Fix batch predict of cls and rec (#1089)

* fixbug_bs=1 of predict_cls\rec

deploy/python/predict_cls.py

@@ -27,7 +27,6 @@ from utils.get_image_list import get_image_list
 from python.preprocess import create_operators
 from python.postprocess import build_postprocess
 
-
 class ClsPredictor(Predictor):
     def __init__(self, config):
         super().__init__(config["Global"])
@@ -59,6 +58,8 @@ class ClsPredictor(Predictor):
         input_tensor.copy_from_cpu(image)
         self.paddle_predictor.run()
         batch_output = output_tensor.copy_to_cpu()
+        if self.postprocess is not None:
+            batch_output = self.postprocess(batch_output)
         return batch_output
 
 
@@ -66,14 +67,38 @@ def main(config):
     cls_predictor = ClsPredictor(config)
     image_list = get_image_list(config["Global"]["infer_imgs"])
 
-    assert config["Global"]["batch_size"] == 1
-    for idx, image_file in enumerate(image_list):
-        img = cv2.imread(image_file)[:, :, ::-1]
-        output = cls_predictor.predict(img)
-        output = cls_predictor.postprocess(output, [image_file])
-        print(output)
-    return
+    batch_imgs = []
+    batch_names = []
+    cnt = 0
+    for idx, img_path in enumerate(image_list):
+        img = cv2.imread(img_path)
+        if img is None:
+            logger.warning(
+                "Image file failed to read and has been skipped. The path: {}".
+                format(img_path))
+        else:
+            img = img[:, :, ::-1]
+            batch_imgs.append(img)
+            img_name = os.path.basename(img_path)
+            batch_names.append(img_name)
+            cnt += 1
 
+        if cnt % config["Global"]["batch_size"] == 0 or (idx + 1) == len(image_list):
+            if len(batch_imgs) == 0: 
+                continue
+                
+            batch_results = cls_predictor.predict(batch_imgs)
+            for number, result_dict in enumerate(batch_results):
+                filename = batch_names[number]
+                clas_ids = result_dict["class_ids"]
+                scores_str = "[{}]".format(", ".join("{:.2f}".format(
+                    r) for r in result_dict["scores"]))
+                label_names = result_dict["label_names"]
+                print("{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
+                      format(filename, clas_ids, scores_str, label_names))
+            batch_imgs = []
+            batch_names = []
+    return
 
 if __name__ == "__main__":
     args = config.parse_args()

deploy/python/predict_rec.py

@@ -54,12 +54,14 @@ class RecPredictor(Predictor):
         input_tensor.copy_from_cpu(image)
         self.paddle_predictor.run()
         batch_output = output_tensor.copy_to_cpu()
-        
+
         if feature_normalize:
             feas_norm = np.sqrt(
                 np.sum(np.square(batch_output), axis=1, keepdims=True))
             batch_output = np.divide(batch_output, feas_norm)
-            
+
+        if self.postprocess is not None:
+            batch_output = self.postprocess(batch_output)
         return batch_output
 
 
@@ -67,14 +69,33 @@ def main(config):
     rec_predictor = RecPredictor(config)
     image_list = get_image_list(config["Global"]["infer_imgs"])
 
-    assert config["Global"]["batch_size"] == 1
-    for idx, image_file in enumerate(image_list):
-        batch_input = []
-        img = cv2.imread(image_file)[:, :, ::-1]
-        output = rec_predictor.predict(img)
-        if rec_predictor.postprocess is not None:
-            output = rec_predictor.postprocess(output)
-        print(output)
+    batch_imgs = []
+    batch_names = []
+    cnt = 0
+    for idx, img_path in enumerate(image_list):
+        img = cv2.imread(img_path)
+        if img is None:
+            logger.warning(
+                "Image file failed to read and has been skipped. The path: {}".
+                format(img_path))
+        else:
+            img = img[:, :, ::-1]
+            batch_imgs.append(img)
+            img_name = os.path.basename(img_path)
+            batch_names.append(img_name)
+            cnt += 1
+
+        if cnt % config["Global"]["batch_size"] == 0 or (idx + 1) == len(image_list):
+            if len(batch_imgs) == 0: 
+                continue
+                
+            batch_results = rec_predictor.predict(batch_imgs)
+            for number, result_dict in enumerate(batch_results):
+                filename = batch_names[number]
+                print("{}:\t{}".format(filename, result_dict))
+            batch_imgs = []
+            batch_names = []
+
     return