/
main.py
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main.py
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import torch
import torchnet as tnt
from torch.autograd import Variable
from torch.optim import Adam
from torchnet.engine import Engine
from torchnet.logger import VisdomPlotLogger, VisdomLogger
from torchvision.utils import make_grid
from tqdm import tqdm
import config
import utils
from capsnet import CapsuleNet
from loss import CapsuleLoss
def processor(sample):
data, labels, training = sample
data = utils.augmentation(data.unsqueeze(1).float() / 255.0)
labels = torch.eye(config.NUM_CLASSES).index_select(dim=0, index=labels)
data = Variable(data)
labels = Variable(labels)
if torch.cuda.is_available():
data = data.cuda()
labels = labels.cuda()
if training:
classes, reconstructions = model(data, labels)
else:
classes, reconstructions = model(data)
loss = capsule_loss(data, labels, classes, reconstructions)
return loss, classes
def on_sample(state):
state['sample'].append(state['train'])
def reset_meters():
meter_accuracy.reset()
meter_loss.reset()
confusion_meter.reset()
def on_forward(state):
meter_accuracy.add(state['output'].data, state['sample'][1])
confusion_meter.add(state['output'].data, state['sample'][1])
meter_loss.add(state['loss'].data[0])
def on_start_epoch(state):
reset_meters()
state['iterator'] = tqdm(state['iterator'])
def on_end_epoch(state):
print('[Epoch %d] Training Loss: %.4f (Accuracy: %.2f%%)' % (
state['epoch'], meter_loss.value()[0], meter_accuracy.value()[0]))
train_loss_logger.log(state['epoch'], meter_loss.value()[0])
train_accuracy_logger.log(state['epoch'], meter_accuracy.value()[0])
reset_meters()
engine.test(processor, utils.get_iterator(False))
test_loss_logger.log(state['epoch'], meter_loss.value()[0])
test_accuracy_logger.log(state['epoch'], meter_accuracy.value()[0])
confusion_logger.log(confusion_meter.value())
print('[Epoch %d] Testing Loss: %.4f (Accuracy: %.2f%%)' % (
state['epoch'], meter_loss.value()[0], meter_accuracy.value()[0]))
torch.save(model.state_dict(), 'epochs/epoch_%d.pt' % state['epoch'])
# reconstruction visualization
test_sample = next(iter(utils.get_iterator(False)))
ground_truth = (test_sample[0].unsqueeze(1).float() / 255.0)
if torch.cuda.is_available():
_, reconstructions = model(Variable(ground_truth).cuda())
else:
_, reconstructions = model(Variable(ground_truth))
reconstruction = reconstructions.cpu().view_as(ground_truth).data
ground_truth_logger.log(
make_grid(ground_truth, nrow=int(config.BATCH_SIZE ** 0.5), normalize=True, range=(0, 1)).numpy())
reconstruction_logger.log(
make_grid(reconstruction, nrow=int(config.BATCH_SIZE ** 0.5), normalize=True, range=(0, 1)).numpy())
if __name__ == "__main__":
model = CapsuleNet()
if torch.cuda.is_available():
model.cuda()
print("# parameters:", sum(param.numel() for param in model.parameters()))
optimizer = Adam(model.parameters())
engine = Engine()
meter_loss = tnt.meter.AverageValueMeter()
meter_accuracy = tnt.meter.ClassErrorMeter(accuracy=True)
confusion_meter = tnt.meter.ConfusionMeter(config.NUM_CLASSES, normalized=True)
train_loss_logger = VisdomPlotLogger('line', opts={'title': 'Train Loss'})
train_accuracy_logger = VisdomPlotLogger('line', opts={'title': 'Train Accuracy'})
test_loss_logger = VisdomPlotLogger('line', opts={'title': 'Test Loss'})
test_accuracy_logger = VisdomPlotLogger('line', opts={'title': 'Test Accuracy'})
confusion_logger = VisdomLogger('heatmap', opts={'title': 'Confusion Matrix',
'columnnames': list(range(config.NUM_CLASSES)),
'rownames': list(range(config.NUM_CLASSES))})
ground_truth_logger = VisdomLogger('image', opts={'title': 'Ground Truth'})
reconstruction_logger = VisdomLogger('image', opts={'title': 'Reconstruction'})
capsule_loss = CapsuleLoss()
engine.hooks['on_sample'] = on_sample
engine.hooks['on_forward'] = on_forward
engine.hooks['on_start_epoch'] = on_start_epoch
engine.hooks['on_end_epoch'] = on_end_epoch
engine.train(processor, utils.get_iterator(True), maxepoch=config.NUM_EPOCHS, optimizer=optimizer)