test

llava/eval/model_vqa_demo.py

@@ -31,10 +31,8 @@ def get_chunk(lst, n, k):
 def eval_model(image, prompt):
     parser = argparse.ArgumentParser()
     args = parser.parse_args()
-    #args.model_path = '/mnt/disk1/zian/LLaVA/checkpoints/llava-v1.5-7b-my_merged-finetune-mixed-lora-test-0628-1'
-    #args.model_path = None
-    args.model_base = None
-    args.model_path = 'teowu/llava_v1.5_7b_qinstruct_preview_v0.1'
+    args.model_path = 'qwmqza/VLC_llava_v1.5_cot_lora_v0'
+    args.model_base = 'teowu/llava_v1.5_7b_qinstruct_preview_v0.1'
     args.image = image
     args.prompt = prompt
     args.conv_mode = "llava_v1"
@@ -95,4 +93,4 @@ if __name__ == "__main__":
     prompt = 'Please describe this image.'
 
     outputs = eval_model(image, prompt)
-    print("processed.")
+    print(outputs)

llava/eval/model_vqa_vlc.py

@@ -0,0 +1,101 @@
+import argparse
+import torch
+torch.backends.cuda.enable_mem_efficient_sdp(False)
+torch.backends.cuda.enable_flash_sdp(False)
+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
+
+
+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]
+
+
+def eval_model(image, prompt, args):
+
+    args.model_base = 'teowu/llava_v1.5_7b_qinstruct_preview_v0.1'
+    args.image = image
+    args.prompt = prompt
+    args.conv_mode = "llava_v1"
+    args.num_chunks = 1
+    args.chunk_idx = 0
+    args.temperature = 0.2
+    args.top_p = None
+    args.num_beams = 1
+    # Model
+    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.to(dtype=torch.bfloat16)
+
+    idx = 'test'
+    image_file = args.image
+    qs = args.prompt
+    cur_prompt = qs
+    if model.config.mm_use_im_start_end:
+        qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+    else:
+        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+
+    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()
+
+    image = Image.open(image_file).convert('RGB')
+    image_tensor = process_images([image], image_processor, model.config)[0]
+    image_tensor = image_tensor.to(model.device, dtype=torch.bfloat16)
+    print(qs)
+    print(torch.version.cuda)
+
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor.unsqueeze(0).cuda(),
+            image_sizes=[image.size],
+            do_sample=True if args.temperature > 0 else False,
+            temperature=args.temperature,
+            top_p=args.top_p,
+            num_beams=args.num_beams,
+            # no_repeat_ngram_size=3,
+            max_new_tokens=1024,
+            use_cache=True)
+
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+    print(outputs)
+    return outputs
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    args = parser.parse_args()
+    parser.add_argument("--model-path", type=str, default="./checkpoints/VLC_llava_v1.5_cot_lora_v0")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
+    image = '/mnt/disk1/zian/LLaVA/my_test_image/blur_image1.jpg'
+    prompt = 'Please describe this image.'
+    args.model_path = '/mnt/disk1/zian/LLaVA/checkpoints/qinstruct_llava_v1.5_cot_lora_v5'
+
+    outputs = eval_model(image, prompt, args)
+    print(outputs)