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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.
from typing import Optional
import omegaconf
import torch
import torchvision.transforms.functional as transforms_F
from cosmos_transfer1.diffusion.datasets.augmentors.control_input import Augmentor
from cosmos_transfer1.diffusion.datasets.dataset_utils import obtain_augmentation_size, obtain_image_size
class ReflectionPadding(Augmentor):
def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
super().__init__(input_keys, output_keys, args)
def __call__(self, data_dict: dict) -> dict:
r"""Performs reflection padding. This function also returns a padding mask.
Args:
data_dict (dict): Input data dict
Returns:
data_dict (dict): Output dict where images are center cropped.
"""
assert self.args is not None, "Please specify args in augmentation"
if self.output_keys is None:
self.output_keys = self.input_keys
# Obtain image and augmentation sizes
orig_w, orig_h = obtain_image_size(data_dict, self.input_keys)
target_size = obtain_augmentation_size(data_dict, self.args)
assert isinstance(target_size, (tuple, omegaconf.listconfig.ListConfig)), "Please specify target size as tuple"
target_w, target_h = target_size
target_w = int(target_w)
target_h = int(target_h)
# Calculate padding vals
padding_left = int((target_w - orig_w) / 2)
padding_right = target_w - orig_w - padding_left
padding_top = int((target_h - orig_h) / 2)
padding_bottom = target_h - orig_h - padding_top
padding_vals = [padding_left, padding_top, padding_right, padding_bottom]
for inp_key, out_key in zip(self.input_keys, self.output_keys):
if max(padding_vals[0], padding_vals[2]) >= orig_w or max(padding_vals[1], padding_vals[3]) >= orig_h:
# In this case, we can't perform reflection padding. This is because padding values
# are larger than the image size. So, perform edge padding instead.
data_dict[out_key] = transforms_F.pad(data_dict[inp_key], padding_vals, padding_mode="edge")
else:
# Perform reflection padding
data_dict[out_key] = transforms_F.pad(data_dict[inp_key], padding_vals, padding_mode="reflect")
if out_key != inp_key:
del data_dict[inp_key]
# Return padding_mask when padding is performed.
# Padding mask denotes which pixels are padded.
padding_mask = torch.ones((1, target_h, target_w))
padding_mask[:, padding_top : (padding_top + orig_h), padding_left : (padding_left + orig_w)] = 0
data_dict["padding_mask"] = padding_mask
data_dict["image_size"] = torch.tensor([target_h, target_w, orig_h, orig_w], dtype=torch.float)
return data_dict
class ResizeSmallestSide(Augmentor):
def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
super().__init__(input_keys, output_keys, args)
def __call__(self, data_dict: dict) -> dict:
r"""Performs resizing to smaller side
Args:
data_dict (dict): Input data dict
Returns:
data_dict (dict): Output dict where images are resized
"""
if self.output_keys is None:
self.output_keys = self.input_keys
assert self.args is not None, "Please specify args in augmentations"
for inp_key, out_key in zip(self.input_keys, self.output_keys):
out_size = obtain_augmentation_size(data_dict, self.args)
assert isinstance(out_size, int), "Arg size in resize should be an integer"
data_dict[out_key] = transforms_F.resize(
data_dict[inp_key],
size=out_size, # type: ignore
interpolation=getattr(self.args, "interpolation", transforms_F.InterpolationMode.BICUBIC),
antialias=True,
)
if out_key != inp_key:
del data_dict[inp_key]
return data_dict
class ResizeLargestSide(Augmentor):
def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
super().__init__(input_keys, output_keys, args)
def __call__(self, data_dict: dict) -> dict:
r"""Performs resizing to larger side
Args:
data_dict (dict): Input data dict
Returns:
data_dict (dict): Output dict where images are resized
"""
if self.output_keys is None:
self.output_keys = self.input_keys
assert self.args is not None, "Please specify args in augmentations"
for inp_key, out_key in zip(self.input_keys, self.output_keys):
out_size = obtain_augmentation_size(data_dict, self.args)
assert isinstance(out_size, int), "Arg size in resize should be an integer"
orig_w, orig_h = obtain_image_size(data_dict, self.input_keys)
scaling_ratio = min(out_size / orig_w, out_size / orig_h)
target_size = [int(scaling_ratio * orig_h), int(scaling_ratio * orig_w)]
data_dict[out_key] = transforms_F.resize(
data_dict[inp_key],
size=target_size,
interpolation=getattr(self.args, "interpolation", transforms_F.InterpolationMode.BICUBIC),
antialias=True,
)
if out_key != inp_key:
del data_dict[inp_key]
return data_dict
class ResizeSmallestSideAspectPreserving(Augmentor):
def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
super().__init__(input_keys, output_keys, args)
def __call__(self, data_dict: dict) -> dict:
r"""Performs aspect-ratio preserving resizing.
