Cleanup dataset UT: test_batch, save_and_check support

tests/ut/python/dataset/test_batch.py

@@ -37,6 +37,7 @@ def test_batch_01():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, drop_remainder)
 
+    assert sum([1 for _ in data1]) == 6
     filename = "batch_01_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -56,6 +57,7 @@ def test_batch_02():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, drop_remainder=drop_remainder)
 
+    assert sum([1 for _ in data1]) == 2
     filename = "batch_02_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -75,6 +77,7 @@ def test_batch_03():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size=batch_size, drop_remainder=drop_remainder)
 
+    assert sum([1 for _ in data1]) == 4
     filename = "batch_03_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -94,6 +97,7 @@ def test_batch_04():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, drop_remainder)
 
+    assert sum([1 for _ in data1]) == 2
     filename = "batch_04_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -111,6 +115,7 @@ def test_batch_05():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size)
 
+    assert sum([1 for _ in data1]) == 12
     filename = "batch_05_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -130,6 +135,7 @@ def test_batch_06():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(drop_remainder=drop_remainder, batch_size=batch_size)
 
+    assert sum([1 for _ in data1]) == 1
     filename = "batch_06_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -152,6 +158,7 @@ def test_batch_07():
     data1 = data1.batch(num_parallel_workers=num_parallel_workers, drop_remainder=drop_remainder,
                     batch_size=batch_size)
 
+    assert sum([1 for _ in data1]) == 3
     filename = "batch_07_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -171,6 +178,7 @@ def test_batch_08():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, num_parallel_workers=num_parallel_workers)
 
+    assert sum([1 for _ in data1]) == 2
     filename = "batch_08_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -190,6 +198,7 @@ def test_batch_09():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, drop_remainder=drop_remainder)
 
+    assert sum([1 for _ in data1]) == 1
     filename = "batch_09_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -209,6 +218,7 @@ def test_batch_10():
     data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
     data1 = data1.batch(batch_size, drop_remainder=drop_remainder)
 
+    assert sum([1 for _ in data1]) == 0
     filename = "batch_10_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
@@ -228,10 +238,30 @@ def test_batch_11():
     data1 = ds.TFRecordDataset(DATA_DIR, schema_file)
     data1 = data1.batch(batch_size)
 
+    assert sum([1 for _ in data1]) == 1
     filename = "batch_11_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
 
 
+def test_batch_12():
+    """
+    Test batch: batch_size boolean value True, treated as valid value 1
+    """
+    logger.info("test_batch_12")
+    # define parameters
+    batch_size = True
+    parameters = {"params": {'batch_size': batch_size}}
+
+    # apply dataset operations
+    data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
+    data1 = data1.batch(batch_size=batch_size)
+
+    assert sum([1 for _ in data1]) == 12
+    filename = "batch_12_result.npz"
+    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+
+
+
 def test_batch_exception_01():
     """
     Test batch exception: num_parallel_workers=0
@@ -302,7 +332,7 @@ def test_batch_exception_04():
 
 def test_batch_exception_05():
     """
-    Test batch exception: batch_size wrong type, boolean value False
+    Test batch exception: batch_size boolean value False, treated as invalid value 0
     """
     logger.info("test_batch_exception_05")
 
@@ -317,23 +347,6 @@ def test_batch_exception_05():
         assert "batch_size" in str(e)
 
 
-def skip_test_batch_exception_06():
-    """
-    Test batch exception: batch_size wrong type, boolean value True
-    """
-    logger.info("test_batch_exception_06")
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, shuffle=ds.Shuffle.FILES)
-    try:
-        data1 = data1.batch(batch_size=True)
-        sum([1 for _ in data1])
-
-    except BaseException as e:
-        logger.info("Got an exception in DE: {}".format(str(e)))
-        assert "batch_size" in str(e)
-
-
 def test_batch_exception_07():
     """
     Test batch exception: drop_remainder wrong type
@@ -473,12 +486,12 @@ if __name__ == '__main__':
     test_batch_09()
     test_batch_10()
     test_batch_11()
+    test_batch_12()
     test_batch_exception_01()
     test_batch_exception_02()
     test_batch_exception_03()
     test_batch_exception_04()
     test_batch_exception_05()
-    skip_test_batch_exception_06()
     test_batch_exception_07()
     test_batch_exception_08()
     test_batch_exception_09()

tests/ut/python/dataset/test_center_crop.py

@@ -28,7 +28,7 @@ SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 def test_center_crop_op(height=375, width=375, plot=False):
     """
-    Test random_vertical
+    Test CenterCrop
     """
     logger.info("Test CenterCrop")
 
