def _load(self, filePath):
checkpoint = torch.load(filePath)
model = MyModel(
device, checkpoint['inputSize'], checkpoint['gatedCnnOutputSize'],
checkpoint['gatedCnnStride1'], checkpoint['gatedCnnStride2'],
checkpoint['gatedCnnKernel1'], checkpoint['gatedCnnKernel2'],
checkpoint['lstmLayer'], checkpoint['lstmHiddenSize'],
checkpoint['fcOutputSize'], checkpoint['dropout'])
model.load_state_dict(checkpoint['stateDict'])
model.eval()
if self.device.type == 'cpu':
model.cpu()
else:
model.cuda(device=self.device)
return model
dtype=torch.float32).to(device))
#criterion = MyLoss2(weights = torch.tensor(train_dataset.get_class_weights2(), dtype=torch.float32).to(device))
optimizer = optim.SGD(model.parameters(), lr=LR)
#scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=2, factor = 0.5)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
#scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10,15,20,25,30], gamma=0.1)
train_dataloader = DataLoader(train_dataset,\
batch_size = BATCHSIZE,\
shuffle = True,
num_workers=4
)
validation_dataloader = DataLoader(validation_dataset,\
batch_size = BATCHSIZE,\
shuffle = False
)
pre_score = 0
for epoch in range(EPOCHS):
train(model, train_dataloader, criterion, optimizer, device)
if (epoch + 1) % 5 == 0:
score = validation(model, validation_dataloader, criterion, None,
device)
if score > pre_score:
pre_score = score
model = model.cpu()
torch.save(model.state_dict(), save_path)
model = model.to(device)
scheduler.step(score)
