forked from mindspore-Ecosystem/mindspore
590 lines
22 KiB
Python
590 lines
22 KiB
Python
# Copyright 2019 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 RandomCrop op in DE
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"""
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import numpy as np
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import mindspore.dataset.transforms.transforms as ops
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import mindspore.dataset.vision.transforms as vision
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import mindspore.dataset.vision.utils as mode
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import mindspore.dataset as ds
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from mindspore import log as logger
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from util import save_and_check_md5, visualize_list, config_get_set_seed, \
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config_get_set_num_parallel_workers, diff_mse
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GENERATE_GOLDEN = False
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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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def test_random_crop_op_c(plot=False):
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"""
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Test RandomCrop Op in c transforms
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"""
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logger.info("test_random_crop_op_c")
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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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random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200])
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decode_op = vision.Decode()
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_crop_op, 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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data2 = data2.map(operations=decode_op, input_columns=["image"])
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image_cropped = []
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image = []
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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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image1 = item1["image"]
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image2 = item2["image"]
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image_cropped.append(image1)
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image.append(image2)
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if plot:
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visualize_list(image, image_cropped)
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def test_random_crop_op_py(plot=False):
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"""
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Test RandomCrop op in py transforms
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"""
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logger.info("test_random_crop_op_py")
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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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transforms1 = [
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vision.Decode(True),
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vision.RandomCrop([512, 512], [200, 200, 200, 200]),
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vision.ToTensor()
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]
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transform1 = ops.Compose(transforms1)
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data1 = data1.map(operations=transform1, input_columns=["image"])
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# Second dataset
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# Second dataset for comparison
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms2 = [
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vision.Decode(True),
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vision.ToTensor()
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]
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transform2 = ops.Compose(transforms2)
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data2 = data2.map(operations=transform2, input_columns=["image"])
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crop_images = []
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original_images = []
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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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crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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crop_images.append(crop)
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original_images.append(original)
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if plot:
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visualize_list(original_images, crop_images)
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def test_random_crop_01_c():
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"""
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Test RandomCrop op with C implementation: size is a single integer, expected to pass
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"""
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logger.info("test_random_crop_01_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: If size is an int, a square crop of size (size, size) is returned.
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random_crop_op = vision.RandomCrop(512)
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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filename = "random_crop_01_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_01_py():
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"""
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Test RandomCrop op with transforms: size is a single integer, expected to pass
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"""
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logger.info("test_random_crop_01_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: If size is an int, a square crop of size (size, size) is returned.
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transforms = [
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vision.Decode(True),
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vision.RandomCrop(512),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_crop_01_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_02_c():
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"""
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Test RandomCrop op with C implementation: size is a list/tuple with length 2, expected to pass
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"""
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logger.info("test_random_crop_02_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: If size is a sequence of length 2, it should be (height, width).
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random_crop_op = vision.RandomCrop([512, 375])
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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filename = "random_crop_02_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_02_py():
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"""
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Test RandomCrop op with transforms: size is a list/tuple with length 2, expected to pass
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"""
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logger.info("test_random_crop_02_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: If size is a sequence of length 2, it should be (height, width).
