fix accidental change overwrite fix 2 updated inits from 0 to 0.0 for float improvd python ut comments updated macropull/2920/head
Danish Farid
5 years ago
parent
faa1084bd8
commit
78c370f72a
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# Copyright 2020 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 RandomCropAndResizeWithBBox op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as c_vision
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from mindspore import log as logger
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from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
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config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
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GENERATE_GOLDEN = False
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# Updated VOC dataset with correct annotations - DATA_DIR
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DATA_DIR_VOC = "../data/dataset/testVOC2012_2"
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# COCO dataset - DATA_DIR, ANNOTATION_DIR
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DATA_DIR_COCO = ["../data/dataset/testCOCO/train/", "../data/dataset/testCOCO/annotations/train.json"]
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def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
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tests with MD5 check, expected to pass
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"""
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logger.info("test_random_resized_crop_with_bbox_op_c")
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original_seed = config_get_set_seed(23415)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op])
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filename = "random_resized_crop_with_bbox_01_c_result.npz"
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save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp)
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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_resized_crop_with_bbox_op_coco_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
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Testing with Coco dataset
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"""
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logger.info("test_random_resized_crop_with_bbox_op_coco_c")
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# load dataset
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dataCoco1 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
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decode=True, shuffle=False)
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dataCoco2 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
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decode=True, shuffle=False)
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test_op = c_vision.RandomResizedCropWithBBox((512, 512), (0.5, 1), (0.5, 1))
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dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
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output_columns=["image", "bbox"],
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columns_order=["image", "bbox"],
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operations=[test_op])
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
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def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied,
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tests on dynamically generated edge case, expected to pass
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"""
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logger.info("test_random_resized_crop_with_bbox_op_edge_c")
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
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# maps to convert data into valid edge case data
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dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
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# Test Op added to list of Operations here
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp)
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def test_random_resized_crop_with_bbox_op_invalid_c():
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"""
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Tests RandomResizedCropWithBBox on invalid constructor parameters, expected to raise ValueError
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"""
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logger.info("test_random_resized_crop_with_bbox_op_invalid_c")
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# Load dataset, only Augmented Dataset as test will raise ValueError
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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try:
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# If input range of scale is not in the order of (min, max), ValueError will be raised.
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test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 0.5), (0.5, 0.5))
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op])
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for _ in dataVoc2.create_dict_iterator():
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break
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except ValueError as err:
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logger.info("Got an exception in DE: {}".format(str(err)))
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assert "Input range is not valid" in str(err)
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def test_random_resized_crop_with_bbox_op_invalid2_c():
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"""
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Tests RandomResizedCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
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"""
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logger.info("test_random_resized_crop_with_bbox_op_invalid2_c")
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# Load dataset # only loading the to AugDataset as test will fail on this
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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try:
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# If input range of ratio is not in the order of (min, max), ValueError will be raised.
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test_op = c_vision.RandomResizedCropWithBBox((256, 512), (1, 1), (1, 0.5))
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op])
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for _ in dataVoc2.create_dict_iterator():
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break
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except ValueError as err:
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logger.info("Got an exception in DE: {}".format(str(err)))
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assert "Input range is not valid" in str(err)
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def test_random_resized_crop_with_bbox_op_bad_c():
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"""
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Test RandomCropWithBBox op with invalid bounding boxes, expected to catch multiple errors.
