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wyli merged 1 commit into from
Apr 24, 2020
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296 fix PEP8 and string format in notebooks and examples #301

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22 changes: 11 additions & 11 deletions examples/classification_3d/densenet_evaluation_array.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,16 +24,16 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand All @@ -54,7 +54,7 @@
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())

# Create DenseNet121
device = torch.device("cuda:0")
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
Expand Down
22 changes: 11 additions & 11 deletions examples/classification_3d/densenet_evaluation_dict.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,16 +24,16 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand All @@ -55,7 +55,7 @@
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())

# Create DenseNet121
device = torch.device("cuda:0")
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
Expand Down
54 changes: 27 additions & 27 deletions examples/classification_3d/densenet_training_array.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,26 +25,26 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand Down Expand Up @@ -81,7 +81,7 @@
val_loader = DataLoader(val_ds, batch_size=2, num_workers=2, pin_memory=torch.cuda.is_available())

# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda:0")
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
Expand All @@ -99,7 +99,7 @@
writer = SummaryWriter()
for epoch in range(5):
print('-' * 10)
print('Epoch {}/{}'.format(epoch + 1, 5))
print('epoch {}/{}'.format(epoch + 1, 5))
model.train()
epoch_loss = 0
step = 0
Expand All @@ -113,11 +113,11 @@
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print("%d/%d, train_loss:%0.4f" % (step, epoch_len, loss.item()))
print('{}/{}, train_loss: {:.4f}'.format(step, epoch_len, loss.item()))
writer.add_scalar('train_loss', loss.item(), epoch_len * epoch + step)
epoch_loss /= step
epoch_loss_values.append(epoch_loss)
print("epoch %d average loss:%0.4f" % (epoch + 1, epoch_loss))
print('epoch {} average loss: {:.4f}'.format(epoch + 1, epoch_loss))

if (epoch + 1) % val_interval == 0:
model.eval()
Expand All @@ -137,8 +137,8 @@
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), 'best_metric_model.pth')
print('saved new best metric model')
print("current epoch %d current accuracy: %0.4f best accuracy: %0.4f at epoch %d"
% (epoch + 1, metric, best_metric, best_metric_epoch))
print('current epoch: {} current accuracy: {:.4f} best accuracy: {:.4f} at epoch {}'.format(
epoch + 1, metric, best_metric, best_metric_epoch))
writer.add_scalar('val_accuracy', metric, epoch + 1)
print('train completed, best_metric: %0.4f at epoch: %d' % (best_metric, best_metric_epoch))
print('train completed, best_metric: {:.4f} at epoch: {}'.format(best_metric, best_metric_epoch))
writer.close()
54 changes: 27 additions & 27 deletions examples/classification_3d/densenet_training_dict.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,26 +25,26 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI314-IOP-0889-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI249-Guys-1072-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI609-HH-2600-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI173-HH-1590-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI020-Guys-0700-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI342-Guys-0909-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI134-Guys-0780-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI577-HH-2661-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI066-Guys-0731-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI130-HH-1528-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand Down Expand Up @@ -85,7 +85,7 @@
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())

# Create DenseNet121, CrossEntropyLoss and Adam optimizer
device = torch.device("cuda:0")
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
Expand All @@ -101,7 +101,7 @@
writer = SummaryWriter()
for epoch in range(5):
print('-' * 10)
print('Epoch {}/{}'.format(epoch + 1, 5))
print('epoch {}/{}'.format(epoch + 1, 5))
model.train()
epoch_loss = 0
step = 0
Expand All @@ -115,10 +115,10 @@
optimizer.step()
epoch_loss += loss.item()
epoch_len = len(train_ds) // train_loader.batch_size
print("%d/%d, train_loss:%0.4f" % (step, epoch_len, loss.item()))
print('{}/{}, train_loss: {:.4f}'.format(step, epoch_len, loss.item()))
writer.add_scalar('train_loss', loss.item(), epoch_len * epoch + step)
epoch_loss /= step
print("epoch %d average loss:%0.4f" % (epoch + 1, epoch_loss))
print('epoch {} average loss: {:.4f}'.format(epoch + 1, epoch_loss))

if (epoch + 1) % val_interval == 0:
model.eval()
Expand All @@ -138,8 +138,8 @@
best_metric_epoch = epoch + 1
torch.save(model.state_dict(), 'best_metric_model.pth')
print('saved new best metric model')
print("current epoch %d current accuracy: %0.4f current AUC: %0.4f best accuracy: %0.4f at epoch %d"
% (epoch + 1, acc_metric, auc_metric, best_metric, best_metric_epoch))
print('current epoch: {} current accuracy: {:.4f} current AUC: {:.4f} best accuracy: {:.4f} at epoch {}'.format(
epoch + 1, acc_metric, auc_metric, best_metric, best_metric_epoch))
writer.add_scalar('val_accuracy', acc_metric, epoch + 1)
print('train completed, best_metric: %0.4f at epoch: %d' % (best_metric, best_metric_epoch))
print('train completed, best_metric: {:.4f} at epoch: {}'.format(best_metric, best_metric_epoch))
writer.close()
22 changes: 11 additions & 11 deletions examples/classification_3d_ignite/densenet_evaluation_array.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,16 +27,16 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand All @@ -58,7 +58,7 @@
in_channels=1,
out_channels=2,
)
device = torch.device("cuda:0")
device = torch.device('cuda:0')

metric_name = 'Accuracy'
# add evaluation metric to the evaluator engine
Expand Down
22 changes: 11 additions & 11 deletions examples/classification_3d_ignite/densenet_evaluation_dict.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,16 +26,16 @@

# IXI dataset as a demo, downloadable from https://brain-development.org/ixi-dataset/
images = [
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
'/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz',
'/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
Expand All @@ -58,7 +58,7 @@
in_channels=1,
out_channels=2,
)
device = torch.device("cuda:0")
device = torch.device('cuda:0')


def prepare_batch(batch, device=None, non_blocking=False):
Expand Down
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