def train(self):
self.print_args()
self.audio_dumps_folder = os.path.join(self.audio_dumps_path,
self.workname, 'train')
create_folder(self.audio_dumps_folder)
self.visual_dumps_folder = os.path.join(self.visual_dumps_path,
self.workname, 'train')
create_folder(self.visual_dumps_folder)
training_data = UnetInput('train')
self.train_loader = torch.utils.data.DataLoader(training_data,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=10)
validation_data = UnetInput('val')
self.val_loader = torch.utils.data.DataLoader(validation_data,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=10)
for self.epoch in range(self.start_epoch, self.EPOCHS):
with train(self):
self.run_epoch(self.train_iter_logger)
self.scheduler.step(self.loss)
with val(self):
self.run_epoch()
stop = self.EarlyStopChecker.check_improvement(
self.loss_.data.tuple['val'].epoch_array.val, self.epoch)
if stop:
print('Early Stopping Epoch : [{0}], '
'Best Checkpoint Epoch : [{1}]'.format(
self.epoch, self.EarlyStopChecker.best_epoch))
break
def train(self):
self.print_args()
self.audio_dumps_folder = os.path.join(self.audio_dumps_path, self.workname, 'train')
create_folder(self.audio_dumps_folder)
self.visual_dumps_folder = os.path.join(self.visual_dumps_path, self.workname, 'train')
create_folder(self.visual_dumps_folder)
training_data = UnetInput('train')
self.train_loader = torch.utils.data.DataLoader(training_data,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=10)
self.train_batches = len(self.train_loader)
validation_data = UnetInput('val')
self.val_loader = torch.utils.data.DataLoader(validation_data,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=10)
self.val_batches = len(self.val_loader)
self.avg_cost = np.zeros([self.EPOCHS, self.K], dtype=np.float32)
self.lambda_weight = np.ones([len(SOURCES_SUBSET), self.EPOCHS])
for self.epoch in range(self.start_epoch, self.EPOCHS):
self.cost = list(torch.zeros(self.K))
# apply Dynamic Weight Average
if self.epoch == 0 or self.epoch == 1:
self.lambda_weight[:, self.epoch] = 1.0
else:
if K == 2:
self.w_1 = self.avg_cost[self.epoch - 1, 0] / self.avg_cost[self.epoch - 2, 0]
self.w_2 = self.avg_cost[self.epoch - 1, 1] / self.avg_cost[self.epoch - 2, 1]
exp_sum = (np.exp(self.w_1 / self.DWA_T) + np.exp(self.w_2 / self.DWA_T))
self.lambda_weight[0, self.epoch] = 2 * np.exp(self.w_1 / self.DWA_T) / exp_sum
self.lambda_weight[1, self.epoch] = 2 * np.exp(self.w_2 / self.DWA_T) / exp_sum
elif K == 4:
self.w_1 = self.avg_cost[self.epoch - 1, 0] / self.avg_cost[self.epoch - 2, 0]
self.w_2 = self.avg_cost[self.epoch - 1, 1] / self.avg_cost[self.epoch - 2, 1]
self.w_3 = self.avg_cost[self.epoch - 1, 2] / self.avg_cost[self.epoch - 2, 2]
self.w_4 = self.avg_cost[self.epoch - 1, 3] / self.avg_cost[self.epoch - 2, 3]
exp_sum = np.exp(self.w_1 / self.DWA_T) + np.exp(self.w_2 / self.DWA_T) + np.exp(
self.w_3 / self.DWA_T) + np.exp(self.w_4 / self.DWA_T)
self.lambda_weight[0, self.epoch] = 4 * np.exp(self.w_1 / self.DWA_T) / exp_sum
self.lambda_weight[1, self.epoch] = 4 * np.exp(self.w_2 / self.DWA_T) / exp_sum
self.lambda_weight[2, self.epoch] = 4 * np.exp(self.w_3 / self.DWA_T) / exp_sum
self.lambda_weight[3, self.epoch] = 4 * np.exp(self.w_4 / self.DWA_T) / exp_sum
with train(self):
self.run_epoch(self.train_iter_logger)
self.scheduler.step(self.loss)
with val(self):
self.run_epoch()
stop = self.EarlyStopChecker.check_improvement(self.loss_.data.tuple['val'].epoch_array.val,
self.epoch)
if stop:
print('Early Stopping Epoch : [{0}]'.format(self.epoch))
break
print(
'Epoch: {:04d} | TRAIN: {:.4f} {:.4f}'.format(self.epoch,
self.avg_cost[self.epoch, 0],
self.avg_cost[self.epoch, 1]))