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force device to be str #10

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52 changes: 26 additions & 26 deletions truenet/true_net/truenet_test_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,10 +32,10 @@ def main(sub_name_dicts, eval_params, intermediate=False, model_dir=None,
use_cpu = eval_params['Use_CPU']
if use_cpu is True:
device = torch.device("cpu")
print('testfunction:device used:' + device)
print('testfunction:device used:' + str(device))
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('testfunction:device used:' + device)
print('testfunction:device used:' + str(device))
nclass = eval_params['Nclass']
num_channels = eval_params['Numchannels']

Expand Down Expand Up @@ -82,72 +82,72 @@ def main(sub_name_dicts, eval_params, intermediate=False, model_dir=None,
for sub in range(len(sub_name_dicts)):
if verbose:
print('Predicting output for subject ' + str(sub+1) + '...', flush=True)

test_sub_dict = [sub_name_dicts[sub]]
basename = test_sub_dict[0]['basename']

probs_combined = []
flair_path = test_sub_dict[0]['flair_path']
flair_hdr = nib.load(flair_path).header
probs_axial = truenet_evaluate.evaluate_truenet(test_sub_dict, model_axial, eval_params, device,
probs_axial = truenet_evaluate.evaluate_truenet(test_sub_dict, model_axial, eval_params, device,
mode='axial', verbose=verbose)
probs_axial = truenet_data_postprocessing.resize_to_original_size(probs_axial, test_sub_dict,
probs_axial = truenet_data_postprocessing.resize_to_original_size(probs_axial, test_sub_dict,
plane='axial')
probs_combined.append(probs_axial)

if intermediate:
save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_axial.nii.gz')
preds_axial = truenet_data_postprocessing.get_final_3dvolumes(probs_axial, test_sub_dict)
if verbose:
print('Saving the intermediate Axial prediction ...', flush=True)

newhdr = flair_hdr.copy()
newobj = nib.nifti1.Nifti1Image(preds_axial, None, header=newhdr)
nib.save(newobj, save_path)
probs_sagittal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_sagittal, eval_params, device,
nib.save(newobj, save_path)

probs_sagittal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_sagittal, eval_params, device,
mode='sagittal', verbose=verbose)
probs_sagittal = truenet_data_postprocessing.resize_to_original_size(probs_sagittal, test_sub_dict,
probs_sagittal = truenet_data_postprocessing.resize_to_original_size(probs_sagittal, test_sub_dict,
plane='sagittal')
probs_combined.append(probs_sagittal)

if intermediate:
save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_sagittal.nii.gz')
preds_sagittal = truenet_data_postprocessing.get_final_3dvolumes(probs_sagittal, test_sub_dict)
if verbose:
print('Saving the intermediate Sagittal prediction ...', flush=True)

newhdr = flair_hdr.copy()
newobj = nib.nifti1.Nifti1Image(preds_sagittal, None, header=newhdr)
nib.save(newobj, save_path)
probs_coronal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_coronal, eval_params, device,
mode='coronal', verbose=verbose)
probs_coronal = truenet_data_postprocessing.resize_to_original_size(probs_coronal, test_sub_dict,
nib.save(newobj, save_path)

probs_coronal = truenet_evaluate.evaluate_truenet(test_sub_dict, model_coronal, eval_params, device,
mode='coronal', verbose=verbose)
probs_coronal = truenet_data_postprocessing.resize_to_original_size(probs_coronal, test_sub_dict,
plane='coronal')
probs_combined.append(probs_coronal)

if intermediate:
save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '_coronal.nii.gz')
preds_coronal = truenet_data_postprocessing.get_final_3dvolumes(probs_coronal, test_sub_dict)
if verbose:
print('Saving the intermediate Coronal prediction ...', flush=True)

newhdr = flair_hdr.copy()
newobj = nib.nifti1.Nifti1Image(preds_coronal, None, header=newhdr)
nib.save(newobj, save_path)
nib.save(newobj, save_path)

probs_combined = np.array(probs_combined)
prob_mean = np.mean(probs_combined,axis=0)

save_path = os.path.join(output_dir,'Predicted_probmap_truenet_' + basename + '.nii.gz')
pred_mean = truenet_data_postprocessing.get_final_3dvolumes(prob_mean, test_sub_dict)
if verbose:
print('Saving the final prediction ...', flush=True)

newhdr = flair_hdr.copy()
newobj = nib.nifti1.Nifti1Image(pred_mean, None, header=newhdr)
nib.save(newobj, save_path)
nib.save(newobj, save_path)

if verbose:
print('Testing complete for all subjects!', flush=True)
21 changes: 7 additions & 14 deletions truenet/utils/truenet_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def select_train_val_names(data_path,val_numbers):
:return:
'''
val_ids = random.choices(list(np.arange(len(data_path))),k=val_numbers)
train_ids = np.setdiff1d(np.arange(len(data_path)),val_ids)
train_ids = np.setdiff1d(np.arange(len(data_path)),val_ids)
data_path_train = [data_path[ind] for ind in train_ids]
data_path_val = [data_path[ind] for ind in val_ids]
return data_path_train,data_path_val,val_ids
Expand All @@ -40,7 +40,7 @@ def freeze_layer_for_finetuning(model, layer_to_ft, verbose=False):
model_layers_tobe_ftd = []
for layer_id in layer_to_ft:
model_layers_tobe_ftd.append(model_layer_names[layer_id-1])

for name, child in model.module.named_children():
if name in model_layers_tobe_ftd:
if verbose:
Expand All @@ -54,24 +54,24 @@ def freeze_layer_for_finetuning(model, layer_to_ft, verbose=False):
print(name + ' is frozen', flush=True)
for param in child.parameters():
param.requires_grad = False

return model


def loading_model(model_name, model, device, mode='weights'):
if mode == 'weights':
if device == 'cpu':
print('utils:device used:' + device)
print('utils:device used:' + str(device))
axial_state_dict = torch.load(model_name, map_location='cpu')
else:
print('utils:device used:' + device)
print('utils:device used:' + str(device))
axial_state_dict = torch.load(model_name)
else:
if device == 'cpu':
print('utils:device used:' + device)
print('utils:device used:' + str(device))
ckpt = torch.load(model_name, map_location='cpu')
else:
print('utils:device used:' + device)
print('utils:device used:' + str(device))
ckpt = torch.load(model_name)
axial_state_dict = ckpt['model_state_dict']

Expand Down Expand Up @@ -175,10 +175,3 @@ def save_checkpoint(self, val_loss, val_acc, best_val_acc, model, epoch, optimiz
else:
if self.verbose:
print('Validation loss increased; Exiting without saving the model ...')