def get_transforms(stage: str = None,
mode: str = None,
image_size=224,
one_hot_classes=None):
pre_transform_fn = pre_transforms(image_size=image_size)
if mode == "train":
post_transform_fn = Compose([hard_transform(), post_transforms()])
elif mode in ["valid", "infer"]:
post_transform_fn = post_transforms()
else:
raise NotImplementedError()
if mode in ["train", "valid"]:
result = MixinAdapter(
mixin=RotateMixin(input_key="image",
output_key="rotation_factor",
targets_key="targets",
one_hot_classes=one_hot_classes),
pre_transforms=Augmentor(
dict_key="image",
augment_fn=lambda x: pre_transform_fn(image=x)["image"]),
post_transforms=Augmentor(
dict_key="image",
augment_fn=lambda x: post_transform_fn(image=x)["image"]))
elif mode in ["infer"]:
result_fn = Compose([pre_transform_fn, post_transform_fn])
result = Augmentor(
dict_key="image",
augment_fn=lambda x: result_fn(image=x)["image"])
else:
raise NotImplementedError()
return result
def get_transforms(stage: str = None,
mode: str = None,
input_size: int = 224):
train_image_transforms = [
OpticalDistortion(distort_limit=0.3, p=0.3),
JpegCompression(quality_lower=50, p=0.8),
HorizontalFlip(p=0.5),
MotionBlur(p=0.5),
ShiftScaleRotate(shift_limit=0.1,
scale_limit=0.2,
rotate_limit=20,
p=0.5),
RandomBrightnessContrast(brightness_limit=0.3,
contrast_limit=0.2,
p=0.4),
HueSaturationValue(hue_shift_limit=3,
sat_shift_limit=20,
val_shift_limit=30,
p=0.4),
CLAHE(clip_limit=2, p=0.3)
]
infer_image_transforms = [
Resize(input_size, input_size),
Normalize(),
ToTorchTensor(p=1.0)
]
stack = TorchStack()
train_images_fn = GroupTransform(transforms=train_image_transforms +
infer_image_transforms)
valid_images_fn = GroupTransform(transforms=infer_image_transforms)
def train_aug_fn(images):
images = train_images_fn(images)
images = stack(images)
return images
def valid_aug_fn(images):
images = valid_images_fn(images)
images = stack(images)
return images
train_transforms = Augmentor(dict_key="features",
augment_fn=lambda x: train_aug_fn(x))
valid_transforms = Augmentor(dict_key="features",
augment_fn=lambda x: valid_aug_fn(x))
if mode == "train":
return train_transforms
else:
return valid_transforms
def _get_transform(**params) -> Callable:
key_value_flag = params.pop("_key_value", False)
if key_value_flag:
transforms_composition = {
transform_key: ConfigExperiment._get_transform( # noqa: WPS437
**transform_params)
for transform_key, transform_params in params.items()
}
transform = AugmentorCompose({
key: Augmentor(
dict_key=key,
augment_fn=transform,
input_key=key,
output_key=key,
)
for key, transform in transforms_composition.items()
})
else:
if "transforms" in params:
transforms_composition = [
ConfigExperiment._get_transform( # noqa: WPS437
**transform_params)
for transform_params in params["transforms"]
]
params.update(transforms=transforms_composition)
transform = TRANSFORMS.get_from_params(**params)
return transform
def prepare_transforms(*, mode, stage=None, **kwargs):
if mode == "train":
transform_fn = train_transform(image_size=IMG_SIZE)
elif mode in ["valid", "infer"]:
transform_fn = valid_transform(image_size=IMG_SIZE)
else:
raise NotImplementedError
return Augmentor(dict_key="image",
augment_fn=lambda x: transform_fn(image=x)["image"])
def get_transforms():
train_transforms = compose(
[pre_transforms(),
hard_transforms(),
post_transforms()])
valid_transforms = compose([pre_transforms(), post_transforms()])
show_transforms = compose([pre_transforms(), hard_transforms()])
# Takes an image from the input dictionary by the key `dict_key`
# and performs `train_transforms` on it.
train_data_transforms = Augmentor(
dict_key="features",
augment_fn=lambda x: train_transforms(image=x)["image"])
