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