merge

app/service/outfit_matcher/outfit_evaluator.py

@@ -379,7 +379,6 @@ class OutfitMaterTypeAware(OutfitMatcher):
         Returns:
             scores: List of float
         """
-<<<<<<< HEAD
         outfit_images, outfit_categories = self.preprocess(outfits, features)
         scores = []
         for images, categories in zip(outfit_images, outfit_categories):
@@ -400,28 +399,3 @@ class OutfitMaterTypeAware(OutfitMatcher):
 
         scores = np.stack(scores, axis=0)
         return scores.flatten()
-=======
-        image, category, mask = self.preprocess(outfits)
-        client = httpclient.InferenceServerClient(url=f"{OM_TRITON_IP}:{OM_TRITON_PORT}")
-        # 输入集
-        inputs = [
-            httpclient.InferInput("input__0", image.shape, datatype="FP32"),
-            httpclient.InferInput("input__1", category.shape, datatype="INT16"),
-            httpclient.InferInput("input__2", mask.shape, datatype="FP32"),
-        ]
-        inputs[0].set_data_from_numpy(image.astype(np.float32), binary_data=True)
-        inputs[1].set_data_from_numpy(category.astype(np.int16), binary_data=True)
-        inputs[2].set_data_from_numpy(mask.astype(np.float32), binary_data=True)
-        # 输出集
-        outputs = [
-            httpclient.InferRequestedOutput("output__0", binary_data=True),
-            httpclient.InferRequestedOutput("output__1", binary_data=True)
-        ]
-        results = client.infer(model_name="outfit_matcher_type_aware", inputs=inputs, outputs=outputs)
-        # 推理
-        # 取结果
-        scores = torch.from_numpy(results.as_numpy("output__0"))  # Shape (N, 1)
-        features = torch.from_numpy(results.as_numpy("output__1"))  # Shape (N, 64)
-
-        return scores, features
->>>>>>> 1f23781b16e59bfbcbbb4d252e6a61685267e6c7

app/service/outfit_matcher/service.py

@@ -18,38 +18,43 @@ if __name__ == '__main__':
     all_items = param["query"] + param["database"]
     unextracted_item = []
     prepared_feature = {}
+
+    # 拿到所有需要提取特征的图片
     for item in all_items:
         if f'{item["item_name"]}.npy' not in os.listdir("feature"):
             unextracted_item.append(item)
     if len(unextracted_item) > 0:
+        # 通过backbone模型提取图片特征
         extracted_features = backbone_service.get_result(unextracted_item)
         for i, item in enumerate(unextracted_item):
-            np.save(f'feature/{item["item_name"]}.npy', extracted_features[i])
-    for item in all_items:
-        if item["item_name"] not in prepared_feature.keys():
-            prepared_feature[item["item_name"]] = np.load(f'feature/{item["item_name"]}.npy')
-    for item in tqdm(param["query"] * 10):
-        outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
-<<<<<<< HEAD
-        scores = service.get_result(outfits, prepared_feature)
-=======
-        scores, features = service.get_result(outfits)
             # save  features
-
             # 链接milvus
-
+            # TODO
+            np.save(f'feature/{item["item_name"]}.npy', extracted_features[i])
             # 存入数据库
             # 关闭链接
 
->>>>>>> 1f23781b16e59bfbcbbb4d252e6a61685267e6c7
+    # TODO 读取本次任务需要的图片特征
-        # print(scores)
+    for item in all_items:
-        # print(len(scores))
+        if item["item_name"] not in prepared_feature.keys():
+            prepared_feature[item["item_name"]] = np.load(f'feature/{item["item_name"]}.npy')
+
+    # 开始服装搭配任务
+    for item in tqdm(param["query"] * 10):
+        # 根据一定规则生成outfit
+        outfits = fashion_dataset.generate_outfit(item, param["topk"], param["max_outfits"])
+        # 根据模型对生成的outfit打分
+        scores = service.get_result(outfits, prepared_feature)
+        # 对评分排序，拿到最好的topk个outfit输出
+        sorted_indices = np.argsort(scores)[:param["topk"]]  # type-aware
+        outfits = [outfits[i] for i in sorted_indices]  # 最好的五个
+
+        # 结果可视化
         # service.visualize(outfits, scores, param["topk"], best=True,
         #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
         #                                            f"{item['item_name']}_best_{param['topk']}.png"))
         # service.visualize(outfits, scores, param["topk"], best=False,
         #                   output_path=os.path.join(r"D:\PhD_Study\MIXI\mitu\image\123",
         #                                            f"{item['item_name']}_worst_{param['topk']}.png"))
-        sorted_indices = np.argsort(scores)[:param["topk"]]  # type-aware
+
-        outfits = [outfits[i] for i in sorted_indices]  # 最好的五个