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transforms = [
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vision.Decode(True),
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vision.RandomCrop([512, 375]),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_crop_02_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_03_c():
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"""
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Test RandomCrop op with C implementation: input image size == crop size, expected to pass
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"""
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logger.info("test_random_crop_03_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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random_crop_op = vision.RandomCrop([2268, 4032])
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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filename = "random_crop_03_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_03_py():
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"""
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Test RandomCrop op with transforms: input image size == crop size, expected to pass
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"""
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logger.info("test_random_crop_03_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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transforms = [
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vision.Decode(True),
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vision.RandomCrop([2268, 4032]),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_crop_03_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_04_c():
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"""
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Test RandomCrop op with C implementation: input image size < crop size, expected to fail
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"""
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logger.info("test_random_crop_04_c")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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random_crop_op = vision.RandomCrop([2268, 4033])
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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try:
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data.create_dict_iterator(num_epochs=1).__next__()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "crop size is bigger than the image dimensions" in str(e)
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def test_random_crop_04_py():
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"""
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Test RandomCrop op with transforms:
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input image size < crop size, expected to fail
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"""
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logger.info("test_random_crop_04_py")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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transforms = [
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vision.Decode(True),
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vision.RandomCrop([2268, 4033]),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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try:
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data.create_dict_iterator(num_epochs=1).__next__()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Crop size" in str(e)
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def test_random_crop_05_c():
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"""
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Test RandomCrop op with C implementation:
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input image size < crop size but pad_if_needed is enabled,
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expected to pass
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"""
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logger.info("test_random_crop_05_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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random_crop_op = vision.RandomCrop([2268, 4033], [200, 200, 200, 200], pad_if_needed=True)
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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filename = "random_crop_05_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_05_py():
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"""
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Test RandomCrop op with transforms:
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input image size < crop size but pad_if_needed is enabled,
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expected to pass
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"""
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logger.info("test_random_crop_05_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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transforms = [
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vision.Decode(True),
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vision.RandomCrop([2268, 4033], [200, 200, 200, 200], pad_if_needed=True),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_crop_05_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_06_c():
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"""
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Test RandomCrop op with C implementation:
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invalid size, expected to raise TypeError
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"""
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logger.info("test_random_crop_06_c")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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# Note: if size is neither an int nor a list of length 2, an exception will raise
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random_crop_op = vision.RandomCrop([512, 512, 375])
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Size should be a single integer" in str(e)
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def test_random_crop_06_py():
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"""
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Test RandomCrop op with transforms:
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invalid size, expected to raise TypeError
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"""
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logger.info("test_random_crop_06_py")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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# Note: if size is neither an int nor a list of length 2, an exception will raise
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transforms = [
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vision.Decode(True),
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vision.RandomCrop([512, 512, 375]),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Size should be a single integer" in str(e)
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def test_random_crop_07_c():
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"""
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Test RandomCrop op with C implementation:
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padding_mode is Border.CONSTANT and fill_value is 255 (White),
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expected to pass
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"""
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logger.info("test_random_crop_07_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The padding_mode is default as Border.CONSTANT and set filling color to be white.
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random_crop_op = vision.RandomCrop(512, [200, 200, 200, 200], fill_value=(255, 255, 255))
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decode_op = vision.Decode()
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_crop_op, input_columns=["image"])
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filename = "random_crop_07_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_07_py():
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"""
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Test RandomCrop op with transforms:
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padding_mode is Border.CONSTANT and fill_value is 255 (White),
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expected to pass
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"""
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logger.info("test_random_crop_07_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The padding_mode is default as Border.CONSTANT and set filling color to be white.
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transforms = [
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vision.Decode(True),
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vision.RandomCrop(512, [200, 200, 200, 200], fill_value=(255, 255, 255)),
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vision.ToTensor()
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]
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transform = ops.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_crop_07_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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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_random_crop_08_c():
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"""
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Test RandomCrop op with C implementation: padding_mode is Border.EDGE,
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expected to pass
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"""
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logger.info("test_random_crop_08_c")
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original_seed = config_get_set_seed(0)
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|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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|
|
|
# Generate dataset
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|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The padding_mode is Border.EDGE.
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|
random_crop_op = vision.RandomCrop(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
|
|
decode_op = vision.Decode()
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|
data = data.map(operations=decode_op, input_columns=["image"])
|
|
data = data.map(operations=random_crop_op, input_columns=["image"])
|
|
|
|
filename = "random_crop_08_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
# Restore config setting
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
def test_random_crop_08_py():
|
|
"""
|
|
Test RandomCrop op with transforms: padding_mode is Border.EDGE,
|
|
expected to pass
|
|
"""
|
|
logger.info("test_random_crop_08_py")
|
|
original_seed = config_get_set_seed(0)
