Cleanup dataset UT: util.py internals

tests/ut/python/dataset/test_HWC2CHW.py

@@ -69,8 +69,8 @@ def test_HWC2CHW_md5():
     data1 = data1.map(input_columns=["image"], operations=decode_op)
     data1 = data1.map(input_columns=["image"], operations=hwc2chw_op)
 
-    # expected md5 from images
-    filename = "test_HWC2CHW_01_result.npz"
+    # Compare with expected md5 from images
+    filename = "HWC2CHW_01_result.npz"
     save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
@@ -103,9 +103,9 @@ def test_HWC2CHW_comp(plot=False):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
 
-        # compare images between that applying c_transform and py_transform
+        # Compare images between that applying c_transform and py_transform
         mse = diff_mse(py_image, c_image)
-        # the images aren't exactly the same due to rounding error
+        # Note: The images aren't exactly the same due to rounding error
         assert mse < 0.001
 
         image_c_transposed.append(item1["image"].copy())

tests/ut/python/dataset/test_center_crop.py

@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+import numpy as np
 import mindspore.dataset.transforms.vision.c_transforms as vision
 import mindspore.dataset.transforms.vision.py_transforms as py_vision
-import numpy as np
-import matplotlib.pyplot as plt
 import mindspore.dataset as ds
 from mindspore import log as logger
 from util import diff_mse, visualize, save_and_check_md5
@@ -60,15 +59,14 @@ def test_center_crop_md5(height=375, width=375):
     logger.info("Test CenterCrop")
 
     # First dataset
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle =False)
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     decode_op = vision.Decode()
     # 3 images [375, 500] [600, 500] [512, 512]
     center_crop_op = vision.CenterCrop([height, width])
     data1 = data1.map(input_columns=["image"], operations=decode_op)
     data1 = data1.map(input_columns=["image"], operations=center_crop_op)
-    # expected md5 from images 
-
-    filename = "test_center_crop_01_result.npz"
+    # Compare with expected md5 from images
+    filename = "center_crop_01_result.npz"
     save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
@@ -89,7 +87,7 @@ def test_center_crop_comp(height=375, width=375, plot=False):
     data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     transforms = [
         py_vision.Decode(),
-        py_vision.CenterCrop([height, width]), 
+        py_vision.CenterCrop([height, width]),
         py_vision.ToTensor()
     ]
     transform = py_vision.ComposeOp(transforms)
@@ -100,27 +98,28 @@ def test_center_crop_comp(height=375, width=375, plot=False):
     for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
         c_image = item1["image"]
         py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
-        # the images aren't exactly the same due to rouding error 
-        assert (diff_mse(py_image, c_image) < 0.001)
+        # Note: The images aren't exactly the same due to rounding error
+        assert diff_mse(py_image, c_image) < 0.001
         image_cropped.append(item1["image"].copy())
         image.append(item2["image"].copy())
     if plot:
         visualize(image, image_cropped)
 
 
-def test_crop_grayscale(height=375, width=375): 
+def test_crop_grayscale(height=375, width=375):
     """
-    Test that centercrop works with pad and grayscale images 
+    Test that centercrop works with pad and grayscale images
     """
-    def channel_swap(image): 
+
+    def channel_swap(image):
         """
         Py func hack for our pytransforms to work with c transforms
         """
         return (image.transpose(1, 2, 0) * 255).astype(np.uint8)
-    
+
     transforms = [
         py_vision.Decode(),
-        py_vision.Grayscale(1), 
+        py_vision.Grayscale(1),
         py_vision.ToTensor(),
         (lambda image: channel_swap(image))
     ]
@@ -129,16 +128,16 @@ def test_crop_grayscale(height=375, width=375):
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
     data1 = data1.map(input_columns=["image"], operations=transform())
 
