72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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import torch
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import numpy as np
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class Cutout:
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"""Applies random cutout augmentation by masking patches in an image.
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This technique randomly cuts out square patches from the image to
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augment the dataset, helping the model become invariant to occlusions.
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Args:
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n_holes (int): Number of patches to remove from the image.
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length (int): Side length (in pixels) of each square patch.
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"""
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def __init__(self, n_holes, length):
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"""
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Initializes the Cutout class with the number of patches to be removed
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and the size of each patch.
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Args:
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n_holes (int): Number of patches (holes) to cut out from the image.
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length (int): Size of each square patch.
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"""
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self.n_holes = n_holes # Number of holes (patches) to remove.
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self.length = length # Side length of each square patch.
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def __call__(self, img):
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"""
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Applies the cutout augmentation on the input image.
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Args:
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img (Tensor): The input image tensor with shape (C, H, W),
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where C is the number of channels, H is the height,
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and W is the width of the image.
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Returns:
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Tensor: The augmented image tensor with `n_holes` patches of size
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`length x length` cut out, filled with zeros.
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"""
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# Get the height and width of the image (ignoring the channel dimension)
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height, width = img.size(1), img.size(2)
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# Create a mask initialized with ones, same height and width as the image
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# (each pixel is set to 1, representing no masking initially)
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mask = np.ones((height, width), dtype=np.float32)
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# Randomly remove `n_holes` patches from the image
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for _ in range(self.n_holes):
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# Randomly choose the center of a patch (x_center, y_center)
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y_center = np.random.randint(height)
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x_center = np.random.randint(width)
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# Define the coordinates of the patch based on the center
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# and ensure the patch stays within the image boundaries.
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y1 = np.clip(y_center - self.length // 2, 0, height)
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y2 = np.clip(y_center + self.length // 2, 0, height)
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x1 = np.clip(x_center - self.length // 2, 0, width)
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x2 = np.clip(x_center + self.length // 2, 0, width)
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# Set the mask to 0 for the patch (mark the patch as cut out)
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mask[y1:y2, x1:x2] = 0.0
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# Convert the mask from numpy array to a PyTorch tensor
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mask_tensor = torch.from_numpy(mask).expand_as(img)
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# Multiply the input image by the mask (cut out the selected patches)
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return img * mask_tensor
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