Image is resized to the dimension which has the smaller ratio of (size / target_size).
First we compute (w_img / w_target) and (h_img / h_target) and resize the image
to the dimension that has the smaller of these ratios.
Args:
data_dict (dict): Input data dict
Returns:
data_dict (dict): Output dict where images are resized
"""
if self.output_keys is None:
self.output_keys = self.input_keys
assert self.args is not None, "Please specify args in augmentations"
img_size = obtain_augmentation_size(data_dict, self.args)
assert isinstance(
img_size, (tuple, omegaconf.listconfig.ListConfig)
), f"Arg size in resize should be a tuple, get {type(img_size)}, {img_size}"
img_w, img_h = img_size
orig_w, orig_h = obtain_image_size(data_dict, self.input_keys)
scaling_ratio = max((img_w / orig_w), (img_h / orig_h))
target_size = (int(scaling_ratio * orig_h + 0.5), int(scaling_ratio * orig_w + 0.5))
assert (
target_size[0] >= img_h and target_size[1] >= img_w
), f"Resize error. orig {(orig_w, orig_h)} desire {img_size} compute {target_size}"
for inp_key, out_key in zip(self.input_keys, self.output_keys):
data_dict[out_key] = transforms_F.resize(
data_dict[inp_key],
size=target_size, # type: ignore
interpolation=getattr(self.args, "interpolation", transforms_F.InterpolationMode.BICUBIC),
antialias=True,
)
if out_key != inp_key:
del data_dict[inp_key]
return data_dict
class ResizeLargestSideAspectPreserving(Augmentor):
def __init__(self, input_keys: list, output_keys: Optional[list] = None, args: Optional[dict] = None) -> None:
super().__init__(input_keys, output_keys, args)
def __call__(self, data_dict: dict) -> dict:
r"""Performs aspect-ratio preserving resizing.
Image is resized to the dimension which has the larger ratio of (size / target_size).
First we compute (w_img / w_target) and (h_img / h_target) and resize the image
to the dimension that has the larger of these ratios.
Args:
data_dict (dict): Input data dict
Returns:
data_dict (dict): Output dict where images are resized
"""
if self.output_keys is None:
self.output_keys = self.input_keys
assert self.args is not None, "Please specify args in augmentations"
img_size = obtain_augmentation_size(data_dict, self.args)
assert isinstance(
img_size, (tuple, omegaconf.listconfig.ListConfig)
), f"Arg size in resize should be a tuple, get {type(img_size)}, {img_size}"
img_w, img_h = img_size
orig_w, orig_h = obtain_image_size(data_dict, self.input_keys)
scaling_ratio = min((img_w / orig_w), (img_h / orig_h))
target_size = (int(scaling_ratio * orig_h + 0.5), int(scaling_ratio * orig_w + 0.5))
assert (
target_size[0] <= img_h and target_size[1] <= img_w
), f"Resize error. orig {(orig_w, orig_h)} desire {img_size} compute {target_size}"
for inp_key, out_key in zip(self.input_keys, self.output_keys):
data_dict[out_key] = transforms_F.resize(
data_dict[inp_key],
size=target_size, # type: ignore
interpolation=getattr(self.args, "interpolation", transforms_F.InterpolationMode.BICUBIC),
antialias=True,
)
if out_key != inp_key:
del data_dict[inp_key]
return data_dict
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