优化代码

LPRNet_ORT_infer.py

@@ -26,9 +26,7 @@ def LPRNetPreprocess(image):
     return img
 
 def LPRNetPostprocess(infer_res):
-    preb_label = []
-    for j in range(infer_res.shape[1]):
-        preb_label.append(np.argmax(infer_res[:, j], axis=0))
+    preb_label = np.argmax(infer_res, axis=0)
     no_repeat_blank_label = []
     pre_c = preb_label[0]
     if pre_c != len(CHARS) - 1:
@@ -51,7 +49,9 @@ def LPRNetInference(args):
 
     if os.path.isdir(args.imgpath):
         images = os.listdir(args.imgpath)
-        count = 0
+        Tp = 0
+        Tn_1 = 0
+        Tn_2 = 0
         time1 = time.perf_counter()
         for image in images:
             img = LPRNetPreprocess(os.path.join(args.imgpath, image))
@@ -59,11 +59,16 @@ def LPRNetInference(args):
             preb = sess.run(None, input_feed={sess.get_inputs()[0].name: img})[0]
             result = LPRNetPostprocess(preb)
             if result == image[:-4]:
-                count += 1
-            print('Inference Result:', result)
-        time2 = time.perf_counter()
-        print('accuracy rate:', count / len(images))
-        print('average time', (time2 - time1)/count*1000)        
+                Tp += 1
+            elif len(result) != len(image[:-4]):
+                Tn_1 += 1
+            else:
+                Tn_2 += 1
+            print(image + ' Inference Result:', result)
+        time2 = time.perf_counter() 
+        Acc = Tp * 1.0 / (Tp + Tn_1 + Tn_2)
+        print("[Info] Test Accuracy: {} [{}:{}:{}:{}]".format(Acc, Tp, Tn_1, Tn_2, (Tp+Tn_1+Tn_2)))
+        print("[Info] Test Speed: {}s 1/{}]".format((time2 - time1) / len(images), len(images)))     
     else:
         img = LPRNetPreprocess(args.imgpath)
         intput = sess.get_inputs()[0].shape

LPRNet_migraphx_infer.py

@@ -28,9 +28,7 @@ def LPRNetPreprocess(image):
     return img
 
 def LPRNetPostprocess(infer_res):
-    preb_label = []
-    for j in range(infer_res.shape[1]):
-        preb_label.append(np.argmax(infer_res[:, j], axis=0))
+    preb_label = np.argmax(infer_res, axis=0)
     no_repeat_blank_label = []
     pre_c = preb_label[0]
     if pre_c != len(CHARS) - 1:
@@ -57,7 +55,9 @@ def LPRNetInference(args):
 
     if os.path.isdir(args.imgpath):
         images = os.listdir(args.imgpath)
-        count = 0
+        Tp = 0
+        Tn_1 = 0
+        Tn_2 = 0
         time1 = time.perf_counter()
         for image in images:
             img = LPRNetPreprocess(os.path.join(args.imgpath, image))
@@ -67,11 +67,16 @@ def LPRNetInference(args):
             results = model.run({inputName: migraphx.argument(img)})
             result = LPRNetPostprocess(np.array(results[0]))
             if result == image[:-4]:
-                count += 1
-            print('Inference Result:', result)
+                Tp += 1
+            elif len(result) != len(image[:-4]):
+                Tn_1 += 1
+            else:
+                Tn_2 += 1
+            print(image + ' Inference Result:', result)
         time2 = time.perf_counter()
-        print('accuracy rate:', count / len(images))
-        print('average time', (time2 - time1)/count*1000) 
+        Acc = Tp * 1.0 / (Tp + Tn_1 + Tn_2)
+        print("[Info] Test Accuracy: {} [{}:{}:{}:{}]".format(Acc, Tp, Tn_1, Tn_2, (Tp+Tn_1+Tn_2)))
+        print("[Info] Test Speed: {}s 1/{}]".format((time2 - time1) / len(images), len(images))) 
     else:
         img = LPRNetPreprocess(args.imgpath)
         inputName=model.get_parameter_names()[0]