feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/design_batch/utils/design_ensemble.py

@@ -16,7 +16,7 @@ import torch
 import torch.nn.functional as F
 import tritonclient.http as httpclient
 
-from app.core.config import *
+from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
 
 """
     keypoint
@@ -91,29 +91,29 @@ def seg_preprocess(img_path):
 
 
 # @ RunTime
-def get_seg_result(image_id, image):
+def get_seg_result(image):
     image, ori_shape = seg_preprocess(image)
     client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}")
     transformed_img = image.astype(np.float32)
     # 输入集
     inputs = [
-        httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
+        httpclient.InferInput(DESIGN_MODEL_NAME, transformed_img.shape, datatype="FP32")
     ]
     inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
     # 输出集
     outputs = [
-        httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
+        httpclient.InferRequestedOutput("seg_input__0", binary_data=True),
     ]
-    results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs)
+    results = client.infer(model_name=DESIGN_MODEL_NAME, inputs=inputs, outputs=outputs)
     # 推理
     # 取结果
-    inference_output1 = results.as_numpy(SEGMENTATION['output'])
-    seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape)
+    inference_output1 = results.as_numpy("seg_input__0")
+    seg_result = seg_postprocess(inference_output1, ori_shape)
     return seg_result
 
 
 # no cache
-def seg_postprocess(image_id, output, ori_shape):
+def seg_postprocess(output, ori_shape):
     seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
     seg_pred = seg_logit.cpu().numpy()
     return seg_pred[0]