fix sr typo (#7262)

* fix sr typo

* rm eval visual

tools/infer_sr.py

@@ -63,14 +63,14 @@ def main():
         elif op_name in ['SRResize']:
             op[op_name]['infer_mode'] = True
         elif op_name == 'KeepKeys':
-            op[op_name]['keep_keys'] = ['imge_lr']
+            op[op_name]['keep_keys'] = ['img_lr']
         transforms.append(op)
     global_config['infer_mode'] = True
     ops = create_operators(transforms, global_config)
 
-    save_res_path = config['Global'].get('save_res_path', "./infer_result")
-    if not os.path.exists(os.path.dirname(save_res_path)):
-        os.makedirs(os.path.dirname(save_res_path))
+    save_visual_path = config['Global'].get('save_visual', "infer_result/")
+    if not os.path.exists(os.path.dirname(save_visual_path)):
+        os.makedirs(os.path.dirname(save_visual_path))
 
     model.eval()
     for file in get_image_file_list(config['Global']['infer_img']):
@@ -87,7 +87,7 @@ def main():
         fm_sr = (sr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
         fm_lr = (lr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
         img_name_pure = os.path.split(file)[-1]
-        cv2.imwrite("infer_result/sr_{}".format(img_name_pure),
+        cv2.imwrite("{}/sr_{}".format(save_visual_path, img_name_pure),
                     fm_sr[:, :, ::-1])
         logger.info("The visualized image saved in infer_result/sr_{}".format(
             img_name_pure))

tools/program.py

@@ -231,7 +231,8 @@ def train(config,
 
     use_srn = config['Architecture']['algorithm'] == "SRN"
     extra_input_models = [
-        "SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN", "VisionLAN", "RobustScanner"
+        "SRN", "NRTR", "SAR", "SEED", "SVTR", "SPIN", "VisionLAN",
+        "RobustScanner"
     ]
     extra_input = False
     if config['Architecture']['algorithm'] == 'Distillation':
@@ -503,16 +504,6 @@ def eval(model,
                         preds = model(batch)
                         sr_img = preds["sr_img"]
                         lr_img = preds["lr_img"]
-
-                        for i in (range(sr_img.shape[0])):
-                            fm_sr = (sr_img[i].numpy() * 255).transpose(
-                                1, 2, 0).astype(np.uint8)
-                            fm_lr = (lr_img[i].numpy() * 255).transpose(
-                                1, 2, 0).astype(np.uint8)
-                            cv2.imwrite("output/images/{}_{}_sr.jpg".format(
-                                sum_images, i), fm_sr)
-                            cv2.imwrite("output/images/{}_{}_lr.jpg".format(
-                                sum_images, i), fm_lr)
                     else:
                         preds = model(images)
                 preds = to_float32(preds)
@@ -525,16 +516,6 @@ def eval(model,
                     preds = model(batch)
                     sr_img = preds["sr_img"]
                     lr_img = preds["lr_img"]
-
-                    for i in (range(sr_img.shape[0])):
-                        fm_sr = (sr_img[i].numpy() * 255).transpose(
-                            1, 2, 0).astype(np.uint8)
-                        fm_lr = (lr_img[i].numpy() * 255).transpose(
-                            1, 2, 0).astype(np.uint8)
-                        cv2.imwrite("output/images/{}_{}_sr.jpg".format(
-                            sum_images, i), fm_sr)
-                        cv2.imwrite("output/images/{}_{}_lr.jpg".format(
-                            sum_images, i), fm_lr)
                 else:
                     preds = model(images)