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mindspore/tests/ut/python/dataset/test_sampler.py

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# Copyright 2020-2022 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.
# ==============================================================================
import numpy as np
import pytest
import mindspore.dataset as ds
from mindspore import log as logger
from util import dataset_equal
# test5trainimgs.json contains 5 images whose un-decoded shape is [83554, 54214, 65512, 54214, 64631]
# the label of each image is [0,0,0,1,1] each image can be uniquely identified
# via the following lookup table (dict){(83554, 0): 0, (54214, 0): 1, (54214, 1): 2, (65512, 0): 3, (64631, 1): 4}
def test_sequential_sampler(print_res=False):
"""
Feature: SequentialSampler op
Description: Test SequentialSampler op with various num_samples and num_repeats args combinations
Expectation: Output is equal to the expected output
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
def test_config(num_samples, num_repeats=None):
sampler = ds.SequentialSampler(num_samples=num_samples)
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
if num_repeats is not None:
data1 = data1.repeat(num_repeats)
res = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
logger.info("item[image].shape[0]: {}, item[label].item(): {}"
.format(item["image"].shape[0], item["label"].item()))
res.append(map_[(item["image"].shape[0], item["label"].item())])
if print_res:
logger.info("image.shapes and labels: {}".format(res))
return res
assert test_config(num_samples=3, num_repeats=None) == [0, 1, 2]
assert test_config(num_samples=None, num_repeats=2) == [0, 1, 2, 3, 4] * 2
assert test_config(num_samples=4, num_repeats=2) == [0, 1, 2, 3] * 2
def test_random_sampler(print_res=False):
"""
Feature: RandomSampler op
Description: Test RandomSampler with various replacement, num_samples, and num_repeats args combinations
Expectation: Output is equal to the expected output
"""
ds.config.set_seed(1234)
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
def test_config(replacement, num_samples, num_repeats):
sampler = ds.RandomSampler(replacement=replacement, num_samples=num_samples)
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
data1 = data1.repeat(num_repeats)
res = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(map_[(item["image"].shape[0], item["label"].item())])
if print_res:
logger.info("image.shapes and labels: {}".format(res))
return res
# this tests that each epoch COULD return different samples than the previous epoch
assert len(set(test_config(replacement=False, num_samples=2, num_repeats=6))) > 2
# the following two tests test replacement works
ordered_res = [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4]
assert sorted(test_config(replacement=False, num_samples=None, num_repeats=4)) == ordered_res
assert sorted(test_config(replacement=True, num_samples=None, num_repeats=4)) != ordered_res
def test_random_sampler_multi_iter(print_res=False):
"""
Feature: RandomSampler op
Description: Test RandomSampler with multiple iteration based on num_repeats
Expectation: Output is equal to the expected output
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
def test_config(replacement, num_samples, num_repeats, validate):
sampler = ds.RandomSampler(replacement=replacement, num_samples=num_samples)
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
while num_repeats > 0:
res = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(map_[(item["image"].shape[0], item["label"].item())])
if print_res:
logger.info("image.shapes and labels: {}".format(res))
if validate != sorted(res):
break
num_repeats -= 1
assert num_repeats > 0
test_config(replacement=True, num_samples=5, num_repeats=5, validate=[0, 1, 2, 3, 4, 5])
def test_sampler_py_api():
"""
Feature: Sampler op
Description: Test add_child op of a Sampler op to a Sampler op
Expectation: Runs successfully
"""
sampler = ds.SequentialSampler().parse()
sampler1 = ds.RandomSampler().parse()
sampler1.add_child(sampler)
def test_python_sampler():
"""
Feature: Python Sampler op
Description: Test Python Sampler op with and without inheritance
Expectation: Output is equal to the expected output
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
class Sp1(ds.Sampler):
def __iter__(self):
return iter([i for i in range(self.dataset_size)])
class Sp2(ds.Sampler):
def __init__(self, num_samples=None):
super(Sp2, self).__init__(num_samples)
# at this stage, self.dataset_size and self.num_samples are not yet known
self.cnt = 0
def __iter__(self): # first epoch, all 0, second epoch all 1, third all 2 etc.. ...
