367 lines
14 KiB
Python
367 lines
14 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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"""
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Test SVHN dataset operators
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"""
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import os
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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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from scipy.io import loadmat
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import mindspore.dataset as ds
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testSVHNData"
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WRONG_DIR = "../data/dataset/testMnistData"
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def load_mat(mode, path):
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"""
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Feature: load_mat.
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Description: Load .mat file.
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Expectation: Get .mat of svhn dataset.
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"""
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filename = mode + "_32x32.mat"
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mat_data = loadmat(os.path.realpath(os.path.join(path, filename)))
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data = np.transpose(mat_data['X'], [3, 0, 1, 2])
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label = mat_data['y'].astype(np.uint32).squeeze()
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np.place(label, label == 10, 0)
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return data, label
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def load_svhn(path, usage):
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"""
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Feature: load_svhn.
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Description: Load svhn.
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Expectation: Get data of svhn dataset.
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"""
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assert usage in ["train", "test", "extra", "all"]
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usage_all = ["train", "test", "extra"]
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data = np.array([], dtype=np.uint8)
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label = np.array([], dtype=np.uint32)
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if usage == "all":
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for _usage in usage_all:
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current_data, current_label = load_mat(_usage, path)
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data = np.concatenate((data, current_data)) if data.size else current_data
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label = np.concatenate((label, current_label)) if label.size else current_label
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else:
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data, label = load_mat(usage, path)
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return data, label
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def visualize_dataset(images, labels):
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"""
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Feature: visualize_dataset.
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Description: Visualize svhn dataset.
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Expectation: Plot images.
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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])
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plt.title(labels[i])
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plt.show()
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def test_svhn_content_check():
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"""
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Feature: test_svhn_content_check.
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Description: Validate SVHNDataset image readings.
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Expectation: Get correct number of data and correct content.
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"""
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logger.info("Test SVHNDataset Op with content check")
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2, shuffle=False)
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images, labels = load_svhn(DATA_DIR, "train")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, data in enumerate(train_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 2
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test_data = ds.SVHNDataset(DATA_DIR, "test", num_samples=4, shuffle=False)
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images, labels = load_svhn(DATA_DIR, "test")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, data in enumerate(test_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 4
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extra_data = ds.SVHNDataset(DATA_DIR, "extra", num_samples=6, shuffle=False)
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images, labels = load_svhn(DATA_DIR, "extra")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, data in enumerate(extra_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 6
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all_data = ds.SVHNDataset(DATA_DIR, "all", num_samples=12, shuffle=False)
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images, labels = load_svhn(DATA_DIR, "all")
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num_iter = 0
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# in this example, each dictionary has keys "image" and "label".
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for i, data in enumerate(all_data.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(data["image"], images[i])
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np.testing.assert_array_equal(data["label"], labels[i])
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num_iter += 1
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assert num_iter == 12
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def test_svhn_basic():
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"""
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Feature: test_svhn_basic.
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Description: Test basic usage of SVHNDataset.
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Expectation: Get correct number of data.
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"""
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logger.info("Test SVHNDataset Op")
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# case 1: test loading whole dataset.
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default_data = ds.SVHNDataset(DATA_DIR)
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num_iter = 0
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for _ in default_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 12
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all_data = ds.SVHNDataset(DATA_DIR, "all")
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 12
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# case 2: test num_samples.
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2)
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num_iter = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 2
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# case 3: test repeat.
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2)
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train_data = train_data.repeat(5)
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num_iter = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 10
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# case 4: test batch with drop_remainder=False.
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2)
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assert train_data.get_dataset_size() == 2
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assert train_data.get_batch_size() == 1
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train_data = train_data.batch(batch_size=2) # drop_remainder is default to be False.
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assert train_data.get_batch_size() == 2
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assert train_data.get_dataset_size() == 1
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num_iter = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 1
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# case 5: test batch with drop_remainder=True.
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2)
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assert train_data.get_dataset_size() == 2
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assert train_data.get_batch_size() == 1
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train_data = train_data.batch(batch_size=2, 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() == 2
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num_iter = 0
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for _ in train_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 1
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# case 6: test num_parallel_workers>1
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shared_mem_flag = ds.config.get_enable_shared_mem()
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ds.config.set_enable_shared_mem(False)
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all_data = ds.SVHNDataset(DATA_DIR, "all", num_parallel_workers=2)
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num_iter = 0
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for _ in all_data.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 12
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ds.config.set_enable_shared_mem(shared_mem_flag)
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# case 7: test map method
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input_columns = ["image"]
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image1, image2 = [], []
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dataset = ds.SVHNDataset(DATA_DIR, "all")
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for data in dataset.create_dict_iterator(output_numpy=True):
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image1.extend(data['image'])
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operations = [(lambda x: x + x)]
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dataset = dataset.map(input_columns=input_columns, operations=operations)
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for data in dataset.create_dict_iterator(output_numpy=True):
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image2.extend(data['image'])
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assert len(image1) == len(image2)
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# case 8: test batch
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dataset = ds.SVHNDataset(DATA_DIR, "all")
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dataset = dataset.batch(batch_size=3)
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num_iter = 0
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for data in dataset.create_dict_iterator(output_numpy=True):
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num_iter += 1
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assert num_iter == 4
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def test_svhn_sequential_sampler():
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"""
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Feature: test_svhn_sequential_sampler.
