param.requires_grad = False
for param in model.resnet.parameters():
param.requires_grad = False
else:
print_log('\n\nno freezing', log=args.logfile)
print_log('loading data', log=args.logfile)
if args.train: trainer = Trainer(args)
if args.val: validator = Validator(args)
if args.train:
loss_history_train, loss_history_val = [], []
accu_history_train, accu_history_val = [], []
for epoch in range(args.start_epoch, args.end_epoch):
loss_train, accu_train = trainer.epoch(model, epoch)
if args.val: loss_val, accu_val = validator.epoch(model, epoch)
# plot figure
loss_history_train.append([loss_train])
loss_history_val.append([loss_val])
accu_history_train.append([accu_train])
accu_history_val.append([accu_val])
plot_loss(loss_history_train, loss_history_val, save_dir=args.save_dir)
plot_accu(accu_history_train, accu_history_val, save_dir=args.save_dir)
if args.test:
tester = Tester(args)
tester.epoch(model)
if args.val: validator.epoch(model, epoch=0)
args.logfile.close()
model = LipRead(options)
load_model(model, state_dict, grad_states) # load weights and freeze states
last_epoch = states["epoch"]
# save current options
with open(os.path.join(result_dir, "options_used.toml"), 'w') as f:
toml.dump(options, f)
if(options["general"]["usecudnnbenchmark"] and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
if options["training"]["train"]:
trainer = Trainer(options)
if(options["validation"]["validate"]):
validator = Validator(options, result_dir)
final_epoch = args.final_epoch if args.final_epoch is not None else options["training"]["epochs"]
for epoch in range(last_epoch + 1, final_epoch):
loss = trainer.epoch(model, epoch) if options["training"]["train"] else ''
accuracy = validator.epoch(model, epoch) if options["validation"]["validate"] else ''
csv.add(epoch, accuracy=accuracy, loss=loss)
save_checkpoint(result_dir, epoch, model, options=options)
# save the final model
torch.save(model.state_dict(), os.path.join(result_dir, "epoch{}.pt".format(epoch)))
print_log('creating the model', log=options["general"]["logfile"])
model = LipRead(options)
print_log('loading model', log=options["general"]["logfile"])
if options["general"]["loadpretrainedmodel"]:
print_log('loading the pretrained model at %s' %
options["general"]["pretrainedmodelpath"],
log=options["general"]["logfile"])
model.load_state_dict(torch.load(
options["general"]["pretrainedmodelpath"])) #Create the model.
if options["general"]["usecudnn"]:
model = model.cuda(
options["general"]["gpuid"]) #Move the model to the GPU.
print_log('loading data', log=options["general"]["logfile"])
if options["training"]["train"]: trainer = Trainer(options)
if options["validation"]["validate"]:
validator = Validator(options)
validator.epoch(model, epoch=0)
if options["training"]["train"]:
for epoch in range(options["training"]["startepoch"],
options["training"]["endepoch"]):
trainer.epoch(model, epoch)
if options["validation"]["validate"]: validator.epoch(model, epoch)
# if options["testing"]["test"]:
# tester = Tester(options)
# tester.epoch(model)
options["general"]["logfile"].close()
if (options["general"]["usecudnnbenchmark"]
and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
#Create the model.
model = LipRead(options)
if (options["general"]["loadpretrainedmodel"]):
model.load_state_dict(
torch.load(options["general"]["pretrainedmodelpath"],
map_location=lambda storage, loc: storage))
#model = torch.load('/home/admin2/grp7/Lipreading-PyTorch/trainedmodel.pt',map_location=lambda storage, loc: storage)
#Move the model to the GPU.
if (options["general"]["usecudnn"]):
model = model.cuda(options["general"]["gpuid"])
#if(options["general"]["usecudnn"]):
# model = model.cuda(options["general"]["gpuid"])
#self.model = model.CPU().double()
trainer = Trainer(options)
validator = Validator(options)
for epoch in range(options["training"]["startepoch"],
options["training"]["epochs"]):
if (options["training"]["train"]):
trainer.epoch(model, epoch)
if (options["validation"]["validate"]):
validator.epoch(model)
