96 lines
3.8 KiB
Python
96 lines
3.8 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import List, Optional, Tuple
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import numpy as np
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import torch
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import torch.nn.functional as F
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from mmpretrain.registry import BATCH_AUGMENTS
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from .cutmix import CutMix
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@BATCH_AUGMENTS.register_module()
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class ResizeMix(CutMix):
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r"""ResizeMix Random Paste layer for a batch of data.
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The ResizeMix will resize an image to a small patch and paste it on another
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image. It's proposed in `ResizeMix: Mixing Data with Preserved Object
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Information and True Labels <https://arxiv.org/abs/2012.11101>`_
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Args:
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alpha (float): Parameters for Beta distribution to generate the
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mixing ratio. It should be a positive number. More details
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can be found in :class:`Mixup`.
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lam_min(float): The minimum value of lam. Defaults to 0.1.
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lam_max(float): The maximum value of lam. Defaults to 0.8.
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interpolation (str): algorithm used for upsampling:
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'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' |
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'area'. Defaults to 'bilinear'.
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prob (float): The probability to execute resizemix. It should be in
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range [0, 1]. Defaults to 1.0.
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cutmix_minmax (List[float], optional): The min/max area ratio of the
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patches. If not None, the bounding-box of patches is uniform
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sampled within this ratio range, and the ``alpha`` will be ignored.
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Otherwise, the bounding-box is generated according to the
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``alpha``. Defaults to None.
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correct_lam (bool): Whether to apply lambda correction when cutmix bbox
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clipped by image borders. Defaults to True
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**kwargs: Any other parameters accpeted by :class:`CutMix`.
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Note:
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The :math:`\lambda` (``lam``) is the mixing ratio. It's a random
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variable which follows :math:`Beta(\alpha, \alpha)` and is mapped
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to the range [``lam_min``, ``lam_max``].
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.. math::
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\lambda = \frac{Beta(\alpha, \alpha)}
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{\lambda_{max} - \lambda_{min}} + \lambda_{min}
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And the resize ratio of source images is calculated by :math:`\lambda`:
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.. math::
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\text{ratio} = \sqrt{1-\lambda}
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"""
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def __init__(self,
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alpha: float,
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lam_min: float = 0.1,
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lam_max: float = 0.8,
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interpolation: str = 'bilinear',
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cutmix_minmax: Optional[List[float]] = None,
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correct_lam: bool = True):
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super().__init__(
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alpha=alpha, cutmix_minmax=cutmix_minmax, correct_lam=correct_lam)
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self.lam_min = lam_min
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self.lam_max = lam_max
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self.interpolation = interpolation
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def mix(self, batch_inputs: torch.Tensor,
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batch_scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Mix the batch inputs and batch one-hot format ground truth.
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Args:
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batch_inputs (Tensor): A batch of images tensor in the shape of
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``(N, C, H, W)``.
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batch_scores (Tensor): A batch of one-hot format labels in the
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shape of ``(N, num_classes)``.
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Returns:
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Tuple[Tensor, Tensor): The mixed inputs and labels.
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"""
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lam = np.random.beta(self.alpha, self.alpha)
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lam = lam * (self.lam_max - self.lam_min) + self.lam_min
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img_shape = batch_inputs.shape[-2:]
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batch_size = batch_inputs.size(0)
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index = torch.randperm(batch_size)
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(y1, y2, x1, x2), lam = self.cutmix_bbox_and_lam(img_shape, lam)
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batch_inputs[:, :, y1:y2, x1:x2] = F.interpolate(
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batch_inputs[index],
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size=(y2 - y1, x2 - x1),
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mode=self.interpolation,
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align_corners=False)
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mixed_scores = lam * batch_scores + (1 - lam) * batch_scores[index, :]
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return batch_inputs, mixed_scores
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