def test_cnn(output_dir,
data_loader,
subset_name,
split,
criterion,
cnn_index,
model_options,
gpu=False):
for selection in ["best_balanced_accuracy", "best_loss"]:
# load the best trained model during the training
model = create_model(model_options.model,
gpu,
dropout=model_options.dropout)
model, best_epoch = load_model(model,
os.path.join(output_dir,
'fold-%i' % split,
'models',
'cnn-%i' % cnn_index,
selection),
gpu=gpu,
filename='model_best.pth.tar')
results_df, metrics = test(model, data_loader, gpu, criterion,
model_options.mode)
print("%s level balanced accuracy is %f" %
(model_options.mode, metrics['balanced_accuracy']))
mode_level_to_tsvs(output_dir,
results_df,
metrics,
split,
selection,
model_options.mode,
dataset=subset_name,
cnn_index=cnn_index)
def group_backprop(options):
main_logger = return_logger(options.verbose, "main process")
options = translate_parameters(options)
fold_list = [
fold for fold in os.listdir(options.model_path) if fold[:5:] == "fold-"
]
if len(fold_list) == 0:
raise ValueError("No folds were found at path %s" % options.model_path)
for fold in fold_list:
main_logger.info(fold)
for selection in options.selection:
results_path = path.join(options.model_path, fold, 'gradients',
selection, options.name)
model_options = argparse.Namespace()
model_options = read_json(
model_options, path.join(options.model_path,
'commandline.json'))
model_options = translate_parameters(model_options)
model_options.gpu = options.gpu
if options.tsv_path is None:
options.tsv_path = model_options.tsv_path
if options.input_dir is None:
options.input_dir = model_options.input_dir
if options.target_diagnosis is None:
options.target_diagnosis = options.diagnosis
criterion = get_criterion(model_options.loss)
# Data management (remove data not well predicted by the CNN)
training_df = load_data_test(options.tsv_path, [options.diagnosis],
baseline=options.baseline)
training_df.reset_index(drop=True, inplace=True)
# Model creation
_, all_transforms = get_transforms(
model_options.mode,
minmaxnormalization=model_options.minmaxnormalization)
data_example = return_dataset(model_options.mode,
options.input_dir,
training_df,
model_options.preprocessing,
train_transformations=None,
all_transformations=all_transforms,
params=options)
model = create_model(model_options, data_example.size)
model_dir = os.path.join(options.model_path, fold, 'models',
selection)
model, best_epoch = load_model(model,
model_dir,
gpu=options.gpu,
filename='model_best.pth.tar')
options.output_dir = results_path
commandline_to_json(options, logger=main_logger)
# Keep only subjects who were correctly / wrongly predicted by the network
training_df = sort_predicted(model,
training_df,
options.input_dir,
model_options,
criterion,
options.keep_true,
batch_size=options.batch_size,
num_workers=options.num_workers,
gpu=options.gpu)
if len(training_df) > 0:
# Save the tsv files used for the saliency maps
training_df.to_csv(path.join('data.tsv'),
sep='\t',
index=False)
data_train = return_dataset(model_options.mode,
options.input_dir,
training_df,
model_options.preprocessing,
train_transformations=None,
all_transformations=all_transforms,
params=options)
train_loader = DataLoader(data_train,
batch_size=options.batch_size,
shuffle=True,
num_workers=options.num_workers,
pin_memory=True)
interpreter = VanillaBackProp(model, gpu=options.gpu)
cum_map = 0
for data in train_loader:
if options.gpu:
input_batch = data['image'].cuda()
else:
input_batch = data['image']
maps = interpreter.generate_gradients(
input_batch,
data_train.diagnosis_code[options.target_diagnosis])
cum_map += maps.sum(axis=0)
mean_map = cum_map / len(data_train)
if len(data_train.size) == 4:
if options.nifti_template_path is not None:
image_nii = nib.load(options.nifti_template_path)
affine = image_nii.affine
else:
affine = np.eye(4)
mean_map_nii = nib.Nifti1Image(mean_map[0], affine)
nib.save(mean_map_nii, path.join(results_path,
"map.nii.gz"))
np.save(path.join(results_path, "map.npy"), mean_map[0])
else:
jpg_path = path.join(results_path, "map.jpg")
plt.imshow(mean_map[0],
cmap="coolwarm",
vmin=-options.vmax,
vmax=options.vmax)
plt.colorbar()
plt.savefig(jpg_path)
plt.close()
numpy_path = path.join(results_path, "map.npy")
np.save(numpy_path, mean_map[0])
else:
main_logger.warn("There are no subjects for the given options")
def main(options):
# Initialize the model
model = create_model(options.network, options.gpu)
transformations = transforms.Compose([MinMaxNormalization()])
