def test3d(config):
prepare_dirs_and_logger(config)
tf.set_random_seed(config.random_seed)
batch_manager = BatchManager(config)
# batch test
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.allow_soft_placement = True
sess_config.log_device_placement = False
sess = tf.Session(config=sess_config)
batch_manager.start_thread(sess)
x, y = batch_manager.batch()
x_ = x.eval(session=sess)
batch_manager.stop_thread()
x_ = (x_ + 1) * 127.5 # [0, 255]
x_ = np.mean(x_, axis=1) # yx
save_image(x_, '{}/x_fixed.png'.format(config.model_dir))
# random pick from parameter space
sample = batch_manager.random_list(config.batch_size)
save_image(sample['xy'], '{}/xy.png'.format(config.model_dir))
save_image(sample['zy'], '{}/zy.png'.format(config.model_dir))
save_image(sample['xym'], '{}/xym.png'.format(config.model_dir))
save_image(sample['zym'], '{}/zym.png'.format(config.model_dir))
with open('{}/sample.txt'.format(config.model_dir), 'w') as f:
f.write(str(sample['p']))
f.write(str(sample['z']))
def main(config):
prepare_dirs_and_logger(config)
batch_manager = BatchManager(config)
# test
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.allow_soft_placement = True
sess_config.log_device_placement = False
sess = tf.Session(config=sess_config)
batch_manager.init_it(sess)
x, y = batch_manager.batch()
x_, y_ = sess.run([x, y])
print(x_.shape, y_.shape)
x, y = batch_manager.batch(is_window=True)
x_, y_ = sess.run([x, y])
print(x_.shape, y_.shape)
batch_manager.init_test_it()
x, y = batch_manager.test_batch()
x_, y_ = sess.run([x, y])
print(x_.shape, y_.shape)
x, y = batch_manager.test_batch(is_window=True)
x_, y_ = sess.run([x, y])
print(x_.shape, y_.shape)
print('batch manager test done')
def main(config):
prepare_dirs_and_logger(config)
tf.compat.v1.set_random_seed(config.random_seed)
if 'nn' in config.arch:
from data_nn import BatchManager
else:
from data import BatchManager
batch_manager = BatchManager(config)
if config.is_3d:
trainer = Trainer3(config, batch_manager)
else:
trainer = Trainer(config, batch_manager)
print("---------------------------------")
print("| |")
print("| |")
print("| prepare trainer |")
print("| is done |")
print("| |")
print("| |")
print("---------------------------------")
if config.is_train:
save_config(config)
trainer.train()
else:
if not config.load_path:
raise Exception(
"[!] You should specify `load_path` to load a pretrained model"
)
trainer.test()
def main(config):
prepare_dirs_and_logger(config)
tf.set_random_seed(config.random_seed)
from data import BatchManager
batch_manager = BatchManager(config)
trainer = Trainer_tumor(config, batch_manager)
if config.is_train:
save_config(config)
trainer.train()
else:
if not config.load_path:
raise Exception(
"[!] You shou/home/tudorld specify `load_path` to load a pretrained model"
)
trainer.test()
def test2d(config):
prepare_dirs_and_logger(config)
tf.set_random_seed(config.random_seed)
batch_manager = BatchManager(config)
# thread test
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.allow_soft_placement = True
sess_config.log_device_placement = False
sess = tf.Session(config=sess_config)
batch_manager.start_thread(sess)
x, y = batch_manager.batch() # [-1, 1]
x_ = x.eval(session=sess)
# y_ = y.eval(session=sess)
batch_manager.stop_thread()
x_w = vort_np(x_)
x_w /= np.abs(x_w).max()
x_w = (x_w + 1) * 0.5
x_w = np.uint8(plt.cm.RdBu(x_w[..., 0]) * 255)[..., :3]
x_ = (x_ + 1) * 127.5 # [0, 255]
b_ch = np.ones([config.batch_size, config.res_y, config.res_x, 1]) * 127.5
x_ = np.concatenate((x_, b_ch), axis=-1)
x_ = np.concatenate((x_, x_w), axis=0)
save_image(x_, '{}/x_fixed.png'.format(config.model_dir))
# random pick from parameter space
x, pi, zi = batch_manager.random_list(config.batch_size)
x_w = vort_np(x / 127.5 - 1)
x_w /= np.abs(x_w).max()
x_w = (x_w + 1) * 0.5
x_w = np.uint8(plt.cm.RdBu(x_w[..., 0]) * 255)[..., :3]
x = np.concatenate((x, x_w), axis=0)
save_image(x, '{}/x.png'.format(config.model_dir))
with open('{}/x_p.txt'.format(config.model_dir), 'w') as f:
f.write(str(pi))
f.write(str(zi))