attribute 模型名称错误

test/attribute_recognition_model_infer/attribute_recognition_model_infer.py

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+from pprint import pprint
+
+import cv2
+import mmcv
+import numpy as np
+import tritonclient.http as httpclient
+import torch
+
+from app.core.config import ATT_TRITON_PORT, ATT_TRITON_IP
+
+model_list = ['bottom_design', 'bottom_length', 'bottom_material', 'bottom_OPType_B', 'bottom_print', 'bottom_Silhouette_B', 'bottom_Softness_B', 'bottom_sub-Type', 'category', 'dress_collar', 'dress_design', 'dress_length', 'dress_material', 'dress_neckline', 'dress_print', 'dress_silohouette12', 'dress_sleeve_length', 'dress_sleeve_shape', 'dress_sleeve_shoulder', 'dress_softness', 'dress_type', 'jumpsuit_collar', 'jumpsuit_design', 'jumpsuit_length', 'jumpsuit_material', 'jumpsuit_optype',
+              'jumpsuit_print', 'jumpsuit_sleeve_length', 'jumpsuit_sleeve_shape', 'jumpsuit_sleeve_shoulder', 'jumpsuit_softness', 'jumpsuit_subtype', 'outwear_material', 'outwear_outear_length', 'outwear_outer_collar', 'outwear_outer_design', 'outwear_outer_optype', 'outwear_outer_silhouette', 'outwear_outer_sleeve_length', 'outwear_outer_sleeve_shape', 'outwear_outer_sleeve_shoulder', 'outwear_outer_softness', 'outwear_print', 'top_Collar', 'top_Design', 'top_length', 'top_material',
+              'top_Neckline', 'top_optype', 'top_print', 'top_Silhouette', 'top_Sleeve_length', 'top_Sleeve_shape', 'top_Sleeve_shoulder', 'top_Softness', 'top_type']
+
+
+def preprocess(img):
+    img = mmcv.imread(img)
+    ori_shape = img.shape[:2]
+    img_scale = (224, 224)
+    scale_factor = []
+    img, x, y = mmcv.imresize(img, img_scale, return_scale=True)
+    scale_factor.append(x)
+    scale_factor.append(y)
+    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
+    return preprocessed_img, ori_shape
+
+
+def get_attribute(model_save_name, sample):
+    triton_client = httpclient.InferenceServerClient(url=f"{ATT_TRITON_IP}:{ATT_TRITON_PORT}")
+    inputs = [
+        httpclient.InferInput("input__0", sample.shape, datatype="FP32")
+    ]
+    inputs[0].set_data_from_numpy(sample, binary_data=True)
+    results = triton_client.infer(model_name=model_save_name, inputs=inputs)
+    inference_output = torch.from_numpy(results.as_numpy(f"output__0"))
+    scores = inference_output.detach().numpy()
+    pprint(scores)
+    print(f"{model_save_name} is ok")
+
+
+image, shape = preprocess(cv2.imread("test_top1.jpg"))
+except_model = []
+
+for model in model_list:
+    try:
+        get_attribute(model, image)
+    except Exception as e:
+        print(e)
+        except_model.append(model)
+print(except_model)