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Running
on
Zero
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 | |
# 模型配置 | |
CHECKPOINTS = { | |
"TokenOCR-4096-English-seg": "/path/to/TokenOCR_4096_English_seg", | |
"TokenOCR-2048-Bilingual-seg": "/path/to/TokenOCR_2048_Binlinual_seg", | |
"R50":"model/checkpoint.pth", | |
"R50_siglip": "/path/to/R50_siglip_checkpoint.pth" | |
} | |
# 全局变量 | |
current_vis = [] | |
current_bpe = [] | |
current_index = 0 | |
def load_model(check_type): | |
device = torch.device("cpu") | |
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 'TokenOCR' in check_type: | |
model_path = CHECKPOINTS[check_type] | |
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False) | |
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 | |
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(device)) | |
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 | |
return image, current_vis[0], current_bpe[0] | |
# 事件处理函数 | |
def update_index(change): | |
global 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>" | |
# Gradio界面 | |
with gr.Blocks(title="BPE Visualization Demo") as demo: | |
gr.Markdown("## BPE Visualization Demo - TokenOCR基座模型能力可视化") | |
with gr.Row(): | |
with gr.Column(scale=0.5): | |
model_type = gr.Dropdown( | |
choices=["TokenOCR-4096-English-seg", "TokenOCR-2048-Bilingual-seg", "R50", "R50_siglip"], | |
label="Select model type", | |
value="R50" # 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 | |
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) | |
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])), | |
inputs=index_slider, | |
outputs=[heatmap, bpe_display] | |
) | |
if __name__ == "__main__": | |
demo.launch() |