diff --git a/ppcls/test/demo.jpeg b/ppcls/test/demo.jpeg
deleted file mode 100644
index fd91ab95eab4cb7e34590324c050f5cbd400cbad..0000000000000000000000000000000000000000
Binary files a/ppcls/test/demo.jpeg and /dev/null differ
diff --git a/ppcls/test/test_download.py b/ppcls/test/test_download.py
deleted file mode 100644
index 4657444934ccade27801b6e6c8a2ab40a185363e..0000000000000000000000000000000000000000
--- a/ppcls/test/test_download.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-import dl
-import os
-import shutil
-
-url = "https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar"
-
-
-class DownloadDecompressTestCase(unittest.TestCase):
-    def setUp(self):
-        print("Test Download and Decompress Function...")
-
-    def test_decompress(self):
-        if os.path.exists('./ResNet50_vd_pretrained'):
-            shutil.rmtree('./ResNet50_vd_pretrained')
-        if os.path.exists("./ResNet50_vd_pretrained.tar"):
-            shutil.rmtree("./ResNet50_vd_pretrained.tar")
-
-        dl.decompress(dl.download(url, "./"))
-        self.assertTrue(os.path.exists("./ResNet50_vd_pretrained"))
-        shutil.rmtree('./ResNet50_vd_pretrained')
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/ppcls/test/test_imaug.py b/ppcls/test/test_imaug.py
deleted file mode 100644
index e11e3a98db933a4d623667f227b68549f696870c..0000000000000000000000000000000000000000
--- a/ppcls/test/test_imaug.py
+++ /dev/null
@@ -1,271 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from ppcls.data.imaug import DecodeImage
-from ppcls.data.imaug import ResizeImage
-from ppcls.data.imaug import RandCropImage
-from ppcls.data.imaug import RandFlipImage
-from ppcls.data.imaug import NormalizeImage
-from ppcls.data.imaug import ToCHWImage
-
-from ppcls.data.imaug import ImageNetPolicy
-from ppcls.data.imaug import RandAugment
-from ppcls.data.imaug import Cutout
-
-from ppcls.data.imaug import HideAndSeek
-from ppcls.data.imaug import RandomErasing
-from ppcls.data.imaug import GridMask
-
-from ppcls.data.imaug import MixupOperator
-from ppcls.data.imaug import CutmixOperator
-from ppcls.data.imaug import FmixOperator
-
-from ppcls.data.imaug import transform
-
-import numpy as np
-
-fname = './test/demo.jpeg'
-size = 224
-img_mean = [0.485, 0.456, 0.406]
-img_std = [0.229, 0.224, 0.225]
-img_scale = 1.0 / 255.0
-
-decode_op = DecodeImage()
-randcrop_op = RandCropImage(size=(size, size))
-randflip_op = RandFlipImage(flip_code=1)
-
-normalize_op = NormalizeImage(
-    scale=img_scale, mean=img_mean, std=img_std, order='')
-tochw_op = ToCHWImage()
-
-data = open(fname).read()
-
-
-def print_function_name(func):
-    """ print function name"""
-
-    def wrapper(*args, **kwargs):
-        """ wrapper """
-        print("========Test Fuction: [%s]:" % (func.__name__))
-        func(*args, **kwargs)
-        print("========Test Fuction: [%s] done!\n" % (func.__name__))
-
-    return wrapper
-
-
-@print_function_name
-def test_decode():
-    """ test decode operator """
-    img = decode_op(data)
-    print('img shape is %s' % (str(img.shape)))
-
-
-@print_function_name
-def test_randcrop():
-    """ test randcrop operator """
-    img = decode_op(data)
-    img = randcrop_op(img)
-    assert img.shape == (size, size, 3), \
-            'image shape[%s] should be equal to [%s]' % (img.shape, (size, size, 3))
-
-
-@print_function_name
-def test_randflip():
-    """ test randflip operator """
-    import cv2
-    img = transform(data, [decode_op, randcrop_op])
-    for i in xrange(10):
-        flip_img = randflip_op(img)
-        if np.array_equal(cv2.flip(img, 1), flip_img):
-            break
-    assert np.array_equal(cv2.flip(img, 1),
-                          flip_img), 'you should check randcrop operator'
-
-
-@print_function_name
-def test_normalize():
-    """ test normalize operator """
-    img = transform(data, [decode_op, randcrop_op])
-
-    norm_img = normalize_op(img)
-    assert norm_img.dtype == np.float32, 'img.dtype should be float32 after normalizing'
-    assert norm_img.shape == (size, size, 3), \
-            'image shape[%s] should be equal to [%s]' % (norm_img.shape, (size, size, 3))
-    print('max value of the img after normalizing is : %f' %
-          (np.max(norm_img.flatten())))
