test

demo.py

@@ -0,0 +1,164 @@
+import argparse
+import random
+
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import gradio as gr
+
+from minigpt4.common.config import Config
+from minigpt4.common.dist_utils import get_rank
+from minigpt4.common.registry import registry
+from eval_scripts.conversation import Chat, CONV_VISION
+# NOTE&TODO: put this before minigpt4 import will cause circular import 
+# possibly because `imagebind` imports `minigpt4` and `minigpt4` also imports `imagebind`
+from imagebind.models.image_bind import ModalityType
+
+# imports modules for registration
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Qualitative")
+    parser.add_argument("--cfg-path", required=True, 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))
+
+# TODO: Fix hard-coding  `cc12m`
+vis_processor_cfg = cfg.datasets_cfg.cc12m.vis_processor.train
+vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
+processors = {ModalityType.VISION: vis_processor}
+chat = Chat(model, processors, device='cuda:{}'.format(args.gpu_id))
+print('Initialization Finished')
+
+
+# ========================================
+#             Gradio Setting
+# ========================================
+
+def gradio_reset(chat_state, emb_list):
+    if chat_state is not None:
+        chat_state.messages = []
+    if emb_list is not None:
+        emb_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, emb_list
+
+
+def upload_img(gr_img, text_input, chat_state):
+    if gr_img is None:
+        return None, None, gr.update(interactive=True), chat_state, None
+    chat_state = CONV_VISION.copy()
+    emb_list = []
+    chat.upload_img(gr_img, chat_state, emb_list)
+    return gr.update(interactive=False), \
+           gr.update(interactive=True, placeholder='Type and press Enter'), \
+           gr.update(value="Start Chatting", interactive=False), \
+           chat_state, emb_list
+
+
+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, emb_list, num_beams, temperature):
+    llm_message = chat.answer(conversation=chat_state,
+                              emb_list=emb_list,
+                              num_beams=num_beams,
+                              temperature=temperature,
+                              max_new_tokens=300,
+                              max_length=2000)[0]
+    chatbot[-1][1] = llm_message
+    return chatbot, chat_state, emb_list
+
+
+title = """<h1 align="center">Demo of BindGPT-4</h1>"""
+description = """<h3>This is the demo of BindGPT-4. Upload your images and start chatting!</h3>"""
+# article = """<p><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a></p><p><a href='https://raw.githubusercontent.com/Vision-CAIR/MiniGPT-4/main/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></p>
+# """
+
+# TODO show examples below
+
+with gr.Blocks() as demo:
+    gr.Markdown(title)
+    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")
+
+            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()
+            emb_list = gr.State()
+            chatbot = gr.Chatbot(label='BindGPT-4')
+            text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
+
+    upload_button.click(upload_img, [image, text_input, chat_state],
+                        [image, text_input, upload_button, chat_state, emb_list])
+
+    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
+        gradio_answer, [chatbot, chat_state, emb_list, num_beams, temperature], [chatbot, chat_state, emb_list]
+    )
+    clear.click(gradio_reset, [chat_state, emb_list], [chatbot, image, text_input, upload_button, chat_state, emb_list],
+                queue=False)
+
+demo.launch(share=True, enable_queue=True)

minigpt4/models/bind_gpt4.py

@@ -45,7 +45,7 @@ class BindGPT4(BaseModel):
         self.multimodal_encoder = imagebind_huge(pretrained=True, freeze_imagebind=freeze_imagebind)
         print('Loading ImageBind Done')
 
-        print('Loading LLAMA')
+        print(f'Loading LLAMA from {llama_model}')
         self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
         self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token