updating python unit tests based on floating point bounding boxes + adding coco dataset in unit tests
avakh
5 years ago
parent
9ba937b1af
commit
421b80bf8f
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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 the random resize with bounding boxes 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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DATA_DIR = "../data/dataset/testVOC2012_2"
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DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
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"../data/dataset/testCOCO/annotations/train.json"] # DATA_DIR, ANNOTATION_DIR
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def test_random_resize_with_bbox_op_voc_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied
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testing with VOC dataset
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"""
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logger.info("test_random_resize_with_bbox_op_voc_c")
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original_seed = config_get_set_seed(123)
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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, task="Detection", mode="train",
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decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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test_op = c_vision.RandomResizeWithBBox(100)
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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_resize_with_bbox_op_01_c_voc_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_resize_with_bbox_op_rand_coco_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied,
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tests with MD5 check, expected to pass
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testing with COCO dataset
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"""
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logger.info("test_random_resize_with_bbox_op_rand_coco_c")
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original_seed = config_get_set_seed(231)
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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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dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
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decode=True, shuffle=False)
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dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
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decode=True, shuffle=False)
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test_op = c_vision.RandomResizeWithBBox(200)
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# map to apply ops
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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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filename = "random_resize_with_bbox_op_01_c_coco_result.npz"
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save_and_check_md5(dataCoco2, filename, generate_golden=GENERATE_GOLDEN)
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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, annot_name="bbox")
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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_resize_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 RandomresizeWithBBox Op applied,
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applied on dynamically generated edge case, expected to pass. edge case is when bounding
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box has dimensions as the image itself.
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"""
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logger.info("test_random_resize_with_bbox_op_edge_c")
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dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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test_op = c_vision.RandomResizeWithBBox(500)
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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: (
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img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
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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: (
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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_resize_with_bbox_op_invalid_c():
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"""
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Test RandomResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError
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"""
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logger.info("test_random_resize_with_bbox_op_invalid_c")
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try:
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# zero value for resize
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c_vision.RandomResizeWithBBox(0)
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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 is not" in str(err)
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try:
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# one of the size values is zero
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c_vision.RandomResizeWithBBox((0, 100))
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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 is not" in str(err)
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try:
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# negative value for resize
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c_vision.RandomResizeWithBBox(-10)
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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 is not" in str(err)
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try:
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# invalid input shape
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c_vision.RandomResizeWithBBox((100, 100, 100))
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except TypeError as err:
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logger.info("Got an exception in DE: {}".format(str(err)))
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assert "Size should be" in str(err)
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def test_random_resize_with_bbox_op_bad_c():
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"""
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Tests RandomResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors
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"""
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logger.info("test_random_resize_with_bbox_op_bad_c")
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test_op = c_vision.RandomResizeWithBBox((400, 300))
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data_voc2 = ds.VOCDataset(DATA_DIR, 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, 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, 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, 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_resize_with_bbox_op_voc_c(plot_vis=False)
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test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False)
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test_random_resize_with_bbox_op_edge_c(plot_vis=False)
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test_random_resize_with_bbox_op_invalid_c()
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test_random_resize_with_bbox_op_bad_c()
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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 the resize with bounding boxes 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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save_and_check_md5
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GENERATE_GOLDEN = False
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DATA_DIR = "../data/dataset/testVOC2012_2"
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DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
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"../data/dataset/testCOCO/annotations/train.json"] # DATA_DIR, ANNOTATION_DIR
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def test_resize_with_bbox_op_voc_c(plot_vis=False):
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"""
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Prints images and bboxes side by side with and without ResizeWithBBox Op applied
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testing with VOC dataset
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"""
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logger.info("test_resize_with_bbox_op_voc_c")
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# Load dataset
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dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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test_op = c_vision.ResizeWithBBox(100)
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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 = "resize_with_bbox_op_01_c_voc_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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def test_resize_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 ResizeWithBBox Op applied,
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tests with MD5 check, expected to pass
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Testing with COCO dataset
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"""
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logger.info("test_resize_with_bbox_op_coco_c")
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# Load dataset
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dataCOCO1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
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decode=True, shuffle=False)
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dataCOCO2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
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decode=True, shuffle=False)
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test_op = c_vision.ResizeWithBBox(200)
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# map to apply ops
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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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filename = "resize_with_bbox_op_01_c_coco_result.npz"
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save_and_check_md5(dataCOCO2, filename, generate_golden=GENERATE_GOLDEN)
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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, annot_name="bbox")
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def test_resize_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 ResizeWithBBox Op applied,
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applied on dynamically generated edge case, expected to pass. edge case is when bounding
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box has dimensions as the image itself.
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"""
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logger.info("test_resize_with_bbox_op_edge_c")
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dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
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decode=True, shuffle=False)
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test_op = c_vision.ResizeWithBBox(500)
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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: (
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img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])
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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: (
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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_resize_with_bbox_op_invalid_c():
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"""
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Test ResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError
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"""
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logger.info("test_resize_with_bbox_op_invalid_c")
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try:
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# invalid interpolation value
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c_vision.ResizeWithBBox(400, interpolation="invalid")
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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 "interpolation" in str(err)
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def test_resize_with_bbox_op_bad_c():
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"""
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Tests ResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors
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"""
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logger.info("test_resize_with_bbox_op_bad_c")
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test_op = c_vision.ResizeWithBBox((200, 300))
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data_voc2 = ds.VOCDataset(DATA_DIR, 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, 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, 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, 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_resize_with_bbox_op_voc_c(plot_vis=False)
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test_resize_with_bbox_op_coco_c(plot_vis=False)
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test_resize_with_bbox_op_edge_c(plot_vis=False)
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test_resize_with_bbox_op_invalid_c()
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test_resize_with_bbox_op_bad_c()
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