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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. 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.
"""Augmentations for tokenizer training (image and video)"""
from cosmos_predict1.tokenizer.training.datasets.augmentors import (
CenterCrop,
CropResizeAugmentor,
HorizontalFlip,
Normalize,
RandomReverse,
ReflectionPadding,
ResizeSmallestSideAspectPreserving,
UnsqueezeImage,
)
from cosmos_predict1.tokenizer.training.datasets.utils import (
VIDEO_KEY,
VIDEO_RES_SIZE_INFO,
VIDEO_VAL_CROP_SIZE_INFO,
get_crop_size_info,
)
from cosmos_predict1.utils import log
from cosmos_predict1.utils.lazy_config import LazyCall, LazyDict
_PROB_OF_CROP_ONLY: float = 0.1
def video_train_augmentations(
input_keys: list[str],
resolution: str = "1080",
crop_height: int = 256,
) -> dict[str, LazyDict]:
[_video_key] = input_keys
crop_sizes = get_crop_size_info(crop_height)
log.info(f"[video] training crop_height={crop_height} and crop_sizes: {crop_sizes}.")
augmentations = {
"crop_resize": LazyCall(CropResizeAugmentor)(
input_keys=[_video_key],
output_keys=[VIDEO_KEY],
crop_args={"size": crop_sizes},
resize_args={"size": VIDEO_RES_SIZE_INFO[resolution]},
args={"prob": _PROB_OF_CROP_ONLY},
),
"random_reverse": LazyCall(RandomReverse)(
input_keys=[VIDEO_KEY],
args={"prob": 0.5},
),
"reflection_padding": LazyCall(ReflectionPadding)(
input_keys=[VIDEO_KEY],
args={"size": crop_sizes},
),
"horizontal_flip": LazyCall(HorizontalFlip)(
input_keys=[VIDEO_KEY],
args={"size": crop_sizes},
),
"normalize": LazyCall(Normalize)(
input_keys=[VIDEO_KEY],
args={"mean": 0.5, "std": 0.5},
),
"unsqueeze_padding": LazyCall(UnsqueezeImage)(input_keys=["padding_mask"]),
}
return augmentations
def video_val_augmentations(
input_keys: list[str], resolution: str = "1080", crop_height: int = None
) -> dict[str, LazyDict]:
[_video_key] = input_keys
if crop_height is None:
crop_sizes = VIDEO_VAL_CROP_SIZE_INFO[resolution]
else:
crop_sizes = get_crop_size_info(crop_height)
log.info(f"[video] validation crop_sizes: {crop_sizes}.")
augmenations = {
"resize_smallest_side_aspect_ratio_preserving": LazyCall(ResizeSmallestSideAspectPreserving)(
input_keys=[VIDEO_KEY],
args={"size": VIDEO_RES_SIZE_INFO[resolution]},
),
"center_crop": LazyCall(CenterCrop)(
input_keys=[VIDEO_KEY],
args={"size": crop_sizes},
),
"reflection_padding": LazyCall(ReflectionPadding)(
input_keys=[VIDEO_KEY],
args={"size": crop_sizes},
),
"normalize": LazyCall(Normalize)(
input_keys=[VIDEO_KEY],
args={"mean": 0.5, "std": 0.5},
),
"unsqueeze_padding": LazyCall(UnsqueezeImage)(input_keys=["padding_mask"]),
}
return augmenations
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