def save_ouputs(self, data):
output_dir = self.config['testing']['output_dir']
ignore_dir = self.config['testing'].get('filename_ignore_dir', True)
save_prob = self.config['testing'].get('save_probability', False)
label_source = self.config['testing'].get('label_source', None)
label_target = self.config['testing'].get('label_target', None)
filename_replace_source = self.config['testing'].get(
'filename_replace_source', None)
filename_replace_target = self.config['testing'].get(
'filename_replace_target', None)
if (not os.path.exists(output_dir)):
os.mkdir(output_dir)
names, pred = data['names'], data['predict']
prob = scipy.special.softmax(pred, axis=1)
output = np.asarray(np.argmax(prob, axis=1), np.uint8)
if ((label_source is not None) and (label_target is not None)):
output = convert_label(output, label_source, label_target)
# save the output and (optionally) probability predictions
root_dir = self.config['dataset']['root_dir']
for i in range(len(names)):
save_name = names[i].split('/')[-1] if ignore_dir else \
names[i].replace('/', '_')
if ((filename_replace_source is not None)
and (filename_replace_target is not None)):
save_name = save_name.replace(filename_replace_source,
filename_replace_target)
print(save_name)
save_name = "{0:}/{1:}".format(output_dir, save_name)
save_nd_array_as_image(output[i], save_name,
root_dir + '/' + names[i])
save_name_split = save_name.split('.')
if (not save_prob):
continue
if ('.nii.gz' in save_name):
save_prefix = '.'.join(save_name_split[:-2])
save_format = 'nii.gz'
else:
save_prefix = '.'.join(save_name_split[:-1])
save_format = save_name_split[-1]
class_num = prob.shape[1]
for c in range(0, class_num):
temp_prob = prob[i][c]
prob_save_name = "{0:}_prob_{1:}.{2:}".format(
save_prefix, c, save_format)
if (len(temp_prob.shape) == 2):
temp_prob = np.asarray(temp_prob * 255, np.uint8)
save_nd_array_as_image(temp_prob, prob_save_name,
root_dir + '/' + names[i])
def __infer(self):
device = torch.device(self.config['testing']['device_name'])
self.net.to(device)
# laod network parameters and set the network as evaluation mode
self.checkpoint = torch.load(self.config['testing']['checkpoint_name'])
self.net.load_state_dict(self.checkpoint['model_state_dict'])
self.net.train()
output_dir = self.config['testing']['output_dir']
save_probability = self.config['testing']['save_probability']
label_source = self.config['testing']['label_source']
label_target = self.config['testing']['label_target']
class_num = self.config['network']['class_num']
mini_batch_size = self.config['testing']['mini_batch_size']
mini_patch_inshape = self.config['testing']['mini_patch_shape']
mini_patch_outshape = None
# automatically infer outupt shape
if (mini_patch_inshape is not None):
patch_inshape = [1, self.config['dataset']['modal_num']
] + mini_patch_inshape
testx = np.random.random(patch_inshape)
testx = torch.from_numpy(testx)
testx = torch.tensor(testx)
testx = testx.to(device)
testy = self.net(testx)
testy = testy.detach().cpu().numpy()
mini_patch_outshape = testy.shape[2:]
print('mini patch in shape', mini_patch_inshape)
print('mini patch out shape', mini_patch_outshape)
start_time = time.time()
with torch.no_grad():
for data in self.test_loder:
images = data['image'].double()
names = data['names']
print(names[0])
data['predict'] = volume_infer(images, self.net, device,
class_num, mini_batch_size,
mini_patch_inshape,
mini_patch_outshape)
for i in reversed(range(len(self.transform_list))):
if (self.transform_list[i].inverse):
data = self.transform_list[
i].inverse_transform_for_prediction(data)
output = np.argmax(data['predict'][0], axis=0)
output = np.asarray(output, np.uint8)
if ((label_source is not None) and (label_target is not None)):
output = convert_label(output, label_source, label_target)
# save the output and (optionally) probability predictions
root_dir = self.config['dataset']['root_dir']
save_name = names[0].split('/')[-1]
save_name = "{0:}/{1:}".format(output_dir, save_name)
save_nd_array_as_image(output, save_name,
root_dir + '/' + names[0])
if (save_probability):
save_name_split = save_name.split('.')
