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@ -30,39 +30,21 @@ from PIL import Image |
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from joblib import load |
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import paddlers |
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from .functions import normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, \ |
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horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, vertical_flip_rle, crop_poly, \ |
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crop_rle, expand_poly, expand_rle, resize_poly, resize_rle, dehaze, select_bands, \ |
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to_intensity, to_uint8, img_flip, img_simple_rotate |
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from .functions import ( |
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normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, |
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horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, |
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vertical_flip_rle, crop_poly, crop_rle, expand_poly, expand_rle, |
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resize_poly, resize_rle, dehaze, select_bands, to_intensity, to_uint8, |
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img_flip, img_simple_rotate, decode_seg_mask) |
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__all__ = [ |
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"Compose", |
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"DecodeImg", |
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"Resize", |
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"RandomResize", |
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"ResizeByShort", |
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"RandomResizeByShort", |
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"ResizeByLong", |
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"RandomHorizontalFlip", |
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"RandomVerticalFlip", |
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"Normalize", |
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"CenterCrop", |
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"RandomCrop", |
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"RandomScaleAspect", |
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"RandomExpand", |
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"Pad", |
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"MixupImage", |
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"RandomDistort", |
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"RandomBlur", |
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"RandomSwap", |
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"Dehaze", |
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"ReduceDim", |
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"SelectBand", |
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"ArrangeSegmenter", |
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"ArrangeChangeDetector", |
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"ArrangeClassifier", |
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"ArrangeDetector", |
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"RandomFlipOrRotate", |
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"Compose", "DecodeImg", "Resize", "RandomResize", "ResizeByShort", |
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"RandomResizeByShort", "ResizeByLong", "RandomHorizontalFlip", |
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"RandomVerticalFlip", "Normalize", "CenterCrop", "RandomCrop", |
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"RandomScaleAspect", "RandomExpand", "Pad", "MixupImage", "RandomDistort", |
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"RandomBlur", "RandomSwap", "Dehaze", "ReduceDim", "SelectBand", |
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"ArrangeSegmenter", "ArrangeChangeDetector", "ArrangeClassifier", |
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"ArrangeDetector", "RandomFlipOrRotate", "ReloadMask" |
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] |
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interp_dict = { |
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@ -74,6 +56,71 @@ interp_dict = { |
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} |
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class Compose(object): |
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""" |
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Apply a series of data augmentation strategies to the input. |
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All input images should be in Height-Width-Channel ([H, W, C]) format. |
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Args: |
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transforms (list[paddlers.transforms.Transform]): List of data preprocess or |
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augmentation operators. |
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Raises: |
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TypeError: Invalid type of transforms. |
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ValueError: Invalid length of transforms. |
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""" |
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def __init__(self, transforms): |
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super(Compose, self).__init__() |
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if not isinstance(transforms, list): |
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raise TypeError( |
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"Type of transforms is invalid. Must be a list, but received is {}." |
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.format(type(transforms))) |
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if len(transforms) < 1: |
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raise ValueError( |
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"Length of transforms must not be less than 1, but received is {}." |
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.format(len(transforms))) |
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transforms = copy.deepcopy(transforms) |
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self.arrange = self._pick_arrange(transforms) |
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self.transforms = transforms |
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def __call__(self, sample): |
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""" |
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This is equivalent to sequentially calling compose_obj.apply_transforms() |
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and compose_obj.arrange_outputs(). |
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""" |
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sample = self.apply_transforms(sample) |
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sample = self.arrange_outputs(sample) |
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return sample |
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def apply_transforms(self, sample): |
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for op in self.transforms: |
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# Skip batch transforms amd mixup |
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if isinstance(op, (paddlers.transforms.BatchRandomResize, |
