mindspore2022/tests/ut/python/dataset/test_HWC2CHW.py

# Copyright 2020-2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""
Testing HWC2CHW op in DE
"""
import numpy as np
import pytest
import mindspore.dataset as ds
import mindspore.dataset.transforms.py_transforms
import mindspore.dataset.vision.c_transforms as c_vision
import mindspore.dataset.vision.py_transforms as py_vision
from mindspore import log as logger
from util import diff_mse, visualize_list, save_and_check_md5

GENERATE_GOLDEN = False

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


def test_HWC2CHW_callable():
    """
    Test HWC2CHW is callable
    """
    logger.info("Test HWC2CHW callable")
    img = np.zeros([50, 50, 3])
    assert img.shape == (50, 50, 3)

    # test one tensor
    img1 = c_vision.HWC2CHW()(img)
    assert img1.shape == (3, 50, 50)

    # test one tensor with 5 channels
    img2 = np.zeros([50, 50, 5])
    assert img2.shape == (50, 50, 5)
    img3 = c_vision.HWC2CHW()(img2)
    assert img3.shape == (5, 50, 50)

    # test input multiple tensors
    with pytest.raises(RuntimeError) as info:
        imgs = [img, img]
        _ = c_vision.HWC2CHW()(*imgs)
    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)

    with pytest.raises(RuntimeError) as info:
        _ = c_vision.HWC2CHW()(img, img)
    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)


def test_HWC2CHW_multi_channels():
    """
    Feature: Test HWC2CHW feature
    Description: The input is a HWC format array with 5 channels
    Expectation: success
    """
    logger.info("Test HWC2CHW with data of 5 channels")

    # create numpy array in HWC format with shape (4, 2, 5) like a fake image with 5 channels
    raw_data = np.random.rand(4, 2, 5).astype(np.float32)
    expect_output = np.transpose(raw_data, (2, 0, 1))

    # NumpySliceDataset support accept data stored in list, tuple etc, here only one row data in list.
    input_data = np.array([raw_data])
    dataset = ds.NumpySlicesDataset(input_data, column_names=["col1"], shuffle=False)

    hwc2chw = c_vision.HWC2CHW()
    dataset = dataset.map(hwc2chw, input_columns=["col1"])
    for item in dataset.create_tuple_iterator(output_numpy=True):
        assert np.allclose(item[0], expect_output)


def test_HWC2CHW(plot=False):
    """
    Test HWC2CHW
    """
    logger.info("Test HWC2CHW")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    decode_op = c_vision.Decode()
    hwc2chw_op = c_vision.HWC2CHW()
    data1 = data1.map(operations=decode_op, input_columns=["image"])
    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])

    # Second dataset
    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    data2 = data2.map(operations=decode_op, input_columns=["image"])

    image_transposed = []
    image = []
    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
        transposed_item = item1["image"].copy()
        original_item = item2["image"].copy()
        image_transposed.append(transposed_item.transpose(1, 2, 0))
        image.append(original_item)

        # check if the shape of data is transposed correctly
        # transpose the original image from shape (H,W,C) to (C,H,W)
        mse = diff_mse(transposed_item, original_item.transpose(2, 0, 1))
        assert mse == 0
    if plot:
        visualize_list(image, image_transposed)


def test_HWC2CHW_md5():
    """
    Test HWC2CHW(md5)
    """
    logger.info("Test HWC2CHW with md5 comparison")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    decode_op = c_vision.Decode()
    hwc2chw_op = c_vision.HWC2CHW()
    data1 = data1.map(operations=decode_op, input_columns=["image"])
    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])

    # Compare with expected md5 from images
    filename = "HWC2CHW_01_result.npz"
    save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)


def test_HWC2CHW_comp(plot=False):
    """
    Test HWC2CHW between python and c image augmentation
    """
    logger.info("Test HWC2CHW with c_transform and py_transform comparison")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    decode_op = c_vision.Decode()
    hwc2chw_op = c_vision.HWC2CHW()
    data1 = data1.map(operations=decode_op, input_columns=["image"])
    data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])

    # Second dataset
    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms = [
        py_vision.Decode(),
        py_vision.ToTensor(),
        py_vision.HWC2CHW()
    ]
    transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
    data2 = data2.map(operations=transform, input_columns=["image"])

    image_c_transposed = []
    image_py_transposed = []
    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
        c_image = item1["image"]
        py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)

        # Compare images between that applying c_transform and py_transform
        mse = diff_mse(py_image, c_image)
        # Note: The images aren't exactly the same due to rounding error
        assert mse < 0.001
        image_c_transposed.append(c_image.transpose(1, 2, 0))
        image_py_transposed.append(py_image.transpose(1, 2, 0))
    if plot:
        visualize_list(image_c_transposed, image_py_transposed, visualize_mode=2)


if __name__ == '__main__':
    test_HWC2CHW_callable()
    test_HWC2CHW_multi_channels()
    test_HWC2CHW(True)
    test_HWC2CHW_md5()
    test_HWC2CHW_comp(True)