329 lines
12 KiB
Python
329 lines
12 KiB
Python
# Copyright 2021-2022 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""
|
|
Test KMnist dataset operators
|
|
"""
|
|
|
|
import os
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.vision as vision
|
|
from mindspore import log as logger
|
|
|
|
DATA_DIR = "../data/dataset/testMnistData"
|
|
|
|
|
|
def load_kmnist(path):
|
|
"""
|
|
Feature: load_kmnist.
|
|
Description: Load KMnistDataset.
|
|
Expectation: Get data of KMnistDataset.
|
|
"""
|
|
labels_path = os.path.realpath(os.path.join(path, 't10k-labels-idx1-ubyte'))
|
|
images_path = os.path.realpath(os.path.join(path, 't10k-images-idx3-ubyte'))
|
|
with open(os.path.realpath(labels_path), 'rb') as lbpath:
|
|
lbpath.read(8)
|
|
labels = np.fromfile(lbpath, dtype=np.uint8)
|
|
with open(os.path.realpath(images_path), 'rb') as imgpath:
|
|
imgpath.read(16)
|
|
images = np.fromfile(imgpath, dtype=np.uint8)
|
|
images = images.reshape(-1, 28, 28, 1)
|
|
return images, labels
|
|
|
|
|
|
def visualize_dataset(images, labels):
|
|
"""
|
|
Feature: visualize_dataset.
|
|
Description: Visualize KMnistDataset.
|
|
Expectation: Plot images.
|
|
"""
|
|
num_samples = len(images)
|
|
for i in range(num_samples):
|
|
plt.subplot(1, num_samples, i + 1)
|
|
plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)
|
|
plt.title(labels[i])
|
|
plt.show()
|
|
|
|
|
|
def test_kmnist_content_check():
|
|
"""
|
|
Feature: test_kmnist_content_check.
|
|
Description: Validate KMnistDataset image readings.
|
|
Expectation: Get correct value.
|
|
"""
|
|
logger.info("Test KMnistDataset Op with content check")
|
|
data1 = ds.KMnistDataset(DATA_DIR, num_samples=100, shuffle=False)
|
|
images, labels = load_kmnist(DATA_DIR)
|
|
num_iter = 0
|
|
# in this example, each dictionary has keys "image" and "label"
|
|
image_list, label_list = [], []
|
|
for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
image_list.append(data["image"])
|
|
label_list.append("label {}".format(data["label"]))
|
|
np.testing.assert_array_equal(data["image"], images[i])
|
|
np.testing.assert_array_equal(data["label"], labels[i])
|
|
num_iter += 1
|
|
assert num_iter == 100
|
|
|
|
|
|
def test_kmnist_basic():
|
|
"""
|
|
Feature: test_kmnist_basic.
|
|
Description: Test basic usage of KMnistDataset.
|
|
Expectation: Get correct data.
|
|
"""
|
|
logger.info("Test KMnistDataset Op")
|
|
|
|
# case 1: test loading whole dataset
|
|
data1 = ds.KMnistDataset(DATA_DIR)
|
|
num_iter1 = 0
|
|
for _ in data1.create_dict_iterator(num_epochs=1):
|
|
num_iter1 += 1
|
|
assert num_iter1 == 10000
|
|
|
|
# case 2: test num_samples
|
|
data2 = ds.KMnistDataset(DATA_DIR, num_samples=500)
|
|
num_iter2 = 0
|
|
for _ in data2.create_dict_iterator(num_epochs=1):
|
|
num_iter2 += 1
|
|
assert num_iter2 == 500
|
|
|
|
# case 3: test repeat
|
|
data3 = ds.KMnistDataset(DATA_DIR, num_samples=200)
|
|
data3 = data3.repeat(5)
|
|
num_iter3 = 0
|
|
for _ in data3.create_dict_iterator(num_epochs=1):
|
|
num_iter3 += 1
|
|
assert num_iter3 == 1000
|
|
|
|
# case 4: test batch with drop_remainder=False
|
|
data4 = ds.KMnistDataset(DATA_DIR, num_samples=100)
|
|
assert data4.get_dataset_size() == 100
|
|
assert data4.get_batch_size() == 1
|
|
data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
|
|
assert data4.get_dataset_size() == 15
|
|
assert data4.get_batch_size() == 7
|
|
num_iter4 = 0
|
|
for _ in data4.create_dict_iterator(num_epochs=1):
|
|
num_iter4 += 1
|
|
assert num_iter4 == 15
|
|
|
|
# case 5: test batch with drop_remainder=True
|
|
data5 = ds.KMnistDataset(DATA_DIR, num_samples=100)
|
|
assert data5.get_dataset_size() == 100
|
|
assert data5.get_batch_size() == 1
|
|
data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
|
|
assert data5.get_dataset_size() == 14
|
|
assert data5.get_batch_size() == 7
|
|
num_iter5 = 0
|
|
for _ in data5.create_dict_iterator(num_epochs=1):
|
|
num_iter5 += 1
|
|
assert num_iter5 == 14
|
|
|
|
# case 6: test get_col_names
|
|
data6 = ds.KMnistDataset(DATA_DIR, "train", num_samples=10)
|
|
assert data6.get_col_names() == ["image", "label"]
|
|
|
|
#case 7: test batch
|
|
data7 = ds.KMnistDataset(DATA_DIR, num_samples=200)
|
|
data7 = data7.batch(100, drop_remainder=True)
|
|
num_iter7 = 0
|
|
for _ in data7.create_dict_iterator(num_epochs=1):
|
|
num_iter7 += 1
|
|
assert num_iter7 == 2
|
|
|
|
|
|
def test_kmnist_pk_sampler():
|
|
"""
|
|
Feature: test_kmnist_pk_sampler.