-    print('min value of the img after normalizing is : %f' %
-          (np.min(norm_img.flatten())))
-
-
-@print_function_name
-def test_tochw():
-    """ test  tochw operator """
-    img = transform(data, [decode_op, randcrop_op, randflip_op, normalize_op])
-
-    tochw_img = tochw_op(img)
-    assert tochw_img.dtype == np.float32, 'img.dtype should be float32 after tochw'
-    assert tochw_img.shape == (3, size, size), \
-            'image shape[%s] should be equal to [%s]' % (tochw_img.shape, (3, size, size))
-
-
-@print_function_name
-def test_autoaugment():
-    """ test autoaugment operator """
-    from PIL import Image
-    autoaugment_op = ImageNetPolicy()
-    img = transform(data, [decode_op, randcrop_op])
-
-    aa_img = autoaugment_op(img)
-    assert aa_img.dtype == np.uint8, 'img.dtype should be uint8 after autoaugment'
-    assert aa_img.shape == (size, size, 3), \
-            'image shape[%s] should be equal to [%s]' % (aa_img.shape, (size, size, 3))
-
-
-@print_function_name
-def test_randaugment():
-    """ test randaugment operator """
-    from PIL import Image
-    randaugment_op = RandAugment(3, 1)
-    img = transform(data, [decode_op, randcrop_op])
-
-    ra_img = randaugment_op(img)
-    assert ra_img.dtype == np.uint8, 'img.dtype should be uint8 after randaugment'
-    assert ra_img.shape == (size, size, 3), \
-            'image shape[%s] should be equal to [%s]' % (ra_img.shape, (size, size, 3))
-
-
-@print_function_name
-def test_cutout():
-    """ test cutout operator """
-    cutout_op = Cutout()
-    img = transform(data, [decode_op, randcrop_op])
-
-    cutout_img = cutout_op(img)
-    assert cutout_img.dtype == np.uint8, 'img.dtype should be uint8 after cutout'
-    assert cutout_img.shape == (size, size, 3), \
-            'image shape[%s] should be equal to [%s]' % (cutout_img.shape, (size, size, 3))
-
-
-@print_function_name
-def test_hideandseek():
-    """ test hide and seek operator """
-    img = transform(
-        data, [decode_op, randcrop_op, randflip_op, normalize_op, tochw_op])
-
-    hide_and_seek_op = HideAndSeek()
-    hs_img = hide_and_seek_op(img)
-    assert hs_img.dtype == np.float32, 'img.dtype should be float32 after hide and seek'
-    assert hs_img.shape == (3, size, size), \
-            'image shape[%s] should be equal to [%s]' % (hs_img.shape, (3, size, size))
-
-
-@print_function_name
-def test_randerasing():
-    """ test randerasing operator """
-    img = transform(
-        data, [decode_op, randcrop_op, randflip_op, normalize_op, tochw_op])
-
-    randomerasing_op = RandomErasing()
-    re_img = randomerasing_op(img)
-    assert re_img.dtype == np.float32, 'img.dtype should be float32 after randomerasing'
-    assert re_img.shape == (3, size, size), \
-            'image shape[%s] should be equal to [%s]' % (re_img.shape, (3, size, size))
-
-
-@print_function_name
-def test_gridmask():
-    """ test gridmask operator """
-    img = transform(
-        data, [decode_op, randcrop_op, randflip_op, normalize_op, tochw_op])
-
-    gridmask_op = GridMask(
-        d1=96, d2=224, rotate=360, ratio=0.6, mode=1, prob=0.8)
-    gm_img = gridmask_op(img)
-    assert gm_img.dtype == np.float32, 'img.dtype should be float32 after gridmask'
-    assert gm_img.shape == (3, size, size), \
-            'image shape[%s] should be equal to [%s]' % (gr_img.shape, (3, size, size))
-
-
-def generate_batch(batch_size=32):
-    """ generate_batch """
-    import random
-    ops = [decode_op, randcrop_op, randflip_op, normalize_op, tochw_op]
-    batch = [(transform(data, ops), random.randint(0, 1000))
-             for i in xrange(batch_size)]
-    return batch
-
-
-def test_batch_operator(operator, batch_size):
-    """ test batch operator """
-    batch = generate_batch(batch_size)
-    assert len(batch) == batch_size, \
-            'num of samples not equal to batch_size: %d != %d' % (len(batch), batch_size)
-
-    assert len(batch[0]) == 2, \
-            'length of sample not equal to 2: %d != 2' % (len(batch[0]))
-
-    import time
-    tic = time.time()
-    new_batch = operator(batch)
-    cost = time.time() - tic
-    print("operator cost: %.4fms" % (cost * 1000))
-
-    assert len(batch) == len(new_batch), \
