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Bobholamovic 3 years ago
parent 9a15ed9043
commit 670519bf26
7 changed files with 47 additions and 44 deletions
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  1. 12
      paddlers/tasks/change_detector.py
  2. 2
      paddlers/tasks/classifier.py
  3. 2
      paddlers/tasks/object_detector.py
  4. 10
      paddlers/tasks/segmenter.py
  5. 8
      paddlers/transforms/batch_operators.py
  6. 34
      paddlers/transforms/operators.py
  7. 23
      tests/testing_utils.py

12
paddlers/tasks/change_detector.py
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@ -501,7 +501,7 @@ class BaseChangeDetector(BaseModel):
Do inference.
Args:
Args:
img_file(List[tuple], Tuple[str or np.ndarray]):
img_file (list[tuple] | tuple[str | np.ndarray]):
Tuple of image paths or decoded image data for bi-temporal images, which also could constitute a list,
meaning all image pairs to be predicted as a mini-batch.
transforms(paddlers.transforms.Compose or None, optional):
@ -556,14 +556,14 @@ class BaseChangeDetector(BaseModel):
Do inference.
Args:
Args:
img_file(List[str]):
img_file(list[str]):
List of image paths.
save_dir(str):
Directory that contains saved geotiff file.
block_size(List[int] or Tuple[int], int):
The size of block.
overlap(List[int] or Tuple[int], int):
The overlap between two blocks. Defaults to 36.
block_size(list[int] | tuple[int] | int, optional):
Size of block.
overlap(list[int] | tuple[int] | int, optional):
Overlap between two blocks. Defaults to 36.
transforms(paddlers.transforms.Compose or None, optional):
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
"""

2
paddlers/tasks/classifier.py
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@ -410,7 +410,7 @@ class BaseClassifier(BaseModel):
Do inference.
Args:
Args:
img_file(List[np.ndarray or str], str or np.ndarray):
img_file(list[np.ndarray | str] | str | np.ndarray):
Image path or decoded image data, which also could constitute a list, meaning all images to be
predicted as a mini-batch.
transforms(paddlers.transforms.Compose or None, optional):

2
paddlers/tasks/object_detector.py
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@ -511,7 +511,7 @@ class BaseDetector(BaseModel):
"""
Do inference.
Args:
img_file(List[np.ndarray or str], str or np.ndarray):
img_file(list[np.ndarray | str] | str | np.ndarray):
Image path or decoded image data, which also could constitute a list,meaning all images to be
predicted as a mini-batch.
transforms(paddlers.transforms.Compose or None, optional):

10
paddlers/tasks/segmenter.py
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@ -478,7 +478,7 @@ class BaseSegmenter(BaseModel):
Do inference.
Args:
Args:
img_file(List[np.ndarray or str], str or np.ndarray):
img_file(list[np.ndarray | str] | str | np.ndarray):
Image path or decoded image data, which also could constitute a list,meaning all images to be
predicted as a mini-batch.
transforms(paddlers.transforms.Compose or None, optional):
@ -533,10 +533,10 @@ class BaseSegmenter(BaseModel):
Image path.
save_dir(str):
Directory that contains saved geotiff file.
block_size(List[int] or Tuple[int], int):
The size of block.
overlap(List[int] or Tuple[int], int):
The overlap between two blocks. Defaults to 36.
block_size(list[int] | tuple[int] | int):
Size of block.
overlap(list[int] | tuple[int] | int, optional):
Overlap between two blocks. Defaults to 36.
transforms(paddlers.transforms.Compose or None, optional):
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
"""

