You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
240 lines
8.1 KiB
240 lines
8.1 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
|
|
from __future__ import division
|
|
|
|
import unittest
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.io import Dataset, IterableDataset, TensorDataset, \
|
|
ComposeDataset, ChainDataset, DataLoader, random_split, Subset
|
|
|
|
IMAGE_SIZE = 32
|
|
|
|
|
|
class RandomDataset(Dataset):
|
|
def __init__(self, sample_num):
|
|
self.sample_num = sample_num
|
|
|
|
def __len__(self):
|
|
return self.sample_num
|
|
|
|
def __getitem__(self, idx):
|
|
np.random.seed(idx)
|
|
image = np.random.random([IMAGE_SIZE]).astype('float32')
|
|
label = np.random.randint(0, 9, (1, )).astype('int64')
|
|
return image, label
|
|
|
|
|
|
class RandomIterableDataset(IterableDataset):
|
|
def __init__(self, sample_num):
|
|
self.sample_num = sample_num
|
|
|
|
def __iter__(self):
|
|
for i in range(self.sample_num):
|
|
np.random.seed(i)
|
|
image = np.random.random([IMAGE_SIZE]).astype('float32')
|
|
label = np.random.randint(0, 9, (1, )).astype('int64')
|
|
yield image, label
|
|
|
|
|
|
class TestTensorDataset(unittest.TestCase):
|
|
def run_main(self, num_workers, places):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
place = paddle.CPUPlace()
|
|
with fluid.dygraph.guard(place):
|
|
input_np = np.random.random([16, 3, 4]).astype('float32')
|
|
input = paddle.to_tensor(input_np)
|
|
label_np = np.random.random([16, 1]).astype('int32')
|
|
label = paddle.to_tensor(label_np)
|
|
|
|
dataset = TensorDataset([input, label])
|
|
assert len(dataset) == 16
|
|
dataloader = DataLoader(
|
|
dataset,
|
|
places=place,
|
|
num_workers=num_workers,
|
|
batch_size=1,
|
|
drop_last=True)
|
|
|
|
for i, (input, label) in enumerate(dataloader()):
|
|
assert len(input) == 1
|
|
assert len(label) == 1
|
|
assert input.shape == [1, 3, 4]
|
|
assert label.shape == [1, 1]
|
|
assert isinstance(input, paddle.Tensor)
|
|
assert isinstance(label, paddle.Tensor)
|
|
assert np.allclose(input.numpy(), input_np[i])
|
|
assert np.allclose(label.numpy(), label_np[i])
|
|
|
|
def test_main(self):
|
|
places = [paddle.CPUPlace()]
|
|
if paddle.is_compiled_with_cuda():
|
|
places.append(paddle.CUDAPlace(0))
|
|
for p in places:
|
|
self.run_main(num_workers=0, places=p)
|
|
|
|
|
|
class TestComposeDataset(unittest.TestCase):
|
|
def test_main(self):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
dataset1 = RandomDataset(10)
|
|
dataset2 = RandomDataset(10)
|
|
dataset = ComposeDataset([dataset1, dataset2])
|
|
assert len(dataset) == 10
|
|
|
|
for i in range(len(dataset)):
|
|
input1, label1, input2, label2 = dataset[i]
|
|
input1_t, label1_t = dataset1[i]
|
|
input2_t, label2_t = dataset2[i]
|
|
assert np.allclose(input1, input1_t)
|
|
assert np.allclose(label1, label1_t)
|
|
assert np.allclose(input2, input2_t)
|
|
assert np.allclose(label2, label2_t)
|
|
|
|
|
|
class TestRandomSplitApi(unittest.TestCase):
|
|
def test_main(self):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
dataset1, dataset2 = paddle.io.random_split(range(5), [1, 4])
|
|
|
|
self.assertTrue(len(dataset1) == 1)
|
|
self.assertTrue(len(dataset2) == 4)
|
|
|
|
elements_list = list(range(5))
|
|
|
|
for _, val in enumerate(dataset1):
|
|
elements_list.remove(val)
|
|
