updating python unit tests based on floating point bounding boxes + adding...

updating python unit tests based on floating point bounding boxes + adding coco dataset in unit tests

tests/ut/python/dataset/test_random_resize_with_bbox.py

+# 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 the random resize with bounding boxes 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
+
+DATA_DIR = "../data/dataset/testVOC2012_2"
+DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
+              "../data/dataset/testCOCO/annotations/train.json"]  # DATA_DIR, ANNOTATION_DIR
+
+
+def test_random_resize_with_bbox_op_voc_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied
+    testing with VOC dataset
+    """
+    logger.info("test_random_resize_with_bbox_op_voc_c")
+    original_seed = config_get_set_seed(123)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+    # Load dataset
+    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    test_op = c_vision.RandomResizeWithBBox(100)
+
+    # map to apply ops
+    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            operations=[test_op])
+
+    filename = "random_resize_with_bbox_op_01_c_voc_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_resize_with_bbox_op_rand_coco_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied,
+    tests with MD5 check, expected to pass
+    testing with COCO dataset
+    """
+    logger.info("test_random_resize_with_bbox_op_rand_coco_c")
+    original_seed = config_get_set_seed(231)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    # Load dataset
+    dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
+                               decode=True, shuffle=False)
+
+    dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
+                               decode=True, shuffle=False)
+
+    test_op = c_vision.RandomResizeWithBBox(200)
+
+    # map to apply ops
+
+    dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
+                              output_columns=["image", "bbox"],
+                              columns_order=["image", "bbox"],
+                              operations=[test_op])
+
+    filename = "random_resize_with_bbox_op_01_c_coco_result.npz"
+    save_and_check_md5(dataCoco2, filename, generate_golden=GENERATE_GOLDEN)
+
+    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, annot_name="bbox")
+
+    # Restore config setting
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_random_resize_with_bbox_op_edge_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without RandomresizeWithBBox Op applied,
+    applied on dynamically generated edge case, expected to pass. edge case is when bounding
+    box has dimensions as the image itself.
+    """
+    logger.info("test_random_resize_with_bbox_op_edge_c")
+    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    test_op = c_vision.RandomResizeWithBBox(500)
+
+    # 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))])
+
+    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_resize_with_bbox_op_invalid_c():
+    """
+    Test RandomResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError
+    """
+    logger.info("test_random_resize_with_bbox_op_invalid_c")
+
+    try:
+        # zero value for resize
+        c_vision.RandomResizeWithBBox(0)
+
+    except ValueError as err:
+        logger.info("Got an exception in DE: {}".format(str(err)))
+        assert "Input is not" in str(err)
+
+    try:
+        # one of the size values is zero
+        c_vision.RandomResizeWithBBox((0, 100))
+
+    except ValueError as err:
+        logger.info("Got an exception in DE: {}".format(str(err)))
+        assert "Input is not" in str(err)
+
+    try:
+        # negative value for resize
+        c_vision.RandomResizeWithBBox(-10)
+
+    except ValueError as err:
+        logger.info("Got an exception in DE: {}".format(str(err)))
+        assert "Input is not" in str(err)
+
+    try:
+        # invalid input shape
+        c_vision.RandomResizeWithBBox((100, 100, 100))
+
+    except TypeError as err:
+        logger.info("Got an exception in DE: {}".format(str(err)))
+        assert "Size should be" in str(err)
+
+
+def test_random_resize_with_bbox_op_bad_c():
+    """
+    Tests RandomResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors
+    """
+    logger.info("test_random_resize_with_bbox_op_bad_c")
+    test_op = c_vision.RandomResizeWithBBox((400, 300))
+
+    data_voc2 = ds.VOCDataset(DATA_DIR, 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, 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, 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, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
+
+
+if __name__ == "__main__":
+    test_random_resize_with_bbox_op_voc_c(plot_vis=False)
+    test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False)
+    test_random_resize_with_bbox_op_edge_c(plot_vis=False)
+    test_random_resize_with_bbox_op_invalid_c()
+    test_random_resize_with_bbox_op_bad_c()

tests/ut/python/dataset/test_resize_with_bbox.py

+# 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 the resize with bounding boxes 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, \
+    save_and_check_md5
+
+GENERATE_GOLDEN = False
+
+DATA_DIR = "../data/dataset/testVOC2012_2"
+DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
+              "../data/dataset/testCOCO/annotations/train.json"]  # DATA_DIR, ANNOTATION_DIR
+
+
+def test_resize_with_bbox_op_voc_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without ResizeWithBBox Op applied
+    testing with VOC dataset
+    """
+    logger.info("test_resize_with_bbox_op_voc_c")
+
+    # Load dataset
+    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    test_op = c_vision.ResizeWithBBox(100)
+
+    # map to apply ops
+    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            operations=[test_op])
+
+    filename = "resize_with_bbox_op_01_c_voc_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)
+
+
+def test_resize_with_bbox_op_coco_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without ResizeWithBBox Op applied,
+    tests with MD5 check, expected to pass
+    Testing with COCO dataset
+    """
+    logger.info("test_resize_with_bbox_op_coco_c")
+
+    # Load dataset
+    dataCOCO1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
+                               decode=True, shuffle=False)
+
+    dataCOCO2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
+                               decode=True, shuffle=False)
+
+    test_op = c_vision.ResizeWithBBox(200)
+
+    # map to apply ops
+
+    dataCOCO2 = dataCOCO2.map(input_columns=["image", "bbox"],
+                              output_columns=["image", "bbox"],
+                              columns_order=["image", "bbox"],
+                              operations=[test_op])
+
+    filename = "resize_with_bbox_op_01_c_coco_result.npz"
+    save_and_check_md5(dataCOCO2, filename, generate_golden=GENERATE_GOLDEN)
+
+    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, annot_name="bbox")
+
+
+def test_resize_with_bbox_op_edge_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without ResizeWithBBox Op applied,
+    applied on dynamically generated edge case, expected to pass. edge case is when bounding
+    box has dimensions as the image itself.
+    """
+    logger.info("test_resize_with_bbox_op_edge_c")
+    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
+                             decode=True, shuffle=False)
+
+    test_op = c_vision.ResizeWithBBox(500)
+
+    # 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))])
+
+    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_resize_with_bbox_op_invalid_c():
+    """
+    Test ResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError
+    """
+    logger.info("test_resize_with_bbox_op_invalid_c")
+
+    try:
+        # invalid interpolation value
+        c_vision.ResizeWithBBox(400, interpolation="invalid")
+
+    except ValueError as err:
+        logger.info("Got an exception in DE: {}".format(str(err)))
+        assert "interpolation" in str(err)
+
+
+def test_resize_with_bbox_op_bad_c():
+    """
+    Tests ResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors
+    """
+    logger.info("test_resize_with_bbox_op_bad_c")
+    test_op = c_vision.ResizeWithBBox((200, 300))
+
+    data_voc2 = ds.VOCDataset(DATA_DIR, 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, 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, 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, task="Detection", mode="train", decode=True, shuffle=False)
+    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
+
+
+if __name__ == "__main__":
+    test_resize_with_bbox_op_voc_c(plot_vis=False)
+    test_resize_with_bbox_op_coco_c(plot_vis=False)
+    test_resize_with_bbox_op_edge_c(plot_vis=False)
+    test_resize_with_bbox_op_invalid_c()
+    test_resize_with_bbox_op_bad_c()