LR_batch_dims = (1, LR_height, LR_width, 1)
#setting input placeholders
HR_depth_batch_input = tf.placeholder(tf.float32, HR_batch_dims)
HR_inten_batch_input = tf.placeholder(tf.float32, HR_batch_dims)
LR_depth_batch_input = tf.placeholder(tf.float32, LR_batch_dims)
coar_inter_dep_batch = tf.image.resize_images(
LR_depth_batch_input, tf.constant(HR_patch_size, dtype=tf.int32),
tf.image.ResizeMethod.BICUBIC)
#gen_network construction
inten_feature = bnb.inten_feature_extraction(HR_inten_batch_input)
inten_1x_ten = bnb.inten_residual_block(inten_feature, 1)
dep_2x_ten = bnb.LR_dep_feature_extraction(LR_depth_batch_input)
dep_2x_ten_up = bnb.feature_up_unit(dep_2x_ten, 1)
dep_ten = bnb.LR_dep_fusion(dep_2x_ten_up[0], inten_1x_ten, fusion_stage=1)
HR_gen_dep = bnb.LR_dep_reconstruction(dep_2x_ten_up[0], dep_ten,
coar_inter_dep_batch)
#define loss for gen
saver_full = tf.train.Saver()
#begin comp_gen testing
with tf.Session() as sess:
model_path = "/media/kenny/Data/trained_models/multi_dense_guide_resnet/noise-free/l1loss/2x/full_model1/2x_ny_full_model.ckpt-99"
saver_full.restore(sess, model_path)
ten_fets = sess.run(HR_gen_dep,
feed_dict={
HR_inten_batch_input: val_inten,
HR_depth_batch_input: val_gth_dep,
LR_depth_batch_input: val_LR_dep
#setting input placeholders
HR_depth_batch_input = tf.placeholder(tf.float32, HR_batch_dims)
HR_inten_batch_input = tf.placeholder(tf.float32, HR_batch_dims)
LR_depth_batch_input = tf.placeholder(tf.float32, LR_batch_dims)
coar_inter_dep_batch = tf.image.resize_images(
LR_depth_batch_input, tf.constant(HR_patch_size, dtype=tf.int32),
tf.image.ResizeMethod.BICUBIC)
#gen_network construction
inten_feature = bnb.inten_feature_extraction(HR_inten_batch_input)
inten_1x_ten = bnb.inten_residual_block(inten_feature, 1)
inten_ten, inten_2x_down = bnb.inten_residual_block(inten_1x_ten, 2)
inten_2x_down_up = bnb.feature_up_unit(inten_2x_down, 1)
dep_4x_ten = bnb.LR_dep_feature_extraction(LR_depth_batch_input)
dep_4x_ten_up = bnb.feature_up_unit(dep_4x_ten, 2)
dep_2x_ten = bnb.LR_dep_fusion(dep_4x_ten_up[0], inten_2x_down, fusion_stage=1)
dep_2x_ten_up = bnb.feature_up_unit(dep_2x_ten, 1)
dep_ten = bnb.LR_dep_fusion(tf.concat([dep_4x_ten_up[1], dep_2x_ten_up[0]], 3),
tf.concat([inten_2x_down_up[0], inten_1x_ten], 3),
fusion_stage=2)
HR_gen_dep = bnb.LR_dep_reconstruction(dep_4x_ten_up[1], dep_ten,
coar_inter_dep_batch)
#define loss for gen
saver_full = tf.train.Saver()
#begin comp_gen testing
with tf.Session() as sess:
model_path = "/media/kenny/Data/trained_models/multi_dense_guide_resnet/noise-free/l1loss/4x/full_model3/4x_ny_full_model.ckpt-13"
saver_full.restore(sess, model_path)
ten_fets = sess.run(HR_gen_dep,