|
|
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
# Note: The padding_mode is Border.EDGE.
|
|
transforms = [
|
|
vision.Decode(True),
|
|
vision.RandomCrop(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE),
|
|
vision.ToTensor()
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data = data.map(operations=transform, input_columns=["image"])
|
|
|
|
filename = "random_crop_08_py_result.npz"
|
|
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
|
|
|
# Restore config setting
|
|
ds.config.set_seed(original_seed)
|
|
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
|
|
|
def test_random_crop_09():
|
|
"""
|
|
Test RandomCrop op: invalid type of input image format, expected to raise RuntimeError
|
|
"""
|
|
logger.info("test_random_crop_09")
|
|
|
|
# Generate dataset
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
transforms = [
|
|
vision.Decode(True),
|
|
vision.ToTensor(),
|
|
# Note: Input is wrong image format
|
|
vision.RandomCrop(512)
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data = data.map(operations=transform, input_columns=["image"])
|
|
try:
|
|
data.create_dict_iterator(num_epochs=1).__next__()
|
|
except RuntimeError as e:
|
|
logger.info("Got an exception in DE: {}".format(str(e)))
|
|
assert "Unexpected error. Pad: input shape is not <H,W,C> or <H, W>, got rank: 3" in str(e)
|
|
|
|
def test_random_crop_comp(plot=False):
|
|
"""
|
|
Test RandomCrop and compare between python and c image augmentation
|
|
"""
|
|
logger.info("Test RandomCrop with c_transform and py_transform comparison")
|
|
cropped_size = 512
|
|
|
|
# First dataset
|
|
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
random_crop_op = vision.RandomCrop(cropped_size)
|
|
decode_op = vision.Decode()
|
|
data1 = data1.map(operations=decode_op, input_columns=["image"])
|
|
data1 = data1.map(operations=random_crop_op, input_columns=["image"])
|
|
|
|
# Second dataset
|
|
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
transforms = [
|
|
vision.Decode(True),
|
|
vision.RandomCrop(cropped_size),
|
|
vision.ToTensor()
|
|
]
|
|
transform = ops.Compose(transforms)
|
|
data2 = data2.map(operations=transform, input_columns=["image"])
|
|
|
|
image_c_cropped = []
|
|
image_py_cropped = []
|
|
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)
|
|
image_c_cropped.append(c_image)
|
|
image_py_cropped.append(py_image)
|
|
if plot:
|
|
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
|
|
|
|
def test_random_crop_09_c():
|
|
"""
|
|
Test RandomCrop with different fields.
|
|
"""
|
|
logger.info("Test RandomCrop with different fields.")
|
|
|
|
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
|
data = data.map(operations=ops.Duplicate(), input_columns=["image"],
|
|
output_columns=["image", "image_copy"], column_order=["image", "image_copy"])
|
|
random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200])
|
|
decode_op = vision.Decode()
|
|
|
|
data = data.map(operations=decode_op, input_columns=["image"])
|
|
data = data.map(operations=decode_op, input_columns=["image_copy"])
|
|
data = data.map(operations=random_crop_op, input_columns=["image", "image_copy"])
|
|
|
|
num_iter = 0
|
|
for data1 in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
image = data1["image"]
|
|
image_copy = data1["image_copy"]
|
|
mse = diff_mse(image, image_copy)
|
|
assert mse == 0
|
|
num_iter += 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_random_crop_01_c()
|
|
test_random_crop_02_c()
|
|
test_random_crop_03_c()
|
|
test_random_crop_04_c()
|
|
test_random_crop_05_c()
|
|
test_random_crop_06_c()
|
|
test_random_crop_07_c()
|
|
test_random_crop_08_c()
|
|
test_random_crop_01_py()
|
|
test_random_crop_02_py()
|
|
test_random_crop_03_py()
|
|
test_random_crop_04_py()
|
|
test_random_crop_05_py()
|
|
test_random_crop_06_py()
|
|
test_random_crop_07_py()
|
|
test_random_crop_08_py()
|
|
test_random_crop_09()
|
|
test_random_crop_op_c(True)
|
|
test_random_crop_op_py(True)
|
|
test_random_crop_comp(True)
|
|
test_random_crop_09_c()
|