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

# 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
import mindspore.dataset.vision as vision


DATA_DIR_TF2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
SCHEMA_DIR_TF2 = "../data/dataset/test_tf_file_3_images/datasetSchema.json"


def test_tf_skip():
    """
    Feature: Skip op
    Description: Test simple skip op usage with TFRecordDataset
    Expectation: Output is equal to the expected output
    """
    data1 = ds.TFRecordDataset(DATA_DIR_TF2, SCHEMA_DIR_TF2, shuffle=False)

    resize_height, resize_width = 32, 32
    decode_op = vision.Decode()
    resize_op = vision.Resize((resize_height, resize_width), interpolation=ds.transforms.vision.Inter.LINEAR)
    data1 = data1.map(operations=decode_op, input_columns=["image"])
    data1 = data1.map(operations=resize_op, input_columns=["image"])
    data1 = data1.skip(2)

    num_iter = 0
    for _ in data1.create_dict_iterator(num_epochs=1):
        num_iter += 1
    assert num_iter == 1


def generator_md():
    """
    create a dataset with [0, 1, 2, 3, 4]
    """
    for i in range(5):
        yield (np.array([i]),)


def test_generator_skip():
    """
    Feature: Skip op
    Description: Test simple skip op usage with GeneratorDataset with num_parallel_workers=4
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"], num_parallel_workers=4)

    # Here ds1 should be [3, 4]
    ds1 = ds1.skip(3)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 2
    assert buf == [3, 4]


def test_skip_1():
    """
    Feature: Skip op
    Description: Test skip op using input count > 0
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be []
    ds1 = ds1.skip(7)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert buf == []


def test_skip_2():
    """
    Feature: Skip op
    Description: Test skip op using input count=0
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [0, 1, 2, 3, 4]
    ds1 = ds1.skip(0)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 5
    assert buf == [0, 1, 2, 3, 4]


def test_skip_repeat_1():
    """
    Feature: Skip op
    Description: Test skip op after a repeat op
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
    ds1 = ds1.repeat(2)

    # Here ds1 should be [3, 4, 0, 1, 2, 3, 4]
    ds1 = ds1.skip(3)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 7
    assert buf == [3, 4, 0, 1, 2, 3, 4]


def test_skip_repeat_2():
    """
    Feature: Skip op
    Description: Test skip op followed by a repeat op
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [3, 4]
    ds1 = ds1.skip(3)

    # Here ds1 should be [3, 4, 3, 4]
    ds1 = ds1.repeat(2)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 4
    assert buf == [3, 4, 3, 4]


def test_skip_repeat_3():
    """
    Feature: Skip op
    Description: Test skip op by applying repeat -> skip -> repeat
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
    ds1 = ds1.repeat(2)

    # Here ds1 should be [3, 4]
    ds1 = ds1.skip(8)

    # Here ds1 should be [3, 4, 3, 4, 3, 4]
    ds1 = ds1.repeat(3)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 6
    assert buf == [3, 4, 3, 4, 3, 4]


def test_skip_take_1():
    """
    Feature: Skip op
    Description: Test skip op after applying take op
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [0, 1, 2, 3]
    ds1 = ds1.take(4)

    # Here ds1 should be [2, 3]
    ds1 = ds1.skip(2)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 2
    assert buf == [2, 3]


def test_skip_take_2():
    """
    Feature: Skip op
    Description: Test skip op followed by a take op
    Expectation: Output is equal to the expected output
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    # Here ds1 should be [2, 3, 4]
    ds1 = ds1.skip(2)

