Pytorch_CNN-RNN/utils/dataset_sd_mean_finder.py

from preprocess import prepare_datasets
from train_methods import train, load, evaluate, predict
from CNN import CNN_Net
from torch.utils.data import DataLoader

model_filepath = '/data/data_wnx1/rschuurs/Pytorch_CNN-RNN'
CNN_filepath = '/data/data_wnx1/rschuurs/Pytorch_CNN-RNN/cnn_net.pth'       # cnn_net.pth
# small dataset
# mri_datapath = '/data/data_wnx1/rschuurs/Pytorch_CNN-RNN/PET_volumes_customtemplate_float32/'   # Small Test
# big dataset
mri_datapath = '/data/data_wnx1/_Data/AlzheimersDL/CNN+RNN-2class-1cnn+data/PET_volumes_customtemplate_float32/'   # Real data
csv_datapath = '../LP_ADNIMERGE.csv'
                        # '/data/data_wnx1/rschuurs/Pytorch_CNN-RNN/LP_ADNIMERGE.csv')


# LOADING DATA
val_split = 0.2     # % of val and test, rest will be train
seed = 12       # TODO Randomize seed

properties = {
    "batch_size":32,
    "padding":0,
    "dilation":1,
    "groups":1,
    "bias":True,
    "padding_mode":"zeros",
    "drop_rate":0,
    "epochs": 20,
    "lr": [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6],  # Unused
    'momentum':[0.99, 0.97, 0.95, 0.9],  # Unused
    'weight_decay':[1e-3, 1e-4, 1e-5, 0]    # Unused
}



# TODO: Datasets include multiple labels, such as medical info
training_data, val_data, test_data = prepare_datasets(mri_datapath, csv_datapath, val_split, seed)

# Create data loaders
train_dataloader = DataLoader(training_data, batch_size=properties['batch_size'], shuffle=True, drop_last=True)
val_dataloader = DataLoader(val_data, batch_size=properties['batch_size'], shuffle=True)        # Used during training
test_dataloader = DataLoader(test_data, batch_size=properties['batch_size'], shuffle=True)      # Used at end for graphs

print("STARTING")


# HERE'S ACTUAL CODE
mean = 0.
std = 0.
nb_samples = 0.
for data in train_dataloader:
    print(data)
    batch_samples = data.size(0)
    data = data.view(batch_samples, data.size(1), -1)
    mean += data.mean(2).sum(0)
    std += data.std(2).sum(0)
    nb_samples += batch_samples

mean /= nb_samples
std /= nb_samples

print(mean)
print(std)



mean = 0.
std = 0.
nb_samples = 0.
for data in val_dataloader:
    batch_samples = data.size(0)
    data = data.view(batch_samples, data.size(1), -1)
    mean += data.mean(2).sum(0)
    std += data.std(2).sum(0)
    nb_samples += batch_samples

mean /= nb_samples
std /= nb_samples

print(mean)
print(std)



mean = 0.
std = 0.
nb_samples = 0.
for data in test_dataloader:
    batch_samples = data.size(0)
    data = data.view(batch_samples, data.size(1), -1)
    mean += data.mean(2).sum(0)
    std += data.std(2).sum(0)
    nb_samples += batch_samples

mean /= nb_samples
std /= nb_samples

print(mean)
print(std)