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single_fnet.py
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single_fnet.py
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"""
Model for single network direct regression
"""
import os
import time
import argparse
import data_loader
import numpy as np
import tensorflow as tf
from models import HomographyNet
from evaluation import sampson_dist, sym_epilolar_dist, epipolar_constraint,epipolar_constraint_abs
class SingleFNet(object):
def __init__(self, tr_data_loader, val_data_loader, test_data_loader=None, net=None,
lr=0.001, l1_weight=0., l2_weight=2., batch_size=128,
gradient_clipping=False, prefix="test", use_internal_layer=False,
use_coor=False, use_idx=False, norm_method='norm', resume=None):
self.resume = resume
self.tr_data_loader = tr_data_loader
self.val_data_loader = val_data_loader
self.test_data_loader = test_data_loader
self.l1_weight = l1_weight
self.l2_weight = l2_weight
self.batch_size = batch_size
self.prefix = prefix
self.lr = lr
self.gc = gradient_clipping
self.use_internal_layer = use_internal_layer
self.norm_method = norm_method
self.use_coor = use_coor
print("Use coor in Single FNet:%s"%self.use_coor)
self.use_idx = use_idx
if net == None:
self.net = HomographyNet(
use_reconstruction_module=self.use_internal_layer,
norm_method=self.norm_method,
use_coor=self.use_coor, use_idx=self.use_idx)
else:
self.net = net
self.metrics = {
"sampson_dist " : sampson_dist,
"sym_epilolar_dist " : sym_epilolar_dist,
"epi_constraint " : epipolar_constraint,
"epi_constraint_abs" : epipolar_constraint_abs
}
# image shape required for UCN
self.img_shape = list(self.tr_data_loader.img_shape())[:2] + [1]
self.image_size_x = self.img_shape[0]
self.image_size_y = self.img_shape[1]
self.image_channels = self.img_shape[2]
# Graph
gpu_options = tf.GPUOptions(allow_growth=True)
self.train_graph = tf.Graph()
with self.train_graph.as_default():
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options),
graph=self.train_graph)
# Place holders.
self.x1 = tf.placeholder(
tf.float32, [None, self.image_size_x, self.image_size_y, self.image_channels], name='x1')
self.x2 = tf.placeholder(
tf.float32, [None, self.image_size_x, self.image_size_y, self.image_channels], name='x2')
# self.correspondences = tf.placeholder(tf.float32, [None, self.nb_corres, 4], name='corres')
# self.x = tf.placeholder(tf.float32, shape=(
# [None] + list(self.tr_data_loader.img_shape())))
self.y = tf.placeholder(tf.float32, shape=(
[None] + list(self.tr_data_loader.fmat_shape())))
print("Building training graph...")
self.y_, self.loss, self.l1_loss, self.l2_loss = \
self.build_graph(self.tr_data_loader, is_training=True, is_reuse=False)
# Optimizers.
print("Building optimizer...")
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.optim = tf.train.AdamOptimizer(learning_rate=self.lr)
grads_and_vars = self.optim.compute_gradients(
self.loss, var_list=self.net.vars)
if self.gc:
grads_and_vars = [\
(tf.clip_by_norm(g, clip_norm=1.) if g is not None else g, v) \
for g,v in grads_and_vars if g is not None]
self.train_op = self.optim.apply_gradients(grads_and_vars)
# Validation
print("Building validation graph...")
self.val_y_, self.val_l1_loss, self.val_l2_loss, self.val_loss= \
self.build_graph(self.val_data_loader, is_training=False, is_reuse=True)
# Testing
print("Building testing graph...")
self.test_y_, self.test_l1_loss, self.test_l2_loss, self.test_loss= \
self.build_graph(self.test_data_loader, is_training=False, is_reuse=True)
# Summary
tf.summary.scalar("l1_loss", self.l1_loss)
tf.summary.scalar("l2_loss", self.l2_loss)
tf.summary.scalar("loss", self.loss)
self.tr_summary = tf.summary.merge_all()
self.tr_log_writer = tf.summary.FileWriter("log/single_fnet_%s" %self.prefix, \
self.sess.graph, flush_secs=30)
self.tr_saver = tf.train.Saver()
def build_graph(self, data_loader, is_training=True, is_reuse=False):