    # Here ds1 should be [2, 3]
    ds1 = ds1.take(2)

    buf = []
    for data in ds1.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(data[0][0])
    assert len(buf) == 2
    assert buf == [2, 3]


def generator_1d():
    for i in range(64):
        yield (np.array([i]),)


def test_skip_filter_1():
    """
    Feature: Skip op
    Description: Test skip op followed by a filter op
    Expectation: Output is equal to the expected output
    """
    dataset = ds.GeneratorDataset(generator_1d, ['data'])
    dataset = dataset.skip(5)
    dataset = dataset.filter(predicate=lambda data: data < 11, num_parallel_workers=4)

    buf = []
    for item in dataset.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(item[0][0])
    assert buf == [5, 6, 7, 8, 9, 10]


def test_skip_filter_2():
    """
    Feature: Skip op
    Description: Test skip op after filter op is applied
    Expectation: Output is equal to the expected output
    """
    dataset = ds.GeneratorDataset(generator_1d, ['data'])
    dataset = dataset.filter(predicate=lambda data: data < 11, num_parallel_workers=4)
    dataset = dataset.skip(5)

    buf = []
    for item in dataset.create_tuple_iterator(num_epochs=1, output_numpy=True):
        buf.append(item[0][0])
    assert buf == [5, 6, 7, 8, 9, 10]


def test_skip_exception_1():
    """
    Feature: Skip op
    Description: Test skip op using input count=-1
    Expectation: Error is raised as expected
    """
    data1 = ds.GeneratorDataset(generator_md, ["data"])

    try:
        data1 = data1.skip(count=-1)
        num_iter = 0
        for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
            num_iter += 1

    except ValueError as e:
        assert "Input count is not within the required interval" in str(e)


def test_skip_exception_2():
    """
    Feature: Skip op
    Description: Test skip op using input count=-2
    Expectation: Error is raised as expected
    """
    ds1 = ds.GeneratorDataset(generator_md, ["data"])

    with pytest.raises(ValueError) as e:
        ds1 = ds1.skip(-2)
    assert "Input count is not within the required interval" in str(e.value)


def test_skip_with_generator_dataset_multi_process():
    """
    Feature: Skip op
    Description: Test skip op when using GeneratorDataset(..., num_parallel_workers=2, ...)
    Expectation: Error is raised as expected
    """

    # construct data and label
    data1 = np.array(np.random.sample(size=(300, 300, 3)) * 255, dtype=np.uint8)
    data2 = np.array(np.random.sample(size=(300, 300, 3)) * 255, dtype=np.uint8)
    data3 = np.array(np.random.sample(size=(300, 300, 3)) * 255, dtype=np.uint8)
    data4 = np.array(np.random.sample(size=(300, 300, 3)) * 255, dtype=np.uint8)

    label = [1, 2, 3, 4]

    # load the data and label by NumpySlicesDataset
    dataset = ds.NumpySlicesDataset(([data1, data2, data3, data4], label), ["data", "label"], num_parallel_workers=2)

    dataset_train, dataset_val = dataset.split([0.5, 0.5])

    # apply the transform to data
    dataset_train = dataset_train.map(operations=vision.RandomCrop(size=(250, 250)), input_columns="data")

    # batch
    dataset_train = dataset_train.batch(batch_size=2)

    # create iterator
    epochs = 2
    ds_iter = dataset_train.create_dict_iterator(output_numpy=True, num_epochs=epochs)
    count = 0
    for _ in range(epochs):
        for item in ds_iter:
            assert item["data"].shape == (2, 250, 250, 3)
            count += 1
    assert count == 2

    # create val iterator
    epochs = 2
    ds_iter = dataset_val.create_dict_iterator(output_numpy=True, num_epochs=epochs)
    count = 0
    for _ in range(epochs):
        for item in ds_iter:
            assert item["data"].shape == (300, 300, 3)
            count += 1
    assert count == 4


if __name__ == "__main__":
    test_tf_skip()
    test_generator_skip()
    test_skip_1()
    test_skip_2()
    test_skip_repeat_1()
    test_skip_repeat_2()
    test_skip_repeat_3()
    test_skip_take_1()
    test_skip_take_2()
    test_skip_filter_1()
    test_skip_filter_2()
    test_skip_exception_1()
    test_skip_exception_2()
    test_skip_with_generator_dataset_multi_process()