# Similarly for the validation part of the dataset.
# we only perform squaring, normalization and ToTensor
valid_data_transforms = Augmentor(
dict_key="features",
augment_fn=lambda x: valid_transforms(image=x)["image"])
return train_data_transforms, valid_data_transforms, show_transforms
def get_transform(phase, img_size):
assert phase in {'train', 'valid'}, f'invalid phase: {phase}'
if phase == 'train':
transforms = compose([
pre_transforms(image_size=img_size),
hard_transforms(),
post_transforms()
])
else:
transforms = compose([pre_transforms(), post_transforms()])
data_transforms = Augmentor(
dict_key="features", augment_fn=lambda x: transforms(image=x)["image"])
return data_transforms
def get_transforms(stage: str = None,
mode: str = None,
image_size: int = 224,
one_hot_classes: int = None):
pre_transform_fn = pre_transforms(image_size=image_size)
if mode == "train":
post_transform_fn = Compose(
[hard_transform(image_size=image_size),
post_transforms()])
elif mode in ["valid", "infer"]:
post_transform_fn = post_transforms()
else:
raise NotImplementedError()
result_fn = Compose([pre_transform_fn, post_transform_fn])
result = Augmentor(dict_key="image",
augment_fn=lambda x: result_fn(image=x)["image"])
return result
pre_transforms(config.size),
hard_transforms(),
post_transforms()
])
valid_transforms = plant.compose([
pre_transforms(config.size),
post_transforms()
])
show_transforms = plant.compose([
pre_transforms(config.size),
hard_transforms()
])
train_data_transforms = Augmentor(
dict_key="features",
augment_fn=lambda x: train_transforms(image=x)["image"]
)
valid_data_transforms = Augmentor(
dict_key="features",
augment_fn=lambda x: valid_transforms(image=x)["image"]
)
loaders = plant.get_loaders(
train_transforms_fn=train_data_transforms,
valid_transforms_fn=valid_data_transforms,
batch_size=config.batch_size,
config=config
)
model = plant.get_model(config.model_name, config.num_classes)
data = list(zip(images, masks))
train_data = data[:n_images // 2]
valid_data = data[n_images // 2:]
# Data loaders
augmentations = [
# TODO specify augmentations (e.g. histogram normalization)
SegAugmentWrapper(RandomFlip(0.5),
image_key='features',
mask_key='targets')
]
transformations = [
Augmentor(
dict_key="features",
augment_fn=lambda x: \
torch.from_numpy(x.copy().astype(np.float32) / 256.).unsqueeze_(0).float()),
Augmentor(
dict_key="targets",
augment_fn=lambda x: \
torch.from_numpy(x.copy()).unsqueeze_(0).float()),
]
train_data_transform = transforms.Compose(augmentations + transformations)
valid_data_transform = transforms.Compose(transformations)
open_fn = lambda x: {"features": x[0], "targets": x[1]}
train_loader = UtilsFactory.create_loader(train_data,
open_fn=open_fn,
dict_transform=train_data_transform,
RandomContrast(),
RandomBrightness(),
], p=0.5),
HueSaturationValue(p=0.5),
], p=p)
AUG_TRAIN = strong_aug(p=0.75)
AUG_INFER = Compose([
Resize(IMG_SIZE, IMG_SIZE),
Normalize(),
])
TRAIN_TRANSFORM_FN = [
Augmentor(
dict_key="image",
augment_fn=lambda x: AUG_TRAIN(image=x)["image"]),
]
INFER_TRANSFORM_FN = [
Augmentor(
dict_key="image",
augment_fn=lambda x: AUG_INFER(image=x)["image"]),
Augmentor(
dict_key="image",
augment_fn=lambda x: torch.tensor(x).permute(2, 0, 1)),
]
# ---- Data ----
# In[ ]:
import collections
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
from catalyst.data.augmentor import Augmentor
from catalyst.dl.utils import UtilsFactory
bs = 1
num_workers = 0
data_transform = transforms.Compose([
Augmentor(
dict_key="features",
augment_fn=lambda x: \
torch.from_numpy(x.copy().astype(np.float32) / 255.).unsqueeze_(0)),
Augmentor(
dict_key="features",
augment_fn=transforms.Normalize(
(0.5, ),
(0.5, ))),
Augmentor(
dict_key="targets",
augment_fn=lambda x: \
torch.from_numpy(x.copy().astype(np.float32) / 255.).unsqueeze_(0))
])
open_fn = lambda x: {"features": x[0], "targets": x[1]}
loaders = collections.OrderedDict()
def create_dataloders(
train_file: str,
valid_file: str,
root_folder: str,
meta_info_file: str,
num_classes: int,
one_hot_encoding: bool,
bs: int,
num_workers: int,
augmenters: Dict = None,
):
train_data = _prepare(train_file, root_folder)
valid_data = _prepare(valid_file, root_folder)
train_augmenter = augmenters['train']
valid_augmenter = augmenters['valid']
train_transforms_fn = transforms.Compose([
Augmentor(
dict_key="features",
augment_fn=lambda x: train_augmenter(samples=x, sample_rate=16000))
])
# Similarly for the validation part of the dataset.
# we only perform squaring, normalization and ToTensor
valid_transforms_fn = transforms.Compose([
Augmentor(
dict_key="features",
augment_fn=lambda x: valid_augmenter(samples=x, sample_rate=16000))
])
compose = [
AudioReader(
input_key="filepath",
output_key="features",
),
ScalarReader(input_key="label",
output_key="targets",
default_value=-1,
dtype=np.int64),
]
if one_hot_encoding:
compose.append(
ScalarReader(
input_key="label",
output_key="targets_one_hot",
default_value=-1,
dtype=np.int64,
one_hot_classes=num_classes,
))
open_fn = ReaderCompose(compose)
train_loader = catalyst_utils.get_loader(
train_data,
open_fn=open_fn,
dict_transform=train_transforms_fn,
batch_size=bs,
num_workers=num_workers,
shuffle=
True, # shuffle data only if Sampler is not specified (PyTorch requirement)
)
valid_loader = catalyst_utils.get_loader(
valid_data,
open_fn=open_fn,
dict_transform=valid_transforms_fn,
batch_size=bs,
num_workers=1,
shuffle=False,
)
loaders = OrderedDict()
loaders["train"] = train_loader
loaders["valid"] = valid_loader
return loaders