if ('.nii.gz' in save_name):
save_prefix = '.'.join(save_name_split[:-2])
save_format = 'nii.gz'
else:
save_prefix = '.'.join(save_name_split[:-1])
save_format = save_name_split[-1]
prob = scipy.special.softmax(data['predict'][0], axis=0)
class_num = prob.shape[0]
for c in range(0, class_num):
temp_prob = prob[c]
prob_save_name = "{0:}_prob_{1:}.{2:}".format(
save_prefix, c, save_format)
save_nd_array_as_image(temp_prob, prob_save_name,
root_dir + '/' + names[0])
avg_time = (time.time() - start_time) / len(self.test_loder)
print("average testing time {0:}".format(avg_time))
def infer(self):
device = torch.device(self.config['testing']['device_name'])
self.net.to(device)
# laod network parameters and set the network as evaluation mode
self.checkpoint = torch.load(self.config['testing']['checkpoint_name'],
map_location=device)
self.net.load_state_dict(self.checkpoint['model_state_dict'])
if (self.config['testing']['evaluation_mode'] == True):
self.net.eval()
if (self.config['testing']['test_time_dropout'] == True):
def test_time_dropout(m):
if (type(m) == nn.Dropout):
print('dropout layer')
m.train()
self.net.apply(test_time_dropout)
output_dir = self.config['testing']['output_dir']
class_num = self.config['network']['class_num']
mini_batch_size = self.config['testing']['mini_batch_size']
mini_patch_inshape = self.config['testing']['mini_patch_input_shape']
mini_patch_outshape = self.config['testing']['mini_patch_output_shape']
mini_patch_stride = self.config['testing']['mini_patch_stride']
output_num = self.config['testing'].get('output_num', 1)
multi_pred_avg = self.config['testing'].get('multi_pred_avg', False)
save_probability = self.config['testing'].get('save_probability',
False)
save_var = self.config['testing'].get('save_multi_pred_var', False)
label_source = self.config['testing'].get('label_source', None)
label_target = self.config['testing'].get('label_target', None)
filename_replace_source = self.config['testing'].get(
'filename_replace_source', None)
filename_replace_target = self.config['testing'].get(
'filename_replace_target', None)
if (not os.path.exists(output_dir)):
os.mkdir(output_dir)
# automatically infer outupt shape
# if(mini_patch_inshape is not None):
# patch_inshape = [1, self.config['dataset']['modal_num']] + mini_patch_inshape
# testx = np.random.random(patch_inshape)
# testx = torch.from_numpy(testx)
# testx = torch.tensor(testx)
# testx = self.convert_tensor_type(testx)
# testx = testx.to(device)
# testy = self.net(testx)
# if(isinstance(testy, tuple) or isinstance(testy, list)):
# testy = testy[0]
# testy = testy.detach().cpu().numpy()
# mini_patch_outshape = testy.shape[2:]
# print('mini patch in shape', mini_patch_inshape)
# print('mini patch out shape', mini_patch_outshape)
infer_time_list = []
with torch.no_grad():
for data in self.test_loder:
images = self.convert_tensor_type(data['image'])
names = data['names']
print(names[0])
# for debug
# for i in range(images.shape[0]):
# image_i = images[i][0]
# label_i = images[i][0]
# image_name = "temp/{0:}_image.nii.gz".format(names[0])
# label_name = "temp/{0:}_label.nii.gz".format(names[0])
# save_nd_array_as_image(image_i, image_name, reference_name = None)
# save_nd_array_as_image(label_i, label_name, reference_name = None)
# continue
start_time = time.time()
data['predict'] = volume_infer(images, self.net, device,
class_num, mini_batch_size,
mini_patch_inshape,
mini_patch_outshape,
mini_patch_stride, output_num)
for i in reversed(range(len(self.transform_list))):
if (self.transform_list[i].inverse):
data = self.transform_list[
i].inverse_transform_for_prediction(data)
predict_list = [data['predict']]
if (isinstance(data['predict'], tuple)
or isinstance(data['predict'], list)):
predict_list = data['predict']
infer_time = time.time() - start_time
infer_time_list.append(infer_time)
prob_list = [
scipy.special.softmax(predict[0], axis=0)
for predict in predict_list
]
if (multi_pred_avg):
if (output_num == 1):
raise ValueError(
"multiple predictions expected, but output_num was set to 1"
)
if (output_num != len(prob_list)):
raise ValueError(
"expected output_num was set to {0:}, but {1:} outputs obtained"
.format(output_dir, len(prob_list)))
prob_stack = np.asarray(prob_list, np.float32)
prob = np.mean(prob_stack, axis=0)
var = np.var(prob_stack, axis=0)
else:
prob = prob_list[0]
# output = predict_list[2][0]
output = np.asarray(np.argmax(prob, axis=0), np.uint8)
if ((label_source is not None) and (label_target is not None)):
output = convert_label(output, label_source, label_target)
# save the output and (optionally) probability predictions
root_dir = self.config['dataset']['root_dir']
save_name = names[0].split('/')[-1]
if ((filename_replace_source is not None)
and (filename_replace_target is not None)):
save_name = save_name.replace(filename_replace_source,
filename_replace_target)
save_name = "{0:}/{1:}".format(output_dir, save_name)
save_nd_array_as_image(output, save_name,
root_dir + '/' + names[0])
save_name_split = save_name.split('.')