@@ -55,7 +55,7 @@ def test_center_crop_op(height=375, width=375, plot=False):
 
 def test_center_crop_md5(height=375, width=375):
     """
-    Test random_vertical
+    Test CenterCrop
     """
     logger.info("Test CenterCrop")
 
@@ -69,13 +69,12 @@ def test_center_crop_md5(height=375, width=375):
     # expected md5 from images 
 
     filename = "test_center_crop_01_result.npz"
-    parameters = {"params": {}}
-    save_and_check_md5(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_center_crop_comp(height=375, width=375, plot=False):
     """
-    Test random_vertical between python and c image augmentation
+    Test CenterCrop between python and c image augmentation
     """
     logger.info("Test CenterCrop")
 
@@ -114,4 +113,4 @@ if __name__ == "__main__":
     test_center_crop_op(300, 600)
     test_center_crop_op(600, 300)
     test_center_crop_md5(600, 600)
-    test_center_crop_comp()
\ No newline at end of file
+    test_center_crop_comp()

tests/ut/python/dataset/test_decode.py

@@ -18,9 +18,7 @@ Testing Decode op in DE
 import cv2
 import mindspore.dataset.transforms.vision.c_transforms as vision
 import numpy as np
-
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]

tests/ut/python/dataset/test_random_color_adjust.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomRotation op in DE
+Testing RandomColorAdjust op in DE
 """
 import matplotlib.pyplot as plt
 import mindspore.dataset.transforms.vision.c_transforms as c_vision

tests/ut/python/dataset/test_random_erasing.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomRotation op in DE
+Testing RandomErasing op in DE
 """
 import matplotlib.pyplot as plt
 import numpy as np

tests/ut/python/dataset/test_random_resize.py

@@ -17,9 +17,7 @@ Testing the resize op in DE
 """
 import matplotlib.pyplot as plt
 import mindspore.dataset.transforms.vision.c_transforms as vision
-
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]

tests/ut/python/dataset/test_type_cast.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing RandomRotation op in DE
+Testing TypeCast op in DE
 """
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
 import mindspore.dataset.transforms.vision.py_transforms as py_vision
@@ -31,9 +31,9 @@ SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 def test_type_cast():
     """
-    Test type_cast_op
+    Test TypeCast op
     """
-    logger.info("test_type_cast_op")
+    logger.info("test_type_cast")
 
     # First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
@@ -71,9 +71,9 @@ def test_type_cast():
 
 def test_type_cast_string():
     """
-    Test type_cast_op
+    Test TypeCast op
     """
-    logger.info("test_type_cast_op")
+    logger.info("test_type_cast_string")
 
     # First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)

tests/ut/python/dataset/test_zip.py

@@ -44,8 +44,7 @@ def test_zip_01():
     dataz = ds.zip((data1, data2))
     # Note: zipped dataset has 5 rows and 7 columns
     filename = "zip_01_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_02():
@@ -59,8 +58,7 @@ def test_zip_02():
     dataz = ds.zip((data1, data2))
     # Note: zipped dataset has 3 rows and 4 columns
     filename = "zip_02_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_03():
@@ -74,8 +72,7 @@ def test_zip_03():
     dataz = ds.zip((data1, data2))
     # Note: zipped dataset has 3 rows and 7 columns
     filename = "zip_03_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_04():
@@ -90,8 +87,7 @@ def test_zip_04():
     dataz = ds.zip((data1, data2, data3))
     # Note: zipped dataset has 3 rows and 9 columns
     filename = "zip_04_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_05():
@@ -109,8 +105,7 @@ def test_zip_05():
     dataz = ds.zip((data1, data2))
     # Note: zipped dataset has 5 rows and 9 columns
     filename = "zip_05_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_06():
@@ -129,8 +124,7 @@ def test_zip_06():
     dataz = dataz.repeat(2)
     # Note: resultant dataset has 10 rows and 9 columns
     filename = "zip_06_result.npz"
-    parameters = {"params": {}}
-    save_and_check_dict(dataz, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(dataz, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_zip_exception_01():

tests/ut/python/dataset/util.py

@@ -15,15 +15,15 @@
 
 import json
 import os
+import hashlib
 import numpy as np
 import matplotlib.pyplot as plt
-import hashlib
-
 #import jsbeautifier
 from mindspore import log as logger
 