-    # if input is grayscale, the output dimensions should be single channel 
+    # If input is grayscale, the output dimensions should be single channel
     crop_gray = vision.CenterCrop([height, width])
     data1 = data1.map(input_columns=["image"], operations=crop_gray)
 
     for item1 in data1.create_dict_iterator():
         c_image = item1["image"]
-        
-        # check that the image is grayscale
-        assert (len(c_image.shape) == 3 and c_image.shape[2] == 1) 
-        
+
+        # Check that the image is grayscale
+        assert (c_image.ndim == 3 and c_image.shape[2] == 1)
+
 
 if __name__ == "__main__":
     test_center_crop_op(600, 600)

tests/ut/python/dataset/test_general.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# 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 util import save_and_check
-
-import mindspore.dataset as ds
-from mindspore import log as logger
-
-DATA_DIR = ["../data/dataset/testTFTestAllTypes/test.data"]
-SCHEMA_DIR = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
-COLUMNS = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float",
-           "col_sint16", "col_sint32", "col_sint64"]
-GENERATE_GOLDEN = False
-
-
-def test_case_columns_list():
-    """
-    a simple repeat operation.
-    """
-    logger.info("Test Simple Repeat")
-    # define parameters
-    repeat_count = 2
-    parameters = {"params": {'repeat_count': repeat_count}}
-    columns_list = ["col_sint64", "col_sint32"]
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=columns_list, shuffle=False)
-    data1 = data1.repeat(repeat_count)
-
-    filename = "columns_list_result.npz"
-    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)

tests/ut/python/dataset/test_project.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import mindspore.dataset.transforms.vision.c_transforms as vision
+import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore.common import dtype as mstype
-from util import ordered_save_and_check
+from util import save_and_check_tuple
 
-import mindspore.dataset as ds
 
 DATA_DIR_TF = ["../data/dataset/testTFTestAllTypes/test.data"]
 SCHEMA_DIR_TF = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
@@ -32,7 +31,7 @@ def test_case_project_single_column():
     data1 = data1.project(columns=columns)
 
     filename = "project_single_column_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_project_multiple_columns_in_order():
@@ -43,7 +42,7 @@ def test_case_project_multiple_columns_in_order():
     data1 = data1.project(columns=columns)
 
     filename = "project_multiple_columns_in_order_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_project_multiple_columns_out_of_order():
@@ -54,7 +53,7 @@ def test_case_project_multiple_columns_out_of_order():
     data1 = data1.project(columns=columns)
 
     filename = "project_multiple_columns_out_of_order_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_project_map():
@@ -68,7 +67,7 @@ def test_case_project_map():
     data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
 
     filename = "project_map_after_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_map_project():
@@ -83,7 +82,7 @@ def test_case_map_project():
     data1 = data1.project(columns=columns)
 
     filename = "project_map_before_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_project_between_maps():
@@ -107,7 +106,7 @@ def test_case_project_between_maps():
     data1 = data1.map(input_columns=["col_3d"], operations=type_cast_op)
 
     filename = "project_between_maps_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_project_repeat():
@@ -121,7 +120,7 @@ def test_case_project_repeat():
     data1 = data1.repeat(repeat_count)
 
     filename = "project_before_repeat_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_repeat_project():
@@ -136,7 +135,7 @@ def test_case_repeat_project():
     data1 = data1.project(columns=columns)
 
     filename = "project_after_repeat_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_case_map_project_map_project():
@@ -155,4 +154,4 @@ def test_case_map_project_map_project():
     data1 = data1.project(columns=columns)
 
     filename = "project_alternate_parallel_inline_result.npz"
-    ordered_save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_tuple(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)

tests/ut/python/dataset/test_repeat.py

@@ -13,11 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 import mindspore.dataset.transforms.vision.c_transforms as vision
-from util import save_and_check
-
 import mindspore.dataset as ds
 import numpy as np
 from mindspore import log as logger
+from util import save_and_check
 
 DATA_DIR_TF = ["../data/dataset/testTFTestAllTypes/test.data"]
 SCHEMA_DIR_TF = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
@@ -25,13 +24,6 @@ COLUMNS_TF = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float",
               "col_sint16", "col_sint32", "col_sint64"]
 GENERATE_GOLDEN = False
 
-# Data for CIFAR and MNIST are not part of build tree
-# They need to be downloaded directly
-# prep_data.py can be exuted or code below
-# import sys
-# sys.path.insert(0,"../../data")
-# import prep_data
-# prep_data.download_all_for_test("../../data")
 IMG_DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
 IMG_SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
@@ -41,7 +33,7 @@ SCHEMA_DIR_TF2 = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 def test_tf_repeat_01():
     """
-    a simple repeat operation.
+    Test a simple repeat operation.
     """
     logger.info("Test Simple Repeat")
     # define parameters
@@ -58,7 +50,7 @@ def test_tf_repeat_01():
 
