forked from huawei/mindspore2022
480 lines
19 KiB
Python
480 lines
19 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 EMnist 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.c_transforms as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testEMnistDataset"
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def load_emnist(path, usage, name):
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"""
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load EMnist data
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"""
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image_path = []
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label_path = []
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image_ext = "images-idx3-ubyte"
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label_ext = "labels-idx1-ubyte"
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train_prefix = "emnist-" + name + "-train-"
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test_prefix = "emnist-" + name + "-test-"
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assert usage in ["train", "test", "all"]
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if usage == "train":
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image_path.append(os.path.realpath(os.path.join(path, train_prefix + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, train_prefix + label_ext)))
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elif usage == "test":
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image_path.append(os.path.realpath(os.path.join(path, test_prefix + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, test_prefix + label_ext)))
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elif usage == "all":
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image_path.append(os.path.realpath(os.path.join(path, test_prefix + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, test_prefix + label_ext)))
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image_path.append(os.path.realpath(os.path.join(path, train_prefix + image_ext)))
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label_path.append(os.path.realpath(os.path.join(path, train_prefix + label_ext)))
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assert len(image_path) == len(label_path)
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images = []
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labels = []
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for i, _ in enumerate(image_path):
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with open(image_path[i], 'rb') as image_file:
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image_file.read(16)
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image = np.fromfile(image_file, dtype=np.uint8)
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image = image.reshape(-1, 28, 28, 1)
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images.append(image)
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with open(label_path[i], 'rb') as label_file:
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label_file.read(8)
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label = np.fromfile(label_file, dtype=np.uint8)
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labels.append(label)
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images = np.concatenate(images, 0)
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labels = np.concatenate(labels, 0)
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return images, labels
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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_emnist_content_check():
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"""
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Validate EMnistDataset image readings
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"""
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logger.info("Test EMnistDataset Op with content check")
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# train mnist
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train_data = ds.EMnistDataset(DATA_DIR, name="mnist", usage="train", num_samples=10, shuffle=False)
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images, labels = load_emnist(DATA_DIR, "train", "mnist")
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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(train_data.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 == 10
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# train byclass
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train_data = ds.EMnistDataset(DATA_DIR, name="byclass", usage="train", num_samples=10, shuffle=False)
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images, labels = load_emnist(DATA_DIR, "train", "byclass")
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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(train_data.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 == 10
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# test
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test_data = ds.EMnistDataset(DATA_DIR, name="mnist", usage="test", num_samples=10, shuffle=False)
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images, labels = load_emnist(DATA_DIR, "test", "mnist")
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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(test_data.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 == 10
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def test_emnist_basic():
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"""
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Validate EMnistDataset
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"""
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logger.info("Test EMnistDataset Op")
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# case 1: test loading whole dataset
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train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train")
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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 == 10
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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test")
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num_iter = 0
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for _ in test_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 2: test num_samples
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train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=5)
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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 == 5
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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=5)
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num_iter2 = 0
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for _ in test_data.create_dict_iterator(num_epochs=1):
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num_iter2 += 1
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assert num_iter2 == 5
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# case 3: test repeat
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train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=2)
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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 == 10
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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=2)
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test_data = test_data.repeat(5)
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num_iter3 = 0
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for _ in test_data.create_dict_iterator(num_epochs=1):
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num_iter3 += 1
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assert num_iter3 == 10
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# case 4: test batch with drop_remainder=False
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train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=10)
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assert train_data.get_dataset_size() == 10
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assert train_data.get_batch_size() == 1
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train_data = train_data.batch(batch_size=7) # 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() == 7
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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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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
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assert test_data.get_dataset_size() == 10
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assert test_data.get_batch_size() == 1
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test_data = test_data.batch(
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batch_size=7) # drop_remainder is default to be False
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assert test_data.get_dataset_size() == 2
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assert test_data.get_batch_size() == 7
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num_iter4 = 0
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for _ in test_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.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=10)
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assert train_data.get_dataset_size() == 10
