def train_main():
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.05)
criterion = nn.BCELoss()
print(model)
batch_size = 25
train_loader = get_train_loader(batch_size)
validation_loader = get_validation_loader(batch_size)
log = get_tensorboard('simple')
epochs = 50
start_time = datetime.now()
for epoch in range(1, epochs + 1):
train(model, train_loader, criterion, optimizer, epoch, log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, epoch, log)
end_time = datetime.now()
print('Total training time: {}.'.format(end_time - start_time))
torch.save(model.state_dict(), model_file)
print('Wrote model to', model_file)
def train_main():
model = Net()
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
print('Using multi-gpu with {} GPUs!'.format(num_gpus))
model = nn.DataParallel(model)
model.to(device)
optimizer = optim.SGD(model.parameters(), lr=0.05)
criterion = nn.BCELoss()
print(model)
batch_size = 25 * num_gpus
train_loader = get_train_loader(batch_size)
validation_loader = get_validation_loader(batch_size)
log = get_tensorboard('simple_multigpu')
epochs = 50
start_time = datetime.now()
for epoch in range(1, epochs + 1):
train(model, train_loader, criterion, optimizer, epoch, log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, epoch, log)
end_time = datetime.now()
print('Total training time: {}.'.format(end_time - start_time))
torch.save(model.module.state_dict(), model_file)
print('Wrote model to', model_file)
def train_main():
model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.1)
criterion = nn.BCELoss()
print(model)
batch_size = 25
train_loader = get_train_loader_hdf5(batch_size)
validation_loader = get_validation_loader_hdf5(batch_size)
# for data, target in train_loader:
# print(data.shape, data.dtype)
# print(target.shape, target.dtype)
# print(target[0])
# break
# return
log = get_tensorboard('simple')
epochs = 20
warmup_epochs = 1
tot_time = 0
for epoch in range(1, epochs + 1):
start_time = datetime.now()
train(model, train_loader, criterion, optimizer, epoch, log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, epoch, log)
end_time = datetime.now()
epoch_time = (end_time - start_time).total_seconds()
txt = 'Epoch took {:.2f} seconds.'.format(epoch_time)
if epoch > warmup_epochs:
tot_time += epoch_time
secs_per_epoch = tot_time / (epoch - warmup_epochs)
txt += ' Running average: {:.2f}'.format(secs_per_epoch)
print(txt)
print('Total training time: {:.2f}, {:.2f} secs/epoch.'.format(
tot_time, secs_per_epoch))
torch.save(model.state_dict(), model_file)
print('Wrote model to', model_file)
def train_main():
model = Net().to(device)
# optimizer = optim.SGD(model.parameters(), lr=0.05)
print(model)
# Horovod: broadcast parameters.
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
# Horovod: scale learning rate by the number of GPUs.
lr = 0.05
optimizer = optim.SGD(model.parameters(), lr=lr * hvd.size())
# Horovod: wrap optimizer with DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(
optimizer, named_parameters=model.named_parameters())
criterion = nn.BCELoss()
batch_size = 25
train_loader = get_train_loader(batch_size)
validation_loader = get_validation_loader(batch_size)
log = get_tensorboard('simple')
epochs = 50
start_time = datetime.now()
for epoch in range(1, epochs + 1):
train(model, train_loader, criterion, optimizer, epoch, log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, epoch, log)
end_time = datetime.now()
print('Total training time: {}.'.format(end_time - start_time))
torch.save(model.state_dict(), model_file)
print('Wrote model to', model_file)
def train_main():
# Learning 1: New layers
model = PretrainedNet().to(device)
params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.SGD(params, lr=0.01)
criterion = nn.BCELoss()
print(model)
batch_size = 25
train_loader = get_train_loader(batch_size)
validation_loader = get_validation_loader(batch_size)
log = get_tensorboard('pretrained')
epochs = 10
start_time = datetime.now()
for epoch in range(1, epochs + 1):
train(model, train_loader, criterion, optimizer, epoch, log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, epoch, log)
end_time = datetime.now()
print('Total training time: {}.'.format(end_time - start_time))
torch.save(model.state_dict(), model_file)
print('Wrote model to', model_file)
# Learning 2: Fine-tuning
log = get_tensorboard('finetuned')
for name, layer in model.vgg_features.named_children():
note = ' '
for param in layer.parameters():
note = '-'
if int(name) >= 24:
param.requires_grad = True
note = '+'
print(name, note, layer, len(param))
params = filter(lambda p: p.requires_grad, model.parameters())
# optimizer = optim.SGD(model.parameters(), lr=1e-3)
optimizer = optim.RMSprop(params, lr=1e-5)
criterion = nn.BCELoss()
print(model)
prev_epochs = epoch
epochs = 20
start_time = datetime.now()
for epoch in range(1, epochs + 1):
train(model, train_loader, criterion, optimizer, prev_epochs + epoch,
log)
with torch.no_grad():
print('\nValidation:')
evaluate(model, validation_loader, criterion, prev_epochs + epoch,
log)
end_time = datetime.now()
print('Total training time: {}.'.format(end_time - start_time))
torch.save(model.state_dict(), model_file_ft)
print('Wrote finetuned model to', model_file_ft)