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DCRNN_PyTorch
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Merge 0e57379372 into d92490b808

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Seth Ockerman 2024-07-17 13:49:06 -05:00 committed by GitHub
parent d92490b808 0e57379372
commit 6d336630be
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2 changed files with 14 additions and 3 deletions
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2
lib/utils.py
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@ -186,7 +186,7 @@ def load_dataset(dataset_dir, batch_size, test_batch_size=None, **kwargs):
for category in ['train', 'val', 'test']: for category in ['train', 'val', 'test']:
data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0]) data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0]) data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0])
data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size, shuffle=True) data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size, shuffle=False)
data['val_loader'] = DataLoader(data['x_val'], data['y_val'], test_batch_size, shuffle=False) data['val_loader'] = DataLoader(data['x_val'], data['y_val'], test_batch_size, shuffle=False)
data['test_loader'] = DataLoader(data['x_test'], data['y_test'], test_batch_size, shuffle=False) data['test_loader'] = DataLoader(data['x_test'], data['y_test'], test_batch_size, shuffle=False)
data['scaler'] = scaler data['scaler'] = scaler

17
model/pytorch/dcrnn_supervisor.py
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@ -174,12 +174,23 @@ class DCRNNSupervisor:
self.dcrnn_model = self.dcrnn_model.train() self.dcrnn_model = self.dcrnn_model.train()
train_iterator = self._data['train_loader'].get_iterator()
losses = []
# shuffle the batches
train_iterator = self._data['train_loader'].get_iterator()
all_train = np.array([(x,y) for _, (x, y) in enumerate(train_iterator)])
permutation = np.random.permutation(all_train.shape[0])
all_train = all_train[permutation]
losses = []
start_time = time.time() start_time = time.time()
for _, (x, y) in enumerate(train_iterator): for batch in all_train:
x = batch[0]
y = batch[1]
optimizer.zero_grad() optimizer.zero_grad()
x, y = self._prepare_data(x, y) x, y = self._prepare_data(x, y)