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paddlers.transforms.BatchRandomResizeByShort, |
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MixupImage)): |
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continue |
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sample = op(sample) |
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return sample |
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def arrange_outputs(self, sample): |
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if self.arrange is not None: |
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sample = self.arrange(sample) |
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return sample |
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def _pick_arrange(self, transforms): |
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arrange = None |
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for idx, op in enumerate(transforms): |
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if isinstance(op, Arrange): |
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if idx != len(transforms) - 1: |
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raise ValueError( |
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"Arrange operator must be placed at the end of the list." |
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) |
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arrange = transforms.pop(idx) |
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return arrange |
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class Transform(object): |
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""" |
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Parent class of all data augmentation operations |
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@ -178,14 +225,14 @@ class DecodeImg(Transform): |
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elif ext == '.npy': |
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return np.load(img_path) |
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else: |
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raise TypeError('Image format {} is not supported!'.format(ext)) |
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raise TypeError("Image format {} is not supported!".format(ext)) |
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def apply_im(self, im_path): |
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if isinstance(im_path, str): |
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try: |
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image = self.read_img(im_path) |
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except: |
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raise ValueError('Cannot read the image file {}!'.format( |
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raise ValueError("Cannot read the image file {}!".format( |
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im_path)) |
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else: |
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image = im_path |
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@ -217,7 +264,9 @@ class DecodeImg(Transform): |
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Returns: |
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dict: Decoded sample. |
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""" |
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if 'image' in sample: |
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sample['image_ori'] = copy.deepcopy(sample['image']) |
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sample['image'] = self.apply_im(sample['image']) |
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if 'image2' in sample: |
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sample['image2'] = self.apply_im(sample['image2']) |
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@ -227,6 +276,7 @@ class DecodeImg(Transform): |
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sample['image'] = self.apply_im(sample['image_t1']) |
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sample['image2'] = self.apply_im(sample['image_t2']) |
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if 'mask' in sample: |
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sample['mask_ori'] = copy.deepcopy(sample['mask']) |
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sample['mask'] = self.apply_mask(sample['mask']) |
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im_height, im_width, _ = sample['image'].shape |
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se_height, se_width = sample['mask'].shape |
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@ -234,6 +284,7 @@ class DecodeImg(Transform): |
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raise ValueError( |
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"The height or width of the image is not same as the mask.") |
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if 'aux_masks' in sample: |
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sample['aux_masks_ori'] = copy.deepcopy(sample['aux_masks_ori']) |
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sample['aux_masks'] = list( |
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map(self.apply_mask, sample['aux_masks'])) |
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# TODO: check the shape of auxiliary masks |
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@ -244,61 +295,6 @@ class DecodeImg(Transform): |
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return sample |
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class Compose(Transform): |
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""" |
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Apply a series of data augmentation to the input. |
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All input images are in Height-Width-Channel ([H, W, C]) format. |
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Args: |
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transforms (list[paddlers.transforms.Transform]): List of data preprocess or augmentations. |
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Raises: |
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TypeError: Invalid type of transforms. |
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ValueError: Invalid length of transforms. |
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""" |
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def __init__(self, transforms, to_uint8=True): |
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super(Compose, self).__init__() |
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if not isinstance(transforms, list): |
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raise TypeError( |
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'Type of transforms is invalid. Must be a list, but received is {}' |
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.format(type(transforms))) |
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if len(transforms) < 1: |
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raise ValueError( |
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'Length of transforms must not be less than 1, but received is {}' |
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.format(len(transforms))) |
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self.transforms = transforms |
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self.decode_image = DecodeImg(to_uint8=to_uint8) |