 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):
@@ -44,15 +44,6 @@ def save_golden_dict(cur_dir, golden_ref_dir, result_dict):
     np.savez(golden_ref_dir, np.array(list(result_dict.items())))
 
 
-def save_golden_md5(cur_dir, golden_ref_dir, result_dict):
-    """
-    Save the dictionary (both keys and values) as the golden result in .npz file
-    """
-    logger.info("cur_dir is {}".format(cur_dir))
-    logger.info("golden_ref_dir is {}".format(golden_ref_dir))
-    np.savez(golden_ref_dir, np.array(list(result_dict.items())))
-
-
 def compare_to_golden(golden_ref_dir, result_dict):
     """
     Compare as numpy arrays the test result to the golden result
@@ -67,7 +58,7 @@ 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))
+    np.testing.assert_equal(result_dict, dict(golden_array))
     # assert result_dict == dict(golden_array)
 
 
@@ -83,7 +74,6 @@ def save_json(filename, parameters, result_dict):
     fout.write(jsbeautifier.beautify(json.dumps(out_dict), options))
 
 
-
 def save_and_check(data, parameters, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as numpy array) with golden file.
@@ -111,11 +101,12 @@ def save_and_check(data, parameters, filename, generate_golden=False):
 
     compare_to_golden(golden_ref_dir, result_dict)
 
-    # Save to a json file for inspection
-    # save_json(filename, parameters, result_dict)
+    if SAVE_JSON:
+        # Save result to a json file for inspection
+        save_json(filename, parameters, result_dict)
 
 
-def save_and_check_dict(data, parameters, filename, generate_golden=False):
+def save_and_check_dict(data, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as dictionary) with golden file.
     Use create_dict_iterator to access the dataset.
@@ -140,11 +131,13 @@ def save_and_check_dict(data, parameters, filename, generate_golden=False):
 
     compare_to_golden_dict(golden_ref_dir, result_dict)
 
-    # Save to a json file for inspection
-    # save_json(filename, parameters, result_dict)
+    if SAVE_JSON:
+        # Save result to a json file for inspection
+        parameters = {"params": {}}
+        save_json(filename, parameters, result_dict)
 
 
-def save_and_check_md5(data, parameters, filename, generate_golden=False):
+def save_and_check_md5(data, filename, generate_golden=False):
     """
     Save the dataset dictionary and compare (as dictionary) with golden file (md5).
     Use create_dict_iterator to access the dataset.
@@ -197,8 +190,9 @@ def ordered_save_and_check(data, parameters, filename, generate_golden=False):
 
     compare_to_golden(golden_ref_dir, result_dict)
 
-    # Save to a json file for inspection
-    # save_json(filename, parameters, result_dict)
+    if SAVE_JSON:
+        # Save result to a json file for inspection
+        save_json(filename, parameters, result_dict)
 
 
 def diff_mse(in1, in2):
@@ -211,24 +205,18 @@ def diff_me(in1, in2):
     return mse / 255 * 100
 
 
-def diff_ssim(in1, in2):
-    from skimage.measure import compare_ssim as ssim
-    val = ssim(in1, in2, multichannel=True)
-    return (1 - val) * 100
-
-
 def visualize(image_original, image_transformed):
     """
     visualizes the image using DE op and Numpy op
     """
-    num = len(image_cropped)
+    num = len(image_transformed)
     for i in range(num):
         plt.subplot(2, num, i + 1)
         plt.imshow(image_original[i])
         plt.title("Original image")
 
         plt.subplot(2, num, i + num + 1)
-        plt.imshow(image_cropped[i])
+        plt.imshow(image_transformed[i])
         plt.title("Transformed image")
 
     plt.show()