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from typing import List
import numpy as np
import torch
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from transformers import BertTokenizerFast, DistilBertTokenizerFast
from data_augmentation.randaugment import FIX_MATCH_AUGMENTATION_POOL, RandAugment
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_MEAN = [0.485, 0.456, 0.406]
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_STD = [0.229, 0.224, 0.225]
def initialize_transform(
transform_name, config, dataset, is_training, additional_transform_name=None
):
"""
By default, transforms should take in `x` and return `transformed_x`.
For transforms that take in `(x, y)` and return `(transformed_x, transformed_y)`,
set `do_transform_y` to True when initializing the WILDSSubset.
"""
if transform_name is None:
return None
elif transform_name == "bert":
return initialize_bert_transform(config)
elif transform_name == 'rxrx1':
return initialize_rxrx1_transform(is_training)
# For images
normalize = True
if transform_name == "image_base":
transform_steps = get_image_base_transform_steps(config, dataset)
elif transform_name == "image_resize":
transform_steps = get_image_resize_transform_steps(
config, dataset
)
elif transform_name == "image_resize_and_center_crop":
transform_steps = get_image_resize_and_center_crop_transform_steps(
config, dataset
)
elif transform_name == "poverty":
if not is_training:
return None
transform_steps = []
normalize = False
else:
raise ValueError(f"{transform_name} not recognized")
default_normalization = transforms.Normalize(
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_MEAN,
_DEFAULT_IMAGE_TENSOR_NORMALIZATION_STD,
)
if additional_transform_name == "fixmatch":
if transform_name == 'poverty':
transformations = add_poverty_fixmatch_transform(config, dataset, transform_steps)
else:
transformations = add_fixmatch_transform(
config, dataset, transform_steps, default_normalization
)
transform = MultipleTransforms(transformations)
elif additional_transform_name == "randaugment":
if transform_name == 'poverty':
transform = add_poverty_rand_augment_transform(
config, dataset, transform_steps
)
else:
transform = add_rand_augment_transform(
config, dataset, transform_steps, default_normalization
)
elif additional_transform_name == "weak":
transform = add_weak_transform(
config, dataset, transform_steps, normalize, default_normalization
)
else:
if transform_name != "poverty":
# The poverty data is already a tensor at this point
transform_steps.append(transforms.ToTensor())
if normalize:
transform_steps.append(default_normalization)
transform = transforms.Compose(transform_steps)
return transform
def initialize_bert_transform(config):
def get_bert_tokenizer(model):
if model == "bert-base-uncased":
return BertTokenizerFast.from_pretrained(model)
elif model == "distilbert-base-uncased":
return DistilBertTokenizerFast.from_pretrained(model)
else:
raise ValueError(f"Model: {model} not recognized.")
assert "bert" in config.model
assert config.max_token_length is not None
tokenizer = get_bert_tokenizer(config.model)
def transform(text):
tokens = tokenizer(
text,
padding="max_length",
truncation=True,
max_length=config.max_token_length,
return_tensors="pt",
)
if config.model == "bert-base-uncased":
x = torch.stack(
(
tokens["input_ids"],
tokens["attention_mask"],
tokens["token_type_ids"],
),
dim=2,
)
elif config.model == "distilbert-base-uncased":
x = torch.stack((tokens["input_ids"], tokens["attention_mask"]), dim=2)
x = torch.squeeze(x, dim=0) # First shape dim is always 1
return x
return transform
def initialize_rxrx1_transform(is_training):
def standardize(x: torch.Tensor) -> torch.Tensor:
mean = x.mean(dim=(1, 2))
std = x.std(dim=(1, 2))
std[std == 0.] = 1.
return TF.normalize(x, mean, std)
t_standardize = transforms.Lambda(lambda x: standardize(x))
angles = [0, 90, 180, 270]
def random_rotation(x: torch.Tensor) -> torch.Tensor:
angle = angles[torch.randint(low=0, high=len(angles), size=(1,))]
if angle > 0:
x = TF.rotate(x, angle)
return x
t_random_rotation = transforms.Lambda(lambda x: random_rotation(x))
if is_training:
transforms_ls = [
t_random_rotation,
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
t_standardize,
]
else:
transforms_ls = [
transforms.ToTensor(),
t_standardize,
]
transform = transforms.Compose(transforms_ls)
return transform
def get_image_base_transform_steps(config, dataset) -> List:
transform_steps = []
if dataset.original_resolution is not None and min(
dataset.original_resolution
) != max(dataset.original_resolution):
crop_size = min(dataset.original_resolution)
transform_steps.append(transforms.CenterCrop(crop_size))
if config.target_resolution is not None:
transform_steps.append(transforms.Resize(config.target_resolution))
return transform_steps
def get_image_resize_and_center_crop_transform_steps(config, dataset) -> List:
"""
Resizes the image to a slightly larger square then crops the center.
"""
transform_steps = get_image_resize_transform_steps(config, dataset)
target_resolution = _get_target_resolution(config, dataset)
transform_steps.append(
transforms.CenterCrop(target_resolution),
)
return transform_steps
def get_image_resize_transform_steps(config, dataset) -> List:
"""
Resizes the image to a slightly larger square.
