the visual content that the user provides."
-           "Follow the instructions carefully and explain your answers in detail.",
-            roles=("Human", "Assistant"),
-            messages=[],
-            offset=0,
-            sep_style=SeparatorStyle.SINGLE,
-            sep="###",
-        )
-        img_list = []
-        llm_message = chat.upload_video(gr_video, chat_state, img_list)
-        return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
-    else:
-        # img_list = []
-        return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None
-
-def gradio_ask(user_message, chatbot, chat_state):
-    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)
-    chatbot = chatbot + [[user_message, None]]
-    return '', chatbot, chat_state
-
-
-def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
-    llm_message = chat.answer(conv=chat_state,
-                              img_list=img_list,
-                              num_beams=num_beams,
-                              temperature=temperature,
-                              max_new_tokens=300,
-                              max_length=2000)[0]
-    chatbot[-1][1] = llm_message
-    print(chat_state.get_prompt())
-    print(chat_state)
-    return chatbot, chat_state, img_list
-
-title = """<h1 align="center">Demo of Video-LLaMA</h1>"""
-description = """<h3>This is the demo of Video-LLaMA. Upload your images/videos and start chatting!</h3>"""
-
-
-#TODO show examples below
-
-with gr.Blocks() as demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-
-    with gr.Row():
-        with gr.Column(scale=0.5):
-            video = gr.Video()
-            image = gr.Image(type="pil")
-
-            upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
-            clear = gr.Button("Restart")
-            
-            num_beams = gr.Slider(
-                minimum=1,
-                maximum=10,
-                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='Video-LLaMA')
-            text_input = gr.Textbox(label='User', placeholder='Please upload your image/video first', interactive=False)
-
-    
-    upload_button.click(upload_imgorvideo, [video, image, text_input, chat_state], [video, image, text_input, upload_button, chat_state, img_list])
-    
-    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
-        gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
-    )
-    clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, image, text_input, upload_button, chat_state, img_list], queue=False)
-
-demo.launch(share=False, enable_queue=False)
-
-# %%

video_llama/ckpt/blip2_pretrained_flant5xxl.pth

@@ -1,3 +0,0 @@
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-size 433481467

video_llama/ckpt/finetune-vicuna7b-v2-nofrozen_imageQ.pth

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-oid sha256:46af76d307c14d28c56534e4bf8654343e5512aa1285fc1c1fdb5728c418e7ca
-size 623104000

video_llama/ckpt/pretrain-billa7b-zh.pth

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f50a51db3055e1be6461f6dec833fbbbba28650287d26c8787664c8ee31dcf0f
-size 265435689