Upload inference_llava_mistral.py with huggingface_hub

inference_llava_mistral.py

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+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
+
+from PIL import Image
+import math
+########################################################
+os.environ["CUDA_VISIBLE_DEVICES"] = "7"
+################################################∂########
+
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_lora8_demo'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_iter2000_0709_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_10000_multidig_v1_P020_0709_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_10000_multidig_v1_e1_0709_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_motivation_iter400_e3_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_motivation_iter400_merge'
+# args_model_path =  '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e1000_merge'
+# args_model_path =  '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e050_merge'
+# args_model_path =  '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e020_merge'
+# args_model_path =  '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e010_merge'
+# args_model_path =  '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e005_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_single_e5_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b-aitw_merge'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-vicuna-7b'
+# args_model_path = '/code/ICLR_2024/Model/llava-v1.6-mistral-7b'
+
+
+args_model_path = '/scratch/zbz5349/ICLR_2024/LLaVA_Mobile_V1/checkpoints/llava-v1.6-7b-task-lora_three_blip_e1_lre4_mistral_0823'
+
+# args_model_path = '/data/zbz5349/ICLR_2024/checkpoints/llava-v1.6-7b-task-lora_all_e1_mistral_0807'
+# args_model_path = '/data/zbz5349/ICLR_2024/checkpoints/llava-v1.6-7b-task-lora_all_e5_0802'
+
+# args_model_path = '/data/zbz5349/ICLR_2024/checkpoints/llava-v1.6-7b-task-lora_all_blip_e10_H800_0806'
+
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_general_dual_iter2000_0715_03'
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_general_dual_iter2000_0715'
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_general_dual_non_iter2000_0715'
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_template_H800'
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_template'
+# args_model_path = '/code/ICLR_2024/LLaVA/checkpoints/llava-v1.6-7b-task-lora_single_blip_iter2000_0709'
+# args_model_path = '/code/ICLR_2024/Model/checkpoints/llava-v1.6-7b-task-lora_single_blip_a100_e1000_0703'
+
+
+args_model_base = '/data/zbz5349/ICLR_2024/Model/llava-v1.6-mistral-7b'
+# args_model_base = '/scratch/zbz5349/ICLR_2024/LLaVA_Mobile_V1/init_model/llava-v1.6-vicuna-7b'
+# args_model_base = 'xtuner/llava-phi-3-mini'
+
+
+
+disable_torch_init()
+model_path = os.path.expanduser(args_model_path)
+model_name = get_model_name_from_path(model_path)
+tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args_model_base, model_name)
+
+# ############################
+# # model = model.bfloat16()
+# tokenizer.pad_token = "[PAD]"
+# tokenizer.padding_side = "left"
+# ############################
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+import json
+
+def read_json(file_path): 
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = json.load(file)
+    return data
+
+def write_json(file_path, data):
+    with open(file_path, 'w', encoding='utf-8') as file:
+        json.dump(data, file, ensure_ascii=False, indent=4)
+
+
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/blip/single_blip_test_llava_800_caption_history_without_label_v3.json'
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/blip/single_blip_test_llava_800.json'
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/blip/single_blip_test_llava.json'
+
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/blip/single_blip_test_llava_800.json'
+# args_answers_file = '/code/ICLR_2024/Auto-GUI/dataset/json/single_blip_test_llava_800_all_e1_H800.json' 
+
+args_question_file = '/data/zbz5349/ICLR_2024/data/general_blip_test_llava.json'
+args_answers_file = '/data/zbz5349/ICLR_2024/json/general_blip_test_llava_three_lre4_e1_mistral_0822.json'
+
+
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/blip/install_blip_test_llava.json'
+# args_answers_file = '/code/ICLR_2024/Auto-GUI/dataset/json/install_blip_test_llava_all_e1_H800.json' 
+
+
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/json/general_blip_test_llava_dual_non_400.json'
+# args_answers_file = '/code/ICLR_2024/Auto-GUI/dataset/json/general_blip_test_llava_dual_non_400_2000iter.json' 
+
+# args_question_file = '/code/ICLR_2024/Auto-GUI/dataset/json/general_blip_test_llava_dual_400.json'
+# args_answers_file = '/code/ICLR_2024/Auto-GUI/dataset/json/general_blip_test_llava_dual_400_2000iter.json' 
+
+
+
+args_num_chunks = 1
+args_chunk_idx = 0
+
+questions = json.load(open(os.path.expanduser(args_question_file), "r"))
+questions = get_chunk(questions, args_num_chunks, args_chunk_idx)
+answers_file = os.path.expanduser(args_answers_file)
+os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+ans_file = open(answers_file, "w")
+
+
+
+args_image_folder = '/data/zbz5349/ICLR_2024/data/'
+args_single_pred_prompt = True 
+args_conv_mode = "llava_v0"
+
+
+
+right_answer = []
+for i, line in enumerate(tqdm(questions)):
+
+
+    idx = line["id"]
+    question = line['conversations'][0]
+    qs = question['value'].replace('<image>', '').strip()
+    cur_prompt = qs
+
+    if 'image' in line:
+        image_file = line["image"]
+        image = Image.open(os.path.join(args_image_folder, image_file))
+        image_tensor = process_images([image], image_processor, model.config)[0]
+        images = image_tensor.unsqueeze(0).half().cuda()
+        image_sizes = [image.size]
+        if getattr(model.config, 'mm_use_im_start_end', False):
+            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+        else:
+            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+        cur_prompt = '<image>' + '\n' + cur_prompt
+    else:
+        images = None
+        image_sizes = None
+
+    if args_single_pred_prompt:
+        # qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
+        # cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly."
+
+        qs = qs + '\n'  
+        cur_prompt = cur_prompt  
+
+
+    conv = conv_templates[args_conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+
+
+
+    # import pdb; pdb.set_trace()
+    # try:
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=images,
+            image_sizes=image_sizes,
+            do_sample=True if 0.2 > 0 else False,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True, 
+        )
+
+    # except:
+        # continue
+        # data = [[1, 9123, 8402, 16747, 29918, 3149]]
+        # output_ids = torch.tensor(data, device=input_ids.device)
+
+
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+
+    # import pprint
+    # pprint.pprint(outputs)
+    # print('-------------------------------')
+    # pprint.pprint(line['conversations'][1]['value'])
+    # print('===========================================================')
+
+    temp = {}
+    temp['gt'] = line['conversations'][1]['value']
+    temp['pred'] = outputs
+    right_answer.append(temp)
+
+    write_json(args_answers_file, right_answer)