llm_agent

.gitignore

@@ -36,7 +36,8 @@ tracking/data/boxes_imgs/*
 tracking/data/trackfeats/*
 tracking/data/tracks/*
 tracking/data/handlocal/*
-contrast/feat_extract/*
+contrast/feat_extract/model/__pycache__/*
+std_img*
 .gitignore
 */__pycache__/*
 ckpts/*

Qwen_agent.py

@@ -1,15 +1,18 @@
 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from accelerate import init_empty_weights, load_checkpoint_in_model
 from stream_pipeline import stream_pipeline
 from PIL import Image
 from io import BytesIO
 import torch
 import ast
 import requests
+import random
 
 # default: Load the model on the available device(s)
 model = Qwen2VLForConditionalGeneration.from_pretrained(
     "Qwen/Qwen2-VL-7B-Instruct",
-    torch_dtype="auto",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
     device_map="auto"
 )
 
@@ -30,6 +33,7 @@ def qwen_prompt(img_list, messages):
     inputs = inputs.to("cuda")
 
     # Inference: Generation of the output
+    with torch.no_grad():
         generated_ids = model.generate(**inputs, max_new_tokens=256)
     generated_ids_trimmed = [
         out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
@@ -37,16 +41,23 @@ def qwen_prompt(img_list, messages):
     output_text = processor.batch_decode(
         generated_ids_trimmed, add_special_tokens=False, skip_special_tokens=True, clean_up_tokenization_spaces=False
     )
+    del inputs
+    del generated_ids
+    del generated_ids_trimmed
+    torch.cuda.empty_cache()
 
     return output_text[0]
 
+
 def get_best_image(track_imgs):
+    if len(track_imgs) >= 5:
+        track_imgs = random.sample(track_imgs, 5)
     img_frames = []
     for i in range(len(track_imgs)):
         content = {}
         content['type'] = 'image'
         content['min_pixels'] = 224 * 224
-        content['max_pixels'] = 1280 * 28 * 28
+        content['max_pixels'] = 800 * 800
         img_frames.append(content)
 
     messages = [
@@ -66,6 +77,8 @@ def get_best_image(track_imgs):
 
     output_text = qwen_prompt(track_imgs, messages)
     output_dict = ast.literal_eval(output_text.strip('```python\n'))
+    if output_dict['index'] > len(track_imgs):
+        output_dict['index'] = len(track_imgs)
     best_img = track_imgs[output_dict['index'] - 1]
     