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self.arrange_outputs = None |
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self.apply_im_only = False |
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def __call__(self, sample): |
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if self.apply_im_only: |
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if 'mask' in sample: |
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mask_backup = copy.deepcopy(sample['mask']) |
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del sample['mask'] |
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if 'aux_masks' in sample: |
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aux_masks = copy.deepcopy(sample['aux_masks']) |
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sample = self.decode_image(sample) |
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for op in self.transforms: |
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# skip batch transforms amd mixup |
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if isinstance(op, (paddlers.transforms.BatchRandomResize, |
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paddlers.transforms.BatchRandomResizeByShort, |
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MixupImage)): |
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continue |
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sample = op(sample) |
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if self.arrange_outputs is not None: |
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if self.apply_im_only: |
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sample['mask'] = mask_backup |
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if 'aux_masks' in locals(): |
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sample['aux_masks'] = aux_masks |
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sample = self.arrange_outputs(sample) |
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return sample |
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class Resize(Transform): |
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""" |
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Resize input. |
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@ -323,7 +319,7 @@ class Resize(Transform): |
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def __init__(self, target_size, interp='LINEAR', keep_ratio=False): |
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super(Resize, self).__init__() |
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if not (interp == "RANDOM" or interp in interp_dict): |
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raise ValueError("interp should be one of {}".format( |
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raise ValueError("`interp` should be one of {}.".format( |
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interp_dict.keys())) |
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if isinstance(target_size, int): |
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target_size = (target_size, target_size) |
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@ -331,7 +327,7 @@ class Resize(Transform): |
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if not (isinstance(target_size, |
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(list, tuple)) and len(target_size) == 2): |
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raise TypeError( |
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"target_size should be an int or a list of length 2, but received {}". |
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"`target_size` should be an int or a list of length 2, but received {}.". |
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format(target_size)) |
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# (height, width) |
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self.target_size = target_size |
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@ -443,11 +439,11 @@ class RandomResize(Transform): |
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def __init__(self, target_sizes, interp='LINEAR'): |
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super(RandomResize, self).__init__() |
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if not (interp == "RANDOM" or interp in interp_dict): |
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raise ValueError("interp should be one of {}".format( |
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raise ValueError("`interp` should be one of {}.".format( |
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interp_dict.keys())) |
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self.interp = interp |
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assert isinstance(target_sizes, list), \ |
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"target_size must be a list." |
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"`target_size` must be a list." |
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for i, item in enumerate(target_sizes): |
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if isinstance(item, int): |
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target_sizes[i] = (item, item) |
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@ -478,7 +474,7 @@ class ResizeByShort(Transform): |
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def __init__(self, short_size=256, max_size=-1, interp='LINEAR'): |
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if not (interp == "RANDOM" or interp in interp_dict): |
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raise ValueError("interp should be one of {}".format( |
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raise ValueError("`interp` should be one of {}".format( |
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interp_dict.keys())) |
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super(ResizeByShort, self).__init__() |
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self.short_size = short_size |
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@ -522,11 +518,11 @@ class RandomResizeByShort(Transform): |
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def __init__(self, short_sizes, max_size=-1, interp='LINEAR'): |
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super(RandomResizeByShort, self).__init__() |
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if not (interp == "RANDOM" or interp in interp_dict): |
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raise ValueError("interp should be one of {}".format( |
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raise ValueError("`interp` should be one of {}".format( |
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interp_dict.keys())) |
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self.interp = interp |
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assert isinstance(short_sizes, list), \ |
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"short_sizes must be a list." |
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"`short_sizes` must be a list." |
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self.short_sizes = short_sizes |
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self.max_size = max_size |
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@ -574,6 +570,7 @@ class RandomFlipOrRotate(Transform): |
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# 定义数据增强 |
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train_transforms = T.Compose([ |
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T.DecodeImg(), |