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"""
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logger.info("test_random_resized_crop_with_bbox_op_bad_c")
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test_op = c_vision.RandomResizedCropWithBBox((256, 512), (0.5, 0.5), (0.5, 0.5))
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data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
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data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
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data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
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data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
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if __name__ == "__main__":
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test_random_resized_crop_with_bbox_op_c(plot_vis=True)
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test_random_resized_crop_with_bbox_op_coco_c(plot_vis=True)
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test_random_resized_crop_with_bbox_op_edge_c(plot_vis=True)
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test_random_resized_crop_with_bbox_op_invalid_c()
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test_random_resized_crop_with_bbox_op_invalid2_c()
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test_random_resized_crop_with_bbox_op_bad_c()
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@ -0,0 +1,249 @@
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# Copyright 2020 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 RandomCropWithBBox op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as c_vision
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import mindspore.dataset.transforms.vision.utils as mode
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from mindspore import log as logger
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from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
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config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
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GENERATE_GOLDEN = False
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# Updated VOC dataset with correct annotations - DATA_DIR
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DATA_DIR_VOC = "../data/dataset/testVOC2012_2"
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# COCO dataset - DATA_DIR, ANNOTATION_DIR
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DATA_DIR_COCO = ["../data/dataset/testCOCO/train/", "../data/dataset/testCOCO/annotations/train.json"]
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def test_random_crop_with_bbox_op_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomCropWithBBox Op applied
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"""
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logger.info("test_random_crop_with_bbox_op_c")
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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# define test OP with values to match existing Op UT
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test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp)
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def test_random_crop_with_bbox_op_coco_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
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Testing with Coco dataset
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"""
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logger.info("test_random_crop_with_bbox_op_coco_c")
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# load dataset
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dataCoco1 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
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decode=True, shuffle=False)
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dataCoco2 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
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decode=True, shuffle=False)
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test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
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dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
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output_columns=["image", "bbox"],
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columns_order=["image", "bbox"],
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operations=[test_op])
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
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def test_random_crop_with_bbox_op2_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
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with md5 check, expected to pass
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"""
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logger.info("test_random_crop_with_bbox_op2_c")
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original_seed = config_get_set_seed(593447)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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# define test OP with values to match existing Op unit - test
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test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], fill_value=(255, 255, 255))
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op])
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filename = "random_crop_with_bbox_01_c_result.npz"
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save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp)
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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_with_bbox_op3_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
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with Padding Mode explicitly passed
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"""
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logger.info("test_random_crop_with_bbox_op3_c")
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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# define test OP with values to match existing Op unit - test
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test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
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# map to apply ops
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dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op])
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unaugSamp, augSamp = [], []
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for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
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unaugSamp.append(unAug)
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augSamp.append(Aug)
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if plot_vis:
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visualize_with_bounding_boxes(unaugSamp, augSamp)
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def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomCropWithBBox Op applied,
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applied on dynamically generated edge case, expected to pass
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"""
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logger.info("test_random_crop_with_bbox_op_edge_c")
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
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# define test OP with values to match existing Op unit - test
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test_op = c_vision.RandomCropWithBBox(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE)
|
||||
|
||||
# maps to convert data into valid edge case data
|
||||
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
|
||||
|
||||
# Test Op added to list of Operations here
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp)
|
||||
|
||||
|
||||
def test_random_crop_with_bbox_op_invalid_c():
|
||||
"""
|
||||
Test RandomCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_crop_with_bbox_op_invalid_c")
|
||||
|
||||
# Load dataset
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
try:
|
||||
# define test OP with values to match existing Op unit - test
|
||||
test_op = c_vision.RandomCropWithBBox([512, 512, 375])
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op]) # Add column for "annotation"
|
||||
|
||||
for _ in dataVoc2.create_dict_iterator():
|
||||
break
|
||||
except TypeError as err:
|
||||
logger.info("Got an exception in DE: {}".format(str(err)))
|
||||
assert "Size should be a single integer" in str(err)
|
||||
|
||||
|
||||
def test_random_crop_with_bbox_op_bad_c():
|
||||
"""
|
||||
Tests RandomCropWithBBox Op with invalid bounding boxes, expected to catch multiple errors.
