forked from mindspore-Ecosystem/mindspore
381 lines
16 KiB
Python
381 lines
16 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 RandomRotation in DE
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"""
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import cv2
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import numpy as np
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from PIL import Image
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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.dataset.vision.utils import Inter
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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_random_rotation_op_c(plot=False):
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"""
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Feature: RandomRotation
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Description: Test RandomRotation in Cpp transformations op
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Expectation: The dataset is processed as expected
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"""
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logger.info("test_random_rotation_op_c")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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decode_op = vision.Decode()
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# use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size
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random_rotation_op = vision.RandomRotation((90, 90), expand=True)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_rotation_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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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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if num_iter > 0:
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break
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rotation_de = item1["image"]
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original = item2["image"]
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logger.info("shape before rotate: {}".format(original.shape))
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rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE)
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mse = diff_mse(rotation_de, rotation_cv)
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logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
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assert mse == 0
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num_iter += 1
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if plot:
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visualize_image(original, rotation_de, mse, rotation_cv)
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def test_random_rotation_op_c_area():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation in Cpp transformations op with Interpolation AREA
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Expectation: Number of returned data rows is correct
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"""
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logger.info("test_random_rotation_op_c_area")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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decode_op = vision.Decode()
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# Use [180, 180] to force rotate 180 degrees, expand is set to be True to match output size
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# Use resample with Interpolation AREA
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random_rotation_op = vision.RandomRotation((180, 180), expand=True, resample=Inter.AREA)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_rotation_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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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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rotation_de = item1["image"]
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original = item2["image"]
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logger.info("shape before rotate: {}".format(original.shape))
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rotation_cv = cv2.rotate(original, cv2.ROTATE_180)
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mse = diff_mse(rotation_de, rotation_cv)
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logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
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assert mse == 0
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num_iter += 1
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assert num_iter == 3
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def test_random_rotation_op_py(plot=False):
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"""
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Feature: RandomRotation
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Description: Test RandomRotation in Python transformations op
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Expectation: The dataset is processed as expected
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"""
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logger.info("test_random_rotation_op_py")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size
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transform1 = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.RandomRotation((90, 90), expand=True),
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vision.ToTensor()])
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data1 = data1.map(operations=transform1, 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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transform2 = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.ToTensor()])
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data2 = data2.map(operations=transform2, 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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if num_iter > 0:
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break
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rotation_de = (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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logger.info("shape before rotate: {}".format(original.shape))
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rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE)
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mse = diff_mse(rotation_de, rotation_cv)
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logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse))
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assert mse == 0
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num_iter += 1
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if plot:
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visualize_image(original, rotation_de, mse, rotation_cv)
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def test_random_rotation_op_py_antialias():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation in Python transformations op with resample=Inter.ANTIALIAS
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Expectation: The dataset is processed as expected
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"""
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logger.info("test_random_rotation_op_py_antialias")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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# use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size
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transform1 = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.RandomRotation((90, 90),
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expand=True,
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resample=Inter.ANTIALIAS),
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vision.ToTensor()])
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data1 = data1.map(operations=transform1, input_columns=["image"])
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num_iter = 0
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for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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logger.info("use RandomRotation by Inter.ANTIALIAS process {} images.".format(num_iter))
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assert num_iter == 3
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def test_random_rotation_expand():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with expand
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Expectation: The dataset is processed as expected
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"""
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logger.info("test_random_rotation_op")
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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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# expand is set to be True to match output size
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random_rotation_op = vision.RandomRotation((0, 90), expand=True)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_rotation_op, input_columns=["image"])
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1):
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rotation = item["image"]
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logger.info("shape after rotate: {}".format(rotation.shape))
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num_iter += 1
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def test_random_rotation_md5():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with md5 check
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test RandomRotation with md5 check")
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original_seed = config_get_set_seed(5)
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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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resize_op = vision.RandomRotation((0, 90),
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expand=True,
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resample=Inter.BILINEAR,
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center=(50, 50),
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fill_value=150)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=resize_op, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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transform2 = mindspore.dataset.transforms.Compose([vision.Decode(True),