     return best_img
@@ -74,42 +87,48 @@ def get_product_description(std_img, track_imgs):
     messages = [
         {
             "role": "system",
-            "content": "你是一个在超市工作的chatbot，你现在需要提取商品的信息，信息需要按照以下python dict的格式输出: \
+            "content": "你是一个在超市工作的chatbot，你现在需要提取图像中商品的信息，信息需要按照以下python dict的格式输出，如果 \
-            {\
+            信息模糊不清则输出'未知': \
-                'Text': 商品中提取出的文字信息,  \
+            {    \
-                'Color': 商品的颜色,  \
+                'item1': {\
-                'Shape': 商品的形状,  \
+                    'Text': 第一张图像中商品中提取出的文字信息,  \
-                'Material': 商品的材质,  \
+                    'Color': 第一张图像中商品的颜色,  \
-                'Category': 商品的类别,  \
+                    'Shape': 第一张图像中商品的形状,  \
-                'is_Same': 如果比对的两件商品的['Text', 'Color', 'Shape', 'Material', 'Category']属性中至少有3个相同则输出True，\
+                    'Material': 第一张图像中商品的材质,  \
-                    否则输出False,  \
+                    'Category': 第一张图像中商品的类别,  \
+                } \
+                'item2': {\
+                    'Text': 第二张图像中商品中提取出的文字信息,  \
+                    'Color': 第二张图像中商品的颜色,  \
+                    'Shape': 第二张图像中商品的形状,  \
+                    'Material': 第二张图像中商品的材质,  \
+                    'Category': 第二张图像中商品的类别,  \
+                } \
+                'is_Same': 首先判断'Color'是否一致，如果不一致则返回False，如果一致则判断是否以上两个dict的['Text', 'Shape', 'Material', 'Category']key中至少有3个相同则输出True，\
+                    否则输出False。  \
             } \
             "
         },
-        {
-            "role": "system",
-            "content": [
-                {
-                    "type": "image",
-                    "min_pixels": 224 * 224,
-                    "max_pixels": 1280 * 28 * 28,
-                },
-            ],
-        },
-        {
-            "role": "system",
-            "content": [
-                {
-                    "type": "image",
-                    "min_pixels": 224 * 224,
-                    "max_pixels": 1280 * 28 * 28,
-                },
-            ],
-        },
         {
             "role": "user",
-            "content": "以python dict的形式输出第二张图像的比对信息。"
+            "content": [
-        }
+                {
+                    "type": "image",
+                    "min_pixels": 224 * 224,
+                    "max_pixels": 800 * 800,
+                },
+                {
+                    "type": "image",
+                    "min_pixels": 224 * 224,
+                    "max_pixels": 800 * 800,
+                },
+            ],
+        },
+        # {
+            # "role": "user",
+            # "content": "以python dict的形式输出第二张图像的比对信息。"
+            # "content": "输出一个list，list的内容包含两张图像提取出的dict信息。"
+        # }
     ]
     best_img = get_best_image(track_imgs)
     if std_img is not None:
@@ -124,10 +143,13 @@ def get_product_description(std_img, track_imgs):
 
 def item_analysis(stream_dict):
     track_imgs = stream_pipeline(stream_dict)
+    if len(track_imgs) == 0:
+        return {}
     std_img = None
     if stream_dict['goodsPic'] is not None:
-        response = requests.get(stream_dict['goodsPic'])
+        # response = requests.get(stream_dict['goodsPic'])
-        std_img = Image.open(BytesIO(response.content))
+        # std_img = Image.open(BytesIO(response.content))
+        std_img = Image.open(stream_dict['goodsPic']).convert("RGB")
     description_dict = get_product_description(std_img, track_imgs)
 
     return description_dict

minicpm.py

@@ -0,0 +1,25 @@
+# Load model directly
+from transformers import AutoModel, AutoTokenizer
+import torch
+from PIL import Image
+
+model = AutoModel.from_pretrained(
+    "openbmb/MiniCPM-o-2_6",
+    trust_remote_code=True,
+    attn_implementation='flash_attention_2',
+    torch_dtype=torch.bfloat16,
+    # device_map="auto"
+)
+model = model.eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-o-2_6', use_fast=True, trust_remote_code=True)
+
+img1 = Image.open('/home/ieemoo0337/projects/datasets/constrast_pair/8850813311020/8850813311020.jpg')
+img2 = Image.open('/home/ieemoo0337/projects/datasets/constrast_pair/8850511321499/8850511321499.jpg')
+
+question = '描述第一张图像的1。'
+msgs = [{'role': 'user', 'content': [img1, img2, question]}]
+answer = model.chat(
+    msgs=msgs,
+    tokenizer=tokenizer
+)
+print(answer)

stream_pipeline.py

@@ -37,7 +37,8 @@ def get_optimized_bboxes(event_tracks):
             points.append([int(ele[2]), int(ele[3])])
             labels.append(int(ele[4])) # track_id
     points = np.array(points)
-
+    if len(points) == 0:
+        return []
     partitions, indices = tr.partition(points, progress_bar=False, w_perpendicular=100, w_angular=10)
 
     bboxes_opt = []
@@ -98,6 +99,8 @@ def stream_pipeline(stream_dict):
 
     event_tracks, video_frames = get_tracking_info(**parmDict)
     bboxes_opt = get_optimized_bboxes(event_tracks[0][1])
+    if len(bboxes_opt) == 0:
+        return []
     subimg_dict = save_event_subimgs(video_frames, bboxes_opt)
     
     sub_images = []