def gpu_init(queue):
global gpu_id
gpu_id = queue.get()
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
import tensorflow as tf
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
gpu_list = [x.name for x in local_device_protos if x.device_type == 'GPU']
process = multiprocessing.current_process()
print(gpu_id, process.pid, gpu_list)
global volume_model
import net
volume_model = net.model3d((64, 64, 64),
sz=config.feature_sz,
alpha=config.feature_alpha,
do_features=True)
volume_model.load_weights(weights_file)
for n in range(num_test_volumes):
test_result = volume_model.predict(test_volumes[n:n+1], batch_size=1)[:,:,:,0]
test_p = net.sigmoid_activations(test_result)
fpr = np.count_nonzero(test_p[0,:,:,:] > p_threshold) / test_volumes[n].size
fpr_list.append(fpr)
fpr90 = np.count_nonzero(test_p[0,:,:,:] > 0.880797) / test_volumes[n].size
fpr90_list.append(fpr90)
return np.mean(fpr_list), np.mean(fpr90_list), p_threshold, fpr_list, fpr90_list, p_list
history = {'loss':[], 'acc':[], 'fpr':[], 'p_threshold':[], 'p_list':[]}
history['version'] = subprocess.check_output('git describe --always --dirty', shell=True).decode('ascii').strip()
history['argv'] = sys.argv
model = net.model3d((16, 16, 16), sz=config.feature_sz, alpha=config.feature_alpha)
print(model.summary())
volume_model = net.model3d((64, 64, 64), sz=config.feature_sz, alpha=config.feature_alpha, do_features=True)
def get_optimizer(lr):
if config.optimizer == 'rmsprop':
optimizer = RMSprop(lr=lr)
elif config.optimizer == 'adam':
optimizer = Adam(lr=lr)
elif config.optimizer == 'nadam':
optimizer = Nadam(lr=lr)
elif config.optimizer == 'sgd':
optimizer = SGD(lr=lr, momentum=0.9, nesterov=True)
return optimizer
model.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer=get_optimizer(config.lr))
16:16 + 32, None]
test_nodules = datagen.preprocess(test_nodules)
if config.do_downscale:
test_nodules = skimage.transform.downscale_local_mean(test_nodules,
(1, 2, 2, 2, 1),
clip=False)
test_y = np.concatenate((np.zeros(
(len(X_benign_nodules_test), )), np.ones((len(X_cancer_nodules_test), ))))
history = {'loss': [], 'acc': [], 'val_loss': [], 'val_acc': []}
history['version'] = subprocess.check_output(
'git describe --always --dirty', shell=True).decode('ascii').strip()
history['argv'] = sys.argv
model = net.model3d(config.net_input_vsize,
sz=config.feature_sz,
alpha=config.feature_alpha)
print(model.summary())
def get_optimizer(lr):
if config.optimizer == 'rmsprop':
optimizer = RMSprop(lr=lr)
elif config.optimizer == 'adam':
optimizer = Adam(lr=lr)
elif config.optimizer == 'nadam':
optimizer = Nadam(lr=lr)
elif config.optimizer == 'sgd':
optimizer = SGD(lr=lr, momentum=0.9, nesterov=True)
return optimizer