354 lines
13 KiB
Python
354 lines
13 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing RandomAffine op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.py_transforms as py_vision
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import mindspore.dataset.vision.c_transforms as c_vision
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from mindspore import log as logger
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from util import visualize_list, save_and_check_md5, \
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config_get_set_seed, config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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def test_random_affine_op(plot=False):
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"""
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Test RandomAffine in python transformations
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"""
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logger.info("test_random_affine_op")
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# define map operations
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transforms1 = [
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py_vision.Decode(),
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py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
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py_vision.ToTensor()
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]
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transform1 = mindspore.dataset.transforms.py_transforms.Compose(transforms1)
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transforms2 = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform2 = mindspore.dataset.transforms.py_transforms.Compose(transforms2)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(input_columns=["image"], operations=transform1)
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=transform2)
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image_affine = []
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image_original = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_affine.append(image1)
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image_original.append(image2)
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if plot:
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visualize_list(image_original, image_affine)
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def test_random_affine_op_c(plot=False):
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"""
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Test RandomAffine in C transformations
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"""
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logger.info("test_random_affine_op_c")
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# define map operations
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transforms1 = [
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c_vision.Decode(),
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c_vision.RandomAffine(degrees=0, translate=(0.5, 0.5, 0, 0))
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]
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transforms2 = [
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c_vision.Decode()
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]
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(input_columns=["image"], operations=transforms1)
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=transforms2)
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image_affine = []
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image_original = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image1 = item1["image"]
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image2 = item2["image"]
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image_affine.append(image1)
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image_original.append(image2)
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if plot:
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visualize_list(image_original, image_affine)
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def test_random_affine_md5():
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"""
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Test RandomAffine with md5 comparison
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"""
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logger.info("test_random_affine_md5")
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original_seed = config_get_set_seed(55)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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py_vision.Decode(),
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py_vision.RandomAffine(degrees=(-5, 15), translate=(0.1, 0.3),
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scale=(0.9, 1.1), shear=(-10, 10, -5, 5)),
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py_vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data = data.map(input_columns=["image"], operations=transform)
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# check results with md5 comparison
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filename = "random_affine_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers((original_num_parallel_workers))
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def test_random_affine_c_md5():
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"""
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Test RandomAffine C Op with md5 comparison
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"""
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logger.info("test_random_affine_c_md5")
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original_seed = config_get_set_seed(1)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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c_vision.Decode(),
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c_vision.RandomAffine(degrees=(-5, 15), translate=(-0.1, 0.1, -0.3, 0.3),
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scale=(0.9, 1.1), shear=(-10, 10, -5, 5))
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]
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data = data.map(input_columns=["image"], operations=transforms)
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# check results with md5 comparison
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filename = "random_affine_01_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers((original_num_parallel_workers))
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def test_random_affine_default_c_md5():
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"""
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Test RandomAffine C Op (default params) with md5 comparison
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"""
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logger.info("test_random_affine_default_c_md5")
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original_seed = config_get_set_seed(1)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# define map operations
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transforms = [
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c_vision.Decode(),
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c_vision.RandomAffine(degrees=0)
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]
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data = data.map(input_columns=["image"], operations=transforms)
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# check results with md5 comparison
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filename = "random_affine_01_default_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers((original_num_parallel_workers))
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def test_random_affine_py_exception_non_pil_images():
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"""
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Test RandomAffine: input img is ndarray and not PIL, expected to raise RuntimeError
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"""
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logger.info("test_random_affine_exception_negative_degrees")
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dataset = ds.MnistDataset(MNIST_DATA_DIR, num_parallel_workers=3)
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try:
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transform = mindspore.dataset.transforms.py_transforms.Compose([py_vision.ToTensor(),
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py_vision.RandomAffine(degrees=(15, 15))])
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dataset = dataset.map(input_columns=["image"], operations=transform, num_parallel_workers=3,
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python_multiprocessing=True)
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for _ in dataset.create_dict_iterator(num_epochs=1):
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break
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Pillow image" in str(e)
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def test_random_affine_exception_negative_degrees():
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"""
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Test RandomAffine: input degrees in negative, expected to raise ValueError
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"""
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logger.info("test_random_affine_exception_negative_degrees")
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try:
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_ = py_vision.RandomAffine(degrees=-15)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input degrees is not within the required interval of (0 to inf)."
