forked from mindspore-Ecosystem/mindspore
242 lines
8.3 KiB
Python
242 lines
8.3 KiB
Python
# Copyright 2019-2022 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 CutOut op in DE
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"""
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import numpy as np
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import pytest
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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.c_transforms as c
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import mindspore.dataset.vision.py_transforms as f
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from mindspore import log as logger
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from ..dataset.util import visualize_image, visualize_list, diff_mse, save_and_check_md5, save_and_check_md5_pil, \
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config_get_set_seed, config_get_set_num_parallel_workers
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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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GENERATE_GOLDEN = False
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def test_cut_out_op(plot=False):
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"""
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Feature: CutOut Op
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Description: Test CutOut C++ op
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Expectation: Dataset pipeline runs successfully and results are verified against RandomErasing Op
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"""
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logger.info("test_cut_out")
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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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transforms_1 = [
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f.Decode(),
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f.ToTensor(),
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f.RandomErasing(value='random')
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]
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transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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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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decode_op = c.Decode()
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cut_out_op = c.CutOut(80)
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transforms_2 = [
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decode_op,
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cut_out_op
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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mse = diff_mse(image_1, image_2)
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if plot:
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visualize_image(image_1, image_2, mse)
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def test_cut_out_op_multicut(plot=False):
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"""
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Feature: CutOut Op
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Description: Test CutOut o with multiple patches
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Expectation: Dataset pipeline runs successfully and results are visually verified
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"""
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logger.info("test_cut_out")
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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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transforms_1 = [
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f.Decode(),
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f.ToTensor(),
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]
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transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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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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decode_op = c.Decode()
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cut_out_op = c.CutOut(80, num_patches=10)
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transforms_2 = [
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decode_op,
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cut_out_op
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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image_list_1, image_list_2 = [], []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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image_list_1.append(image_1)
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image_list_2.append(image_2)
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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if plot:
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visualize_list(image_list_1, image_list_2)
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def test_cut_out_md5():
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"""
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Feature: CutOut Op
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Description: Test CutOut C++ op and Cutout Py op with md5 check
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Expectation: Dataset pipeline runs successfully and md5 results are verified
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"""
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logger.info("test_cut_out_md5")
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original_seed = config_get_set_seed(2)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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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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decode_op = c.Decode()
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cut_out_op = c.CutOut(100)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=cut_out_op, input_columns=["image"])
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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f.Decode(),
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f.ToTensor(),
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f.Cutout(100)
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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data2 = data2.map(operations=transform, input_columns=["image"])
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# Compare with expected md5 from images
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filename1 = "cut_out_01_c_result.npz"
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save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
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filename2 = "cut_out_01_py_result.npz"
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save_and_check_md5_pil(data2, filename2, generate_golden=GENERATE_GOLDEN)
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# Restore config
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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_cut_out_comp(plot=False):
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"""
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Feature: CutOut Op
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Description: Test Test CutOut C++ op and Cutout Py op
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Expectation: Dataset pipeline runs successfully and results are visually verified
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"""
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logger.info("test_cut_out_comp")
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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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transforms_1 = [
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f.Decode(),
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f.ToTensor(),
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f.Cutout(200)
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]
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transform_1 = mindspore.dataset.transforms.py_transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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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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transforms_2 = [
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c.Decode(),
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c.CutOut(200)
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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image_list_1, image_list_2 = [], []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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image_list_1.append(image_1)
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image_list_2.append(image_2)
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if plot:
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visualize_list(image_list_1, image_list_2, visualize_mode=2)
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def test_cut_out_validation():
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"""
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Feature: CutOut op
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Description: Test CutOut Op with patch length greater than image dimensions
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Expectation: Raises an exception
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"""
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image = np.random.randn(3, 1024, 856).astype(np.uint8)
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op = f.Cutout(length=1500, num_patches=3)
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with pytest.raises(ValueError) as errinfo:
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op(image)
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assert 'Patch length is too large' in str(errinfo.value)
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if __name__ == "__main__":
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test_cut_out_op(plot=True)
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test_cut_out_op_multicut(plot=True)
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test_cut_out_md5()
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test_cut_out_comp(plot=True)
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test_cut_out_validation()
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