"""
assert dataset.original_resolution is not None
assert config.resize_scale is not None
scaled_resolution = tuple(
int(res * config.resize_scale) for res in dataset.original_resolution
)
return [
transforms.Resize(scaled_resolution)
]
def add_fixmatch_transform(config, dataset, base_transform_steps, normalization):
return (
add_weak_transform(config, dataset, base_transform_steps, True, normalization),
add_rand_augment_transform(config, dataset, base_transform_steps, normalization)
)
def add_poverty_fixmatch_transform(config, dataset, base_transform_steps):
return (
add_weak_transform(config, dataset, base_transform_steps, False, None),
add_poverty_rand_augment_transform(config, dataset, base_transform_steps)
)
def add_weak_transform(config, dataset, base_transform_steps, should_normalize, normalization):
# Adapted from https://github.com/YBZh/Bridging_UDA_SSL
target_resolution = _get_target_resolution(config, dataset)
weak_transform_steps = copy.deepcopy(base_transform_steps)
weak_transform_steps.extend(
[
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(
size=target_resolution,
),
]
)
if should_normalize:
weak_transform_steps.append(transforms.ToTensor())
weak_transform_steps.append(normalization)
return transforms.Compose(weak_transform_steps)
def add_rand_augment_transform(config, dataset, base_transform_steps, normalization):
# Adapted from https://github.com/YBZh/Bridging_UDA_SSL
target_resolution = _get_target_resolution(config, dataset)
strong_transform_steps = copy.deepcopy(base_transform_steps)
strong_transform_steps.extend(
[
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(
size=target_resolution
),
RandAugment(
n=config.randaugment_n,
augmentation_pool=FIX_MATCH_AUGMENTATION_POOL,
),
transforms.ToTensor(),
normalization,
]
)
return transforms.Compose(strong_transform_steps)
def poverty_rgb_color_transform(ms_img, transform):
from wilds.datasets.poverty_dataset import _MEANS_2009_17, _STD_DEVS_2009_17
poverty_rgb_means = np.array([_MEANS_2009_17[c] for c in ['RED', 'GREEN', 'BLUE']]).reshape((-1, 1, 1))
poverty_rgb_stds = np.array([_STD_DEVS_2009_17[c] for c in ['RED', 'GREEN', 'BLUE']]).reshape((-1, 1, 1))
def unnormalize_rgb_in_poverty_ms_img(ms_img):
result = ms_img.detach().clone()
result[:3] = (result[:3] * poverty_rgb_stds) + poverty_rgb_means
return result
def normalize_rgb_in_poverty_ms_img(ms_img):
result = ms_img.detach().clone()
result[:3] = (result[:3] - poverty_rgb_means) / poverty_rgb_stds
return ms_img
color_transform = transforms.Compose([
transforms.Lambda(lambda ms_img: unnormalize_rgb_in_poverty_ms_img(ms_img)),
transform,
transforms.Lambda(lambda ms_img: normalize_rgb_in_poverty_ms_img(ms_img)),
])
# The first three channels of the Poverty MS images are BGR
# So we shuffle them to the standard RGB to do the ColorJitter
# Before shuffling them back
ms_img[:3] = color_transform(ms_img[[2,1,0]])[[2,1,0]] # bgr to rgb to bgr
return ms_img
def add_poverty_rand_augment_transform(config, dataset, base_transform_steps):
def poverty_color_jitter(ms_img):
return poverty_rgb_color_transform(
ms_img,
transforms.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.1))
def ms_cutout(ms_img):
def _sample_uniform(a, b):
return torch.empty(1).uniform_(a, b).item()
assert ms_img.shape[1] == ms_img.shape[2]
img_width = ms_img.shape[1]
cutout_width = _sample_uniform(0, img_width/2)
cutout_center_x = _sample_uniform(0, img_width)
cutout_center_y = _sample_uniform(0, img_width)
x0 = int(max(0, cutout_center_x - cutout_width/2))
y0 = int(max(0, cutout_center_y - cutout_width/2))
x1 = int(min(img_width, cutout_center_x + cutout_width/2))
y1 = int(min(img_width, cutout_center_y + cutout_width/2))
# Fill with 0 because the data is already normalized to mean zero
ms_img[:, x0:x1, y0:y1] = 0
return ms_img
target_resolution = _get_target_resolution(config, dataset)
strong_transform_steps = copy.deepcopy(base_transform_steps)
strong_transform_steps.extend([
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomAffine(degrees=10, translate=(0.1, 0.1), shear=0.1, scale=(0.9, 1.1)),
transforms.Lambda(lambda ms_img: poverty_color_jitter(ms_img)),
transforms.Lambda(lambda ms_img: ms_cutout(ms_img)),
# transforms.Lambda(lambda ms_img: viz(ms_img)),
])
return transforms.Compose(strong_transform_steps)
def _get_target_resolution(config, dataset):
if config.target_resolution is not None:
return config.target_resolution
else:
return dataset.original_resolution
class MultipleTransforms(object):
"""When multiple transformations of the same data need to be returned."""
def __init__(self, transformations):
self.transformations = transformations
def __call__(self, x):
return tuple(transform(x) for transform in self.transformations)
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