Update app.py

app.py

@@ -1,222 +1,3 @@
-# import os
-# import argparse
-# import numpy as np
-# from PIL import Image
-# import torch
-# import torchvision.transforms as T
-# from transformers import AutoTokenizer
-# import gradio as gr
-# from resnet50 import build_model
-# from utils import generate_similiarity_map, post_process, load_tokenizer, build_transform_R50
-# from utils import IMAGENET_MEAN, IMAGENET_STD
-# from internvl.train.dataset import dynamic_preprocess
-# from internvl.model.internvl_chat import InternVLChatModel
-# import spaces
-
-# # 模型配置
-# CHECKPOINTS = {
-#     "TokenFD_4096_English_seg": "TongkunGuan/TokenFD_4096_English_seg",
-#     "TokenFD_2048_Bilingual_seg": "TongkunGuan/TokenFD_2048_Bilingual_seg",
-# }
-
-# # 全局变量
-# HF_TOKEN = os.getenv("HF_TOKEN")
-# current_vis = []
-# current_bpe = []
-# current_index = 0
-
-
-# def load_model(check_type):
-#     # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-#     device = torch.device("cuda")
-#     if check_type == 'R50':
-#         tokenizer = load_tokenizer('tokenizer_path')
-#         model = build_model(argparse.Namespace()).eval()
-#         model.load_state_dict(torch.load(CHECKPOINTS['R50'], map_location='cpu')['model'])
-#         transform = build_transform_R50(normalize_type='imagenet')
-
-#     elif check_type == 'R50_siglip':
-#         tokenizer = load_tokenizer('tokenizer_path')
-#         model = build_model(argparse.Namespace()).eval()
-#         model.load_state_dict(torch.load(CHECKPOINTS['R50_siglip'], map_location='cpu')['model'])
-#         transform = build_transform_R50(normalize_type='imagenet')
-
-#     elif 'TokenFD' in check_type:
-#         model_path = CHECKPOINTS[check_type]
-#         tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False, use_auth_token=HF_TOKEN)
-#         model = InternVLChatModel.from_pretrained(model_path, torch_dtype=torch.bfloat16).eval()
-#         transform = T.Compose([
-#             T.Lambda(lambda img: img.convert('RGB')),
-#             T.Resize((224, 224)),
-#             T.ToTensor(),
-#             T.Normalize(IMAGENET_MEAN, IMAGENET_STD)
-#         ])
-    
-#     return model.to(device), tokenizer, transform, device
-
-# def process_image(model, tokenizer, transform, device, check_type, image, text):
-#     global current_vis, current_bpe, current_index
-#     src_size = image.size
-#     if 'TokenOCR' in check_type:
-#         images, target_ratio = dynamic_preprocess(image, min_num=1, max_num=12, 
-#                                                   image_size=model.config.force_image_size,
-#                                                   use_thumbnail=model.config.use_thumbnail,
-#                                                   return_ratio=True)
-#         pixel_values = torch.stack([transform(img) for img in images]).to(device)
-#     else:
-#         pixel_values = torch.stack([transform(image)]).to(device)
-#         target_ratio = (1, 1)
-
-#     # 文本处理
-#     text += ' '
-#     input_ids = tokenizer(text)['input_ids'][1:]
-#     input_ids = torch.tensor(input_ids, device=device)
-    
-#     # 获取嵌入
-#     with torch.no_grad():
-#         if 'R50' in check_type:
-#             text_embeds = model.language_embedding(input_ids)
-#         else:
-#             text_embeds = model.tok_embeddings(input_ids)
-        
-#         vit_embeds, size1 = model.forward_tokenocr(pixel_values.to(torch.bfloat16).to(device))
-#         print("vit_embeds",vit_embeds)
-#         print("vit_embeds,shape",vit_embeds.shape)
-#         print("target_ratio",target_ratio)
-#         print("check_type",check_type)
-#         vit_embeds, size2 = post_process(vit_embeds, target_ratio, check_type)
-        
-#         # 计算相似度
-#         text_embeds = text_embeds / text_embeds.norm(dim=-1, keepdim=True)
-#         vit_embeds = vit_embeds / vit_embeds.norm(dim=-1, keepdim=True)
-#         similarity = text_embeds @ vit_embeds.T
-#         resized_size = size1 if size1 is not None else size2
-
-#     # print(f"text_embeds shape: {text_embeds.shape}, numel: {text_embeds.numel()}") # text_embeds shape: torch.Size([4, 2048]), numel: 8192
-#     # print(f"vit_embeds shape: {vit_embeds.shape}, numel: {vit_embeds.numel()}") # vit_embeds shape: torch.Size([9728, 2048]), numel: 19922944
-#     # print(f"similarity shape: {similarity.shape}, numel: {similarity.numel()}")# similarity shape: torch.Size([4, 9728]), numel: 38912
-
-
-#     # 生成可视化
-#     attn_map = similarity.reshape(len(text_embeds), resized_size[0], resized_size[1])
-#     # attn_map = similarity.reshape(len(text_embeds), *target_ratio)
-#     all_bpe_strings = [tokenizer.decode(input_id) for input_id in input_ids]
-#     current_vis = generate_similiarity_map([image], attn_map, 
-#                                            [tokenizer.decode([i]) for i in input_ids], 
-#                                            [], target_ratio, src_size)
-    
-#     current_bpe = [tokenizer.decode([i]) for i in input_ids]