# Network.
y_ = self.net(self.x1, self.x2, self.img_shape, is_training, reuse=is_reuse)
# Loss
l1_loss = tf.reduce_mean(tf.abs(self.y - y_)) * self.l1_weight
l2_loss = tf.losses.mean_squared_error(self.y, y_) * self.l2_weight
loss = l1_loss + l2_loss
return y_, loss, l1_loss, l2_loss
def validate(self, iters, best_score, val_data_loader=None):
if val_data_loader == None:
val_data_loader = self.val_data_loader
val_loss = 0
relative = { k:0 for k in self.metrics.keys() }
scores = { k:0 for k in self.metrics.keys() }
base = { k:0 for k in self.metrics.keys() }
# bs = len(self.val_data_loader)
num_batches = len(self.val_data_loader)//self.batch_size+1
print ('num batches: ', num_batches, 'val data: ', len(val_data_loader))
# num_batches = len(self.val_data_loader)//bs+1
for i in range(num_batches):
# bx, by, bp1, bp2 = self.val_data_loader(self.batch_size)
bx, by, bp1, bp2 = self.val_data_loader(self.batch_size)
img1 = bx[:,:,:,0]
img2 = bx[:,:,:,1]
img1 = np.expand_dims(img1, axis=3)
img2 = np.expand_dims(img2, axis=3)
feed_dict = {self.x1 : img1, self.x2 : img2, self.y : by}
loss, fmat = self.sess.run([self.val_loss, self.val_y_], feed_dict)
val_loss += loss
for k in self.metrics.keys():
m = self.metrics[k]
r_score, m_score, base_score = m(fmat, by, bp1, bp2)
scores[k] += m_score.mean()
relative[k] += r_score.mean()
base[k] += base_score.mean()
output_data = {}
print("\nValidation: loss\t%.5f" \
%(val_loss / float(num_batches)))
output_data['val_loss'] = val_loss/float(num_batches)
for k in scores.keys():
output_data[k] = {}
ind = 0
for k in scores.keys():
s = scores[k]
v = base[k]
r = relative[k]
print("\t%s\t%.5f %.5f %.5f"\
%(k, r/float(num_batches), s/float(num_batches), v/float(num_batches)))
best_score[ind][0] = min(r/float(num_batches), best_score[ind][0])
best_score[ind][1] = min(s/float(num_batches), best_score[ind][1])
best_score[ind][2] = min(v/float(num_batches), best_score[ind][2])
ind += 1
output_data[k]['pred'] = s/float(num_batches)
output_data[k]['gtrs'] = v/float(num_batches)
output_data['pred_all'] = scores
output_data['gtrs_all'] = base
# save_path = "log/single_fnet_%s" %self.prefix
# save_file = os.path.join(save_path, "val-iteres%d.npy"%iters)
# np.save(save_file, output_data)
return best_score
def test(self, iters, best_score, test_data_loader=None):
if test_data_loader == None:
test_data_loader = self.test_data_loader
test_loss = 0
relative = { k:0 for k in self.metrics.keys() }
scores = { k:0 for k in self.metrics.keys() }
base = { k:0 for k in self.metrics.keys() }
# bs = len(self.val_data_loader)
num_batches = len(self.test_data_loader)//self.batch_size+1
print ('num batches: ', num_batches, 'val data: ', len(test_data_loader))
# num_batches = len(self.val_data_loader)//bs+1
for i in range(num_batches):
# bx, by, bp1, bp2 = self.val_data_loader(self.batch_size)
bx, by, bp1, bp2 = self.test_data_loader(self.batch_size)
img1 = bx[:,:,:,0]
img2 = bx[:,:,:,1]
img1 = np.expand_dims(img1, axis=3)
img2 = np.expand_dims(img2, axis=3)
feed_dict = {self.x1 : img1, self.x2 : img2, self.y : by}
loss, fmat = self.sess.run([self.test_loss, self.test_y_], feed_dict)
test_loss += loss
for k in self.metrics.keys():
m = self.metrics[k]
r_score, m_score, base_score = m(fmat, by, bp1, bp2)
scores[k] += m_score.mean()
relative[k] += r_score.mean()
base[k] += base_score.mean()
output_data = {}
print("\nTesting: loss\t%.5f" \
%(test_loss / float(num_batches)))
output_data['test_loss'] = test_loss/float(num_batches)
for k in scores.keys():
output_data[k] = {}
ind = 0
for k in scores.keys():
s = scores[k]
v = base[k]
r = relative[k]
print("\t%s\t%.5f %.5f %.5f"\
%(k, r/float(num_batches), s/float(num_batches), v/float(num_batches)))
best_score[ind][0] = min(r/float(num_batches), best_score[ind][0])
best_score[ind][1] = min(s/float(num_batches), best_score[ind][1])
best_score[ind][2] = min(v/float(num_batches), best_score[ind][2])
ind += 1
output_data[k]['pred'] = s/float(num_batches)
output_data[k]['gtrs'] = v/float(num_batches)
output_data['pred_all'] = scores
output_data['gtrs_all'] = base
# save_path = "log/single_fnet_%s" %self.prefix
# save_file = os.path.join(save_path, "val-iteres%d.npy"%iters)
# np.save(save_file, output_data)
return best_score
def train(self, epoches=10, log_interval=10):
ttl_iter = 0
num_batches = len(self.tr_data_loader) // self.batch_size + 1
with self.train_graph.as_default():
if self.resume is None:
print("Initializing variables..")
self.sess.run(tf.global_variables_initializer())
else:
print("Resuming varibles from :%s"%self.resume)
self.tr_saver.restore(self.sess, self.resume)
max_val = 1000000000000000.0
best_score_val = [[max_val]*3 for i in range(4)]
best_score_test = [[max_val]*3 for i in range(4)]
print("Validation")
self.validate(-1, best_score_val)
print("Testing")
self.test(-1, best_score_test)
print("Start training...")
