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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"load model\"\"\"\n",
"import torch\n",
"from PIL import Image, ImageDraw\n",
"from qwen_vl_utils import process_vision_info\n",
"from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor\n",
"import os\n",
"import json\n",
"import codecs \n",
"from peft import PeftModel\n",
"import argparse\n",
"import random \n",
"import re\n",
"\n",
"\n",
"max_pixels_temp = 160*28*28\n",
"max_pixels_narr = 760*28*28\n",
"min_pixels_narr = 240*28*28\n",
"\n",
"\n",
"\n",
"model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
" 'FRank62Wu/ShowUI-Narrator', torch_dtype=\"auto\", device_map=\"cuda\"\n",
")\n",
"\n",
"\n",
"processor = AutoProcessor.from_pretrained('FRank62Wu/ShowUI-Narrator') \n",
"processor.tokenizer.pad_token = processor.tokenizer.eos_token"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<6> and <8>.\n"
]
}
],
"source": [
"\n",
"_SYSTEM_PROMPT='For the given video frames of a GUI action, The frames are decribed in the format of <0> to <{N}>.'\n",
"\n",
"\n",
"\n",
"_SYSTEM_PROMPT_NARR='''You are an ai assistant to narrate the action of the user for the video frames in the following detail.\n",
"'Action': The type of action\n",
"'Element': The target of the action\n",
"'Source': The starting position (Applicable for action type: Drag)\n",
"'Destination': The ending position (Applicable for action type: Drag)\n",
"'Purpose': The intended result of the action\n",
"The Action include left click, right click, double click, drag, or Keyboard type.\n",
"'''\n",
"\n",
"\n",
"Action_no_reference_grounding = [\n",
" 'Describe the start frame and the end frame of the action in this video?',\n",
" 'When Did the action happened in this video? Tell me the start frame and the end frame.',\n",
" 'Locate the start and the end frame of the action in this video',\n",
" \"Observe the cursor in this GUI video, marking start and end frame of the action in video frames.\"\n",
"]\n",
"\n",
"\n",
"Dense_narration_query = ['Narrate the action in the given video.',\n",
" 'Describe the action of the user in the given frames',\n",
" 'Describe the action in this video.',\n",
" 'Narrate the action detail of the user in the video.']\n",
"\n",
"\n",
"\n",
"path_to_data =''\n",
"\n",
"query = _SYSTEM_PROMPT.format(N=9) + ' ' + random.choice(Action_no_reference_grounding)\n",
"messages = [\n",
" {\n",
" 'role': 'user', \n",
" 'content': [\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/0_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/1_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/2_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/3_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/4_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/5_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/6_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/7_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/8_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"image\", \"image\": f\"{path_to_data}/storage/test_benchmark_Act2Cap/303/9_crop.png\",\"max_pixels\": max_pixels_temp},\n",
" {'type':\"text\",'text': query},\n",
" ]\n",
" } \n",
" ]\n",
"\n",
"\n",
"\n",
"## round_1 for temporal grounding\n",
"text = processor.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True,\n",
" )\n",
" \n",
"image_inputs, video_inputs = process_vision_info(messages)\n",
"inputs = processor(\n",
" text=[text],\n",
" images=image_inputs,\n",
" videos=video_inputs,\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
" )\n",
"inputs = inputs.to(\"cuda\")\n",
"generated_ids = model.generate(**inputs, max_new_tokens=128)\n",
"generated_ids_trimmed = [\n",
" out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)\n",
"]\n",
"output_text = processor.batch_decode(\n",
" generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False\n",
")[0]\n",
"\n",
"print(output_text)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"Action\": \"double click\", \"Element\": \"sc2 trans shape button\", \"Source\": null, \"Destination\": null, \"Purpose\": \" Select the SC2 Trans Shape.\"}\n"
]
}
],
"source": [
"# round_2 for dense narration caption\n",
"try:\n",
" matches = re.search(r\"<(\\w+)>.*?<(\\w+)>\", output_text)\n",
" s1, e1 = int(matches.group(1)), int(matches.group(2))\n",
"except:\n",
" s1, e1 =0, 9\n",
" \n",
"\n",
"query = _SYSTEM_PROMPT_NARR + ' ' + random.choice(Dense_narration_query)\n",
"\n",
"selected_images = []\n",
"\n",
"if e1-s1<=3:\n",
" pixels_narr = max_pixels_narr\n",
"else:\n",
" pixels_narr = max_pixels_narr *3 /(e1-s1+1)\n",
" \n",
" \n",
"for idx, each in enumerate(messages[0]['content']):\n",
" if idx >= s1 and idx <= e1:\n",
" new_image = each.copy()\n",
" new_image['max_pixels'] = int(pixels_narr)\n",
" selected_images.append(new_image)\n",
" \n",
" \n",
"messages = [\n",
" {\n",
" 'role': 'user', \n",
" 'content':selected_images+ [{'type':\"text\",'text': query},\n",
" ] \n",
" } \n",
" ]\n",
"\n",
"text = processor.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True,\n",
" )\n",
" \n",
"image_inputs, video_inputs = process_vision_info(messages)\n",
"inputs = processor(\n",
" text=[text],\n",
" images=image_inputs,\n",
" videos=video_inputs,\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
" )\n",
"inputs = inputs.to(\"cuda\")\n",
"generated_ids = model.generate(**inputs, max_new_tokens=128)\n",
"generated_ids_trimmed = [\n",
" out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)\n",
"]\n",
"output_text_narration = processor.batch_decode(\n",
" generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False\n",
")[0]\n",
"\n",
"print(output_text_narration)\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "wqc_qwen2vl",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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