def __init__(self, model, loss_function, trainData, validData, dataset, opt):
super().__init__(model, loss_function, trainData, validData, dataset, opt)
print('Initializing model parameters')
init_model_parameters(model, opt)
# create a multi-gpu runner here
self.runner = MultiprocessingRunner(opt, model, loss_function, device_ids=opt.gpus)
def run(self, save_file=None):
opt = self.opt
model = self.model
optim = self.optim
# Try to load the save_file
checkpoint = None
if save_file:
checkpoint = torch.load(save_file,
map_location=lambda storage, loc: storage)
if checkpoint is not None:
print('Loading model and optim from checkpoint at %s' % save_file)
self.model.load_state_dict(checkpoint['model'])
if opt.reset_optim == False:
self.optim.load_state_dict(checkpoint['optim'])
batchOrder = checkpoint['batchOrder']
iteration = checkpoint['iteration'] + 1
opt.start_epoch = int(
math.floor(float(checkpoint['epoch'] + 1)))
resume = True
else:
batchOrder = None
iteration = 0
resume = False
del checkpoint['model']
del checkpoint['optim']
del checkpoint
else:
batchOrder = None
iteration = 0
print('Initializing model parameters')
init_model_parameters(model, opt)
resume = False
valid_loss = self.eval(self.validData)
valid_ppl = math.exp(min(valid_loss, 100))
print('Validation perplexity: %g' % valid_ppl)
self.start_time = time.time()
for epoch in range(opt.start_epoch, opt.start_epoch + opt.epochs):
print('')
# (1) train for one epoch on the training set
train_loss = self.train_epoch(epoch,
resume=resume,
batchOrder=batchOrder,
iteration=iteration)
train_ppl = math.exp(min(train_loss, 100))
print('Train perplexity: %g' % train_ppl)
# (2) evaluate on the validation set
valid_loss = self.eval(self.validData)
valid_ppl = math.exp(min(valid_loss, 100))
print('Validation perplexity: %g' % valid_ppl)
self.save(epoch, valid_ppl)
batchOrder = None
iteration = None
resume = False
def run(self, checkpoint=None):
opt = self.opt
model = self.model
optim = self.optim
# Try to load the save_file
# checkpoint = None
# if save_file:
# checkpoint = torch.load(save_file, map_location=lambda storage, loc: storage)
if checkpoint is not None:
self.model.load_state_dict(checkpoint['model'])
if not opt.reset_optim:
self.optim.load_state_dict(checkpoint['optim'])
if 'batch_order' in checkpoint:
batch_order = checkpoint['batch_order']
iteration = checkpoint['iteration'] + 1
else:
batch_order = None
iteration = 0
opt.start_epoch = int(
math.floor(float(checkpoint['epoch'] + 1)))
resume = True
if len(self.additional_data) > 0:
if 'additional_batch_order' in checkpoint:
self.additional_batch_order = checkpoint[
'additional_batch_order']
self.additional_data_iteration = checkpoint[
'additional_data_iteration']
else:
self.init_additional_data()
else:
batch_order = None
iteration = 0
resume = False
self.init_additional_data()
del checkpoint['model']
del checkpoint['optim']
del checkpoint
else:
batch_order = None
iteration = 0
print('Initializing model parameters')
init_model_parameters(model, opt)
resume = False
self.init_additional_data()
valid_loss = self.eval(self.valid_data)
valid_ppl = math.exp(min(valid_loss, 100))
print('Validation perplexity: %g' % valid_ppl)
self.start_time = time.time()
for epoch in range(opt.start_epoch, opt.start_epoch + opt.epochs):
print('')
# (1) train for one epoch on the training set
train_loss = self.train_epoch(epoch,
resume=resume,
batch_order=batch_order,
iteration=iteration)
train_ppl = math.exp(min(train_loss, 100))
print('Train perplexity: %g' % train_ppl)
# (2) evaluate on the validation set
valid_loss = self.eval(self.valid_data)
valid_ppl = math.exp(min(valid_loss, 100))
print('Validation perplexity: %g' % valid_ppl)
self.save(epoch, valid_ppl)
batch_order = None
iteration = None
resume = False
