253 lines
11 KiB
Python
253 lines
11 KiB
Python
# Copyright 2021-2022 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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import matplotlib.pyplot as plt
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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.vision as vision
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DATASET_DIR = "../data/dataset/testDIV2KData/div2k"
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def test_div2k_basic(plot=False):
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"""
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Feature: DIV2KDataset
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Description: Test basic read on DIV2KDataset
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Expectation: The dataset is processed as expected
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"""
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usage = "train" # train, valid, all
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downgrade = "bicubic" # bicubic, unknown, mild, difficult, wild
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scale = 2 # 2, 3, 4, 8
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data = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, decode=True)
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count = 0
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hr_images_list = []
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lr_images_list = []
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for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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hr_images_list.append(item['hr_image'])
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lr_images_list.append(item['lr_image'])
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count = count + 1
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assert count == 5
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if plot:
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flag = "{}_{}_{}".format(usage, scale, downgrade)
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visualize_dataset(hr_images_list, lr_images_list, flag)
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def visualize_dataset(hr_images_list, lr_images_list, flag):
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"""
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Helper function to visualize the dataset samples
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"""
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image_num = len(hr_images_list)
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for i in range(image_num):
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plt.subplot(121)
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plt.imshow(hr_images_list[i])
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plt.title('Original')
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plt.subplot(122)
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plt.imshow(lr_images_list[i])
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plt.title(flag)
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plt.savefig('./div2k_{}_{}.jpg'.format(flag, str(i)))
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def test_div2k_basic_func():
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"""
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Feature: DIV2KDataset
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Description: Test basic functions for DIV2KDataset
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Expectation: The dataset is processed as expected
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"""
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# case 0: test usage equal to `all`
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usage = "all" # train, valid, all
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downgrade = "bicubic" # bicubic, unknown, mild, difficult, wild
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scale = 2 # 2, 3, 4, 8
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data0 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale)
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num_iter0 = 0
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for _ in data0.create_dict_iterator(num_epochs=1):
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num_iter0 += 1
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assert num_iter0 == 6
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# case 1: test num_samples
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usage = "train" # train, valid, all
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data1 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_samples=4)
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num_iter1 = 0
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for _ in data1.create_dict_iterator(num_epochs=1):
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num_iter1 += 1
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assert num_iter1 == 4
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# case 2: test repeat
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data2 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_samples=3)
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data2 = data2.repeat(5)
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num_iter2 = 0
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for _ in data2.create_dict_iterator(num_epochs=1):
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num_iter2 += 1
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assert num_iter2 == 15
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# case 3: test batch with drop_remainder=False
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data3 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, decode=True)
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assert data3.get_dataset_size() == 5
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assert data3.get_batch_size() == 1
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resize_op = vision.Resize([100, 100])
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data3 = data3.map(operations=resize_op, input_columns=["hr_image"], num_parallel_workers=1)
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data3 = data3.map(operations=resize_op, input_columns=["lr_image"], num_parallel_workers=1)
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data3 = data3.batch(batch_size=3) # drop_remainder is default to be False
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assert data3.get_dataset_size() == 2
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assert data3.get_batch_size() == 3
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num_iter3 = 0
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for _ in data3.create_dict_iterator(num_epochs=1):
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num_iter3 += 1
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assert num_iter3 == 2
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# case 4: test batch with drop_remainder=True
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data4 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, decode=True)
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assert data4.get_dataset_size() == 5
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assert data4.get_batch_size() == 1
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data4 = data4.map(operations=resize_op, input_columns=["hr_image"], num_parallel_workers=1)
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data4 = data4.map(operations=resize_op, input_columns=["lr_image"], num_parallel_workers=1)
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data4 = data4.batch(batch_size=3, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data4.get_dataset_size() == 1
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assert data4.get_batch_size() == 3
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num_iter4 = 0
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for _ in data4.create_dict_iterator(num_epochs=1):
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num_iter4 += 1
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assert num_iter4 == 1
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# case 5: test get_col_names
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data5 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_samples=1)
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assert data5.get_col_names() == ["hr_image", "lr_image"]
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def test_div2k_sequential_sampler():
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"""
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Feature: DIV2KDataset
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Description: Test DIV2KDataset with SequentialSampler
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Expectation: The dataset is processed as expected
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"""
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usage = "train" # train, valid, all
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downgrade = "bicubic" # bicubic, unknown, mild, difficult, wild
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scale = 2 # 2, 3, 4, 8
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num_samples = 2
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sampler = ds.SequentialSampler(num_samples=num_samples)
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data1 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, sampler=sampler)
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data2 = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shuffle=False,
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num_samples=num_samples)
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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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np.testing.assert_array_equal(item1["hr_image"], item2["hr_image"])
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np.testing.assert_array_equal(item1["lr_image"], item2["lr_image"])
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num_iter += 1
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assert num_iter == num_samples
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def test_div2k_exception():
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"""
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Feature: DIV2KDataset
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Description: Test invalid parameters for DIV2KDataset
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Expectation: Throw correct error as expected
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"""
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usage = "train" # train, valid, all
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downgrade = "bicubic" # bicubic, unknown, mild, difficult, wild
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scale = 2 # 2, 3, 4, 8
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error_msg_1 = "does not exist or is not a directory or permission denied!"
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with pytest.raises(ValueError, match=error_msg_1):
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ds.DIV2KDataset("NoExistsDir", usage=usage, downgrade=downgrade, scale=scale)
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error_msg_2 = r"Input usage is not within the valid set of \['train', 'valid', 'all'\]."
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with pytest.raises(ValueError, match=error_msg_2):
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ds.DIV2KDataset(DATASET_DIR, usage="test", downgrade=downgrade, scale=scale)
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error_msg_3 = r"Input scale is not within the valid set of \[2, 3, 4, 8\]."
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with pytest.raises(ValueError, match=error_msg_3):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, scale=16, downgrade=downgrade)
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error_msg_4 = r"Input downgrade is not within the valid set of .*"
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with pytest.raises(ValueError, match=error_msg_4):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, scale=scale, downgrade="downgrade")
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error_msg_5 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_5):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shuffle=False,
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sampler=ds.PKSampler(3))
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error_msg_6 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_6):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=2, shard_id=0,
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sampler=ds.PKSampler(3))
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error_msg_7 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_7):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=10)
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error_msg_8 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_8):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shard_id=0)
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error_msg_9 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_9):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_9):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_9):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=2, shard_id=5)
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error_msg_10 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_10):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shuffle=False,
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num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_10):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shuffle=False,
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num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_10):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, shuffle=False,
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num_parallel_workers=-2)
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error_msg_11 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_11):
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ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale, num_shards=2, shard_id="0")
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def exception_func(item):
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raise Exception("Error occur!")
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try:
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data = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale)
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data = data.map(operations=exception_func, input_columns=["hr_image"], num_parallel_workers=1)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1):
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num_rows += 1
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assert False
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except RuntimeError as e:
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assert "map operation: [PyFunc] failed. The corresponding data files:" in str(e)
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try:
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data = ds.DIV2KDataset(DATASET_DIR, usage=usage, downgrade=downgrade, scale=scale)
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data = data.map(operations=exception_func, input_columns=["hr_image"], num_parallel_workers=1)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1):
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num_rows += 1
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assert False
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except RuntimeError as e:
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assert "map operation: [PyFunc] failed. The corresponding data files:" in str(e)
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if __name__ == "__main__":
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test_div2k_basic()
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test_div2k_basic_func()
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test_div2k_sequential_sampler()
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test_div2k_exception()
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