 def test_tf_repeat_02():
     """
-    a simple repeat operation to tes infinite
+    Test Infinite Repeat.
     """
     logger.info("Test Infinite Repeat")
     # define parameters
@@ -77,7 +69,10 @@ def test_tf_repeat_02():
 
 
 def test_tf_repeat_03():
-    '''repeat and batch '''
+    """
+    Test Repeat then Batch.
+    """
+    logger.info("Test Repeat then Batch")
     data1 = ds.TFRecordDataset(DATA_DIR_TF2, SCHEMA_DIR_TF2, shuffle=False)
 
     batch_size = 32
@@ -90,15 +85,32 @@ def test_tf_repeat_03():
     data1 = data1.batch(batch_size, drop_remainder=True)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_iter += 1
     logger.info("Number of tf data in data1: {}".format(num_iter))
     assert num_iter == 2
 
 
+def test_tf_repeat_04():
+    """
+    Test a simple repeat operation with column list.
+    """
+    logger.info("Test Simple Repeat Column List")
+    # define parameters
+    repeat_count = 2
+    parameters = {"params": {'repeat_count': repeat_count}}
+    columns_list = ["col_sint64", "col_sint32"]
+    # apply dataset operations
+    data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, columns_list=columns_list, shuffle=False)
+    data1 = data1.repeat(repeat_count)
+
+    filename = "repeat_list_result.npz"
+    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+
+
 def generator():
     for i in range(3):
-        yield np.array([i]),
+        (yield np.array([i]),)
 
 
 def test_nested_repeat1():
@@ -151,7 +163,7 @@ def test_nested_repeat5():
     data = data.repeat(2)
     data = data.repeat(3)
 
-    for i, d in enumerate(data):
+    for _, d in enumerate(data):
         assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
 
     assert sum([1 for _ in data]) == 6
@@ -163,7 +175,7 @@ def test_nested_repeat6():
     data = data.batch(3)
     data = data.repeat(3)
 
-    for i, d in enumerate(data):
+    for _, d in enumerate(data):
         assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
 
     assert sum([1 for _ in data]) == 6
@@ -175,7 +187,7 @@ def test_nested_repeat7():
     data = data.repeat(3)
     data = data.batch(3)
 
-    for i, d in enumerate(data):
+    for _, d in enumerate(data):
         assert np.array_equal(d[0], np.asarray([[0], [1], [2]]))
 
     assert sum([1 for _ in data]) == 6
@@ -232,11 +244,18 @@ def test_nested_repeat11():
 
 
 if __name__ == "__main__":
-    logger.info("--------test tf repeat 01---------")
-    # test_repeat_01()
-
-    logger.info("--------test tf repeat 02---------")
-    # test_repeat_02()
-
-    logger.info("--------test tf repeat 03---------")
+    test_tf_repeat_01()
+    test_tf_repeat_02()
     test_tf_repeat_03()
+    test_tf_repeat_04()
+    test_nested_repeat1()
+    test_nested_repeat2()
+    test_nested_repeat3()
+    test_nested_repeat4()
+    test_nested_repeat5()
+    test_nested_repeat6()
+    test_nested_repeat7()
+    test_nested_repeat8()
+    test_nested_repeat9()
+    test_nested_repeat10()
+    test_nested_repeat11()

tests/ut/python/dataset/util.py

@@ -21,12 +21,13 @@ import matplotlib.pyplot as plt
 #import jsbeautifier
 from mindspore import log as logger
 
+# These are the column names defined in the testTFTestAllTypes dataset
 COLUMNS = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float",
            "col_sint16", "col_sint32", "col_sint64"]
 SAVE_JSON = False
 
 
-def save_golden(cur_dir, golden_ref_dir, result_dict):
+def _save_golden(cur_dir, golden_ref_dir, result_dict):
     """
     Save the dictionary values as the golden result in .npz file
     """
@@ -35,7 +36,7 @@ def save_golden(cur_dir, golden_ref_dir, result_dict):
     np.savez(golden_ref_dir, np.array(list(result_dict.values())))
 
 
-def save_golden_dict(cur_dir, golden_ref_dir, result_dict):
+def _save_golden_dict(cur_dir, golden_ref_dir, result_dict):
     """
     Save the dictionary (both keys and values) as the golden result in .npz file
     """
@@ -44,7 +45,7 @@ def save_golden_dict(cur_dir, golden_ref_dir, result_dict):
     np.savez(golden_ref_dir, np.array(list(result_dict.items())))
 