-#     # current_bpe[-1] = 'Input text'
-#     current_bpe[-1] = text
-#     print("current_vis",len(current_vis))
-#     print("current_bpe",len(current_bpe))
-#     return image, current_vis[0], current_bpe[0]
-
-# # 事件处理函数
-# def update_index(change):
-#     global current_vis, current_bpe, current_index
-#     current_index = max(0, min(len(current_vis) - 1, current_index + change))
-#     return current_vis[current_index], format_bpe_display(current_bpe[current_index])
-
-# def format_bpe_display(bpe):
-#     # 使用HTML标签来设置字体大小、颜色，加粗，并居中
-#     return f"<div style='text-align:center; font-size:20px;'><strong>Current BPE: <span style='color:red;'>{bpe}</span></strong></div>"
-
-# def update_slider_index(x):
-#     print(f"x: {x}, current_vis length: {len(current_vis)}, current_bpe length: {len(current_bpe)}")
-#     if 0 <= x < len(current_vis) and 0 <= x < len(current_bpe):
-#         return current_vis[x], format_bpe_display(current_bpe[x])
-#     else:
-#         return None, "索引超出范围"
-
-# # Gradio界面
-# with gr.Blocks(title="BPE Visualization Demo") as demo:
-#     gr.Markdown("## BPE Visualization Demo - TokenFD基座模型能力可视化")
-    
-#     with gr.Row():
-#         with gr.Column(scale=0.5):
-#             model_type = gr.Dropdown(
-#                 choices=["TokenFD_4096_English_seg", "TokenFD_2048_Bilingual_seg", "R50", "R50_siglip"],
-#                 label="Select model type",
-#                 value="TokenOCR_4096_English_seg"  # 设置默认值为第一个选项
-#             )
-#             image_input = gr.Image(label="Upload images", type="pil")
-#             text_input = gr.Textbox(label="Input text")
-
-#             run_btn = gr.Button("RUN")
-            
-#             gr.Examples(
-#                 examples=[
-#                     [os.path.join("examples", "examples0.jpg"), "Veterans and Benefits"],
-#                     [os.path.join("examples", "examples1.jpg"), "Refreshers"],
-#                     [os.path.join("examples", "examples2.png"), "Vision Transformer"]
-#                 ],
-#                 inputs=[image_input, text_input],
-#                 label="Sample input"
-#             )
-        
-#         with gr.Column(scale=2):
-#             gr.Markdown("<p style='font-size:20px;'><span style='color:red;'>If the input text is not included in the image</span>, the attention map will show a lot of noise (the actual response value is very low), since we normalize the attention map according to the relative value.</p>")
-
-#             with gr.Row():
-#                 orig_img = gr.Image(label="Original picture", interactive=False)
-#                 heatmap = gr.Image(label="BPE visualization", interactive=False)
-            
-#             with gr.Row() as controls:
-#                 prev_btn = gr.Button("⬅ Last", visible=False)
-#                 index_slider = gr.Slider(0, 1, value=0, step=1, label="BPE index", visible=False)
-#                 next_btn = gr.Button("⮕ Next", visible=False)
-            
-#             bpe_display = gr.Markdown("Current BPE: ", visible=False)
-
-#     # 事件处理
-#     @spaces.GPU
-#     def on_run_clicked(model_type, image, text):
-#         global current_vis, current_bpe, current_index
-#         current_index = 0  # Reset index when new image is processed
-#         image, vis, bpe = process_image(*load_model(model_type), model_type, image, text)
-#         # Update the slider range and set value to 0
-#         slider_max_val = len(current_bpe) - 1
-#         bpe_text = format_bpe_display(bpe)
-#         print("current_vis",len(current_vis))
-#         print("current_bpe",len(current_bpe))
-#         return image, vis, bpe_text, slider_max_val
-
-#     run_btn.click(
-#         on_run_clicked,
-#         inputs=[model_type, image_input, text_input],
-#         outputs=[orig_img, heatmap, bpe_display, index_slider],
-#     ).then(
-#         lambda max_val: (gr.update(visible=True), gr.update(visible=True, maximum=max_val, value=0), gr.update(visible=True), gr.update(visible=True)),
-#         inputs=index_slider,
-#         outputs=[prev_btn, index_slider, next_btn, bpe_display],
-#     )
-    
-#     prev_btn.click(
-#         lambda: (*update_index(-1), current_index),
-#         outputs=[heatmap, bpe_display, index_slider]
-#     )
-    
-#     next_btn.click(
-#         lambda: (*update_index(1), current_index),
-#         outputs=[heatmap, bpe_display, index_slider]
-#     )
-    
-#     # index_slider.change(
-#     #     lambda x: (current_vis[x], format_bpe_display(current_bpe[x])) if 0<=x<len(current_vis else (None,"Invaild")
-#     #     inputs=index_slider,
-#     #     outputs=[heatmap, bpe_display]
-#     # )
-
-#     index_slider.change(
-#             update_slider_index,
-#             inputs=index_slider,
-#             outputs=[heatmap, bpe_display]
-#         )
-
-# if __name__ == "__main__":
-#     demo.launch()
-
 import os
 import argparse
 import numpy as np
@@ -240,8 +21,9 @@ CHECKPOINTS = {
 