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Description: Test usage of SVHNDataset with SequentialSampler.
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Expectation: Get correct number of data.
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"""
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logger.info("Test SVHNDataset Op with SequentialSampler")
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num_samples = 2
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sampler = ds.SequentialSampler(num_samples=num_samples)
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train_data_1 = ds.SVHNDataset(DATA_DIR, "train", sampler=sampler)
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train_data_2 = ds.SVHNDataset(DATA_DIR, "train", shuffle=False, num_samples=num_samples)
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label_list_1, label_list_2 = [], []
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num_iter = 0
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for item1, item2 in zip(train_data_1.create_dict_iterator(num_epochs=1),
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train_data_2.create_dict_iterator(num_epochs=1)):
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label_list_1.append(item1["label"].asnumpy())
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label_list_2.append(item2["label"].asnumpy())
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num_iter += 1
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np.testing.assert_array_equal(label_list_1, label_list_2)
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assert num_iter == num_samples
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def test_svhn_exception():
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"""
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Feature: test_svhn_exception.
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Description: Test error cases for SVHNDataset.
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Expectation: Raise exception.
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"""
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logger.info("Test error cases for SVHNDataset")
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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.SVHNDataset(DATA_DIR, "train", shuffle=False, sampler=ds.SequentialSampler(1))
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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.SVHNDataset(DATA_DIR, "train", sampler=ds.SequentialSampler(1), num_shards=2, 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.SVHNDataset(DATA_DIR, "train", 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.SVHNDataset(DATA_DIR, "train", 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.SVHNDataset(DATA_DIR, "train", num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SVHNDataset(DATA_DIR, "train", num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SVHNDataset(DATA_DIR, "train", 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.SVHNDataset(DATA_DIR, "train", shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SVHNDataset(DATA_DIR, "train", shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SVHNDataset(DATA_DIR, "train", 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.SVHNDataset(DATA_DIR, "train", num_shards=2, shard_id="0")
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error_msg_8 = "does not exist or permission denied!"
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with pytest.raises(ValueError, match=error_msg_8):
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train_data = ds.SVHNDataset(WRONG_DIR, "train")
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for _ in train_data.__iter__():
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pass
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def test_svhn_visualize(plot=False):
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"""
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Feature: test_svhn_visualize.
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Description: Visualize SVHNDataset results.
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Expectation: Get correct number of data and plot them.
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"""
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logger.info("Test SVHNDataset visualization")
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train_data = ds.SVHNDataset(DATA_DIR, "train", num_samples=2, 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 == (32, 32, 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 == 2
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if plot:
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visualize_dataset(image_list, label_list)
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def test_svhn_usage():
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"""
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Feature: test_svhn_usage.
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Description: Validate SVHNDataset image readings.
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Expectation: Get correct number of data.
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"""
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logger.info("Test SVHNDataset usage flag")
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def test_config(usage, path=None):
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path = DATA_DIR if path is None else path
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try:
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data = ds.SVHNDataset(path, usage=usage, 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("train") == 2
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assert test_config("test") == 4
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assert test_config("extra") == 6
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assert test_config("all") == 12
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assert "usage is not within the valid set of ['train', 'test', 'extra', 'all']" in test_config("invalid")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
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data_path = None
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# the following tests on the entire datasets.
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if data_path is not None:
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assert test_config("train", data_path) == 2
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assert test_config("test", data_path) == 4
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assert test_config("extra", data_path) == 6
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assert test_config("all", data_path) == 12
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assert ds.SVHNDataset(data_path, usage="train").get_dataset_size() == 2
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assert ds.SVHNDataset(data_path, usage="test").get_dataset_size() == 4
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assert ds.SVHNDataset(data_path, usage="extra").get_dataset_size() == 6
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assert ds.SVHNDataset(data_path, usage="all").get_dataset_size() == 12
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if __name__ == '__main__':
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test_svhn_content_check()
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test_svhn_basic()
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test_svhn_sequential_sampler()
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test_svhn_exception()
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test_svhn_visualize(plot=True)
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test_svhn_usage()
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