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# Copyright 2024 EPFL and Apple Inc.
#
# 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 random
from abc import ABC, abstractmethod
import numpy as np
import torchvision
from fourm.utils import to_2tuple
class AbstractImageAugmenter(ABC):
"""Abstract class for image augmenters.
"""
@abstractmethod
def __call__(self, mod_dict, crop_settings):
pass
class RandomCropImageAugmenter(AbstractImageAugmenter):
def __init__(self, target_size=224, hflip=0.5, crop_scale=(0.2, 1.0), crop_ratio=(0.75, 1.3333), main_domain='rgb'):
self.target_size = to_2tuple(target_size)
self.hflip = hflip
self.crop_scale = crop_scale
self.crop_ratio = crop_ratio
self.main_domain = main_domain
def __call__(self, mod_dict, crop_settings):
if crop_settings is not None:
raise ValueError("Crop settings are provided but not used by this augmenter.")
image = mod_dict[self.main_domain] if self.main_domain is not None else mod_dict[list(mod_dict.keys())[0]]
# With torchvision 0.13+, can also be: orig_size = TF.get_dimensions(image)
orig_width, orig_height = image.size
orig_size = (orig_height, orig_width)
top, left, h, w = torchvision.transforms.RandomResizedCrop.get_params(
image, scale=self.crop_scale, ratio=self.crop_ratio
)
crop_coords = top, left, h, w
flip = random.random() < self.hflip
rand_aug_idx = None
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class NoImageAugmenter(AbstractImageAugmenter): # this is for non-image modalities like poses where we don't do any augs, e.g. during tokenization
def __init__(self, no_aug=True, main_domain='human_poses'):
self.target_size = None #to_2tuple(target_size)
self.no_aug = no_aug
self.main_domain = main_domain
def __call__(self, mod_dict, crop_settings):
# # With torchvision 0.13+, can also be: orig_size = TF.get_dimensions(image)
orig_size = (224, 224)
rand_aug_idx = 0
top, left, h, w, flip = 0, 0, 224, 224, 0
crop_coords = (top, left, h, w)
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class PreTokenizedImageAugmenter(AbstractImageAugmenter):
def __init__(self, target_size, no_aug=False, main_domain='rgb'):
self.target_size = to_2tuple(target_size)
self.no_aug = no_aug
self.main_domain = main_domain
def __call__(self, mod_dict, crop_settings):
# With torchvision 0.13+, can also be: orig_size = TF.get_dimensions(image)
if self.main_domain in mod_dict and 'tok' not in self.main_domain:
image = mod_dict[self.main_domain] if self.main_domain is not None else mod_dict[list(mod_dict.keys())[0]]
orig_width, orig_height = image.size
orig_size = (orig_height, orig_width)
else:
orig_size = None
rand_aug_idx = 0 if self.no_aug else np.random.randint(len(crop_settings))
top, left, h, w, flip = crop_settings[rand_aug_idx]
crop_coords = (top, left, h, w)
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class CenterCropImageAugmenter(AbstractImageAugmenter):
def __init__(self, target_size, hflip=0.0, main_domain='rgb'):
self.target_size = to_2tuple(target_size)
self.hflip = hflip
self.main_domain = main_domain
def __call__(self, mod_dict, crop_settings=None):
image = mod_dict[self.main_domain] if self.main_domain is not None else mod_dict[list(mod_dict.keys())[0]]
orig_width, orig_height = image.size
orig_size = (orig_height, orig_width)
if orig_height > orig_width:
h = w = orig_width
top = (orig_height - orig_width) // 2
left = 0
else:
h = w = orig_height
top = 0
left = (orig_width - orig_height) // 2
crop_coords = (top, left, h, w)
flip = random.random() < self.hflip
rand_aug_idx = None
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class PaddingImageAugmenter(AbstractImageAugmenter):
def __init__(self, target_size, hflip=0.0, main_domain='rgb'):
self.target_size = to_2tuple(target_size)
self.hflip = hflip
self.main_domain = main_domain
def __call__(self, mod_dict, crop_settings):
image = mod_dict[self.main_domain] if self.main_domain is not None else mod_dict[list(mod_dict.keys())[0]]
orig_width, orig_height = image.size
orig_size = (orig_height, orig_width)
h = w = max(orig_width, orig_height)
top = left = 0
crop_coords = (top, left, h, w)
flip = random.random() < self.hflip
rand_aug_idx = None
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class ScaleJitteringImageAugmenter(AbstractImageAugmenter):
def __init__(self, target_size, hflip=0.0, scale=(0.1, 2.0), main_domain='rgb'):
self.target_size = to_2tuple(target_size)
self.hflip = hflip
self.scale = scale
self.main_domain = main_domain
def scale_jitter(self, orig_height, orig_width):
rand_scale = np.random.uniform(self.scale[0], self.scale[1])
max_hw = max(orig_height, orig_width)
h = w = round(max_hw / rand_scale)
top = round(max(0, np.random.uniform(0, orig_height - h)))
left = round(max(0, np.random.uniform(0, orig_width - w)))
return top, left, h, w
def __call__(self, mod_dict, crop_settings):
if crop_settings is not None:
raise ValueError("Crop settings are provided but not used by this augmenter.")
image = mod_dict[self.main_domain] if self.main_domain is not None else mod_dict[list(mod_dict.keys())[0]]
# With torchvision 0.13+, can also be: orig_size = TF.get_dimensions(image)
orig_width, orig_height = image.size
orig_size = (orig_height, orig_width)
crop_coords = self.scale_jitter(orig_height, orig_width)
flip = random.random() < self.hflip
rand_aug_idx = None
return crop_coords, flip, orig_size, self.target_size, rand_aug_idx
class EmptyAugmenter(AbstractImageAugmenter):
def __init__(self):
pass
def __call__(self, mod_dict, crop_settings):
return None, None, None, None, None |