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vision.RandomRotation((0, 90),
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expand=True,
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resample=Inter.BILINEAR,
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center=(50, 50),
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fill_value=150),
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vision.ToTensor()])
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data2 = data2.map(operations=transform2, input_columns=["image"])
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# Compare with expected md5 from images
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filename1 = "random_rotation_01_c_result.npz"
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save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
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filename2 = "random_rotation_01_py_result.npz"
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save_and_check_md5_pil(data2, filename2, generate_golden=GENERATE_GOLDEN)
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# Restore configuration
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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_rotation_diff(plot=False):
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"""
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Feature: RandomRotation
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Description: Test RandomRotation difference between Python and Cpp transformations op
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Expectation: Both datasets are processed the same as expected
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"""
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logger.info("test_random_rotation_op")
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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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rotation_op = vision.RandomRotation((45, 45))
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ctrans = [decode_op,
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rotation_op
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]
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data1 = data1.map(operations=ctrans, input_columns=["image"])
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# Second dataset
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transforms = [
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vision.Decode(True),
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vision.RandomRotation((45, 45)),
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vision.ToTensor(),
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(operations=transform, input_columns=["image"])
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num_iter = 0
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image_list_c, image_list_py = [], []
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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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c_image = item1["image"]
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py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_list_c.append(c_image)
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image_list_py.append(py_image)
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logger.info("shape of c_image: {}".format(c_image.shape))
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logger.info("shape of py_image: {}".format(py_image.shape))
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logger.info("dtype of c_image: {}".format(c_image.dtype))
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logger.info("dtype of py_image: {}".format(py_image.dtype))
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mse = diff_mse(c_image, py_image)
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assert mse < 0.001 # Rounding error
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if plot:
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visualize_list(image_list_c, image_list_py, visualize_mode=2)
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def test_random_rotation_op_exception():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation in Python transformations op with resample=Inter.ANTIALIAS, but center is not None
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Expectation: ValueError
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"""
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logger.info("test_random_rotation_op_exception")
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image = Image.open("../data/dataset/testImageNetData2/train/class1/1_1.jpg")
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with pytest.raises(ValueError) as error_info:
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random_rotation_op = vision.RandomRotation((90, 90), expand=True, resample=Inter.ANTIALIAS, center=(50, 50))
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_ = random_rotation_op(image)
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assert "When using Inter.ANTIALIAS, center needs to be None and angle needs to be an integer multiple of 90." \
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in str(error_info.value)
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def test_random_rotation_op_exception_c_pilcubic():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with resample=Inter.PILCUBIC for NumPy input in eager mode
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Expectation: Exception is raised as expected
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"""
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logger.info("test_random_rotation_op_exception_c_pilcubic")
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image = cv2.imread("../data/dataset/apple.jpg")
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with pytest.raises(RuntimeError) as error_info:
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random_rotation_op = vision.RandomRotation((90, 90), expand=True, resample=Inter.PILCUBIC)
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_ = random_rotation_op(image)
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assert "RandomRotation: Invalid InterpolationMode" in str(error_info.value)
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def test_random_rotation_op_exception_py_pilcubic():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with resample=Inter.PILCUBIC for PIL input in eager mode
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Expectation: Exception is raised as expected
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"""
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logger.info("test_random_rotation_op_exception_py_pilcubic")
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image = Image.open("../data/dataset/apple.jpg").convert("RGB")
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with pytest.raises(TypeError) as error_info:
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random_rotation_op = vision.RandomRotation((90, 90), expand=True, resample=Inter.PILCUBIC)
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_ = random_rotation_op(image)
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assert "Current Interpolation is not supported with PIL input." in str(error_info.value)
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def test_random_rotation_with_channel_5():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with 5 channel image
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Expectation: The image is processed as expected
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"""
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logger.info("test_random_rotation_invalid_dim")
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image = np.random.random((128, 64, 5)).astype(np.float32)
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random_rotation = vision.RandomRotation((90, 90), resample=Inter.NEAREST, expand=True)
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out = random_rotation(image)
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assert out.shape == (64, 128, 5)
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def test_random_rotation_with_channel_5_and_invalid_resample():
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"""
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Feature: RandomRotation
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Description: Test RandomRotation with 5 channel image and Inter.BICUBIC
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Expectation: RuntimeError is raised
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"""
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logger.info("test_random_rotation_with_channel_5_and_invalid_resample")
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image = np.random.random((128, 64, 5)).astype(np.float32)
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with pytest.raises(RuntimeError) as error_info:
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random_rotation = vision.RandomRotation((90, 90), resample=Inter.BICUBIC)
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_ = random_rotation(image)
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assert "interpolation can not be CUBIC when image channel is greater than 4" in str(error_info.value)
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if __name__ == "__main__":
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test_random_rotation_op_c(plot=True)
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test_random_rotation_op_c_area()
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test_random_rotation_op_py(plot=True)
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test_random_rotation_op_py_antialias()
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test_random_rotation_expand()
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test_random_rotation_md5()
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test_rotation_diff(plot=True)
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test_random_rotation_op_exception()
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test_random_rotation_op_exception_c_pilcubic()
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test_random_rotation_op_exception_py_pilcubic()
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test_random_rotation_with_channel_5()
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test_random_rotation_with_channel_5_and_invalid_resample()
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