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T.RandomFlipOrRotate( |
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probs = [0.3, 0.2] # 进行flip增强的概率是0.3，进行rotate增强的概率是0.2，不变的概率是0.5 |
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probsf = [0.3, 0.25, 0, 0, 0] # flip增强时，使用水平flip、垂直flip的概率分别是0.3、0.25，水平且垂直flip、对角线flip、反对角线flip概率均为0，不变的概率是0.45 |
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@ -609,12 +606,12 @@ class RandomFlipOrRotate(Transform): |
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def apply_bbox(self, bbox, mode_id, flip_mode=True): |
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raise TypeError( |
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"Currently, `paddlers.transforms.RandomFlipOrRotate` is not available for object detection tasks." |
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"Currently, RandomFlipOrRotate is not available for object detection tasks." |
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) |
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def apply_segm(self, bbox, mode_id, flip_mode=True): |
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raise TypeError( |
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"Currently, `paddlers.transforms.RandomFlipOrRotate` is not available for object detection tasks." |
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"Currently, RandomFlipOrRotate is not available for object detection tasks." |
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) |
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def get_probs_range(self, probs): |
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@ -845,11 +842,11 @@ class Normalize(Transform): |
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from functools import reduce |
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if reduce(lambda x, y: x * y, std) == 0: |
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raise ValueError( |
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'Std should not contain 0, but received is {}.'.format(std)) |
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"`std` should not contain 0, but received is {}.".format(std)) |
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if reduce(lambda x, y: x * y, |
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[a - b for a, b in zip(max_val, min_val)]) == 0: |
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raise ValueError( |
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'(max_val - min_val) should not contain 0, but received is {}.'. |
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"(`max_val` - `min_val`) should not contain 0, but received is {}.". |
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format((np.asarray(max_val) - np.asarray(min_val)).tolist())) |
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self.mean = mean |
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@ -1153,11 +1150,11 @@ class RandomExpand(Transform): |
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im_padding_value=127.5, |
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label_padding_value=255): |
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super(RandomExpand, self).__init__() |
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assert upper_ratio > 1.01, "expand ratio must be larger than 1.01" |
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assert upper_ratio > 1.01, "`upper_ratio` must be larger than 1.01." |
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self.upper_ratio = upper_ratio |
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self.prob = prob |
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assert isinstance(im_padding_value, (Number, Sequence)), \ |
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"fill value must be either float or sequence" |
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"Value to fill must be either float or sequence." |
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self.im_padding_value = im_padding_value |
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self.label_padding_value = label_padding_value |
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@ -1204,16 +1201,16 @@ class Pad(Transform): |
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if isinstance(target_size, (list, tuple)): |
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if len(target_size) != 2: |
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raise ValueError( |
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'`target_size` should include 2 elements, but it is {}'. |
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"`target_size` should contain 2 elements, but it is {}.". |
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format(target_size)) |
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if isinstance(target_size, int): |
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target_size = [target_size] * 2 |
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assert pad_mode in [ |
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-1, 0, 1, 2 |
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], 'currently only supports four modes [-1, 0, 1, 2]' |
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], "Currently only four modes are supported: [-1, 0, 1, 2]." |
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if pad_mode == -1: |
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assert offsets, 'if pad_mode is -1, offsets should not be None' |
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assert offsets, "if `pad_mode` is -1, `offsets` should not be None." |
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self.target_size = target_size |
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self.size_divisor = size_divisor |
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@ -1314,9 +1311,9 @@ class MixupImage(Transform): |
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""" |
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super(MixupImage, self).__init__() |
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if alpha <= 0.0: |
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raise ValueError("alpha should be positive in {}".format(self)) |
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raise ValueError("`alpha` should be positive in MixupImage.") |
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if beta <= 0.0: |
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raise ValueError("beta should be positive in {}".format(self)) |
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raise ValueError("`beta` should be positive in MixupImage.") |
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self.alpha = alpha |
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self.beta = beta |
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self.mixup_epoch = mixup_epoch |
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@ -1753,55 +1750,56 @@ class RandomSwap(Transform): |
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def apply(self, sample): |
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if 'image2' not in sample: |
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raise ValueError('image2 is not found in the sample.') |
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raise ValueError("'image2' is not found in the sample.") |
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if random.random() < self.prob: |
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sample['image'], sample['image2'] = sample['image2'], sample[ |
