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trainingInitialization.py
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trainingInitialization.py
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from albumentations import (
Compose,
Normalize,
CenterCrop,
HueSaturationValue,
RandomBrightnessContrast,
Emboss,
Sharpen,
CLAHE,
PiecewiseAffine,
GridDistortion,
OpticalDistortion,
ShiftScaleRotate,
Blur,
MedianBlur,
MotionBlur,
OneOf,
GaussNoise,
Transpose,
Flip,
RandomRotate90,
)
from albumentations.pytorch import ToTensorV2
from torchvision import transforms, datasets, models
from torch.utils.data import DataLoader, random_split
from torch import (
cuda,
device,
backends,
autograd,
)
from torch.nn import Linear
def cudaInitialization():
"""Initialize the cuda module for the training, retrieving also the available
device (cuda or CPU).
Returns:
tuple[cuda, str]: tuple containing the module and the string.
"""
# Empty the GPU memory (in case of unallocated elements in it).
cuda.init()
cuda.empty_cache()
cuda.memory_summary(device=None, abbreviated=False)
# Deactivate profiling for lower memory/cpu usage.
autograd.profiler.profile(enabled=False)
autograd.profiler.emit_nvtx(enabled=False)
autograd.set_detect_anomaly(mode=False)
# Return the cuda module and the current device.
return cuda, device("cuda:0" if cuda.is_available() else "cpu")
def initImageTransforms():
"""Return a Composition of all the required augmentations
for training and test.
Training uses real augmentations, whereas Test just uses the
transformations needed to format the images as required by
ImageNet standard.
Returns:
dict: Dictionary {train: augmentations, test: formatting}
"""
return {
# Train uses data augmentation
"train": Compose(
[
RandomRotate90(),
Flip(),
Transpose(),
GaussNoise(p=0.2),
OneOf(
[
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
],
p=0.2,
),
ShiftScaleRotate(
shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2
),
OneOf(
[
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
PiecewiseAffine(p=0.3),
],
p=0.2,
),
OneOf(
[
CLAHE(clip_limit=2),
Sharpen(),
Emboss(),
RandomBrightnessContrast(),
],
p=0.3,
),
HueSaturationValue(p=0.3),
CenterCrop(
height=299, width=299, always_apply=True
), # Image net standards
Normalize(
[0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
), # Imagenet standards
ToTensorV2(),
]
),
"test": transforms.Compose(
[
transforms.Resize(size=299),
transforms.CenterCrop(size=299),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
),
}
def retrieveData(dataPath: str = "DATASET/train/") -> datasets.ImageFolder:
"""Return the data through the ImageFolder pytorch utility.
Args:
dataPath (str, optional): path to the folder containing the images.
Defaults to "DATASET/train/".
Returns:
datasets.ImageFolder: wrapper of images.
"""
return datasets.ImageFolder(root=dataPath)
def initDataLoaders(
data: datasets.ImageFolder,
imageTransforms: dict,
batchSize: int = 16,
numberOfCPUWorkers=0,
) -> tuple[DataLoader, DataLoader, DataLoader]:
"""Splits the data into training, testing and validation loaders for training and testing
purposes.
Args:
imageTransforms (dict): transforms to be applied to the data.
batchSize (int): batch size chosen for the training. Defaults to 62.
numberOfCPUWorkers (int): number of worker threads/processes which will
handle the loading of the data from the memory. Defaults to 4.
ATTENTION: workers number and batch size can afflict heavily the execution
of the program (even causing failures).
Returns:
tuple[DataLoader, DataLoader, DataLoader]: intialized dataloaders.
"""
dataSize = len(data)
trainingDataSize = int(dataSize * 0.8)
validationDataSize = int((dataSize - trainingDataSize) / 2)
testDataSize = int(dataSize - trainingDataSize - validationDataSize)
trainingData, validationData, testData = random_split(
data, [trainingDataSize, validationDataSize, testDataSize]
)
trainingData.dataset.transform = imageTransforms["train"]
validationData.dataset.transform = imageTransforms["test"]
testData.dataset.transform = imageTransforms["test"]
# return Train, Val, and Test loaders.
return (
DataLoader(
trainingData,
num_workers=numberOfCPUWorkers,
pin_memory=True,
batch_size=batchSize,
shuffle=True,
),
DataLoader(
validationData,
num_workers=numberOfCPUWorkers,
pin_memory=True,
batch_size=batchSize,
shuffle=True,
),
DataLoader(
testData,
num_workers=numberOfCPUWorkers,
pin_memory=True,
batch_size=batchSize,
shuffle=True,
),
)
def initModelVGG11(data: datasets.ImageFolder):
"""Build and return a VGG 11 model with pre-trained weights.
The last layer has been readapted to 2 outputs, as required for
a binary classification, all the layers of the model have been
activated for training, and the mapping class-index is stored in
the model.
Args:
data (datasets.ImageFolder): pytorch utility used to read data.
Returns:
_type_: _description_
"""
# Load basic model with pre-trained weights.
model = models.vgg11(weights=models.VGG11_Weights.DEFAULT)
# Train every layer, even feature extractor.
for param in model.parameters():
param.requires_grad = True
# Replace the last Linear layer with an identical layer with just 2 outputs
# and not 1000.
model.classifier[6] = Linear(in_features=4096, out_features=2)
# Initalize classes' indexes for class discrimination.
model.class_to_idx = data.class_to_idx
model.idx_to_class = {idx: class_ for class_, idx in model.class_to_idx.items()}
return model
def activateCuda(cudaModuleRef: cuda, model: models.vgg11) -> bool:
"""Check whether cuda is available and load the model on the GPU.
Args:
cudaModuleRef (cuda): cuda module reference.
model (models.vgg11): CNN model.
Returns:
bool: True if cuda is available, False otherwise.
"""
if cudaModuleRef.is_available():
model.cuda()
backends.cudnn.benchmark = True
return True
return False