# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out LICENSE.md
# Differentiable Augmentation for Data-Efficient GAN Training
# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
# https://arxiv.org/pdf/2006.10738
# Modified from https://github.com/mit-han-lab/data-efficient-gans
import torch
import torch.nn.functional as F
[docs]def apply_diff_aug(data, keys, aug_policy, inplace=False, **kwargs):
r"""Applies differentiable augmentation.
Args:
data (dict): Input data.
keys (list of str): Keys to the data values that we want to apply
differentiable augmentation to.
aug_policy (str): Type of augmentation(s), ``'color'``,
``'translation'``, or ``'cutout'`` separated by ``','``.
"""
if aug_policy == '':
return data
data_aug = data if inplace else {}
for key, value in data.items():
if key in keys:
data_aug[key] = diff_aug(data[key], aug_policy, **kwargs)
else:
data_aug[key] = data[key]
return data_aug
[docs]def diff_aug(x, policy='', channels_first=True, **kwargs):
if policy:
if not channels_first:
x = x.permute(0, 3, 1, 2)
for p in policy.split(','):
for f in AUGMENT_FNS[p]:
x = f(x, **kwargs)
if not channels_first:
x = x.permute(0, 2, 3, 1)
x = x.contiguous()
return x
[docs]def rand_brightness(x, **kwargs):
x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype,
device=x.device) - 0.5)
return x
[docs]def rand_saturation(x, **kwargs):
x_mean = x.mean(dim=1, keepdim=True)
x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype,
device=x.device) * 2) + x_mean
return x
[docs]def rand_contrast(x, **kwargs):
x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype,
device=x.device) + 0.5) + x_mean
return x
[docs]def rand_translation(x, ratio=0.125, **kwargs):
shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(
x.size(3) * ratio + 0.5)
translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1],
device=x.device)
translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1],
device=x.device)
# noinspection PyTypeChecker
grid_batch, grid_x, grid_y = torch.meshgrid(
torch.arange(x.size(0), dtype=torch.long, device=x.device),
torch.arange(x.size(2), dtype=torch.long, device=x.device),
torch.arange(x.size(3), dtype=torch.long, device=x.device),
)
grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
x = x_pad.permute(0, 2, 3, 1).contiguous()[
grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
return x
[docs]def rand_cutout(x, ratio=0.5, **kwargs):
cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2),
size=[x.size(0), 1, 1], device=x.device)
offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2),
size=[x.size(0), 1, 1], device=x.device)
# noinspection PyTypeChecker
grid_batch, grid_x, grid_y = torch.meshgrid(
torch.arange(x.size(0), dtype=torch.long, device=x.device),
torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
)
grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0,
max=x.size(2) - 1)
grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0,
max=x.size(3) - 1)
mask = torch.ones(x.size(0), x.size(2), x.size(3),
dtype=x.dtype, device=x.device)
mask[grid_batch, grid_x, grid_y] = 0
x = x * mask.unsqueeze(1)
return x
[docs]def rand_translation_scale(x, trans_r=0.125, scale_r=0.125,
mode='bilinear', padding_mode='reflection',
**kwargs):
assert x.dim() == 4, "Input must be a 4D tensor."
batch_size = x.size(0)
# Identity transformation.
theta = torch.eye(2, 3, device=x.device).unsqueeze(0).repeat(
batch_size, 1, 1)
# Translation, uniformly sampled from (-trans_r, trans_r).
translate = \
2 * trans_r * torch.rand(batch_size, 2, device=x.device) - trans_r
theta[:, :, 2] += translate
# Scaling, uniformly sampled from (1-scale_r, 1+scale_r).
scale = \
2 * scale_r * torch.rand(batch_size, 2, device=x.device) - scale_r
theta[:, :, :2] += torch.diag_embed(scale)
grid = F.affine_grid(theta, x.size())
x = F.grid_sample(
x.float(), grid.float(), mode=mode, padding_mode=padding_mode)
return x
AUGMENT_FNS = {
'color': [rand_brightness, rand_saturation, rand_contrast],
'translation': [rand_translation],
'translation_scale': [rand_translation_scale],
'cutout': [rand_cutout],
}