Upload 2 files

app.py

@@ -93,7 +93,7 @@ def clear_history(state, state_):
         True, state, state_, state.to_gradio_chatbot(), [])
 
 
-conv_mode = "simple"
+conv_mode = "vicuna_v1"
 handler = Chat(model_path, conv_mode=conv_mode, load_4bit=load_4bit, load_8bit=load_8bit)
 if not os.path.exists("temp"):
     os.makedirs("temp")

demo.py

@@ -0,0 +1,141 @@
+import torch
+from ..constants import *
+from ..conversation import conv_templates, SeparatorStyle
+from ..model.builder import load_pretrained_model
+from ..utils import disable_torch_init
+from ..mm_utils import tokenizer_image_token, KeywordsStoppingCriteria, get_model_name_from_path
+from PIL import Image
+import os
+from decord import VideoReader, cpu
+import numpy as np
+
+
+class Chat:
+    def __init__(self, model_path, conv_mode="simple", load_8bit=False, load_4bit=False):
+        disable_torch_init()
+        model_name = get_model_name_from_path(model_path)
+        self.tokenizer, self.model, self.image_processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit=load_8bit, load_4bit=load_4bit)
+
+        mm_use_im_start_end = getattr(self.model.config, "mm_use_im_start_end", False)
+        mm_use_im_patch_token = getattr(self.model.config, "mm_use_im_patch_token", True)
+        if mm_use_im_patch_token:
+            self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+        if mm_use_im_start_end:
+            self.tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+        self.model.resize_token_embeddings(len(self.tokenizer))
+
+        vision_tower = self.model.get_vision_tower()
+        if not vision_tower.is_loaded:
+            vision_tower.load_model()
+
+        self.image_processor = vision_tower.image_processor
+        self.conv_mode = conv_mode
+        print(self.model)
+
+    def get_prompt(self, qs, state):
+        state.append_message(state.roles[0], qs)
+        state.append_message(state.roles[1], None)
+        return state
+
+    def _get_rawvideo_dec(self, video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224,
+                          video_framerate=1, s=None, e=None):
+        if s is None:
+            start_time, end_time = None, None
+        else:
+            start_time = int(s)
+            end_time = int(e)
+            start_time = start_time if start_time >= 0. else 0.
+            end_time = end_time if end_time >= 0. else 0.
+            if start_time > end_time:
+                start_time, end_time = end_time, start_time
+            elif start_time == end_time:
+                end_time = start_time + 1
+
+        if os.path.exists(video_path):
+            vreader = VideoReader(video_path, ctx=cpu(0))
+        else:
+            print(video_path)
+            raise FileNotFoundError
+
+        fps = vreader.get_avg_fps()
+        f_start = 0 if start_time is None else int(start_time * fps)
+        f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1))
+        num_frames = f_end - f_start + 1
+        if num_frames > 0:
+            sample_fps = int(video_framerate)
+            t_stride = int(round(float(fps) / sample_fps))
+
+            all_pos = list(range(f_start, f_end + 1, t_stride))
+            if len(all_pos) > max_frames:
+                sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)]
+            else:
+                sample_pos = all_pos
+
+            patch_images = [Image.fromarray(f) for f in vreader.get_batch(sample_pos).asnumpy()]
+            return patch_images
+
+    @torch.inference_mode()
+    def generate(self, images_tensor: list, prompt: str, first_run: bool, state):
+        tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
+
+        state = self.get_prompt(prompt, state)
+        prompt = state.get_prompt()
+        print(prompt)
+
+        images_tensor = torch.stack(images_tensor, dim=0)
+        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+
+        temperature = 0.2
+        max_new_tokens = 1024
+
+        stop_str = conv_templates[self.conv_mode].copy().sep if conv_templates[self.conv_mode].copy().sep_style != SeparatorStyle.TWO else \
+        conv_templates[self.conv_mode].copy().sep2
+        keywords = [stop_str]
+        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+
+        with torch.inference_mode():
+            output_ids = model.generate(
+                input_ids,
+                images=images_tensor,
+                do_sample=True,
+                temperature=temperature,
+                num_beams=1,
+                max_new_tokens=max_new_tokens,
+                use_cache=True,
+                stopping_criteria=[stopping_criteria])
+
+        input_token_len = input_ids.shape[1]
+        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+        if n_diff_input_output > 0:
+            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs.strip()
+        if outputs.endswith(stop_str):
+            outputs = outputs[:-len(stop_str)]
+        outputs = outputs.strip()
+
+        print('response', outputs)
+        return outputs, state
+
+
+
+title_markdown = ("""
+<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+  <div>
+    <h1 >Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams</h1>
+  </div>
+</div>
+<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+    <div style="display:flex; gap: 0.25rem;" align="center">
+        <a href="https://invinciblewyq.github.io/vstream-page/"><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+        <a href="https://arxiv.org/abs/2406.08085v1"><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
+        <a href='https://github.com/IVGSZ/Flash-VStream'><img src='https://img.shields.io/badge/Github-Code-blue'></a>
+    </div>
+</div>
+""")
+
+block_css = """
+#buttons button {
+    min-width: min(120px,100%);
+}
+"""