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306 broken pipe issues on Windows machines in examples #307

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Apr 26, 2020
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306 broken pipe issues on Windows machines in examples #307

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0d5438b
Add if __name__ == '__main__' in examples and fix typo in notebook
gml16 Apr 26, 2020
d183167
Fixed flake8 E301
gml16 Apr 26, 2020
eb6d48f
Refactored main def in examples
gml16 Apr 26, 2020
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110 changes: 57 additions & 53 deletions examples/classification_3d/densenet_evaluation_array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,61 +19,65 @@
from monai.data import NiftiDataset, CSVSaver
from monai.transforms import Compose, AddChannel, ScaleIntensity, Resize, ToTensor

monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# 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'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
0, 0, 1, 0, 1, 0, 1, 0, 1, 0
])
# 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'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
0, 0, 1, 0, 1, 0, 1, 0, 1, 0
])

# Define transforms for image
val_transforms = Compose([
ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
ToTensor()
])
# Define transforms for image
val_transforms = Compose([
ScaleIntensity(),
AddChannel(),
Resize((96, 96, 96)),
ToTensor()
])

# Define nifti dataset
val_ds = NiftiDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False)
# create a validation data loader
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
# Define nifti dataset
val_ds = NiftiDataset(image_files=images, labels=labels, transform=val_transforms, image_only=False)
# create a validation data loader
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())

# Create DenseNet121
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
).to(device)
# Create DenseNet121
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
).to(device)

model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
num_correct = 0.
metric_count = 0
saver = CSVSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data[0].to(device), val_data[1].to(device)
val_outputs = model(val_images).argmax(dim=1)
value = torch.eq(val_outputs, val_labels)
metric_count += len(value)
num_correct += value.sum().item()
saver.save_batch(val_outputs, val_data[2])
metric = num_correct / metric_count
print('evaluation metric:', metric)
saver.finalize()
model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
num_correct = 0.
metric_count = 0
saver = CSVSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data[0].to(device), val_data[1].to(device)
val_outputs = model(val_images).argmax(dim=1)
value = torch.eq(val_outputs, val_labels)
metric_count += len(value)
num_correct += value.sum().item()
saver.save_batch(val_outputs, val_data[2])
metric = num_correct / metric_count
print('evaluation metric:', metric)
saver.finalize()

if __name__ == '__main__':
main()
112 changes: 58 additions & 54 deletions examples/classification_3d/densenet_evaluation_dict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,62 +19,66 @@
from monai.transforms import Compose, LoadNiftid, AddChanneld, ScaleIntensityd, Resized, ToTensord
from monai.data import CSVSaver

monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
def main():
monai.config.print_config()
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

# 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'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
0, 0, 1, 0, 1, 0, 1, 0, 1, 0
])
val_files = [{'img': img, 'label': label} for img, label in zip(images, labels)]
# 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'
]
# 2 binary labels for gender classification: man and woman
labels = np.array([
0, 0, 1, 0, 1, 0, 1, 0, 1, 0
])
val_files = [{'img': img, 'label': label} for img, label in zip(images, labels)]

# Define transforms for image
val_transforms = Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
ScaleIntensityd(keys=['img']),
Resized(keys=['img'], spatial_size=(96, 96, 96)),
ToTensord(keys=['img'])
])
# Define transforms for image
val_transforms = Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
ScaleIntensityd(keys=['img']),
Resized(keys=['img'], spatial_size=(96, 96, 96)),
ToTensord(keys=['img'])
])

# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
# create a validation data loader
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())

# Create DenseNet121
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
).to(device)
# Create DenseNet121
device = torch.device('cuda:0')
model = monai.networks.nets.densenet.densenet121(
spatial_dims=3,
in_channels=1,
out_channels=2,
).to(device)

model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
num_correct = 0.
metric_count = 0
saver = CSVSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data['img'].to(device), val_data['label'].to(device)
val_outputs = model(val_images).argmax(dim=1)
value = torch.eq(val_outputs, val_labels)
metric_count += len(value)
num_correct += value.sum().item()
saver.save_batch(val_outputs, {'filename_or_obj': val_data['img.filename_or_obj']})
metric = num_correct / metric_count
print('evaluation metric:', metric)
saver.finalize()
model.load_state_dict(torch.load('best_metric_model.pth'))
model.eval()
with torch.no_grad():
num_correct = 0.
metric_count = 0
saver = CSVSaver(output_dir='./output')
for val_data in val_loader:
val_images, val_labels = val_data['img'].to(device), val_data['label'].to(device)
val_outputs = model(val_images).argmax(dim=1)
value = torch.eq(val_outputs, val_labels)
metric_count += len(value)
num_correct += value.sum().item()
saver.save_batch(val_outputs, {'filename_or_obj': val_data['img.filename_or_obj']})
metric = num_correct / metric_count
print('evaluation metric:', metric)
saver.finalize()

if __name__ == '__main__':
main()
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