|
||||
"""
|
||||
logger.info("test_random_crop_with_bbox_op_bad_c")
|
||||
test_op = c_vision.RandomCropWithBBox([512, 512], [200, 200, 200, 200])
|
||||
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_crop_with_bbox_op_c(plot_vis=True)
|
||||
test_random_crop_with_bbox_op_coco_c(plot_vis=True)
|
||||
test_random_crop_with_bbox_op2_c(plot_vis=True)
|
||||
test_random_crop_with_bbox_op3_c(plot_vis=True)
|
||||
test_random_crop_with_bbox_op_edge_c(plot_vis=True)
|
||||
test_random_crop_with_bbox_op_invalid_c()
|
||||
test_random_crop_with_bbox_op_bad_c()
|
||||
@ -0,0 +1,220 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""
|
||||
Testing RandomVerticalFlipWithBBox op in DE
|
||||
"""
|
||||
import numpy as np
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.vision.c_transforms as c_vision
|
||||
|
||||
from mindspore import log as logger
|
||||
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
|
||||
config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5
|
||||
|
||||
GENERATE_GOLDEN = False
|
||||
|
||||
# Updated VOC dataset with correct annotations - DATA_DIR
|
||||
DATA_DIR_VOC = "../data/dataset/testVOC2012_2"
|
||||
# COCO dataset - DATA_DIR, ANNOTATION_DIR
|
||||
DATA_DIR_COCO = ["../data/dataset/testCOCO/train/", "../data/dataset/testCOCO/annotations/train.json"]
|
||||
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_c(plot_vis=False):
|
||||
"""
|
||||
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_c")
|
||||
# Load dataset
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(1)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp)
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_coco_c(plot_vis=False):
|
||||
"""
|
||||
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
|
||||
Testing with Coco dataset
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_coco_c")
|
||||
# load dataset
|
||||
dataCoco1 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataCoco2 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(1)
|
||||
|
||||
dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
|
||||
output_columns=["image", "bbox"],
|
||||
columns_order=["image", "bbox"],
|
||||
operations=[test_op])
|
||||
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(1)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
|
||||
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=False):
|
||||
"""
|
||||
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
|
||||
tests with MD5 check, expected to pass
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_rand_c")
|
||||
original_seed = config_get_set_seed(29847)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Load dataset
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(0.8)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
filename = "random_vertical_flip_with_bbox_01_c_result.npz"
|
||||
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=False):
|
||||
"""
|
||||
Prints images and bboxes side by side with and without RandomVerticalFlipWithBBox Op applied,
|
||||
applied on dynamically generated edge case, expected to pass
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_edge_c")
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(1)
|
||||
|
||||
# maps to convert data into valid edge case data
|
||||
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
|
||||
|
||||
# Test Op added to list of Operations here
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[lambda img, bboxes: (img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp)
|
||||
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_invalid_c():
|
||||
"""
|
||||
Test RandomVerticalFlipWithBBox Op on invalid constructor parameters, expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_invalid_c")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
try:
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(2)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
for _ in dataVoc2.create_dict_iterator():
|
||||
break
|
||||
|
||||
except ValueError as err:
|
||||
logger.info("Got an exception in DE: {}".format(str(err)))
|
||||
assert "Input is not" in str(err)
|
||||
|
||||
|
||||
def test_random_vertical_flip_with_bbox_op_bad_c():
|
||||
"""
|
||||
Tests RandomVerticalFlipWithBBox Op with invalid bounding boxes, expected to catch multiple errors
|
||||
"""
|
||||
logger.info("test_random_vertical_flip_with_bbox_op_bad_c")
|
||||
test_op = c_vision.RandomVerticalFlipWithBBox(1)
|
||||
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
|
||||
data_voc2 = ds.VOCDataset(DATA_DIR_VOC, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_vertical_flip_with_bbox_op_c(plot_vis=True)
|
||||
test_random_vertical_flip_with_bbox_op_coco_c(plot_vis=True)
|
||||
test_random_vertical_flip_with_bbox_op_rand_c(plot_vis=True)
|
||||
test_random_vertical_flip_with_bbox_op_edge_c(plot_vis=True)
|
||||
test_random_vertical_flip_with_bbox_op_invalid_c()
|
||||
test_random_vertical_flip_with_bbox_op_bad_c()
|
||||
Loading…
Reference in new issue