8
paddlers/transforms/batch_operators.py
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@ -74,7 +74,7 @@ class BatchRandomResize(Transform):
Attention: If interp is 'RANDOM', the interpolation method will be chose randomly.
Args:
target_sizes (List[int], List[list or tuple] or Tuple[list or tuple]):
target_sizes (list[int] | list[list | tuple] | tuple[list | tuple]):
Multiple target sizes, each target size is an int or list/tuple of length 2.
interp ({'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}, optional):
Interpolation method of resize. Defaults to 'LINEAR'.
@ -93,7 +93,7 @@ class BatchRandomResize(Transform):
interp_dict.keys()))
self.interp = interp
assert isinstance(target_sizes, list), \
"target_size must be List"
"target_size must be a list."
for i, item in enumerate(target_sizes):
if isinstance(item, int):
target_sizes[i] = (item, item)
@ -113,7 +113,7 @@ class BatchRandomResizeByShort(Transform):
Attention: If interp is 'RANDOM', the interpolation method will be chose randomly.
Args:
short_sizes (List[int], Tuple[int]): Target sizes of the shorter side of the image(s).
short_sizes (list[int] | tuple[int]): Target sizes of the shorter side of the image(s).
max_size (int, optional): The upper bound of longer side of the image(s).
If max_size is -1, no upper bound is applied. Defaults to -1.
interp ({'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}, optional):
@ -134,7 +134,7 @@ class BatchRandomResizeByShort(Transform):
interp_dict.keys()))
self.interp = interp
assert isinstance(short_sizes, list), \
"short_sizes must be List"
"short_sizes must be a list."
self.short_sizes = short_sizes
self.max_size = max_size

34
paddlers/transforms/operators.py
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@ -250,7 +250,7 @@ class Compose(Transform):
All input images are in Height-Width-Channel ([H, W, C]) format.
Args:
transforms (List[paddlers.transforms.Transform]): List of data preprocess or augmentations.
transforms (list[paddlers.transforms.Transform]): List of data preprocess or augmentations.
Raises:
TypeError: Invalid type of transforms.
ValueError: Invalid length of transforms.
@ -260,7 +260,7 @@ class Compose(Transform):
super(Compose, self).__init__()
if not isinstance(transforms, list):
raise TypeError(
'Type of transforms is invalid. Must be List, but received is {}'
'Type of transforms is invalid. Must be a list, but received is {}'
.format(type(transforms)))
if len(transforms) < 1:
raise ValueError(
@ -308,7 +308,7 @@ class Resize(Transform):
Attention: If interp is 'RANDOM', the interpolation method will be chose randomly.
Args:
target_size (int, List[int] or Tuple[int]): Target size. If int, the height and width share the same target_size.
target_size (int, list[int] | tuple[int]): Target size. If int, the height and width share the same target_size.
Otherwise, target_size represents [target height, target width].
interp ({'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}, optional):
Interpolation method of resize. Defaults to 'LINEAR'.
@ -427,7 +427,7 @@ class RandomResize(Transform):
Attention: If interp is 'RANDOM', the interpolation method will be chose randomly.
Args:
target_sizes (List[int], List[list or tuple] or Tuple[list or tuple]):
target_sizes (list[int] | list[list | tuple] | tuple[list | tuple]):
Multiple target sizes, each target size is an int or list/tuple.
interp ({'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}, optional):
Interpolation method of resize. Defaults to 'LINEAR'.
@ -447,7 +447,7 @@ class RandomResize(Transform):
interp_dict.keys()))
self.interp = interp
assert isinstance(target_sizes, list), \
"target_size must be List"
"target_size must be a list."
for i, item in enumerate(target_sizes):
if isinstance(item, int):
target_sizes[i] = (item, item)
@ -507,7 +507,7 @@ class RandomResizeByShort(Transform):
Attention: If interp is 'RANDOM', the interpolation method will be chose randomly.
Args:
short_sizes (List[int]): Target size of the shorter side of the image(s).
short_sizes (list[int]): Target size of the shorter side of the image(s).
max_size (int, optional): The upper bound of longer side of the image(s). If max_size is -1, no upper bound is applied. Defaults to -1.
interp ({'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4', 'RANDOM'}, optional): Interpolation method of resize. Defaults to 'LINEAR'.
@ -526,7 +526,7 @@ class RandomResizeByShort(Transform):
interp_dict.keys()))
self.interp = interp
assert isinstance(short_sizes, list), \
"short_sizes must be List"
"short_sizes must be a list."
self.short_sizes = short_sizes
self.max_size = max_size
@ -818,16 +818,16 @@ class RandomVerticalFlip(Transform):
class Normalize(Transform):
"""
Apply min-max normalization to the image(s) in input.
Apply normalization to the input image(s). The normalization steps are:
1. im = (im - min_value) * 1 / (max_value - min_value)
2. im = im - mean
3. im = im / std
Args:
mean(List[float] or Tuple[float], optional): Mean of input image(s). Defaults to [0.485, 0.456, 0.406].
std(List[float] or Tuple[float], optional): Standard deviation of input image(s). Defaults to [0.229, 0.224, 0.225].
min_val(List[float] or Tuple[float], optional): Minimum value of input image(s). Defaults to [0, 0, 0, ].
max_val(List[float] or Tuple[float], optional): Max value of input image(s). Defaults to [255., 255., 255.].
mean(list[float] | tuple[float], optional): Mean of input image(s). Defaults to [0.485, 0.456, 0.406].
std(list[float] | tuple[float], optional): Standard deviation of input image(s). Defaults to [0.229, 0.224, 0.225].
min_val(list[float] | tuple[float], optional): Minimum value of input image(s). Defaults to [0, 0, 0, ].
max_val(list[float] | tuple[float], optional): Max value of input image(s). Defaults to [255., 255., 255.].
"""
def __init__(self,
@ -917,12 +917,12 @@ class RandomCrop(Transform):
4. Resize the cropped area to crop_size by crop_size.
Args:
crop_size(int, List[int] or Tuple[int]): Target size of the cropped area. If None, the cropped area will not be
crop_size(int, list[int] | tuple[int]): Target size of the cropped area. If None, the cropped area will not be
resized. Defaults to None.
aspect_ratio (List[float], optional): Aspect ratio of cropped region in [min, max] format. Defaults to [.5, 2.].
thresholds (List[float], optional): Iou thresholds to decide a valid bbox crop.
aspect_ratio (list[float], optional): Aspect ratio of cropped region in [min, max] format. Defaults to [.5, 2.].
thresholds (list[float], optional): Iou thresholds to decide a valid bbox crop.
Defaults to [.0, .1, .3, .5, .7, .9].
scaling (List[float], optional): Ratio between the cropped region and the original image in [min, max] format.
scaling (list[float], optional): Ratio between the cropped region and the original image in [min, max] format.
Defaults to [.3, 1.].
num_attempts (int, optional): The number of tries before giving up. Defaults to 50.
allow_no_crop (bool, optional): Whether returning without doing crop is allowed. Defaults to True.
@ -1140,7 +1140,7 @@ class RandomExpand(Transform):
Args:
upper_ratio(float, optional): The maximum ratio to which the original image is expanded. Defaults to 4..
prob(float, optional): The probability of apply expanding. Defaults to .5.
im_padding_value(List[float] or Tuple[float], optional): RGB filling value for the image. Defaults to (127.5, 127.5, 127.5).
im_padding_value(list[float] | tuple[float], optional): RGB filling value for the image. Defaults to (127.5, 127.5, 127.5).
label_padding_value(int, optional): Filling value for the mask. Defaults to 255.
See Also:

23
tests/testing_utils.py
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@ -58,9 +58,10 @@ class _CommonTestNamespace:
@classmethod
def setUpClass(cls):
'''
"""
Set the decorators for all test function
'''
"""
for key, value in cls.__dict__.items():
if key.startswith('test'):
decorator_func_list = ["_test_places"]
@ -72,9 +73,9 @@ class _CommonTestNamespace:
setattr(cls, key, value)
def _catch_warnings(func):
'''
"""
Catch the warnings and treat them as errors for each test.
'''
"""
def wrapper(self, *args, **kwargs):
with warnings.catch_warnings(record=True) as w:
@ -90,9 +91,9 @@ class _CommonTestNamespace:
return wrapper
def _test_places(func):
'''
"""
Setting the running place for each test.
'''
"""
def wrapper(self, *args, **kwargs):
places = self.places
@ -150,7 +151,7 @@ class _CommonTestNamespace:
expected_result,
rtol=1.e-5,
atol=1.e-8):
'''
"""
Check whether result and expected result are equal, including shape.
Args:
@ -162,7 +163,8 @@ class _CommonTestNamespace:
relative tolerance, default 1.e-5.
atol: float
absolute tolerance, default 1.e-8
'''
"""
self._check_output_impl(result, expected_result, rtol, atol)
def check_output_not_equal(self,
@ -170,7 +172,7 @@ class _CommonTestNamespace:
expected_result,
rtol=1.e-5,
atol=1.e-8):
'''
"""
Check whether result and expected result are not equal, including shape.
Args:
@ -182,7 +184,8 @@ class _CommonTestNamespace:
relative tolerance, default 1.e-5.
atol: float
absolute tolerance, default 1.e-8
'''
"""
self._check_output_impl(
result, expected_result, rtol, atol, equal=False)

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