forked from mindspore-Ecosystem/mindspore
317 lines
11 KiB
Python
317 lines
11 KiB
Python
# Copyright 2019-2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing CutOut op in DE
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"""
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import numpy as np
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.transforms
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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from util import visualize_image, visualize_list, diff_mse, save_and_check_md5, save_and_check_md5_pil, \
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config_get_set_seed, config_get_set_num_parallel_workers
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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GENERATE_GOLDEN = False
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def test_cut_out_op(plot=False):
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"""
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Feature: CutOut op
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Description: Test CutOut op by comparing between Python and Cpp implementation
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Expectation: Both outputs are equal to each other
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"""
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logger.info("test_cut_out")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_1 = [
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vision.Decode(True),
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vision.ToTensor(),
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vision.RandomErasing(value='random')
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]
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transform_1 = mindspore.dataset.transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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cut_out_op = vision.CutOut(80, is_hwc=True)
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transforms_2 = [
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decode_op,
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cut_out_op
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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mse = diff_mse(image_1, image_2)
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if plot:
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visualize_image(image_1, image_2, mse)
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def test_cut_out_op_multicut(plot=False):
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"""
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Feature: CutOut op
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Description: Test CutOut where Python is implemented without RandomErasing and Cpp is implemented with num_patches
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Expectation: Both outputs are equal to each other
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"""
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logger.info("test_cut_out")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_1 = [
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vision.Decode(True),
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vision.ToTensor(),
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]
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transform_1 = mindspore.dataset.transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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cut_out_op = vision.CutOut(80, num_patches=10, is_hwc=True)
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transforms_2 = [
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decode_op,
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cut_out_op
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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image_list_1, image_list_2 = [], []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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image_list_1.append(image_1)
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image_list_2.append(image_2)
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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if plot:
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visualize_list(image_list_1, image_list_2)
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def test_cut_out_md5():
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"""
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Feature: CutOut op
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Description: Test CutOut with md5 comparison check
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Expectation: Passes the md5 check test
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"""
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logger.info("test_cut_out_md5")
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original_seed = config_get_set_seed(2)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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cut_out_op = vision.CutOut(100, is_hwc=True)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=cut_out_op, input_columns=["image"])
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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vision.Decode(True),
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vision.ToTensor(),
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vision.CutOut(100, is_hwc=False)
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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data2 = data2.map(operations=transform, input_columns=["image"])
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# Compare with expected md5 from images
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filename1 = "cut_out_01_c_result.npz"
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save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
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filename2 = "cut_out_02_c_result.npz"
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save_and_check_md5_pil(data2, filename2, generate_golden=GENERATE_GOLDEN)
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# Restore config
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_cut_out_comp_hwc(plot=False):
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"""
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Feature: CutOut op
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Description: Test CutOut with HWC input, Decode(to_pil=True) & ToTensor versus Decode(to_pil=False) comparison
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Expectation: Test succeeds. Manual confirmation of logged info. Manual visualization confirmation
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"""
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logger.info("test_cut_out_comp")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_1 = [
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vision.Decode(True),
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vision.ToTensor(),
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vision.CutOut(250, is_hwc=False)
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]
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transform_1 = mindspore.dataset.transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_2 = [
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vision.Decode(),
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vision.CutOut(250, is_hwc=True)
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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image_list_1, image_list_2 = [], []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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# C image doesn't require transpose
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image_2 = item2["image"]
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image_list_1.append(image_1)
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image_list_2.append(image_2)
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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if plot:
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visualize_list(image_list_2, image_list_1, visualize_mode=2)
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def test_cut_out_comp_chw(plot=False):
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"""
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Feature: CutOut op
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Description: Test CutOut with CHW input, Decode(to_pil=True) & ToTensor versus Decode(to_pil=False) & HWC2CHW
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comparison
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Expectation: Test succeeds. Manual confirmation of logged info
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"""
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logger.info("test_cut_out_comp_chw")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_1 = [
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vision.Decode(),
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vision.HWC2CHW(),
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vision.CutOut(500, num_patches=3, is_hwc=False)
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]
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transform_1 = mindspore.dataset.transforms.Compose(transforms_1)
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data1 = data1.map(operations=transform_1, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms_2 = [
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vision.Decode(True),
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vision.ToTensor(),
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vision.CutOut(500, num_patches=5, is_hwc=False)
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]
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data2 = data2.map(operations=transforms_2, input_columns=["image"])
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num_iter = 0
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image_list_1, image_list_2 = [], []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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if image_1.shape != image_2.shape:
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raise RuntimeError("image_1.shape != image_2.shape: " + str(image_1.shape) + " " + str(image_2.shape))
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image_list_1.append(image_1)
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image_list_2.append(image_2)
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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if plot:
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visualize_list(image_list_1, image_list_2, visualize_mode=1)
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def test_cutout_4channel_chw():
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"""
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Feature: CutOut op
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Description: Test CutOut Op with multi-channel CHW input
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Expectation: Test succeeds.
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"""
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image = np.random.randn(4, 1024, 856).astype(np.uint8)
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op = vision.CutOut(length=500, num_patches=3, is_hwc=False)
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op(image)
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def test_cutout_4channel_hwc():
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"""
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Feature: CutOut op
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Description: Test CutOut Op with multi-channel HWC input
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Expectation: Test succeeds.
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"""
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image = np.random.randn(1024, 856, 4).astype(np.uint8)
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op = vision.CutOut(length=500, num_patches=3, is_hwc=True)
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op(image)
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def test_cut_out_validation():
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"""
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Feature: CutOut op
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Description: Test CutOut Op with patch length greater than image dimensions
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Expectation: Raises an exception
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"""
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image = np.random.randn(3, 1024, 856).astype(np.uint8)
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op = vision.CutOut(length=1500, num_patches=3, is_hwc=False)
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with pytest.raises(RuntimeError) as errinfo:
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op(image)
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assert 'box size is too large for image erase' in str(errinfo.value)
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if __name__ == "__main__":
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test_cut_out_op(plot=True)
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test_cut_out_op_multicut(plot=True)
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test_cut_out_md5()
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test_cut_out_comp_hwc(plot=True)
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test_cut_out_comp_chw(plot=True)
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test_cutout_4channel_chw()
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test_cutout_4channel_hwc()
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test_cut_out_validation()
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