 
-def compare_to_golden(golden_ref_dir, result_dict):
+def _compare_to_golden(golden_ref_dir, result_dict):
     """
     Compare as numpy arrays the test result to the golden result
     """
@@ -53,16 +54,15 @@ def compare_to_golden(golden_ref_dir, result_dict):
     assert np.array_equal(test_array, golden_array)
 
 
-def compare_to_golden_dict(golden_ref_dir, result_dict):
+def _compare_to_golden_dict(golden_ref_dir, result_dict):
     """
     Compare as dictionaries the test result to the golden result
     """
     golden_array = np.load(golden_ref_dir, allow_pickle=True)['arr_0']
     np.testing.assert_equal(result_dict, dict(golden_array))
-    # assert result_dict == dict(golden_array)
 
 
-def save_json(filename, parameters, result_dict):
+def _save_json(filename, parameters, result_dict):
     """
     Save the result dictionary in json file
     """
@@ -78,6 +78,7 @@ def save_and_check(data, parameters, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as numpy array) with golden file.
     Use create_dict_iterator to access the dataset.
+    Note: save_and_check() is deprecated; use save_and_check_dict().
     """
     num_iter = 0
     result_dict = {}
@@ -97,13 +98,13 @@ def save_and_check(data, parameters, filename, generate_golden=False):
     golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename)
     if generate_golden:
         # Save as the golden result
-        save_golden(cur_dir, golden_ref_dir, result_dict)
+        _save_golden(cur_dir, golden_ref_dir, result_dict)
 
-    compare_to_golden(golden_ref_dir, result_dict)
+    _compare_to_golden(golden_ref_dir, result_dict)
 
     if SAVE_JSON:
         # Save result to a json file for inspection
-        save_json(filename, parameters, result_dict)
+        _save_json(filename, parameters, result_dict)
 
 
 def save_and_check_dict(data, filename, generate_golden=False):
@@ -127,14 +128,14 @@ def save_and_check_dict(data, filename, generate_golden=False):
     golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename)
     if generate_golden:
         # Save as the golden result
-        save_golden_dict(cur_dir, golden_ref_dir, result_dict)
+        _save_golden_dict(cur_dir, golden_ref_dir, result_dict)
 
-    compare_to_golden_dict(golden_ref_dir, result_dict)
+    _compare_to_golden_dict(golden_ref_dir, result_dict)
 
     if SAVE_JSON:
         # Save result to a json file for inspection
         parameters = {"params": {}}
-        save_json(filename, parameters, result_dict)
+        _save_json(filename, parameters, result_dict)
 
 
 def save_and_check_md5(data, filename, generate_golden=False):
@@ -159,22 +160,21 @@ def save_and_check_md5(data, filename, generate_golden=False):
     golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename)
     if generate_golden:
         # Save as the golden result
-        save_golden_dict(cur_dir, golden_ref_dir, result_dict)
+        _save_golden_dict(cur_dir, golden_ref_dir, result_dict)
 
-    compare_to_golden_dict(golden_ref_dir, result_dict)
+    _compare_to_golden_dict(golden_ref_dir, result_dict)
 
 
-def ordered_save_and_check(data, parameters, filename, generate_golden=False):
+def save_and_check_tuple(data, parameters, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as numpy array) with golden file.
     Use create_tuple_iterator to access the dataset.
     """
     num_iter = 0
-
     result_dict = {}
 
     for item in data.create_tuple_iterator():  # each data is a dictionary
-        for data_key in range(0, len(item)):
+        for data_key, _ in enumerate(item):
             if data_key not in result_dict:
                 result_dict[data_key] = []
             result_dict[data_key].append(item[data_key].tolist())
@@ -186,13 +186,13 @@ def ordered_save_and_check(data, parameters, filename, generate_golden=False):
     golden_ref_dir = os.path.join(cur_dir, "../../data/dataset", 'golden', filename)
     if generate_golden:
         # Save as the golden result
-        save_golden(cur_dir, golden_ref_dir, result_dict)
+        _save_golden(cur_dir, golden_ref_dir, result_dict)
 
-    compare_to_golden(golden_ref_dir, result_dict)
+    _compare_to_golden(golden_ref_dir, result_dict)
 
     if SAVE_JSON:
         # Save result to a json file for inspection
-        save_json(filename, parameters, result_dict)
+        _save_json(filename, parameters, result_dict)
 
 
 def diff_mse(in1, in2):