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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
def window_partition(x: torch.Tensor, window_size, ndim=3):
"""
Args:
x: (B, Pl, Lat, Lon, C) or (B, Lat, Lon, C)
window_size (tuple[int]): [win_pl, win_lat, win_lon] or [win_lat, win_lon]
ndim (int): dimension of window (3 or 2)
Returns:
windows: (B*num_lon, num_pl*num_lat, win_pl, win_lat, win_lon, C) or (B*num_lon, num_lat, win_lat, win_lon, C)
"""
if ndim == 3:
B, Pl, Lat, Lon, C = x.shape
win_pl, win_lat, win_lon = window_size
x = x.view(
B, Pl // win_pl, win_pl, Lat // win_lat, win_lat, Lon // win_lon, win_lon, C
)
windows = (
x.permute(0, 5, 1, 3, 2, 4, 6, 7)
.contiguous()
.view(-1, (Pl // win_pl) * (Lat // win_lat), win_pl, win_lat, win_lon, C)
)
return windows
elif ndim == 2:
B, Lat, Lon, C = x.shape
win_lat, win_lon = window_size
x = x.view(B, Lat // win_lat, win_lat, Lon // win_lon, win_lon, C)
windows = (
x.permute(0, 3, 1, 2, 4, 5)
.contiguous()
.view(-1, (Lat // win_lat), win_lat, win_lon, C)
)
return windows
def window_reverse(windows, window_size, Pl=1, Lat=1, Lon=1, ndim=3):
"""
Args:
windows: (B*num_lon, num_pl*num_lat, win_pl, win_lat, win_lon, C) or (B*num_lon, num_lat, win_lat, win_lon, C)
window_size (tuple[int]): [win_pl, win_lat, win_lon] or [win_lat, win_lon]
Pl (int): pressure levels
Lat (int): latitude
Lon (int): longitude
ndim (int): dimension of window (3 or 2)
Returns:
x: (B, Pl, Lat, Lon, C) or (B, Lat, Lon, C)
"""
if ndim == 3:
win_pl, win_lat, win_lon = window_size
B = int(windows.shape[0] / (Lon / win_lon))
x = windows.view(
B,
Lon // win_lon,
Pl // win_pl,
Lat // win_lat,
win_pl,
win_lat,
win_lon,
-1,
)
x = x.permute(0, 2, 4, 3, 5, 1, 6, 7).contiguous().view(B, Pl, Lat, Lon, -1)
return x
elif ndim == 2:
win_lat, win_lon = window_size
B = int(windows.shape[0] / (Lon / win_lon))
x = windows.view(B, Lon // win_lon, Lat // win_lat, win_lat, win_lon, -1)
x = x.permute(0, 2, 3, 1, 4, 5).contiguous().view(B, Lat, Lon, -1)
return x
def get_shift_window_mask(input_resolution, window_size, shift_size, ndim=3):
"""
Along the longitude dimension, the leftmost and rightmost indices are actually close to each other.
If half windows apper at both leftmost and rightmost positions, they are dircetly merged into one window.
Args:
input_resolution (tuple[int]): [pressure levels, latitude, longitude] or [latitude, longitude]
window_size (tuple[int]): Window size [pressure levels, latitude, longitude] or [latitude, longitude]
shift_size (tuple[int]): Shift size for SW-MSA [pressure levels, latitude, longitude] or [latitude, longitude]
ndim (int): dimension of window (3 or 2)
Returns:
attn_mask: (n_lon, n_pl*n_lat, win_pl*win_lat*win_lon, win_pl*win_lat*win_lon) or (n_lon, n_lat, win_lat*win_lon, win_lat*win_lon)
"""
if ndim == 3:
Pl, Lat, Lon = input_resolution
win_pl, win_lat, win_lon = window_size
shift_pl, shift_lat, shift_lon = shift_size
img_mask = torch.zeros((1, Pl, Lat, Lon + shift_lon, 1))
elif ndim == 2:
Lat, Lon = input_resolution
win_lat, win_lon = window_size
shift_lat, shift_lon = shift_size
img_mask = torch.zeros((1, Lat, Lon + shift_lon, 1))
if ndim == 3:
pl_slices = (
slice(0, -win_pl),
slice(-win_pl, -shift_pl),
slice(-shift_pl, None),
)
lat_slices = (
slice(0, -win_lat),
slice(-win_lat, -shift_lat),
slice(-shift_lat, None),
)
lon_slices = (
slice(0, -win_lon),
slice(-win_lon, -shift_lon),
slice(-shift_lon, None),
)
cnt = 0
if ndim == 3:
for pl in pl_slices:
for lat in lat_slices:
for lon in lon_slices:
img_mask[:, pl, lat, lon, :] = cnt
cnt += 1
img_mask = img_mask[:, :, :, :Lon, :]
elif ndim == 2:
for lat in lat_slices:
for lon in lon_slices:
img_mask[:, lat, lon, :] = cnt
cnt += 1
img_mask = img_mask[:, :, :Lon, :]
mask_windows = window_partition(
img_mask, window_size, ndim=ndim
) # n_lon, n_pl*n_lat, win_pl, win_lat, win_lon, 1 or n_lon, n_lat, win_lat, win_lon, 1
if ndim == 3:
win_total = win_pl * win_lat * win_lon
elif ndim == 2:
win_total = win_lat * win_lon
mask_windows = mask_windows.view(
mask_windows.shape[0], mask_windows.shape[1], win_total
)
attn_mask = mask_windows.unsqueeze(2) - mask_windows.unsqueeze(3)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
attn_mask == 0, float(0.0)
)
return attn_mask
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