# Define loss and optimizer
loss = torch.nn.CrossEntropyLoss()
if options.split is None:
fold_iterator = range(options.n_splits)
else:
fold_iterator = [options.split]
# Loop on folds
for fi in fold_iterator:
print("Fold %i" % fi)
if options.dataset == 'validation':
_, test_df = load_data(options.diagnosis_tsv_path,
options.diagnoses,
fi,
n_splits=options.n_splits,
baseline=True)
else:
test_df = load_data_test(options.diagnosis_tsv_path,
options.diagnoses)
for n in range(options.num_cnn):
dataset = MRIDataset_patch(options.caps_directory,
test_df,
options.patch_size,
options.patch_stride,
transformations=transformations,
patch_index=n,
prepare_dl=options.prepare_dl)
test_loader = DataLoader(dataset,
batch_size=options.batch_size,
shuffle=False,
num_workers=options.num_workers,
pin_memory=True)
# load the best trained model during the training
model, best_epoch = load_model(
model,
os.path.join(options.output_dir, 'best_model_dir',
"fold_%i" % fi, 'cnn-%i' % n, options.selection),
options.gpu,
filename='model_best.pth.tar')
results_df, metrics = test(model, test_loader, options.gpu, loss)
print("Patch level balanced accuracy is %f" %
metrics['balanced_accuracy'])
# write the test results into the tsv files
patch_level_to_tsvs(options.output_dir,
results_df,
metrics,
fi,
options.selection,
dataset=options.dataset,
cnn_index=n)
print("Selection threshold: ", options.selection_threshold)
soft_voting_to_tsvs(options.output_dir,
fi,
options.selection,
dataset=options.dataset,
num_cnn=options.num_cnn,
selection_threshold=options.selection_threshold)
def test_cnn(output_dir, data_loader, subset_name, split, criterion, cnn_index, model_options, gpu=False, train_begin_time=None):
metric_dict = {}
for selection in ["best_balanced_accuracy", "best_loss"]:
# load the best trained model during the training
if model_options.model == 'UNet3D':
print('********** init UNet3D model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout, device_index=model_options.device, in_channels=model_options.in_channels,
out_channels=model_options.out_channels, f_maps=model_options.f_maps, layer_order=model_options.layer_order, num_groups=model_options.num_groups, num_levels=model_options.num_levels)
elif model_options.model == 'ResidualUNet3D':
print('********** init ResidualUNet3D model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout, device_index=model_options.device, in_channels=model_options.in_channels,
out_channels=model_options.out_channels, f_maps=model_options.f_maps, layer_order=model_options.layer_order, num_groups=model_options.num_groups, num_levels=model_options.num_levels)
elif model_options.model == 'UNet3D_add_more_fc':
print('********** init UNet3D_add_more_fc model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout, device_index=model_options.device, in_channels=model_options.in_channels,
out_channels=model_options.out_channels, f_maps=model_options.f_maps, layer_order=model_options.layer_order, num_groups=model_options.num_groups, num_levels=model_options.num_levels)
elif model_options.model == 'ResidualUNet3D_add_more_fc':
print('********** init ResidualUNet3D_add_more_fc model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout, device_index=model_options.device, in_channels=model_options.in_channels,
out_channels=model_options.out_channels, f_maps=model_options.f_maps, layer_order=model_options.layer_order, num_groups=model_options.num_groups, num_levels=model_options.num_levels)
elif model_options.model == 'VoxCNN':
print('********** init VoxCNN model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, device_index=model_options.device)
elif model_options.model == 'ConvNet3D':
print('********** init ConvNet3D model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, device_index=model_options.device)
elif 'gcn' in model_options.model:
print('********** init {}-{} model for test! **********'.format(model_options.model, model_options.gnn_type))
model = create_model(model_options.model, gpu=model_options.gpu, device_index=model_options.device, gnn_type=model_options.gnn_type,
gnn_dropout=model_options.gnn_dropout,
gnn_dropout_adj=model_options.gnn_dropout_adj,
gnn_non_linear=model_options.gnn_non_linear,
gnn_undirected=model_options.gnn_undirected,
gnn_self_loop=model_options.gnn_self_loop,
gnn_threshold = model_options.gnn_threshold,)
elif model_options.model == 'ROI_GCN':
print('********** init ROI_GCN model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, device_index=model_options.device,