-            'num of samples not equal: %d != %d' % (len(batch), len(new_batch))
-    assert len(new_batch[0]) == 4, \
-            'length of sample not equal to 4: %d != 4' % (len(new_batch[0]))
-
-
-@print_function_name
-def test_mixup():
-    """ test mixup operator """
-    batch_size = 32
-    mixup_op = MixupOperator(alpha=0.2)
-    test_batch_operator(mixup_op, batch_size)
-
-
-@print_function_name
-def test_cutmix():
-    """ test cutmix operator """
-    batch_size = 32
-    cutmix_op = CutmixOperator(alpha=0.2)
-    test_batch_operator(cutmix_op, batch_size)
-
-
-@print_function_name
-def test_fmix():
-    """ test fmix operator """
-    batch_size = 32
-    fmix_op = FmixOperator()
-    test_batch_operator(fmix_op, batch_size)
-
-
-if __name__ == '__main__':
-    test_decode()
-    test_randcrop()
-    test_randflip()
-    test_normalize()
-    test_tochw()
-
-    test_autoaugment()
-    test_randaugment()
-    test_cutout()
-
-    test_hideandseek()
-    test_randerasing()
-    test_gridmask()
-
-    test_mixup()
-    test_cutmix()
-    test_fmix()
diff --git a/ppcls/test/test_super_reader.py b/ppcls/test/test_super_reader.py
deleted file mode 100644
index 3e7ca2be3229d9f6142a33c1e681a2552e674c9a..0000000000000000000000000000000000000000
--- a/ppcls/test/test_super_reader.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from ppcls.data.imaug import DecodeImage
-from ppcls.data.imaug import RandCropImage
-from ppcls.data.imaug import RandFlipImage
-from ppcls.data.imaug import NormalizeImage
-from ppcls.data.imaug import ToCHWImage
-
-from ppcls.data.imaug import ImageNetPolicy
-from ppcls.data.imaug import RandAugment
-from ppcls.data.imaug import Cutout
-
-from ppcls.data.imaug import HideAndSeek
-from ppcls.data.imaug import RandomErasing
-from ppcls.data.imaug import GridMask
-
-from ppcls.data.imaug import MixupOperator
-from ppcls.data.imaug import CutmixOperator
-from ppcls.data.imaug import FmixOperator
-
-from ppcls.data.imaug import transform
-
-import numpy as np
-
-fname = './test/demo.jpeg'
-size = 224
-img_mean = [0.485, 0.456, 0.406]
-img_std = [0.229, 0.224, 0.225]
-img_scale = 1.0 / 255.0
-
-# normal_ops_1
-decode_op = DecodeImage()
-randcrop_op = RandCropImage(size=(size, size))
-
-# trans_ops
-autoaugment_op = ImageNetPolicy()
-randaugment_op = RandAugment(3, 1)
-cutout_op = Cutout()
-
-# normal_ops_2
-randflip_op = RandFlipImage(flip_code=1)
-normalize_op = NormalizeImage(
-    scale=img_scale, mean=img_mean, std=img_std, order='')
-tochw_op = ToCHWImage()
-
-# mask_ops
-hide_and_seek_op = HideAndSeek()
-randomerasing_op = RandomErasing()
-gridmask_op = GridMask(d1=96, d2=224, rotate=360, ratio=0.6, mode=1, prob=0.8)
-
-# batch_ops
-mixup_op = MixupOperator(alpha=0.2)
-cutmix_op = CutmixOperator(alpha=0.2)
-fmix_op = FmixOperator()
-
-
-def fakereader():
-    """ fake reader """
-    import random
-    data = open(fname).read()
-
-    def wrapper():
-        while True:
-            yield (data, random.randint(0, 1000))
-
-    return wrapper
-
-
-def superreader(batch_size=32):
-    """ super reader """
-    normal_ops_1 = [decode_op, randcrop_op]
-    normal_ops_2 = [randflip_op, normalize_op, tochw_op]
-
-    trans_ops = [autoaugment_op, randaugment_op, cutout_op]
-    trans_ops_p = [0.2, 0.3, 0.5]
-    mask_ops = [hide_and_seek_op, randomerasing_op, gridmask_op]
-    mask_ops_p = [0.1, 0.6, 0.3]
-    batch_ops = [mixup_op, cutmix_op, fmix_op]
-    batch_ops_p = [0.3, 0.3, 0.4]
-
-    reader = fakereader()
-
-    def wrapper():
-        batch = []
-        for idx, sample in enumerate(reader()):
-            img, label = sample
-            ops = normal_ops_1 + [np.random.choice(trans_ops, p=trans_ops_p)] +\
-                    normal_ops_2 + [np.random.choice(mask_ops, p=mask_ops_p)]
-            img = transform(img, ops)
-            batch.append((img, label))
-            if (idx + 1) % batch_size == 0:
-                batch = transform(
-                    batch, [np.random.choice(
-                        batch_ops, p=batch_ops_p)])
-                yield batch
-                batch = []
-
-    return wrapper
-
-
-if __name__ == '__main__':
-    reader = superreader(32)
-    for batch in reader():
-        print(len(batch), len(batch[0]), batch[0][0].shape, batch[0][1:])