def __init__(self, config, split, dataset):
self.n_epochs = config.n_epochs
self.split = split
self._time_start = ""
self._time_end = ""
self.epoch = 0
self.name = config.name
# Create output folders
dirname = f'{time.strftime("%Y-%m-%d_%H%M", time.gmtime())}_{self.name}'
self.out_dir = os.path.join(config.test_results_dir, dirname)
os.makedirs(self.out_dir, exist_ok=True)
# Create data loaders
self.train_loader = DataLoader(SlicesDataset(dataset[split["train"]]),
batch_size=config.batch_size,
shuffle=True,
num_workers=0)
self.val_loader = DataLoader(SlicesDataset(dataset[split["val"]]),
batch_size=config.batch_size,
shuffle=True,
num_workers=0)
# we will access volumes directly for testing
self.test_data = dataset[split["test"]]
# Do we have CUDA available?
if not torch.cuda.is_available():
print(
"WARNING: No CUDA device is found. This may take significantly longer!"
)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
# Configure our model and other training implements
self.model = UNet(num_classes=3)
self.model.to(self.device)
# Cross entropy loss
self.loss_function = torch.nn.CrossEntropyLoss()
# We are using standard SGD method to optimize our weights
self.optimizer = optim.Adam(self.model.parameters(),
lr=config.learning_rate)
# Scheduler helps us update learning rate automatically
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, 'min')
# Set up Tensorboard. By default it saves data into runs folder.
self.tensorboard_train_writer = SummaryWriter(comment="_train")
self.tensorboard_val_writer = SummaryWriter(comment="_val")
def __init__(self, config, split, dataset):
self.n_epochs = config.n_epochs
self.split = split
self._time_start = ""
self._time_end = ""
self.epoch = 0
self.name = config.name
# Create output folders
dirname = f'{time.strftime("%Y-%m-%d_%H%M", time.gmtime())}_{self.name}'
self.out_dir = os.path.join(config.test_results_dir, dirname)
os.makedirs(self.out_dir, exist_ok=True)
self.out_images_dir = os.path.join(self.out_dir, "images")
os.makedirs(self.out_images_dir)
# Create data loaders
# Note that we are using a 2D version of UNet here, which means that it will expect
# batches of 2D slices.
self.train_loader = DataLoader(SlicesDataset(dataset[split["train"]]),
batch_size=config.batch_size, shuffle=True, num_workers=0)
self.val_loader = DataLoader(SlicesDataset(dataset[split["val"]]),
batch_size=config.batch_size, shuffle=True, num_workers=0)
# we will access volumes directly for testing
self.test_data = dataset[split["test"]]
# Do we have CUDA available?
if not torch.cuda.is_available():
print("WARNING: No CUDA device is found. This may take significantly longer!")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Configure our model and other training implements
# We will use a recursive UNet model from German Cancer Research Center,
# Division of Medical Image Computing. It is quite complicated and works
# very well on this task.
self.model = UNet(num_classes=3)
self.model.to(self.device)
# We are using a standard cross-entropy loss since the model output is essentially
# a tensor with softmax'd prediction of each pixel's probability of belonging
# to a certain class
self.loss_function = torch.nn.CrossEntropyLoss()
# We are using standard SGD method to optimize our weights
self.optimizer = optim.Adam(self.model.parameters(), lr=config.learning_rate)
# Scheduler helps us update learning rate automatically
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, 'min')
# Set up Tensorboard. By default it saves data into runs folder. You need to launch
self.tensorboard_train_writer = SummaryWriter(comment="_train")
self.tensorboard_val_writer = SummaryWriter(comment="_val")
def __init__(self, config, split, dataset):
self.n_epochs = config.n_epochs
self.split = split
self._time_start = ""
self._time_end = ""
self.epoch = 0
self.name = config.name
# Create output folders
dirname = f'{time.strftime("%Y-%m-%d_%H%M", time.gmtime())}_{self.name}'
self.out_dir = os.path.join(config.test_results_dir, dirname)
os.makedirs(self.out_dir, exist_ok=True)
# Create data loaders
self.train_loader = DataLoader(SlicesDataset(dataset[split["train"]]),
batch_size=config.batch_size,
shuffle=True,
num_workers=0)
self.val_loader = DataLoader(SlicesDataset(dataset[split["val"]]),
batch_size=config.batch_size,
shuffle=True,
num_workers=0)
# access volumes directly for testing
self.test_data = dataset[split["test"]]
if not torch.cuda.is_available():
print(
"WARNING: No CUDA device is found. This may take significantly longer!"
)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
# use a recursive UNet model from German Cancer Research Center, Division of Medical Image Computing
self.model = UNet()
self.model.to(self.device)
# use a standard cross-entropy loss since the model output is essentially
# a tensor with softmax prediction of each pixel's probability of belonging to a certain class
self.loss_function = torch.nn.CrossEntropyLoss()
# use standard SGD method to optimize the weights
self.optimizer = optim.Adam(self.model.parameters(),
lr=config.learning_rate)
# Scheduler helps to update learning rate automatically
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, 'min')