|
|
Description: Test usage of KMnistDataset with PKSampler.
|
|
Expectation: Get correct data.
|
|
"""
|
|
logger.info("Test KMnistDataset Op with PKSampler")
|
|
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]
|
|
sampler = ds.PKSampler(3)
|
|
data = ds.KMnistDataset(DATA_DIR, sampler=sampler)
|
|
num_iter = 0
|
|
label_list = []
|
|
for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
label_list.append(item["label"])
|
|
num_iter += 1
|
|
np.testing.assert_array_equal(golden, label_list)
|
|
assert num_iter == 30
|
|
|
|
|
|
def test_kmnist_sequential_sampler():
|
|
"""
|
|
Feature: test_kmnist_sequential_sampler.
|
|
Description: Test usage of KMnistDataset with SequentialSampler.
|
|
Expectation: Get correct data.
|
|
"""
|
|
logger.info("Test KMnistDataset Op with SequentialSampler")
|
|
num_samples = 50
|
|
sampler = ds.SequentialSampler(num_samples=num_samples)
|
|
data1 = ds.KMnistDataset(DATA_DIR, sampler=sampler)
|
|
data2 = ds.KMnistDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
|
|
label_list1, label_list2 = [], []
|
|
num_iter = 0
|
|
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
|
|
label_list1.append(item1["label"].asnumpy())
|
|
label_list2.append(item2["label"].asnumpy())
|
|
num_iter += 1
|
|
np.testing.assert_array_equal(label_list1, label_list2)
|
|
assert num_iter == num_samples
|
|
|
|
|
|
def test_kmnist_exception():
|
|
"""
|
|
Feature: test_kmnist_exception.
|
|
Description: Test error cases for KMnistDataset.
|
|
Expectation: Raise exception.
|
|
"""
|
|
logger.info("Test error cases for KMnistDataset")
|
|
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
|
|
with pytest.raises(RuntimeError, match=error_msg_1):
|
|
ds.KMnistDataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
|
|
|
|
error_msg_2 = "sampler and sharding cannot be specified at the same time"
|
|
with pytest.raises(RuntimeError, match=error_msg_2):
|
|
ds.KMnistDataset(DATA_DIR, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
|
|
|
|
error_msg_3 = "num_shards is specified and currently requires shard_id as well"
|
|
with pytest.raises(RuntimeError, match=error_msg_3):
|
|
ds.KMnistDataset(DATA_DIR, num_shards=10)
|
|
|
|
error_msg_4 = "shard_id is specified but num_shards is not"
|
|
with pytest.raises(RuntimeError, match=error_msg_4):
|
|
ds.KMnistDataset(DATA_DIR, shard_id=0)
|
|
|
|
error_msg_5 = "Input shard_id is not within the required interval"
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.KMnistDataset(DATA_DIR, num_shards=5, shard_id=-1)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.KMnistDataset(DATA_DIR, num_shards=5, shard_id=5)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.KMnistDataset(DATA_DIR, num_shards=2, shard_id=5)
|
|
|
|
error_msg_6 = "num_parallel_workers exceeds"
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
|
|
|
|
error_msg_7 = "Argument shard_id"
|
|
with pytest.raises(TypeError, match=error_msg_7):
|
|
ds.KMnistDataset(DATA_DIR, num_shards=2, shard_id="0")
|
|
|
|
def exception_func(item):
|
|
raise Exception("Error occur!")
|
|
|
|
error_msg_8 = "The corresponding data files"
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
data = ds.KMnistDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
data = ds.KMnistDataset(DATA_DIR)
|
|
data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
|
|
data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
data = ds.KMnistDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
|
|
|
|
def test_kmnist_visualize(plot=False):
|
|
"""
|
|
Feature: test_kmnist_visualize.
|
|
Description: Visualize KMnistDataset results.
|
|
Expectation: Get correct data and plot them.
|
|
"""
|
|
logger.info("Test KMnistDataset visualization")
|
|
|
|
data1 = ds.KMnistDataset(DATA_DIR, num_samples=10, shuffle=False)
|
|
num_iter = 0
|
|
image_list, label_list = [], []
|
|
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
image = item["image"]
|
|
label = item["label"]
|
|
image_list.append(image)
|
|
label_list.append("label {}".format(label))
|
|
assert isinstance(image, np.ndarray)
|
|
assert image.shape == (28, 28, 1)
|
|
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_kmnist_usage():
|
|
"""
|
|
Feature: test_kmnist_usage.
|
|
Description: Validate KMnistDataset image readings.
|
|
Expectation: Get correct data.
|
|
"""
|
|
logger.info("Test KMnistDataset usage flag")
|
|
|
|
def test_config(usage, kmnist_path=None):
|
|
kmnist_path = DATA_DIR if kmnist_path is None else kmnist_path
|
|
try:
|
|
data = ds.KMnistDataset(kmnist_path, 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("test") == 10000
|
|
assert test_config("all") == 10000
|
|
assert "KMnistDataset API can't read the data file (interface mismatch or no data found)" in test_config("train")
|
|
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 kmnist 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) == 60000
|
|
assert test_config("test", all_files_path) == 10000
|
|
assert test_config("all", all_files_path) == 70000
|
|
assert ds.KMnistDataset(all_files_path, usage="train").get_dataset_size() == 60000
|
|
assert ds.KMnistDataset(all_files_path, usage="test").get_dataset_size() == 10000
|
|
assert ds.KMnistDataset(all_files_path, usage="all").get_dataset_size() == 70000
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_kmnist_content_check()
|
|
test_kmnist_basic()
|
|
test_kmnist_pk_sampler()
|
|
test_kmnist_sequential_sampler()
|
|
test_kmnist_exception()
|
|
test_kmnist_visualize(plot=True)
|
|
test_kmnist_usage()
|