print('tr_mask:',tr_mask.shape)
print('val_mask:',val_mask.shape)
tr_dataset=TensorDataset(tr_data,tr_mask)
val_dataset=TensorDataset(val_data,val_mask)
device='cuda:0'
batch_size = 8
torch.backends.cudnn.deterministic = True
train_loader=DataLoader(dataset=tr_dataset,batch_size=batch_size,shuffle=True,num_workers=4)
val_loader=DataLoader(dataset=val_dataset,batch_size=batch_size,shuffle=True,num_workers=4)
del tr_data,tr_mask,val_data,val_mask,tr_dataset,val_dataset
gc.collect()
print('Data loading')
# fix random seed for reproducibility
# Build model
model = M.Hybrid_U_Net(input_size = (3,256,256)).to(device)
print('Training')
loss_function=nn.BCELoss()
optimiser=optim.Adam(model.parameters(),lr=1e-4)
scheduler=optim.lr_scheduler.ReduceLROnPlateau(optimiser,mode='min',eps=1e-5,patience=10,factor=0.8,verbose=True)
nb_epoch = 100
val_loss_best=30
val_accuracy_best=0.90
for epoch in range(nb_epoch):
print('epoch:',epoch)
model.train()
train_loss=0
train_num=0
train_correct=0
for input,label in train_loader:
input=input.to(device)
#========= CONFIG FILE TO READ FROM ======= #=========================================== #run the training on invariant or local path_data = './DRIVE_datasets_training_testing/' #original test images (for FOV selection) DRIVE_test_imgs_original = path_data + 'DRIVE_dataset_imgs_test.hdf5' test_imgs_orig = load_hdf5(DRIVE_test_imgs_original) full_img_height = test_imgs_orig.shape[2] full_img_width = test_imgs_orig.shape[3] #the border masks provided by the DRIVE DRIVE_test_border_masks = path_data + 'DRIVE_dataset_borderMasks_test.hdf5' test_border_masks = load_hdf5(DRIVE_test_border_masks) device = 'cuda:0' model = M.Hybrid_U_Net(input_size=(1, 64, 64)).to(device) # dimension of the patches patch_height = 64 patch_width = 64 # the stride in case output with average stride_height = 5 stride_width = 5 # model name name_experiment = 'output' path_experiment = './' + name_experiment + '/' # N full images to be predicted Imgs_to_test = 2 # Grouping of the predicted images N_visual = 1 # ====== average mode ===========