forked from mindspore-Ecosystem/mindspore
!3102 [MD] Add additional parameter checks in RandomCropWithBBoxOp and RandomResizeWithBbox
Merge pull request !3102 from nhussain/random_crop_fixes
This commit is contained in:
commit
a84092e89d
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@ -189,8 +189,10 @@ def type_check_list(args, types, arg_names):
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Exception: when the type is not correct, otherwise nothing.
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"""
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type_check(args, (list, tuple,), arg_names)
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if len(args) != len(arg_names):
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if len(args) != len(arg_names) and not isinstance(arg_names, str):
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raise ValueError("List of arguments is not the same length as argument_names.")
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if isinstance(arg_names, str):
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arg_names = ["{0}[{1}]".format(arg_names, i) for i in range(len(args))]
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for arg, arg_name in zip(args, arg_names):
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type_check(arg, types, arg_name)
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@ -686,8 +686,7 @@ def check_concat(method):
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[ds], _ = parse_user_args(method, *args, **kwargs)
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type_check(ds, (list, datasets.Dataset), "datasets")
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if isinstance(ds, list):
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dataset_names = ["dataset[{0}]".format(i) for i in range(len(ds)) if isinstance(ds, list)]
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type_check_list(ds, (datasets.Dataset,), dataset_names)
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type_check_list(ds, (datasets.Dataset,), "dataset")
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return method(self, *args, **kwargs)
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return new_method
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@ -751,8 +750,7 @@ def check_add_column(method):
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if shape is not None:
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type_check(shape, (list,), "shape")
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shape_names = ["shape[{0}]".format(i) for i in range(len(shape))]
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type_check_list(shape, (int,), shape_names)
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type_check_list(shape, (int,), "shape")
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return method(self, *args, **kwargs)
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@ -297,8 +297,7 @@ def check_from_dataset(method):
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if columns is not None:
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if not isinstance(columns, list):
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columns = [columns]
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col_names = ["col_{0}".format(i) for i in range(len(columns))]
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type_check_list(columns, (str,), col_names)
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type_check_list(columns, (str,), "col")
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if freq_range is not None:
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type_check(freq_range, (tuple,), "freq_range")
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@ -78,6 +78,8 @@ def check_fill_value(fill_value):
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def check_padding(padding):
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"""Parsing the padding arguments and check if it is legal."""
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type_check(padding, (tuple, list, numbers.Number), "padding")
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if isinstance(padding, numbers.Number):
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check_value(padding, (0, INT32_MAX), "padding")
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if isinstance(padding, (tuple, list)):
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if len(padding) not in (2, 4):
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raise ValueError("The size of the padding list or tuple should be 2 or 4.")
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@ -163,10 +165,13 @@ def check_random_resize_crop(method):
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check_crop_size(size)
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if scale is not None:
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type_check(scale, (tuple,), "scale")
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type_check_list(scale, (float, int), "scale")
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check_range(scale, [0, FLOAT_MAX_INTEGER])
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if ratio is not None:
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type_check(ratio, (tuple,), "ratio")
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type_check_list(ratio, (float, int), "ratio")
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check_range(ratio, [0, FLOAT_MAX_INTEGER])
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check_positive(ratio[0], "ratio[0]")
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if interpolation is not None:
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type_check(interpolation, (Inter,), "interpolation")
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if max_attempts is not None:
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@ -450,8 +455,7 @@ def check_random_affine(method):
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if translate is not None:
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if type_check(translate, (list, tuple), "translate"):
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translate_names = ["translate_{0}".format(i) for i in range(len(translate))]
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type_check_list(translate, (int, float), translate_names)
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type_check_list(translate, (int, float), "translate")
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if len(translate) != 2:
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raise TypeError("translate should be a list or tuple of length 2.")
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for i, t in enumerate(translate):
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@ -508,8 +512,7 @@ def check_uniform_augment_cpp(method):
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if num_ops > len(operations):
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raise ValueError("num_ops is greater than operations list size")
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tensor_ops = ["tensor_op_{0}".format(i) for i in range(len(operations))]
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type_check_list(operations, (TensorOp,), tensor_ops)
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type_check_list(operations, (TensorOp,), "tensor_ops")
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return method(self, *args, **kwargs)
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@ -134,7 +134,7 @@ def test_from_dataset_exceptions():
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test_config("text", (), 1, "freq_range needs to be a tuple of 2 integers or an int and a None.")
