Fix infer and eval case fail (#2854)

PaddleCV/image_classification/eval.py

@@ -26,7 +26,7 @@ import functools
 
 import paddle
 import paddle.fluid as fluid
-import reader_cv2 as reader 
+import reader_cv2 as reader
 import models
 from utils.learning_rate import cosine_decay
 from utils.utility import add_arguments, print_arguments, check_gpu
@@ -44,6 +44,7 @@ add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.
 add_arg('resize_short_size', int, 256,                "Set resize short size")
 # yapf: enable
 
+
 def eval(args):
     # parameters from arguments
     class_dim = args.class_dim
@@ -93,10 +94,9 @@ def eval(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-
     fluid.io.load_persistables(exe, pretrained_model)
 
-    val_reader = paddle.batch(reader.val(settings=args), batch_size=args.batch_size)
+    val_reader = reader.val(settings=args, batch_size=args.batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
     test_info = [[], [], []]
@@ -127,7 +127,7 @@ def eval(args):
     test_acc5 = np.sum(test_info[2]) / cnt
 
     print("Test_loss {0}, test_acc1 {1}, test_acc5 {2}".format(
-        "%.5f"%test_loss, "%.5f"%test_acc1, "%.5f"%test_acc5))
+        "%.5f" % test_loss, "%.5f" % test_acc1, "%.5f" % test_acc5))
     sys.stdout.flush()
 
 

PaddleCV/image_classification/infer.py

@@ -29,7 +29,7 @@ import paddle.fluid as fluid
 import reader_cv2 as reader
 import models
 import utils
-from utils.utility import add_arguments,print_arguments, check_gpu
+from utils.utility import add_arguments, print_arguments, check_gpu
 
 parser = argparse.ArgumentParser(description=__doc__)
 # yapf: disable
@@ -44,6 +44,7 @@ add_arg('save_inference',   bool, False,                 "Whether to save infere
 add_arg('resize_short_size', int, 256,                  "Set resize short size")
 # yapf: enable
 
+
 def infer(args):
     # parameters from arguments
     class_dim = args.class_dim
@@ -80,17 +81,18 @@ def infer(args):
     fluid.io.load_persistables(exe, pretrained_model)
     if save_inference:
         fluid.io.save_inference_model(
-                dirname=model_name,
-                feeded_var_names=['image'],
-                main_program=test_program,
-                target_vars=out,
-                executor=exe,
-                model_filename='model',
-                params_filename='params')
-        print("model: ",model_name," is already saved")
+            dirname=model_name,
+            feeded_var_names=['image'],
+            main_program=test_program,
+            target_vars=out,
+            executor=exe,
+            model_filename='model',
+            params_filename='params')
+        print("model: ", model_name, " is already saved")
         exit(0)
     test_batch_size = 1
-    test_reader = paddle.batch(reader.test(settings=args), batch_size=test_batch_size)
+
+    test_reader = reader.test(settings=args, batch_size=test_batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image])
 
     TOPK = 1

PaddleCV/image_classification/reader_cv2.py

@@ -243,10 +243,9 @@ def _reader_creator(settings,
                 img_path = os.path.join(data_dir, img_path)
                 batch_data.append([img_path, int(label)])
                 if len(batch_data) == batch_size:
-                    if mode == 'train' or mode == 'val':
+                    if mode == 'train' or mode == 'val' or mode == 'test':
                         yield batch_data
-                    elif mode == 'test':
-                        yield [sample[0] for sample in batch_data]
+
                     batch_data = []
 
         return read_file_list