gnn_type=model_options.gnn_type,
gnn_dropout=model_options.gnn_dropout,
gnn_dropout_adj=model_options.gnn_dropout_adj,
gnn_non_linear=model_options.gnn_non_linear,
gnn_undirected=model_options.gnn_undirected,
gnn_self_loop=model_options.gnn_self_loop,
gnn_threshold = model_options.gnn_threshold,
nodel_vetor_layer=model_options.nodel_vetor_layer,
classify_layer=model_options.classify_layer,
num_node_features=model_options.num_node_features, num_class=model_options.num_class,
roi_size=model_options.roi_size, num_nodes=model_options.num_nodes,
gnn_pooling_layers=model_options.gnn_pooling_layers, global_sort_pool_k=model_options.global_sort_pool_k,
layers=model_options.layers,
shortcut_type=model_options.shortcut_type, use_nl=model_options.use_nl,
dropout=model_options.dropout,
device=model_options.device)
elif model_options.model == 'SwinTransformer3d':
print('********** init SwinTransformer3d model for test! **********')
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout,
device_index=model_options.device,
sw_patch_size=model_options.sw_patch_size,
window_size = model_options.window_size,
mlp_ratio = model_options.mlp_ratio,
drop_rate = model_options.drop_rate,
attn_drop_rate = model_options.attn_drop_rate,
drop_path_rate = model_options.drop_path_rate,
qk_scale = model_options.qk_scale,
embed_dim = model_options.embed_dim,
depths = model_options.depths,
num_heads = model_options.num_heads,
qkv_bias = model_options.qkv_bias,
ape = model_options.ape,
patch_norm = model_options.patch_norm,
)
else:
model = create_model(model_options.model, gpu=model_options.gpu, dropout=model_options.dropout, device_index=model_options.device)
model, best_epoch = load_model(model, os.path.join(output_dir, 'fold-%i' % split, 'models',
'cnn-%i' % cnn_index, selection),
gpu=gpu, filename='model_best.pth.tar', device_index=model_options.device)
results_df, metrics = test(model, data_loader, gpu, criterion, model_options.mode, device_index=model_options.device, train_begin_time=train_begin_time)
print("[%s]: %s level balanced accuracy is %f" % (timeSince(train_begin_time), model_options.mode, metrics['balanced_accuracy']))
print('[{}]: {}_{}_result_df:'.format(timeSince(train_begin_time), subset_name, selection))
print(results_df)
print('[{}]: {}_{}_metrics:\n{}'.format(timeSince(train_begin_time), subset_name, selection, metrics))
wandb.log({'{}_accuracy_{}_singel_model'.format(subset_name, selection): metrics['accuracy'],
'{}_balanced_accuracy_{}_singel_model'.format(subset_name, selection): metrics['balanced_accuracy'],
'{}_sensitivity_{}_singel_model'.format(subset_name, selection): metrics['sensitivity'],
'{}_specificity_{}_singel_model'.format(subset_name, selection): metrics['specificity'],
'{}_ppv_{}_singel_model'.format(subset_name, selection): metrics['ppv'],
'{}_npv_{}_singel_model'.format(subset_name, selection): metrics['npv'],
'{}_total_loss_{}_singel_model'.format(subset_name, selection): metrics['total_loss'],
})
mode_level_to_tsvs(output_dir, results_df, metrics, split, selection, model_options.mode,
dataset=subset_name, cnn_index=cnn_index)
# return metric dict
metric_temp_dict = {'{}_accuracy_{}_singel_model'.format(subset_name, selection): metrics['accuracy'],
'{}_balanced_accuracy_{}_singel_model'.format(subset_name, selection): metrics['balanced_accuracy'],
'{}_sensitivity_{}_singel_model'.format(subset_name, selection): metrics['sensitivity'],
'{}_specificity_{}_singel_model'.format(subset_name, selection): metrics['specificity'],
'{}_ppv_{}_singel_model'.format(subset_name, selection): metrics['ppv'],
'{}_npv_{}_singel_model'.format(subset_name, selection): metrics['npv'],
'{}_total_loss_{}_singel_model'.format(subset_name, selection): metrics['total_loss'],
}
metric_dict.update(metric_temp_dict)
return metric_dict
def individual_backprop(options):
main_logger = return_logger(options.verbose, "main process")
options = translate_parameters(options)
fold_list = [
fold for fold in os.listdir(options.model_path) if fold[:5:] == "fold-"
]
if len(fold_list) == 0:
raise ValueError("No folds were found at path %s" % options.model_path)
model_options = argparse.Namespace()
model_options = read_json(
model_options, path.join(options.model_path, 'commandline.json'))
model_options = translate_parameters(model_options)
model_options.gpu = options.gpu
if model_options.network_type == "multicnn":
raise NotImplementedError(
"The interpretation of multi-CNN is not implemented.")
