feat（新功能）: batch generate product 入参回参修改

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/schemas/generate_image.py

@@ -1,3 +1,5 @@
+from typing import List
+
 from pydantic import BaseModel
 
 
@@ -43,13 +45,18 @@ class GenerateRelightImageModel(BaseModel):
 """
 
 
-class BatchGenerateProductImageModel(BaseModel):
+class ProductItemModel(BaseModel):
     tasks_id: str
+    image_strength: float
     prompt: str
     image_url: str
-    image_strength: float
     product_type: str
-    batch_size: int
+
+
+class BatchGenerateProductImageModel(BaseModel):
+    batch_tasks_id: str
+    user_id: str
+    batch_data_list: List[ProductItemModel]
 
 
 class BatchGenerateRelightImageModel(BaseModel):

app/service/generate_batch_image/service_batch_generate_product_image.py

@@ -19,7 +19,7 @@ from celery import Celery
 from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
-from app.schemas.generate_image import BatchGenerateProductImageModel
+from app.schemas.generate_image import BatchGenerateProductImageModel, ProductItemModel
 from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
 from app.service.utils.oss_client import oss_get_image
 
@@ -35,24 +35,26 @@ category = "product_image"
 
 @celery_app.task
 def batch_generate_product(batch_request_data):
+    batch_size = len(batch_request_data['batch_data_list'])
     logger.info(f"batch_generate_product batch_request_data:{json.dumps(batch_request_data, indent=4)}")
-    tasks_id = batch_request_data['tasks_id']
+    batch_tasks_id = batch_request_data['batch_tasks_id']
-    user_id = tasks_id.rsplit('-', 1)[1]
+    user_id = batch_request_data['user_id']
-    batch_size = batch_request_data['batch_size']
+    result_data_list = []
-    image = pre_processing_image(batch_request_data['image_url'])
+
+    for i, data in enumerate(batch_request_data['batch_data_list']):
+        tasks_id = data['tasks_id']
+        image = pre_processing_image(data['image_url'])
         image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
         images = [image.astype(np.uint8)] * 1
-
+        prompts = [data['prompt']] * 1
-    prompts = [batch_request_data['prompt']] * 1
+        if data['product_type'] == "single":
-
-    if batch_request_data['product_type'] == "single":
             text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
             image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-        image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((-1, 1))
+            image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((-1, 1))
         else:
             text_obj = np.array(prompts, dtype="object").reshape((1))
             image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
-        image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((1))
+            image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((1))
         input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
         input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
         input_image_strength = grpcclient.InferInput("image_strength", image_strength_obj.shape, np_to_triton_dtype(image_strength_obj.dtype))
@@ -63,10 +65,8 @@ def batch_generate_product(batch_request_data):
 
         inputs = [input_text, input_image, input_image_strength]
 
-    image_url_list = []
-    for i in range(batch_size):
         try:
-            if batch_request_data['product_type'] == "single":
+            if data['product_type'] == "single":
                 result = grpc_client.infer(model_name=GPI_MODEL_NAME_SINGLE, inputs=inputs, priority=100)
                 image = result.as_numpy("generated_cnet_image")
             else:
@@ -77,7 +77,7 @@ def batch_generate_product(batch_request_data):
             if 'mask_list' in str(e):
                 e_text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
                 e_image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-                e_image_strength_obj = np.array(batch_request_data['image_strength'], dtype=np.float32).reshape((-1, 1))
+                e_image_strength_obj = np.array(data['image_strength'], dtype=np.float32).reshape((-1, 1))
 
                 e_input_text = grpcclient.InferInput("prompt", e_text_obj.shape, np_to_triton_dtype(e_text_obj.dtype))
                 e_input_image = grpcclient.InferInput("input_image", e_image_obj.shape, "UINT8")
@@ -96,18 +96,29 @@ def batch_generate_product(batch_request_data):
 
         if isinstance(image_result, Image.Image):
             image_url = upload_SDXL_image(image_result, user_id=user_id, category=f"{category}", file_name=f"{tasks_id}-batch-{i}.png")
-            image_url_list.append(image_url)
+            data['product_img'] = image_url
+            result_data_list.append(data)
         else:
             image_url = image_result
-        if DEBUG is False:
+            data['product_img'] = image_url
-            if i + 1 < batch_size:
+            result_data_list.append(data)
+
+        # 发送每条结果
+        if DEBUG:
+            logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+            print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | result_data：{data}")
+        else:
             publish_status(tasks_id, f"{i + 1}/{batch_size}", image_url)
             logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
-                # print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：{i + 1}/{batch_size} | image_url：{image_url}")
+
+    # 任务完成，发送所有数据结果
+    if DEBUG:
+        print(result_data_list)
+        logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
+        print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
     else:
-                publish_status(tasks_id, f"OK", image_url_list)
+        publish_status(batch_tasks_id, f"OK", result_data_list)
-                logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
+        logger.info(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | batch_tasks_id：{batch_tasks_id} | progress：OK | result_data_list：{result_data_list}")
-                # print(f" [x]Queue : {BATCH_GPI_RABBITMQ_QUEUES} | tasks_id：{tasks_id} | progress：OK | image_url：{image_url}")
 
 
 def pre_processing_image(image_url):
@@ -180,12 +191,52 @@ def publish_status(task_id, progress, result):
 
 
 if __name__ == '__main__':
+    # rd = BatchGenerateProductImageModel(
+    #     tasks_id="123-15-51-89",
+    #     image_strength=0.7,
+    #     prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
+    #     image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #     product_type="overall",
+    #     batch_size=20
+    # )
+    # batch_generate_product(rd.dict())
+    # rd = {
+    #     "user_id": "89",
+    #     "batch_data_list": [
+    #         {
+    #             "tasks_id": "A-123-15-51-89",
+    #             "image_strength": 0.7,
+    #             "prompt": " The best quality, ma123sterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
+    #             "image_url": "aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #             "product_type": "overall",
+    #         },
+    #         {
+    #             "tasks_id": "B-123-15-51-89",
+    #             "image_strength": 0.7,
+    #             "prompt": " The best quality, masterpiece, real image.Outwear123,high quality clothing details,8K realistic,HDR",
+    #             "image_url": "aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+    #             "product_type": "overall",
+    #         }
+    #     ]
+    # }
     rd = BatchGenerateProductImageModel(
-        tasks_id="123-15-51-89",
+        batch_tasks_id="abcd",
+        user_id="89",
+        batch_data_list=[
+            ProductItemModel(
+                tasks_id="123-5464",
                 image_strength=0.7,
-        prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
-        image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
                 product_type="overall",
-        batch_size=20
+                image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+                prompt="123"
+            ),
+            ProductItemModel(
+                tasks_id="123-5464123",
+                image_strength=0.7,
+                product_type="overall",
+                image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
+                prompt="123"
+            )
+        ]
     )
     batch_generate_product(rd.dict())