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assert train_data.get_batch_size() == 1
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train_data = train_data.batch(batch_size=7, 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() == 7
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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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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
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assert test_data.get_dataset_size() == 10
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assert test_data.get_batch_size() == 1
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test_data = test_data.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert test_data.get_dataset_size() == 1
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assert test_data.get_batch_size() == 7
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num_iter5 = 0
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for _ in test_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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# case 6: test get_col_names
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dataset = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
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assert dataset.get_col_names() == ["image", "label"]
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def test_emnist_pk_sampler():
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"""
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Test EMnistDataset with PKSampler
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"""
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logger.info("Test EMnistDataset Op with PKSampler")
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golden = [0, 0, 0, 1, 1, 1]
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sampler = ds.PKSampler(3)
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train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", 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 == 6
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sampler = ds.PKSampler(3)
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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", sampler=sampler)
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num_iter = 0
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label_list = []
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for item in test_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 == 6
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def test_emnist_sequential_sampler():
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"""
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Test EMnistDataset with SequentialSampler
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"""
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logger.info("Test EMnistDataset Op with SequentialSampler")
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num_samples = 10
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sampler = ds.SequentialSampler(num_samples=num_samples)
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train_data1 = ds.EMnistDataset(DATA_DIR, "mnist", "train", sampler=sampler)
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train_data2 = ds.EMnistDataset(DATA_DIR, "mnist", "train", 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(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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num_samples = 10
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sampler = ds.SequentialSampler(num_samples=num_samples)
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test_data1 = ds.EMnistDataset(DATA_DIR, "mnist", "test", sampler=sampler)
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test_data2 = ds.EMnistDataset(DATA_DIR, "mnist", "test", 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(test_data1.create_dict_iterator(num_epochs=1),
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test_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_emnist_exception():
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"""
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Test error cases for EMnistDataset
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"""
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logger.info("Test error cases for EMnistDataset")
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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.EMnistDataset(DATA_DIR, "byclass", "train", shuffle=False, sampler=ds.PKSampler(3))
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train", sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=10)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train", shard_id=0)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=5, shard_id=-1)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.EMnistDataset(DATA_DIR, "mnist", "train", num_shards=5, shard_id=5)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=2, shard_id=5)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train", shuffle=False, num_parallel_workers=0)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.EMnistDataset(DATA_DIR, "byclass", "train", shuffle=False, num_parallel_workers=256)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.EMnistDataset(DATA_DIR, "mnist", "train", shuffle=False, num_parallel_workers=-2)
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train", num_shards=2, shard_id="0")
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ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train")
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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.EMnistDataset(DATA_DIR, "mnist", "train")
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data = data.map(operations=vision.Decode(), 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.EMnistDataset(DATA_DIR, "mnist", "train")
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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_emnist_visualize(plot=False):
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"""
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Visualize EMnistDataset results
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"""
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logger.info("Test EMnistDataset visualization")
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train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", 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, 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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test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", 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 test_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, 1)
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|
assert image.dtype == np.uint8
|
|
assert label.dtype == np.uint32
|
|
num_iter += 1
|
|
assert num_iter == 10
|
|
if plot:
|
|
visualize_dataset(image_list, label_list)
|
|
|
|
|
|
def test_emnist_usage():
|
|
"""
|
|
Validate EMnistDataset image readings
|
|
"""
|
|
logger.info("Test EMnistDataset usage flag")
|
|
|
|
def test_config(usage, emnist_path=None):
|
|
emnist_path = DATA_DIR if emnist_path is None else emnist_path
|
|
try:
|
|
data = ds.EMnistDataset(emnist_path, "mnist", usage=usage, shuffle=False)
|
|
num_rows = 0
|
|
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_rows += 1
|
|
except (ValueError, TypeError, RuntimeError) as e:
|
|
return str(e)
|
|
return num_rows
|
|
|
|
assert test_config("train") == 10
|
|
assert test_config("test") == 10
|
|
assert test_config("all") == 20
|
|
|
|
assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid")
|
|
assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
|
|
|
|
# change this directory to the folder that contains all emnist files
|
|
all_files_path = None
|
|
|
|
# the following tests on the entire datasets
|
|
if all_files_path is not None:
|
|
assert test_config("train", all_files_path) == 10000
|
|
assert test_config("test", all_files_path) == 60000
|
|
assert test_config("all", all_files_path) == 70000
|
|
assert ds.EMnistDataset(all_files_path, "mnist", usage="test").get_dataset_size() == 10000
|
|
assert ds.EMnistDataset(all_files_path, "mnist", usage="test").get_dataset_size() == 60000
|
|
assert ds.EMnistDataset(all_files_path, "mnist", usage="all").get_dataset_size() == 70000
|
|
|
|
|
|
def test_emnist_name():
|
|
"""
|
|
Validate EMnistDataset image readings
|
|
"""
|
|
def test_config(name, usage, emnist_path=None):
|
|
emnist_path = DATA_DIR if emnist_path is None else emnist_path
|
|
try:
|
|
data = ds.EMnistDataset(emnist_path, name, usage=usage, shuffle=False)
|
|
num_rows = 0
|
|
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_rows += 1
|
|
except (ValueError, TypeError, RuntimeError) as e:
|
|
return str(e)
|
|
return num_rows
|
|
|
|
assert test_config("mnist", "train") == 10
|
|
assert test_config("mnist", "test") == 10
|
|
assert test_config("byclass", "train") == 10
|
|
assert "name is not within the valid set of " + \
|
|
"['byclass', 'bymerge', 'balanced', 'letters', 'digits', 'mnist']" in test_config("invalid", "train")
|
|
assert "Argument name with value ['list'] is not of type [<class 'str'>]" in test_config(["list"], "train")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_emnist_content_check()
|
|
test_emnist_basic()
|
|
test_emnist_pk_sampler()
|
|
test_emnist_sequential_sampler()
|
|
test_emnist_exception()
|
|
test_emnist_visualize(plot=True)
|
|
test_emnist_usage()
|
|
test_emnist_name()
|