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def test_random_affine_exception_translation_range():
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"""
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Test RandomAffine: translation value is not in [-1, 1], expected to raise ValueError
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"""
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logger.info("test_random_affine_exception_translation_range")
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try:
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_ = c_vision.RandomAffine(degrees=15, translate=(0.1, 1.5))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input translate at 1 is not within the required interval of (-1.0 to 1.0)."
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logger.info("test_random_affine_exception_translation_range")
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try:
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_ = c_vision.RandomAffine(degrees=15, translate=(-2, 1.5))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input translate at 0 is not within the required interval of (-1.0 to 1.0)."
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def test_random_affine_exception_scale_value():
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"""
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Test RandomAffine: scale is not positive, expected to raise ValueError
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"""
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logger.info("test_random_affine_exception_scale_value")
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try:
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_ = py_vision.RandomAffine(degrees=15, scale=(0.0, 1.1))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input scale[0] must be greater than 0."
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try:
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_ = py_vision.RandomAffine(degrees=15, scale=(2.0, 1.1))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input scale[1] must be equal to or greater than scale[0]."
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def test_random_affine_exception_shear_value():
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"""
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Test RandomAffine: shear is a number but is not positive, expected to raise ValueError
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"""
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logger.info("test_random_affine_exception_shear_value")
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try:
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_ = py_vision.RandomAffine(degrees=15, shear=-5)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input shear must be greater than 0."
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try:
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_ = py_vision.RandomAffine(degrees=15, shear=(5, 1))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input shear[1] must be equal to or greater than shear[0]"
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try:
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_ = py_vision.RandomAffine(degrees=15, shear=(5, 1, 2, 8))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input shear[1] must be equal to or greater than shear[0] and " \
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"shear[3] must be equal to or greater than shear[2]."
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try:
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_ = py_vision.RandomAffine(degrees=15, shear=(5, 9, 2, 1))
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Input shear[1] must be equal to or greater than shear[0] and " \
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"shear[3] must be equal to or greater than shear[2]."
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def test_random_affine_exception_degrees_size():
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"""
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Test RandomAffine: degrees is a list or tuple and its length is not 2,
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expected to raise TypeError
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"""
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logger.info("test_random_affine_exception_degrees_size")
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try:
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_ = py_vision.RandomAffine(degrees=[15])
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "If degrees is a sequence, the length must be 2."
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def test_random_affine_exception_translate_size():
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"""
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Test RandomAffine: translate is not list or a tuple of length 2,
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expected to raise TypeError
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"""
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logger.info("test_random_affine_exception_translate_size")
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try:
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_ = py_vision.RandomAffine(degrees=15, translate=(0.1))
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(
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e) == "Argument translate with value 0.1 is not of type (<class 'list'>," \
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" <class 'tuple'>)."
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def test_random_affine_exception_scale_size():
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"""
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Test RandomAffine: scale is not a list or tuple of length 2,
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expected to raise TypeError
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"""
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logger.info("test_random_affine_exception_scale_size")
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try:
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_ = py_vision.RandomAffine(degrees=15, scale=(0.5))
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Argument scale with value 0.5 is not of type (<class 'tuple'>," \
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" <class 'list'>)."
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def test_random_affine_exception_shear_size():
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"""
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Test RandomAffine: shear is not a list or tuple of length 2 or 4,
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expected to raise TypeError
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"""
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logger.info("test_random_affine_exception_shear_size")
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try:
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_ = py_vision.RandomAffine(degrees=15, shear=(-5, 5, 10))
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "shear must be of length 2 or 4."
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if __name__ == "__main__":
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test_random_affine_op(plot=True)
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test_random_affine_op_c(plot=True)
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test_random_affine_md5()
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test_random_affine_c_md5()
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test_random_affine_default_c_md5()
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test_random_affine_py_exception_non_pil_images()
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test_random_affine_exception_negative_degrees()
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test_random_affine_exception_translation_range()
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test_random_affine_exception_scale_value()
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test_random_affine_exception_shear_value()
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test_random_affine_exception_degrees_size()
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test_random_affine_exception_translate_size()
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test_random_affine_exception_scale_size()
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test_random_affine_exception_shear_size()
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