forked from huawei/mindspore2022
304 lines
12 KiB
Python
304 lines
12 KiB
Python
# Copyright 2021 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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Test FakeImage dataset operators
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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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from mindspore import log as logger
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num_images = 50
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image_size = (28, 28, 3)
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num_classes = 10
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base_seed = 0
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def visualize_dataset(images, labels):
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"""
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Helper function to visualize the dataset samples
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"""
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num_samples = len(images)
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for i in range(num_samples):
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plt.subplot(1, num_samples, i + 1)
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plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)
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plt.title(labels[i])
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plt.show()
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def test_fake_image_basic():
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"""
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Feature: FakeImage
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Description: test basic usage of FakeImage
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Expectation: the dataset is as expected
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"""
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logger.info("Test FakeImageDataset Op")
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# case 1: test loading whole dataset
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed)
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num_iter1 = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter1 += 1
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assert num_iter1 == num_images
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# case 2: test num_samples
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_samples=4)
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num_iter2 = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter2 += 1
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assert num_iter2 == 4
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# case 3: test repeat
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_samples=4)
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train_data = train_data.repeat(5)
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num_iter3 = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter3 += 1
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assert num_iter3 == 20
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# case 4: test batch with drop_remainder=False, get_dataset_size, get_batch_size, get_col_names
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_samples=4)
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assert train_data.get_dataset_size() == 4
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assert train_data.get_batch_size() == 1
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assert train_data.get_col_names() == ['image', 'label']
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train_data = train_data.batch(batch_size=3) # drop_remainder is default to be False
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assert train_data.get_dataset_size() == 2
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assert train_data.get_batch_size() == 3
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num_iter4 = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter4 += 1
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assert num_iter4 == 2
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# case 5: test batch with drop_remainder=True
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_samples=4)
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assert train_data.get_dataset_size() == 4
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assert train_data.get_batch_size() == 1
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train_data = train_data.batch(batch_size=3, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert train_data.get_dataset_size() == 1
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assert train_data.get_batch_size() == 3
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num_iter5 = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter5 += 1
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assert num_iter5 == 1
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def test_fake_image_pk_sampler():
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"""
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Feature: FakeImage
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Description: test FakeImageDataset with PKSamplere
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Expectation: the results are as expected
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"""
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logger.info("Test FakeImageDataset Op with PKSampler")
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golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
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#correlation with num_classes
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sampler = ds.PKSampler(3)
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, sampler=sampler)
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num_iter = 0
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label_list = []
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for item in train_data.create_dict_iterator(num_epochs=1, output_numpy=True):
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label_list.append(item["label"])
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num_iter += 1
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np.testing.assert_array_equal(golden, label_list)
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assert num_iter == 30
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def test_fake_image_sequential_sampler():
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"""
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Feature: FakeImage
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Description: test FakeImageDataset with SequentialSampler
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Expectation: the results are as expected
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"""
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logger.info("Test FakeImageDataset Op with SequentialSampler")
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num_samples = 50
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sampler = ds.SequentialSampler(num_samples=num_samples)
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train_data1 = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, sampler=sampler)
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train_data2 = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shuffle=False,
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num_samples=num_samples)
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label_list1, label_list2 = [], []
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num_iter = 0
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for item1, item2 in zip(train_data1.create_dict_iterator(num_epochs=1),
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train_data2.create_dict_iterator(num_epochs=1)):
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label_list1.append(item1["label"].asnumpy())
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label_list2.append(item2["label"].asnumpy())
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num_iter += 1
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np.testing.assert_array_equal(label_list1, label_list2)
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assert num_iter == num_samples
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def test_fake_image_exception():
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"""
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Feature: FakeImage
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Description: test error cases for FakeImageDataset
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Expectation: throw exception correctly
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"""
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logger.info("Test error cases for FakeImageDataset")
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error_msg_1 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_1):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shuffle=False, sampler=ds.PKSampler(3))
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error_msg_2 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, sampler=ds.PKSampler(3), num_shards=2,
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shard_id=0)
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error_msg_3 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_3):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_shards=10)
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error_msg_4 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_4):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shard_id=0)
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error_msg_5 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_5):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_shards=2, shard_id=5)
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error_msg_6 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_6):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, shuffle=False, num_parallel_workers=-2)
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error_msg_7 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_7):
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ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_shards=2, shard_id="0")
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def test_fake_image_visualize(plot=False):
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"""
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Feature: FakeImage
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Description: test FakeImageDataset visualized results
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Expectation: get correct dataset of FakeImage
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"""
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logger.info("Test FakeImageDataset visualization")
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train_data = ds.FakeImageDataset(num_images, image_size, num_classes, base_seed, num_samples=10, shuffle=False)
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num_iter = 0
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image_list, label_list = [], []
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for item in train_data.create_dict_iterator(num_epochs=1, output_numpy=True):
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image = item["image"]
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label = item["label"]
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image_list.append(image)
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label_list.append("label {}".format(label))
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assert isinstance(image, np.ndarray)
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assert image.shape == (28, 28, 3)
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assert image.dtype == np.uint8
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assert label.dtype == np.uint32
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num_iter += 1
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assert num_iter == 10
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if plot:
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visualize_dataset(image_list, label_list)
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def test_fake_image_num_images():
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"""
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Feature: FakeImage
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Description: test FakeImageDataset with num images
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Expectation: throw exception correctly or get correct dataset
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"""
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logger.info("Test FakeImageDataset num_images flag")
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def test_config(test_num_images):
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try:
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data = ds.FakeImageDataset(test_num_images, image_size, num_classes, base_seed, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config(num_images) == num_images
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assert "Input num_images is not within the required interval of [1, 2147483647]." in test_config(-1)
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assert "is not of type [<class 'int'>], but got <class 'str'>." in test_config("10")
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def test_fake_image_image_size():
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"""
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Feature: FakeImage
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Description: test FakeImageDataset with image size
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Expectation: throw exception correctly or get correct dataset
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"""
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logger.info("Test FakeImageDataset image_size flag")
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def test_config(test_image_size):
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try:
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data = ds.FakeImageDataset(num_images, test_image_size, num_classes, base_seed, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config(image_size) == num_images
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assert "Argument image_size[0] with value -1 is not of type [<class 'int'>], but got <class 'str'>."\
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in test_config(("-1", 28, 3))
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assert "image_size should be a list or tuple of length 3, but got 2" in test_config((2, 2))
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assert "Input image_size[0] is not within the required interval of [1, 2147483647]." in test_config((-1, 28, 3))
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def test_fake_image_num_classes():
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"""
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Feature: FakeImage
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Description: test FakeImageDataset with num classes
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Expectation: throw exception correctly or get correct dataset
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"""
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logger.info("Test FakeImageDataset num_classes flag")
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def test_config(test_num_classes):
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try:
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data = ds.FakeImageDataset(num_images, image_size, test_num_classes, base_seed, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config(num_classes) == num_images
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assert "Input num_classes is not within the required interval of [1, 2147483647]." in test_config(-1)
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#should not be negative
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assert "is not of type [<class 'int'>], but got <class 'str'>." in test_config("10")
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if __name__ == '__main__':
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test_fake_image_basic()
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test_fake_image_pk_sampler()
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test_fake_image_sequential_sampler()
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test_fake_image_exception()
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test_fake_image_visualize(plot=True)
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test_fake_image_num_images()
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test_fake_image_image_size()
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test_fake_image_num_classes()
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