for epo in range(epoches):
print("Training epoch : [%03d/%03d]"%(epo, epoches))
for i in range(num_batches):
ttl_iter += 1
bx, by, _, _ = self.tr_data_loader(self.batch_size)
img1 = bx[:,:,:,0]
img2 = bx[:,:,:,1]
img1 = np.expand_dims(img1, axis=3)
img2 = np.expand_dims(img2, axis=3)
feed_dict = {self.x1 : img1, self.x2 : img2, self.y : by}
if i % log_interval == 0:
summary, loss = self.sess.run([
self.tr_summary, self.loss], feed_dict)
print("[epo=%02d/%02d][%05d/%05d] loss = %.5f"\
%(epo, epoches, i, num_batches, loss))
self.tr_log_writer.add_summary(summary, ttl_iter)
self.sess.run(self.train_op, feed_dict)
best_score_val = self.validate(epo, best_score_val)
best_score_test = self.test(epo, best_score_test)
scores = { k:0 for k in self.metrics.keys() }
ind = 0
print ('\nbest validation values till now:')
for k in scores.keys():
print("\t%s\t%.5f %.5f %.5f"\
%(k, best_score_val[ind][0], best_score_val[ind][1], best_score_val[ind][2]))
ind += 1
ind = 0
print ('\nbest test values till now:')
for k in scores.keys():
print("\t%s\t%.5f %.5f %.5f"\
%(k, best_score_test[ind][0], best_score_test[ind][1], best_score_test[ind][2]))
ind += 1
if epo % 50 == 0:
print("Saving the model...")
save_path = "log/single_fnet_%s" %self.prefix
# self.tr_saver.save(self.sess, os.path.join(save_path, "model-%d.ckpt"%epo))
print("Model saved to %s"%save_path)
# Training scripts
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Single FMatrixNet, direct regression.')
parser.add_argument('--gpu-id', default=0, type=int)
parser.add_argument('--prefix', default="default_posidx_absnorm", type=str,
help="Prefix for naming the log/model.")
parser.add_argument('--batch-size', default=4, type=int,
help="Batch size.")
parser.add_argument('--epoches', default=60000, type=int,
help="Number of epoches.")
parser.add_argument('--data-size', default=30000, type=int,
help="Dataset size as number of (img1, img2, fmat) tuple")
parser.add_argument('--l1-weight', default=10., type=float,
help="Weight for L1-loss")
parser.add_argument('--l2-weight', default=1., type=float,
help='Weight for L2-loss')
parser.add_argument('--lr', default=0.001, type=float,
help='Learning rate')
parser.add_argument('--use-gc', default=True, type=bool,
help='Use gradient clipping.')
parser.add_argument('--norm-method', default='norm', type=str,
help='Normalization method: (norm)|(abs)|(last)')
parser.add_argument('--resume', default=None, type=str,
help='Resuming model')
parser.add_argument('--use-coor', default=False, type=bool,
help='Whether using coordinate as input')
parser.add_argument('--use-pos-index', default=False, type=bool,
help='Whether using pos index')
parser.add_argument('--use-reconstruction', default=False, type=bool,
help='Whether using reconstruction layer')
parser.add_argument('--dataset', default='kitti', type=str,
help='whether synthetic(syn) / kitti(kitti) / ALOI(aloi)')
args = parser.parse_args()
print(args)
if args.dataset == 'syn':
tr_ds, val_ds, te_ds = data_loader.make_povray_datasets(
max_num=args.data_size, norm=args.norm_method)
# print('train set size: ', tr_ds.img_shape())
# print('train set fmat size: ', tr_ds.fmat_shape())
elif args.dataset == 'kitti':
tr_ds, val_ds, te_ds = data_loader.make_kitti_datasets(
norm=args.norm_method)
# print('train set size: ', tr_ds.img_shape())
# print('train set fmat size: ', tr_ds.fmat_shape())
elif args.dataset == 'aloi':
tr_ds, val_ds, te_ds = data_loader.make_aloi_datasets(
norm=args.norm_method)
# print('train set size: ', tr_ds.img_shape())
# print('train set fmat size: ', tr_ds.fmat_shape())
elif args.dataset == 'mvs':
tr_ds, val_ds, te_ds = data_loader.make_mvs_datasets(
norm=args.norm_method)
# print('train set size: ', tr_ds.img_shape())
# print('train set fmat size: ', tr_ds.fmat_shape())
model = SingleFNet(tr_ds, val_ds, te_ds, net=None, lr=args.lr,
l1_weight=args.l1_weight, l2_weight=args.l2_weight,
batch_size=args.batch_size, gradient_clipping=args.use_gc,
resume=args.resume, norm_method=args.norm_method,
use_coor=args.use_coor, use_idx=args.use_pos_index,
use_internal_layer=args.use_reconstruction,
prefix="%s_time%d"%(args.prefix, int(time.time())))
model.train(epoches=args.epoches)