|
|
for _, val in enumerate(dataset2):
|
|
elements_list.remove(val)
|
|
|
|
self.assertTrue(len(elements_list) == 0)
|
|
|
|
|
|
class TestRandomSplitError(unittest.TestCase):
|
|
def test_errors(self):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
self.assertRaises(ValueError, paddle.io.random_split, range(5), [3, 8])
|
|
self.assertRaises(ValueError, paddle.io.random_split, range(5), [8])
|
|
self.assertRaises(ValueError, paddle.io.random_split, range(5), [])
|
|
|
|
|
|
class TestSubsetDataset(unittest.TestCase):
|
|
def run_main(self, num_workers, places):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
input_np = np.random.random([5, 3, 4]).astype('float32')
|
|
input = paddle.to_tensor(input_np)
|
|
label_np = np.random.random([5, 1]).astype('int32')
|
|
label = paddle.to_tensor(label_np)
|
|
|
|
dataset = TensorDataset([input, label])
|
|
even_subset = paddle.io.Subset(dataset, [0, 2, 4])
|
|
odd_subset = paddle.io.Subset(dataset, [1, 3])
|
|
|
|
assert len(dataset) == 5
|
|
|
|
def prepare_dataloader(dataset):
|
|
return DataLoader(
|
|
dataset,
|
|
places=places,
|
|
num_workers=num_workers,
|
|
batch_size=1,
|
|
drop_last=True)
|
|
|
|
dataloader = prepare_dataloader(dataset)
|
|
dataloader_even = prepare_dataloader(even_subset)
|
|
dataloader_odd = prepare_dataloader(odd_subset)
|
|
|
|
def assert_basic(input, label):
|
|
assert len(input) == 1
|
|
assert len(label) == 1
|
|
assert input.shape == [1, 3, 4]
|
|
assert label.shape == [1, 1]
|
|
assert isinstance(input, paddle.Tensor)
|
|
assert isinstance(label, paddle.Tensor)
|
|
|
|
elements_list = list()
|
|
for _, (input, label) in enumerate(dataloader()):
|
|
assert_basic(input, label)
|
|
elements_list.append(label)
|
|
|
|
for _, (input, label) in enumerate(dataloader_even()):
|
|
assert_basic(input, label)
|
|
elements_list.remove(label)
|
|
|
|
odd_list = list()
|
|
for _, (input, label) in enumerate(dataloader_odd()):
|
|
assert_basic(input, label)
|
|
odd_list.append(label)
|
|
|
|
self.assertEqual(odd_list, elements_list)
|
|
|
|
def test_main(self):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
places = [paddle.CPUPlace()]
|
|
if paddle.is_compiled_with_cuda():
|
|
places.append(paddle.CUDAPlace(0))
|
|
for p in places:
|
|
self.run_main(num_workers=0, places=p)
|
|
|
|
|
|
class TestChainDataset(unittest.TestCase):
|
|
def run_main(self, num_workers, places):
|
|
paddle.static.default_startup_program().random_seed = 1
|
|
paddle.static.default_main_program().random_seed = 1
|
|
|
|
dataset1 = RandomIterableDataset(10)
|
|
dataset2 = RandomIterableDataset(10)
|
|
dataset = ChainDataset([dataset1, dataset2])
|
|
|
|
samples = []
|
|
for data in iter(dataset):
|
|
samples.append(data)
|
|
assert len(samples) == 20
|
|
|
|
idx = 0
|
|
for image, label in iter(dataset1):
|
|
assert np.allclose(image, samples[idx][0])
|
|
assert np.allclose(label, samples[idx][1])
|
|
idx += 1
|
|
for image, label in iter(dataset2):
|
|
assert np.allclose(image, samples[idx][0])
|
|
assert np.allclose(label, samples[idx][1])
|
|
idx += 1
|
|
|
|
def test_main(self):
|
|
places = [paddle.CPUPlace()]
|
|
if paddle.is_compiled_with_cuda():
|
|
places.append(paddle.CUDAPlace(0))
|
|
for p in places:
|
|
self.run_main(num_workers=0, places=p)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|