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'image'] |
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return sample |
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class ArrangeSegmenter(Transform): |
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class ReloadMask(Transform): |
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def apply(self, sample): |
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sample['mask'] = decode_seg_mask(sample['mask_ori']) |
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if 'aux_masks' in sample: |
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sample['aux_masks'] = list( |
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map(decode_seg_mask, sample['aux_masks_ori'])) |
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return sample |
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class Arrange(Transform): |
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def __init__(self, mode): |
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super(ArrangeSegmenter, self).__init__() |
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super().__init__() |
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if mode not in ['train', 'eval', 'test', 'quant']: |
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raise ValueError( |
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"mode should be defined as one of ['train', 'eval', 'test', 'quant']!" |
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"`mode` should be defined as one of ['train', 'eval', 'test', 'quant']!" |
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) |
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self.mode = mode |
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class ArrangeSegmenter(Arrange): |
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def apply(self, sample): |
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if 'mask' in sample: |
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mask = sample['mask'] |
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mask = mask.astype('int64') |
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image = permute(sample['image'], False) |
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if self.mode == 'train': |
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mask = mask.astype('int64') |
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return image, mask |
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if self.mode == 'eval': |
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mask = np.asarray(Image.open(mask)) |
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mask = mask[np.newaxis, :, :].astype('int64') |
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return image, mask |
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if self.mode == 'test': |
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return image, |
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class ArrangeChangeDetector(Transform): |
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def __init__(self, mode): |
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super(ArrangeChangeDetector, self).__init__() |
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if mode not in ['train', 'eval', 'test', 'quant']: |
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raise ValueError( |
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"mode should be defined as one of ['train', 'eval', 'test', 'quant']!" |
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) |
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self.mode = mode |
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class ArrangeChangeDetector(Arrange): |
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def apply(self, sample): |
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if 'mask' in sample: |
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mask = sample['mask'] |
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mask = mask.astype('int64') |
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image_t1 = permute(sample['image'], False) |
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image_t2 = permute(sample['image2'], False) |
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if self.mode == 'train': |
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mask = mask.astype('int64') |
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masks = [mask] |
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if 'aux_masks' in sample: |
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masks.extend( |
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@ -1810,22 +1808,12 @@ class ArrangeChangeDetector(Transform): |
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image_t1, |
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image_t2, ) + tuple(masks) |
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if self.mode == 'eval': |
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|
mask = np.asarray(Image.open(mask)) |
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mask = mask[np.newaxis, :, :].astype('int64') |
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return image_t1, image_t2, mask |
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|
if self.mode == 'test': |
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|
return image_t1, image_t2, |
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class ArrangeClassifier(Transform): |
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def __init__(self, mode): |
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|
super(ArrangeClassifier, self).__init__() |
|
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|
|
if mode not in ['train', 'eval', 'test', 'quant']: |
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|
raise ValueError( |
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|
|
"mode should be defined as one of ['train', 'eval', 'test', 'quant']!" |
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|
) |
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|
|
self.mode = mode |
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|
class ArrangeClassifier(Arrange): |
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|
def apply(self, sample): |
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|
image = permute(sample['image'], False) |
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|
|
if self.mode in ['train', 'eval']: |
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|
@ -1834,15 +1822,7 @@ class ArrangeClassifier(Transform): |
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|
return image |
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|
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|
|
class ArrangeDetector(Transform): |
|
|
|
|
def __init__(self, mode): |
|
|
|
|
super(ArrangeDetector, self).__init__() |
|
|
|
|
if mode not in ['train', 'eval', 'test', 'quant']: |
|
|
|
|
raise ValueError( |
|
|
|
|
"mode should be defined as one of ['train', 'eval', 'test', 'quant']!" |
|
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|
) |
|
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|
|
self.mode = mode |
|
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|
|
class ArrangeDetector(Arrange): |
|
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|
|
def apply(self, sample): |
|
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|
|
if self.mode == 'eval' and 'gt_poly' in sample: |
|
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|
|
del sample['gt_poly'] |
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cc
2 years ago
committed by
GitHub