def validate(args, checkpoint):
net = MVNet(vmin=-0.5,
vmax=0.5,
vox_bs=args.val_batch_size,
im_bs=args.val_im_batch,
grid_size=args.nvox,
im_h=args.im_h,
im_w=args.im_w,
mode="TEST",
norm=args.norm)
im_dir = SHAPENET_IM
vox_dir = SHAPENET_VOX[args.nvox]
# Setup network
net = model_vlsm(net, im_nets[args.im_net], grid_nets[args.grid_net],
conv_rnns[args.rnn])
sess = tf.Session(config=get_session_config())
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
coord = tf.train.Coordinator()
# Init IoU
iou = init_iou(net.im_batch, args.eval_thresh)
# Init dataset
dset = ShapeNet(im_dir=im_dir,
split_file=args.val_split_file,
vox_dir=vox_dir,
rng_seed=1)
mids = dset.get_smids('val')
logging.info('Testing %d models', len(mids))
items = ['shape_id', 'model_id', 'im', 'K', 'R', 'vol']
dset.init_queue(mids,
args.val_im_batch,
items,
coord,
nepochs=1,
qsize=32,
nthreads=args.prefetch_threads)
# Testing loop
pbar = tqdm(desc='Validating', total=len(mids))
deq_mids, deq_sids = [], []
try:
while not coord.should_stop():
batch_data = dset.next_batch(items, net.batch_size)
if batch_data is None:
continue
deq_sids.append(batch_data['shape_id'])
deq_mids.append(batch_data['model_id'])
num_batch_items = batch_data['K'].shape[0]
batch_data = pad_batch(batch_data, args.val_batch_size)
feed_dict = {net.K: batch_data['K'], net.Rcam: batch_data['R']}
feed_dict[net.ims] = batch_data['im']
pred = sess.run(net.prob_vox, feed_dict=feed_dict)
batch_iou = eval_seq_iou(pred[:num_batch_items],
batch_data['vol'][:num_batch_items],
args.val_im_batch,
thresh=args.eval_thresh)
# Update iou dict
iou = update_iou(batch_iou, iou)
pbar.update(num_batch_items)
except Exception, e:
logger.error(repr(e))
dset.close_queue(e)
if args.ckpt is None:
log_dir = osp.join(args.logdir, key, 'train')
else:
log_dir = args.logdir
# Initialize network parameters
mvnet = MVNet(vmin=-0.5,
vmax=0.5,
vox_bs=args.batch_size,
im_bs=args.im_batch,
grid_size=args.nvox,
im_h=args.im_h,
im_w=args.im_w,
mode="TRAIN",
norm=args.norm)
# Define graph
mvnet = model_vlsm(mvnet, im_nets[args.im_net], grid_nets[args.grid_net],
conv_rnns[args.rnn])
# Set things up
mkdir_p(log_dir)
write_args(args, osp.join(log_dir, 'args.json'))
logger.info('Logging to {:s}'.format(log_dir))
logger.info('\nUsing args:')
pprint(vars(args))
mvnet.print_net()
# Train loop
train(mvnet)