 # 全局变量
 HF_TOKEN = os.getenv("HF_TOKEN")
-current_vis = []
-current_bpe = []
+current_vis = []  # 存储所有 heatmap
+current_bpe = []  # 存储所有 BPE
+current_index = 0  # 当前显示的 heatmap 和 BPE 的索引
 
 def load_model(check_type):
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -271,7 +53,7 @@ def load_model(check_type):
     return model.to(device), tokenizer, transform, device
 
 def process_image(model, tokenizer, transform, device, check_type, image, text):
-    global current_vis, current_bpe
+    global current_vis, current_bpe, current_index
     src_size = image.size
     if 'TokenOCR' in check_type:
         images, target_ratio = dynamic_preprocess(image, min_num=1, max_num=12, 
@@ -313,11 +95,17 @@ def process_image(model, tokenizer, transform, device, check_type, image, text):
     
     current_bpe = [tokenizer.decode([i]) for i in input_ids]
     current_bpe[-1] = text
-    return image, current_vis, current_bpe
+    current_index = 0  # 重置索引
+    return image, current_vis[current_index], format_bpe_display(current_bpe[current_index])
 
 def format_bpe_display(bpe):
     return f"<div style='text-align:center; font-size:20px;'><strong>Current BPE: <span style='color:red;'>{bpe}</span></strong></div>"
 
+def update_index(change):
+    global current_vis, current_bpe, current_index
+    current_index = max(0, min(len(current_vis) - 1, current_index + change))
+    return current_vis[current_index], format_bpe_display(current_bpe[current_index])
+
 # Gradio界面
 with gr.Blocks(title="BPE Visualization Demo") as demo:
     gr.Markdown("## BPE Visualization Demo - TokenFD基座模型能力可视化")
@@ -351,23 +139,33 @@ with gr.Blocks(title="BPE Visualization Demo") as demo:
                 orig_img = gr.Image(label="Original picture", interactive=False)
                 heatmap = gr.Image(label="BPE visualization", interactive=False)
             
-            bpe_display = gr.Markdown("Current BPE: ", visible=False)
+            with gr.Row():
+                prev_btn = gr.Button("⬅ Previous")
+                next_btn = gr.Button("Next ⮕")
+            
+            bpe_display = gr.Markdown("Current BPE: ", visible=True)
 
     # 事件处理
     @spaces.GPU
     def on_run_clicked(model_type, image, text):
-        global current_vis, current_bpe
+        global current_vis, current_bpe, current_index
         image, vis, bpe = process_image(*load_model(model_type), model_type, image, text)
-        bpe_text = format_bpe_display(bpe)
-        return image, vis[0], bpe_text
+        return image, vis, bpe
 
     run_btn.click(
         on_run_clicked,
         inputs=[model_type, image_input, text_input],
         outputs=[orig_img, heatmap, bpe_display],
-    ).then(
-        lambda: (gr.update(visible=True)),
-        outputs=[bpe_display],
+    )
+
+    prev_btn.click(
+        lambda: update_index(-1),
+        outputs=[heatmap, bpe_display]
+    )
+
+    next_btn.click(
+        lambda: update_index(1),
+        outputs=[heatmap, bpe_display]
     )
 
 if __name__ == "__main__":