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# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# Copyright (c) 2020, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import torch
import torch.nn.functional as F
def DiffAugment(x, policy="", channels_first=True):
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)
if not channels_first:
x = x.permute(0, 2, 3, 1)
x = x.contiguous()
return x
def rand_brightness(x):
x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
return x
def rand_saturation(x):
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
def rand_contrast(x):
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
def rand_translation(x, ratio=0.125):
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
)
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
def rand_cutout(x, ratio=0.5):
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
)
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
AUGMENT_FNS = {
"color": [rand_brightness, rand_saturation, rand_contrast],
"translation": [rand_translation],
"cutout": [rand_cutout],
}
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