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test_config("text", (2, 3), 1.2345,
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"Argument top_k with value 1.2345 is not of type (<class 'int'>, <class 'NoneType'>)")
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test_config(23, (2, 3), 1.2345, "Argument col_0 with value 23 is not of type (<class 'str'>,)")
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test_config(23, (2, 3), 1.2345, "Argument col[0] with value 23 is not of type (<class 'str'>,)")
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test_config("text", (100, 1), 12, "frequency range [a,b] should be 0 <= a <= b (a,b are inclusive)")
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test_config("text", (2, 3), 0, "top_k must be greater than 0")
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test_config([123], (2, 3), -1, "top_k must be greater than 0")
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@ -337,6 +337,32 @@ def test_random_crop_and_resize_comp(plot=False):
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visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
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def test_random_crop_and_resize_06():
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"""
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Test RandomCropAndResize with c_transforms: invalid values for scale,
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expected to raise ValueError
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"""
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logger.info("test_random_crop_and_resize_05_c")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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try:
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random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), scale="", ratio=(1, 0.5))
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data = data.map(input_columns=["image"], operations=decode_op)
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data.map(input_columns=["image"], operations=random_crop_and_resize_op)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Argument scale with value \"\" is not of type (<class 'tuple'>,)" in str(e)
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try:
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random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), scale=(1, "2"), ratio=(1, 0.5))
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data = data.map(input_columns=["image"], operations=decode_op)
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data.map(input_columns=["image"], operations=random_crop_and_resize_op)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Argument scale[1] with value 2 is not of type (<class 'float'>, <class 'int'>)." in str(e)
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if __name__ == "__main__":
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test_random_crop_and_resize_op_c(True)
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test_random_crop_and_resize_op_py(True)
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@ -347,4 +373,5 @@ if __name__ == "__main__":
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test_random_crop_and_resize_04_py()
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test_random_crop_and_resize_05_c()
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test_random_crop_and_resize_05_py()
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test_random_crop_and_resize_06()
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test_random_crop_and_resize_comp(True)
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@ -178,13 +178,15 @@ def test_random_crop_with_bbox_op_edge_c(plot_vis=False):
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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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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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# 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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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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@ -239,6 +241,29 @@ def test_random_crop_with_bbox_op_bad_c():
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
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def test_random_crop_with_bbox_op_negative_padding():
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"""
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Test RandomCropWithBBox Op on invalid constructor parameters, expected to raise ValueError
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"""
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logger.info("test_random_crop_with_bbox_op_invalid_c")
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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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test_op = c_vision.RandomCropWithBBox([512, 512], padding=-1)
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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 padding is not within the required interval of (0 to 2147483647)." in str(err)
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if __name__ == "__main__":
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test_random_crop_with_bbox_op_c(plot_vis=True)
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test_random_crop_with_bbox_op_coco_c(plot_vis=True)
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@ -247,3 +272,4 @@ if __name__ == "__main__":
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test_random_crop_with_bbox_op_edge_c(plot_vis=True)
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test_random_crop_with_bbox_op_invalid_c()
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test_random_crop_with_bbox_op_bad_c()
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test_random_crop_with_bbox_op_negative_padding()
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@ -16,9 +16,10 @@
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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 pytest
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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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@ -172,6 +173,18 @@ def test_resize_with_bbox_op_bad_c():
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check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")
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def test_resize_with_bbox_op_params_outside_of_interpolation_dict():
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"""
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Test passing in a invalid key for interpolation
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"""
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logger.info("test_resize_with_bbox_op_params_outside_of_interpolation_dict")
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size = (500, 500)
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more_para = None
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with pytest.raises(KeyError, match="None"):
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c_vision.ResizeWithBBox(size, more_para)
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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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@ -166,10 +166,10 @@ def test_cpp_uniform_augment_exception_pyops(num_ops=2):
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F.Invert()]
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with pytest.raises(TypeError) as e:
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_ = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
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C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Argument tensor_op_5 with value" \
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assert "Argument tensor_ops[5] with value" \
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" <mindspore.dataset.transforms.vision.py_transforms.Invert" in str(e.value)
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assert "is not of type (<class 'mindspore._c_dataengine.TensorOp'>,)" in str(e.value)
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