return iter([self.cnt for i in range(self.num_samples)])
def reset(self):
self.cnt = (self.cnt + 1) % self.dataset_size
def test_config(num_repeats, sampler):
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
if num_repeats is not None:
data1 = data1.repeat(num_repeats)
res = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
logger.info("item[image].shape[0]: {}, item[label].item(): {}"
.format(item["image"].shape[0], item["label"].item()))
res.append(map_[(item["image"].shape[0], item["label"].item())])
# print(res)
return res
def test_generator():
class MySampler(ds.Sampler):
def __iter__(self):
for i in range(99, -1, -1):
yield i
data1 = ds.GeneratorDataset([(np.array(i),) for i in range(100)], ["data"], sampler=MySampler())
i = 99
for data in data1:
assert data[0].asnumpy() == (np.array(i),)
i = i - 1
# This 2nd case is the one that exhibits the same behavior as the case above without inheritance
def test_generator_iter_sampler():
class MySampler():
def __iter__(self):
for i in range(99, -1, -1):
yield i
data1 = ds.GeneratorDataset([(np.array(i),) for i in range(100)], ["data"], sampler=MySampler())
i = 99
for data in data1:
assert data[0].asnumpy() == (np.array(i),)
i = i - 1
assert test_config(2, Sp1(5)) == [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
assert test_config(6, Sp2(2)) == [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0]
test_generator()
test_generator_iter_sampler()
def test_sequential_sampler2():
"""
Feature: SequentialSampler op
Description: Test SequentialSampler op with various start_index and num_samples args combinations
Expectation: Output is equal to the expected output
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
def test_config(start_index, num_samples):
sampler = ds.SequentialSampler(start_index, num_samples)
d = ds.ManifestDataset(manifest_file, sampler=sampler)
res = []
for item in d.create_dict_iterator(num_epochs=1, output_numpy=True):
res.append(map_[(item["image"].shape[0], item["label"].item())])
return res
assert test_config(0, 1) == [0]
assert test_config(0, 2) == [0, 1]
assert test_config(0, 3) == [0, 1, 2]
assert test_config(0, 4) == [0, 1, 2, 3]
assert test_config(0, 5) == [0, 1, 2, 3, 4]
assert test_config(1, 1) == [1]
assert test_config(2, 3) == [2, 3, 4]
assert test_config(3, 2) == [3, 4]
assert test_config(4, 1) == [4]
assert test_config(4, None) == [4]
def test_subset_sampler():
"""
Feature: SubsetSampler op
Description: Test SubsetSampler op with various indices and num_samples args combinations including invalid ones
Expectation: Output is equal to the expected output when input is valid, otherwise exception is raised
"""
def test_config(indices, num_samples=None, exception_msg=None):
def pipeline():
sampler = ds.SubsetSampler(indices, num_samples)
data = ds.NumpySlicesDataset(list(range(0, 10)), sampler=sampler)
data2 = ds.NumpySlicesDataset(list(range(0, 10)), sampler=indices, num_samples=num_samples)
dataset_size = data.get_dataset_size()
dataset_size2 = data.get_dataset_size()
res1 = [d[0] for d in data.create_tuple_iterator(num_epochs=1, output_numpy=True)], dataset_size
res2 = [d[0] for d in data2.create_tuple_iterator(num_epochs=1, output_numpy=True)], dataset_size2
return res1, res2
if exception_msg is None:
res, res2 = pipeline()
res, size = res
res2, size2 = res2
if not isinstance(indices, list):
indices = list(indices)
assert indices[:num_samples] == res
assert len(indices[:num_samples]) == size
assert indices[:num_samples] == res2
assert len(indices[:num_samples]) == size2
else:
with pytest.raises(Exception) as error_info:
pipeline()
print(str(error_info.value))
assert exception_msg in str(error_info.value)
test_config([1, 2, 3])
test_config(list(range(10)))
test_config([0])
test_config([9])
test_config(list(range(0, 10, 2)))
test_config(list(range(1, 10, 2)))
test_config(list(range(9, 0, -1)))
test_config(list(range(9, 0, -2)))
test_config(list(range(8, 0, -2)))
test_config([0, 9, 3, 2])
test_config([0, 0, 0, 0])
test_config([0])
test_config([0, 9, 3, 2], num_samples=2)
test_config([0, 9, 3, 2], num_samples=5)
test_config(np.array([1, 2, 3]))
test_config([20], exception_msg="Sample ID (20) is out of bound, expected range [0, 9]")
test_config([10], exception_msg="Sample ID (10) is out of bound, expected range [0, 9]")
test_config([0, 9, 0, 500], exception_msg="Sample ID (500) is out of bound, expected range [0, 9]")
test_config([0, 9, -6, 2], exception_msg="Sample ID (-6) is out of bound, expected range [0, 9]")
# test_config([], exception_msg="Indices list is empty") # temporary until we check with MindDataset
test_config([0, 9, 3, 2], num_samples=-1,
exception_msg="num_samples exceeds the boundary between 0 and 9223372036854775807(INT64_MAX)")
test_config(np.array([[1], [5]]), num_samples=10,
exception_msg="SubsetSampler: Type of indices element must be int, but got list[0]: [1],"
" type: <class 'numpy.ndarray'>.")