if options.tsv_path is None and options.input_dir is None:
options.multi_cohort = model_options.multi_cohort
if options.tsv_path is None:
options.tsv_path = model_options.tsv_path
if options.input_dir is None:
options.input_dir = model_options.input_dir
if options.target_diagnosis is None:
options.target_diagnosis = options.diagnosis
for fold in fold_list:
main_logger.info(fold)
for selection in options.selection:
results_path = path.join(options.model_path, fold, 'gradients',
selection, options.name)
criterion = get_criterion(model_options.loss)
# Data management (remove data not well predicted by the CNN)
training_df = load_data_test(options.tsv_path, [options.diagnosis],
baseline=options.baseline,
multi_cohort=options.multi_cohort)
training_df.reset_index(drop=True, inplace=True)
# Model creation
_, all_transforms = get_transforms(
model_options.mode,
minmaxnormalization=model_options.minmaxnormalization)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
data_example = return_dataset(
model_options.mode,
options.input_dir,
training_df,
model_options.preprocessing,
train_transformations=None,
all_transformations=all_transforms,
prepare_dl=options.prepare_dl,
multi_cohort=options.multi_cohort,
params=model_options)
model = create_model(model_options, data_example.size)
model_dir = os.path.join(options.model_path, fold, 'models',
selection)
model, best_epoch = load_model(model,
model_dir,
gpu=options.gpu,
filename='model_best.pth.tar')
options.output_dir = results_path
commandline_to_json(options, logger=main_logger)
# Keep only subjects who were correctly / wrongly predicted by the network
training_df = sort_predicted(model,
training_df,
options.input_dir,
model_options,
criterion,
options.keep_true,
batch_size=options.batch_size,
num_workers=options.num_workers,
gpu=options.gpu)
if len(training_df) > 0:
# Save the tsv files used for the saliency maps
training_df.to_csv(path.join('data.tsv'),
sep='\t',
index=False)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
data_train = return_dataset(
model_options.mode,
options.input_dir,
training_df,
model_options.preprocessing,
train_transformations=None,
all_transformations=all_transforms,
prepare_dl=options.prepare_dl,
multi_cohort=options.multi_cohort,
params=model_options)
train_loader = DataLoader(data_train,
batch_size=options.batch_size,
shuffle=True,
num_workers=options.num_workers,
pin_memory=True)
interpreter = VanillaBackProp(model, gpu=options.gpu)
for data in train_loader:
if options.gpu:
input_batch = data['image'].cuda()
else:
input_batch = data['image']
map_np = interpreter.generate_gradients(
input_batch,
data_train.diagnosis_code[options.target_diagnosis])
for i in range(options.batch_size):
single_path = path.join(results_path,
data['participant_id'][i],
data['session_id'][i])
os.makedirs(single_path, exist_ok=True)
if len(data_train.size) == 4:
if options.nifti_template_path is not None:
image_nii = nib.load(
options.nifti_template_path)
affine = image_nii.affine
else:
affine = np.eye(4)
map_nii = nib.Nifti1Image(map_np[i, 0, :, :, :],
affine)
nib.save(map_nii,
path.join(single_path, "map.nii.gz"))
else:
jpg_path = path.join(single_path, "map.jpg")
plt.imshow(map_np[i, 0, :, :],
cmap="coolwarm",
vmin=-options.vmax,
vmax=options.vmax)
plt.colorbar()
plt.savefig(jpg_path)
plt.close()
np.save(path.join(single_path, "map.npy"), map_np[i])