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| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| class Resize(object): | |
| """Resize sample to given size (width, height). | |
| """ | |
| def __init__( | |
| self, | |
| width, | |
| height, | |
| resize_target=True, | |
| keep_aspect_ratio=False, | |
| ensure_multiple_of=1, | |
| resize_method="lower_bound", | |
| ): | |
| """Init. | |
| Args: | |
| width (int): desired output width | |
| height (int): desired output height | |
| resize_target (bool, optional): | |
| True: Resize the full sample (image, mask, target). | |
| False: Resize image only. | |
| Defaults to True. | |
| keep_aspect_ratio (bool, optional): | |
| True: Keep the aspect ratio of the input sample. | |
| Output sample might not have the given width and height, and | |
| resize behaviour depends on the parameter 'resize_method'. | |
| Defaults to False. | |
| ensure_multiple_of (int, optional): | |
| Output width and height is constrained to be multiple of this parameter. | |
| Defaults to 1. | |
| resize_method (str, optional): | |
| "lower_bound": Output will be at least as large as the given size. | |
| "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.) | |
| "minimal": Scale as least as possible. (Output size might be smaller than given size.) | |
| Defaults to "lower_bound". | |
| """ | |
| # print("Params passed to Resize transform:") | |
| # print("\twidth: ", width) | |
| # print("\theight: ", height) | |
| # print("\tresize_target: ", resize_target) | |
| # print("\tkeep_aspect_ratio: ", keep_aspect_ratio) | |
| # print("\tensure_multiple_of: ", ensure_multiple_of) | |
| # print("\tresize_method: ", resize_method) | |
| self.__width = width | |
| self.__height = height | |
| self.__keep_aspect_ratio = keep_aspect_ratio | |
| self.__multiple_of = ensure_multiple_of | |
| self.__resize_method = resize_method | |
| def constrain_to_multiple_of(self, x, min_val=0, max_val=None): | |
| y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int) | |
| if max_val is not None and y > max_val: | |
| y = (np.floor(x / self.__multiple_of) | |
| * self.__multiple_of).astype(int) | |
| if y < min_val: | |
| y = (np.ceil(x / self.__multiple_of) | |
| * self.__multiple_of).astype(int) | |
| return y | |
| def get_size(self, width, height): | |
| # determine new height and width | |
| scale_height = self.__height / height | |
| scale_width = self.__width / width | |
| if self.__keep_aspect_ratio: | |
| if self.__resize_method == "lower_bound": | |
| # scale such that output size is lower bound | |
| if scale_width > scale_height: | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| elif self.__resize_method == "upper_bound": | |
| # scale such that output size is upper bound | |
| if scale_width < scale_height: | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| elif self.__resize_method == "minimal": | |
| # scale as least as possbile | |
| if abs(1 - scale_width) < abs(1 - scale_height): | |
| # fit width | |
| scale_height = scale_width | |
| else: | |
| # fit height | |
| scale_width = scale_height | |
| else: | |
| raise ValueError( | |
| f"resize_method {self.__resize_method} not implemented" | |
| ) | |
| if self.__resize_method == "lower_bound": | |
| new_height = self.constrain_to_multiple_of( | |
| scale_height * height, min_val=self.__height | |
| ) | |
| new_width = self.constrain_to_multiple_of( | |
| scale_width * width, min_val=self.__width | |
| ) | |
| elif self.__resize_method == "upper_bound": | |
| new_height = self.constrain_to_multiple_of( | |
| scale_height * height, max_val=self.__height | |
| ) | |
| new_width = self.constrain_to_multiple_of( | |
| scale_width * width, max_val=self.__width | |
| ) | |
| elif self.__resize_method == "minimal": | |
| new_height = self.constrain_to_multiple_of(scale_height * height) | |
| new_width = self.constrain_to_multiple_of(scale_width * width) | |
| else: | |
| raise ValueError( | |
| f"resize_method {self.__resize_method} not implemented") | |
| return (new_width, new_height) | |
| def __call__(self, x): | |
| width, height = self.get_size(*x.shape[-2:][::-1]) | |
| return nn.functional.interpolate(x, (int(height), int(width)), mode='bilinear', align_corners=True) | |