def test_sampler_chain():
"""
Feature: Chained Sampler
Description: ManifestDataset with sampler chain; add SequentialSampler as a child for DistributedSampler
Expectation: Correct error is raised as expected
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
def test_config(num_shards, shard_id, start_index=0):
sampler = ds.DistributedSampler(num_shards, shard_id, shuffle=False, num_samples=5)
child_sampler = ds.SequentialSampler(start_index)
sampler.add_child(child_sampler)
data1 = ds.ManifestDataset(manifest_file, sampler=sampler)
res = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
logger.info("item[image].shape[0]: {}, item[label].item(): {}"
.format(item["image"].shape[0], item["label"].item()))
res.append(map_[(item["image"].shape[0], item["label"].item())])
return res
assert test_config(2, 0) == [0, 2, 4]
assert test_config(2, 1) == [1, 3, 0]
assert test_config(5, 0) == [0]
assert test_config(5, 1) == [1]
assert test_config(5, 2) == [2]
assert test_config(5, 3) == [3]
assert test_config(5, 4) == [4]
assert test_config(2, 0, 1) == [1, 3]
assert test_config(2, 1, 1) == [2, 4]
assert test_config(5, 0, 1) == [1]
assert test_config(5, 1, 1) == [2]
assert test_config(5, 2, 1) == [3]
assert test_config(5, 3, 1) == [4]
assert test_config(5, 4, 1) == [1]
def test_add_sampler_invalid_input():
"""
Feature: Sampler op
Description: Test use_sampler op when the arg is not an instance of a sample and
another separate case when num_samples and sampler are specified at the same time in dataset arg
Expectation: Correct error is raised as expected
"""
manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
data1 = ds.ManifestDataset(manifest_file)
with pytest.raises(TypeError) as info:
data1.use_sampler(1)
assert "not an instance of a sampler" in str(info.value)
with pytest.raises(TypeError) as info:
data1.use_sampler("sampler")
assert "not an instance of a sampler" in str(info.value)
sampler = ds.SequentialSampler()
with pytest.raises(RuntimeError) as info:
_ = ds.ManifestDataset(manifest_file, sampler=sampler, num_samples=20)
assert "sampler and num_samples cannot be specified at the same time" in str(info.value)
def test_distributed_sampler_invalid_offset():
"""
Feature: DistributedSampler op
Description: Test DistributedSampler op when offset is more than num_shards
Expectation: Error is raised as expected
"""
with pytest.raises(RuntimeError) as info:
_ = ds.DistributedSampler(num_shards=4, shard_id=0, shuffle=False, num_samples=None, offset=5).parse()
assert "DistributedSampler: offset must be no more than num_shards(4)" in str(info.value)
def test_sampler_list():
"""
Feature: Sampler op
Description: Test various sampler args (int and not int) in ImageFolderDataset
Expectation: Output is equal to the expected output when sampler has data type int, otherwise exception is raised
"""
data1 = ds.ImageFolderDataset("../data/dataset/testPK/data", sampler=[1, 3, 5])
data21 = ds.ImageFolderDataset("../data/dataset/testPK/data", shuffle=False).take(2).skip(1)
data22 = ds.ImageFolderDataset("../data/dataset/testPK/data", shuffle=False).take(4).skip(3)
data23 = ds.ImageFolderDataset("../data/dataset/testPK/data", shuffle=False).take(6).skip(5)
dataset_equal(data1, data21 + data22 + data23, 0)
data3 = ds.ImageFolderDataset("../data/dataset/testPK/data", sampler=1)
dataset_equal(data3, data21, 0)
def bad_pipeline(sampler, msg):
with pytest.raises(Exception) as info:
data1 = ds.ImageFolderDataset("../data/dataset/testPK/data", sampler=sampler)
for _ in data1:
pass
assert msg in str(info.value)
bad_pipeline(sampler=[1.5, 7],
msg="Type of indices element must be int, but got list[0]: 1.5, type: <class 'float'>")
bad_pipeline(sampler=["a", "b"],
msg="Type of indices element must be int, but got list[0]: a, type: <class 'str'>.")
bad_pipeline(sampler="a", msg="Unsupported sampler object of type (<class 'str'>)")
bad_pipeline(sampler="", msg="Unsupported sampler object of type (<class 'str'>)")
bad_pipeline(sampler=np.array([[1, 2]]),
msg="Type of indices element must be int, but got list[0]: [1 2], type: <class 'numpy.ndarray'>.")
if __name__ == '__main__':
test_sequential_sampler(True)
test_random_sampler(True)
test_random_sampler_multi_iter(True)
test_sampler_py_api()
test_python_sampler()
test_sequential_sampler2()
test_subset_sampler()
test_sampler_chain()
test_add_sampler_invalid_input()
test_distributed_sampler_invalid_offset()
test_sampler_list()
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