if ('.nii.gz' in save_name):
save_prefix = '.'.join(save_name_split[:-2])
save_format = 'nii.gz'
else:
save_prefix = '.'.join(save_name_split[:-1])
save_format = save_name_split[-1]
if (save_probability):
class_num = prob.shape[0]
for c in range(0, class_num):
temp_prob = prob[c]
prob_save_name = "{0:}_prob_{1:}.{2:}".format(
save_prefix, c, save_format)
if (len(temp_prob.shape) == 2):
temp_prob = np.asarray(temp_prob * 255, np.uint8)
save_nd_array_as_image(temp_prob, prob_save_name,
root_dir + '/' + names[0])
if (save_var):
var = var[1]
var_save_name = "{0:}_var.{1:}".format(
save_prefix, save_format)
save_nd_array_as_image(var, var_save_name,
root_dir + '/' + names[0])
infer_time_list = np.asarray(infer_time_list)
time_avg = infer_time_list.mean()
time_std = infer_time_list.std()
print("testing time {0:} +/- {1:}".format(time_avg, time_std))
def infer(self):
device = torch.device(self.config['testing']['device_name'])
self.net.to(device)
# laod network parameters and set the network as evaluation mode
self.checkpoint = torch.load(self.config['testing']['checkpoint_name'],
map_location=device)
self.net.load_state_dict(self.checkpoint['model_state_dict'])
if (self.config['testing']['evaluation_mode'] == True):
self.net.eval()
if (self.config['testing']['test_time_dropout'] == True):
def test_time_dropout(m):
if (type(m) == nn.Dropout):
print('dropout layer')
m.train()
self.net.apply(test_time_dropout)
output_dir = self.config['testing']['output_dir']
save_probability = self.config['testing']['save_probability']
label_source = self.config['testing']['label_source']
label_target = self.config['testing']['label_target']
class_num = self.config['network']['class_num']
mini_batch_size = self.config['testing']['mini_batch_size']
mini_patch_inshape = self.config['testing']['mini_patch_shape']
mini_patch_stride = self.config['testing']['mini_patch_stride']
filename_replace_source = self.config['testing'][
'filename_replace_source']
filename_replace_target = self.config['testing'][
'filename_replace_target']
mini_patch_outshape = None
# automatically infer outupt shape
if (mini_patch_inshape is not None):
patch_inshape = [1, self.config['dataset']['modal_num']
] + mini_patch_inshape
testx = np.random.random(patch_inshape)
testx = torch.from_numpy(testx)
testx = torch.tensor(testx)
testx = testx.to(device)
testy = self.net(testx)
if (isinstance(testy, tuple) or isinstance(testy, list)):
testy = testy[0]
testy = testy.detach().cpu().numpy()
mini_patch_outshape = testy.shape[2:]
print('mini patch in shape', mini_patch_inshape)
print('mini patch out shape', mini_patch_outshape)
start_time = time.time()
with torch.no_grad():
for data in self.test_loder:
images = self.convert_tensor_type(data['image'])
images = data['image'].float()
names = data['names']
print(names[0])
# for debug
# for i in range(images.shape[0]):
# image_i = images[i][0]
# label_i = images[i][0]
# image_name = "temp/{0:}_image.nii.gz".format(names[0])
# label_name = "temp/{0:}_label.nii.gz".format(names[0])
# save_nd_array_as_image(image_i, image_name, reference_name = None)
# save_nd_array_as_image(label_i, label_name, reference_name = None)
# continue
data['predict'] = volume_infer(images, self.net, device,
class_num, mini_batch_size,
mini_patch_inshape,
mini_patch_outshape,
mini_patch_stride)
for i in reversed(range(len(self.transform_list))):
if (self.transform_list[i].inverse):
data = self.transform_list[
i].inverse_transform_for_prediction(data)
output = np.argmax(data['predict'][0], axis=0)
output = np.asarray(output, np.uint8)
if ((label_source is not None) and (label_target is not None)):
output = convert_label(output, label_source, label_target)
# save the output and (optionally) probability predictions
root_dir = self.config['dataset']['root_dir']
save_name = names[0].split('/')[-1]
if ((filename_replace_source is not None)
and (filename_replace_target is not None)):
save_name = save_name.replace(filename_replace_source,
filename_replace_target)
save_name = "{0:}/{1:}".format(output_dir, save_name)
save_nd_array_as_image(output, save_name,
root_dir + '/' + names[0])
if (save_probability):
save_name_split = save_name.split('.')
if ('.nii.gz' in save_name):
save_prefix = '.'.join(save_name_split[:-2])
save_format = 'nii.gz'
else:
save_prefix = '.'.join(save_name_split[:-1])
save_format = save_name_split[-1]
prob = scipy.special.softmax(data['predict'][0], axis=0)
class_num = prob.shape[0]
for c in range(0, class_num):
temp_prob = prob[c]
prob_save_name = "{0:}_prob_{1:}.{2:}".format(
save_prefix, c, save_format)
if (len(temp_prob.shape) == 2):
temp_prob = np.asarray(temp_prob * 255, np.uint8)
save_nd_array_as_image(temp_prob, prob_save_name,
root_dir + '/' + names[0])
avg_time = (time.time() - start_time) / len(self.test_loder)
print("average testing time {0:}".format(avg_time))
