def main(_):
roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.ROI_SIZE_W, net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='roi_input')
expand_roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.EXPAND_SIZE_W,
net_config.EXPAND_SIZE_H, net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='expand_roi_input')
labels_tensor = tf.placeholder(shape=[None], dtype=np.int32)
is_training_tensor = tf.placeholder(dtype=tf.bool, shape=[])
logits = inference_small(roi_images,
expand_roi_images,
phase_names=['NC', 'ART', 'PV'],
num_classes=4,
is_training=is_training_tensor)
save_model_path = '/home/give/PycharmProjects/MedicalImage/Net/forpatch/ResNetMultiPhaseMultiScale/model/parallel'
train(logits,
roi_images,
expand_roi_images,
labels_tensor,
is_training_tensor,
save_model_path=save_model_path,
step_width=100)
def main(_):
roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.ROI_SIZE_W, net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='roi_input')
expand_roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.EXPAND_SIZE_W,
net_config.EXPAND_SIZE_H, net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='expand_roi_input')
labels_tensor = tf.placeholder(shape=[None], dtype=np.int32)
is_training_tensor = tf.placeholder(dtype=tf.bool, shape=[])
logits = inference_small(roi_images,
expand_roi_images,
phase_names=['NC', 'ART', 'PV'],
num_classes=4,
is_training=is_training_tensor,
point_phase=[2])
save_model_path = '/home/give/PycharmProjects/ICPR2018/DeepLearning/Patch_ROI/models'
train(logits,
roi_images,
expand_roi_images,
labels_tensor,
is_training_tensor,
save_model_path=save_model_path,
step_width=20,
record_loss=False)
def main(_):
roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.ROI_SIZE_W, net_config.ROI_SIZE_H,
net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='roi_input')
expand_roi_images = tf.placeholder(shape=[
net_config.BATCH_SIZE, net_config.EXPAND_SIZE_W,
net_config.EXPAND_SIZE_H, net_config.IMAGE_CHANNEL
],
dtype=np.float32,
name='expand_roi_input')
labels_tensor = tf.placeholder(shape=[None], dtype=np.int32)
is_training_tensor = tf.placeholder(dtype=tf.bool, shape=[])
logits, local_output_tensor, global_output_tensor, represent_feature_tensor = inference_small(
roi_images,
expand_roi_images,
phase_names=['NC', 'ART', 'PV'],
num_classes=4,
is_training=is_training_tensor)
save_model_path = '/home/give/PycharmProjects/MICCAI2018/deeplearning/Co-Occurrence/parameters/1'
train(logits,
local_output_tensor,
global_output_tensor,
represent_feature_tensor,
roi_images,
expand_roi_images,
labels_tensor,
is_training_tensor,
save_model_path=save_model_path,
step_width=100)