forked from mindspore-Ecosystem/mindspore
322 lines
12 KiB
Python
322 lines
12 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 FashionMnist 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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import mindspore.dataset as ds
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testMnistData"
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def load_fashion_mnist(path):
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"""
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Feature: load_fashion_mnist.
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Description: Load FashionMnistDataset.
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Expectation: Get data of FashionMnistDataset.
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"""
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labels_path = os.path.realpath(os.path.join(path, 't10k-labels-idx1-ubyte'))
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images_path = os.path.realpath(os.path.join(path, 't10k-images-idx3-ubyte'))
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with open(labels_path, 'rb') as lbpath:
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lbpath.read(8)
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labels = np.fromfile(lbpath, dtype=np.uint8)
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with open(images_path, 'rb') as imgpath:
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imgpath.read(16)
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images = np.fromfile(imgpath, dtype=np.uint8)
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images = images.reshape(-1, 28, 28, 1)
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return images, labels
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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 FashionMnistDataset.
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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].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_fashion_mnist_content_check():
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"""
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Feature: test_fashion_mnist_content_check.
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Description: Validate FashionMnistDataset image readings.
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Expectation: Get correct value.
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"""
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logger.info("Test FashionMnistDataset Op with content check")
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data1 = ds.FashionMnistDataset(DATA_DIR, num_samples=100, shuffle=False)
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images, labels = load_fashion_mnist(DATA_DIR)
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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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image_list, label_list = [], []
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for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_list.append(data["image"])
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label_list.append("label {}".format(data["label"]))
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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 == 100
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def test_fashion_mnist_basic():
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"""
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Feature: test_fashion_mnist_basic.
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Description: Test basic usage of FashionMnistDataset.
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Expectation: Get correct data.
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"""
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logger.info("Test FashionMnistDataset Op")
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# case 1: test loading whole dataset
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data1 = ds.FashionMnistDataset(DATA_DIR)
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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 == 10000
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# case 2: test num_samples
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data2 = ds.FashionMnistDataset(DATA_DIR, num_samples=500)
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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 == 500
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# case 3: test repeat
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data3 = ds.FashionMnistDataset(DATA_DIR, num_samples=200)
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data3 = data3.repeat(5)
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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 == 1000
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# case 4: test batch with drop_remainder=False
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data4 = ds.FashionMnistDataset(DATA_DIR, num_samples=100)
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assert data4.get_dataset_size() == 100
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assert data4.get_batch_size() == 1
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data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
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assert data4.get_dataset_size() == 15
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assert data4.get_batch_size() == 7
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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 == 15
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# case 5: test batch with drop_remainder=True
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data5 = ds.FashionMnistDataset(DATA_DIR, num_samples=100)
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assert data5.get_dataset_size() == 100
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assert data5.get_batch_size() == 1
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data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data5.get_dataset_size() == 14
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assert data5.get_batch_size() == 7
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num_iter5 = 0
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for _ in data5.create_dict_iterator(num_epochs=1):
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num_iter5 += 1
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assert num_iter5 == 14
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# case 6: test get_col_names
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data6 = ds.FashionMnistDataset(DATA_DIR, "train", num_samples=10)
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assert data6.get_col_names() == ["image", "label"]
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def test_fashion_mnist_pk_sampler():
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"""
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Feature: test_fashion_mnist_pk_sampler.
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Description: Test usage of FashionMnistDataset with PKSampler.
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Expectation: Get correct data.
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"""
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logger.info("Test FashionMnistDataset Op with PKSampler")
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golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
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5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
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sampler = ds.PKSampler(3)
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data = ds.FashionMnistDataset(DATA_DIR, sampler=sampler)
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num_iter = 0
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label_list = []
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for item in 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_fashion_mnist_sequential_sampler():
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"""
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Feature: test_fashion_mnist_sequential_sampler.
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Description: Test usage of FashionMnistDataset with SequentialSampler.
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Expectation: Get correct data.
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"""
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logger.info("Test FashionMnistDataset 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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data1 = ds.FashionMnistDataset(DATA_DIR, sampler=sampler)
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data2 = ds.FashionMnistDataset(DATA_DIR, shuffle=False, 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(data1.create_dict_iterator(num_epochs=1), 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_fashion_mnist_exception():
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"""
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Feature: test_fashion_mnist_exception.
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Description: Test error cases for FashionMnistDataset.
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Expectation: Raise exception.
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"""
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logger.info("Test error cases for FashionMnistDataset")
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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.FashionMnistDataset(DATA_DIR, 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.FashionMnistDataset(DATA_DIR, sampler=ds.PKSampler(3), 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.FashionMnistDataset(DATA_DIR, 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.FashionMnistDataset(DATA_DIR, 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.FashionMnistDataset(DATA_DIR, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.FashionMnistDataset(DATA_DIR, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.FashionMnistDataset(DATA_DIR, 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.FashionMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.FashionMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.FashionMnistDataset(DATA_DIR, 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.FashionMnistDataset(DATA_DIR, 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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error_msg_8 = "The corresponding data files"
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.FashionMnistDataset(DATA_DIR)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.FashionMnistDataset(DATA_DIR)
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data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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data = ds.FashionMnistDataset(DATA_DIR)
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data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
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for _ in data.__iter__():
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pass
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def test_fashion_mnist_visualize(plot=False):
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"""
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Feature: test_fashion_mnist_visualize.
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Description: Visualize FashionMnistDataset results.
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Expectation: Get correct data and plot them.
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"""
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logger.info("Test FashionMnistDataset visualization")
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data1 = ds.FashionMnistDataset(DATA_DIR, 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 data1.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, 1)
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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_fashion_mnist_usage():
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"""
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Feature: test_fashion_mnist_usage.
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Description: Validate FashionMnistDataset image readings.
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Expectation: Get correct data.
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"""
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logger.info("Test FashionMnistDataset usage flag")
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def test_config(usage, fashion_mnist_path=None):
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fashion_mnist_path = DATA_DIR if fashion_mnist_path is None else fashion_mnist_path
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try:
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data = ds.FashionMnistDataset(fashion_mnist_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("test") == 10000
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assert test_config("all") == 10000
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assert "FashionMnistDataset API can't read the data file (interface mismatch or no data found)" \
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in test_config("train")
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assert "usage is not within the valid set of ['train', 'test', '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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# change this directory to the folder that contains all fashionmnist files
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all_files_path = None
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# the following tests on the entire datasets
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if all_files_path is not None:
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assert test_config("train", all_files_path) == 60000
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assert test_config("test", all_files_path) == 10000
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assert test_config("all", all_files_path) == 70000
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assert ds.FashionMnistDataset(all_files_path, usage="train").get_dataset_size() == 60000
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assert ds.FashionMnistDataset(all_files_path, usage="test").get_dataset_size() == 10000
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assert ds.FashionMnistDataset(all_files_path, usage="all").get_dataset_size() == 70000
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if __name__ == '__main__':
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test_fashion_mnist_content_check()
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test_fashion_mnist_basic()
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test_fashion_mnist_pk_sampler()
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test_fashion_mnist_sequential_sampler()
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test_fashion_mnist_exception()
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test_fashion_mnist_visualize(plot=True)
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test_fashion_mnist_usage()
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