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### training dataset with 80/20 split
TRAIN_ROOT_DIR_GMCHALLENGE = "/gdrive/My Drive/DAIR RESOURCES/PyTorch/medical_imaging/train/"

gmdataset_train = mt_datasets.SCGMChallenge2DTrain(root_dir=TRAIN_ROOT_DIR_GMCHALLENGE,
                                                   subj_ids=range(1, 9),
                                                   transform=train_transform,
                                                   slice_filter_fn=mt_filters.SliceFilter())

gmdataset_val = mt_datasets.SCGMChallenge2DTrain(root_dir=TRAIN_ROOT_DIR_GMCHALLENGE,
                                                 subj_ids=range(9, 11),

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train_loader = DataLoader(gmdataset_train, batch_size=16,
                          shuffle=True, pin_memory=True,
                          collate_fn=mt_datasets.mt_collate,
                          num_workers=1)

val_loader = DataLoader(gmdataset_val, batch_size=16,
                        shuffle=True, pin_memory=True,
                        collate_fn=mt_datasets.mt_collate,
                        num_workers=1)

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model = mt_models.Unet(drop_rate=0.4, bn_momentum=0.1)
model.cuda()
num_epochs = 10
initial_lr = 0.001
optimizer = optim.Adam(model.parameters(), lr=initial_lr)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, num_epochs)

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def numeric_score(prediction, groundtruth):
    FP = np.float(np.sum((prediction == 1) & (groundtruth == 0)))
    FN = np.float(np.sum((prediction == 0) & (groundtruth == 1)))
    TP = np.float(np.sum((prediction == 1) & (groundtruth == 1)))
    TN = np.float(np.sum((prediction == 0) & (groundtruth == 0)))
    return FP, FN, TP, TN 
  
def accuracy(prediction, groundtruth):
    FP, FN, TP, TN = numeric_score(prediction, groundtruth)
    N = FP + FN + TP + TN

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for epoch in tqdm(range(1, num_epochs+1)):
    start_time = time.time()

    scheduler.step()

    lr = scheduler.get_lr()[0]

    model.train()
    train_loss_total = 0.0
    num_steps = 0

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!pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms

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BATCH_SIZE = 32

## transformations
transform = transforms.Compose(
    [transforms.ToTensor()])

## download and load training dataset
trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,

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import matplotlib.pyplot as plt
import numpy as np

## functions to show an image
def imshow(img):
    #img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))

## get some random training images

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for images, labels in trainloader:
    print("Image batch dimensions:", images.shape)
    print("Image label dimensions:", labels.shape)
    break