# args.decay_steps = 10000*3
key = time.strftime("%Y-%m-%d_%H%M%S")
init_logging(args.loglevel)
logger = logging.getLogger('mview3d.' + __name__)
if args.ckpt is None:
log_dir = osp.join(args.logdir, key, 'train')
else:
log_dir = args.logdir
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,
norm=args.norm,
mode="TRAIN")
# Define graph
mvnet = model_dlsm(mvnet,
im_nets[args.im_net],
grid_nets[args.grid_net],
conv_rnns[args.rnn],
im_skip=args.im_skip,
ray_samples=args.ray_samples,
sepup=args.sepup,
proj_x=args.proj_x)
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
# Setup network
net = model_dlsm(net,
im_nets[args.im_net],
grid_nets[args.grid_net],
conv_rnns[args.rnn],
im_skip=args.im_skip,
ray_samples=args.ray_samples,
sepup=args.sepup,
proj_x=args.proj_x,
proj_last=True)
sess = tf.Session(config=get_session_config())
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
coord = tf.train.Coordinator()
# Init dataset
dset = ShapeNet(im_dir=im_dir, split_file=args.val_split_file, rng_seed=1)
mids = dset.get_smids('val')
logging.info('Validating %d models', len(mids))
items = ['shape_id', 'model_id', 'im', 'K', 'R', 'depth']
dset.init_queue(mids,
args.val_im_batch,
items,
coord,
nepochs=1,
qsize=32,
nthreads=args.prefetch_threads)
# Init stats
l1_err = []
# 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'],
net.ims: batch_data['im']
}
pred = sess.run(net.depth_out, feed_dict=feed_dict)
batch_err = eval_l1_err(pred[:num_batch_items],
batch_data['depth'][:num_batch_items])
l1_err.extend(batch_err)
pbar.update(num_batch_items)
except Exception, e:
logger.error(repr(e))
dset.close_queue(e)
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)
log_dir = os.path.join('models_lsm_v1/vlsm-release/train')
with open(os.path.join(log_dir, 'args.json'), 'r') as f:
args = json.load(f)
args = Bunch(args)
# Set voxel resolution
voxel_resolution = 32
# Setup TF graph and initialize VLSM model
tf.reset_default_graph()
# Change the ims_per_model to run on different number of views
bs, ims_per_model = 1, 4
ckpt = 'mvnet-100000'
net = MVNet(vmin=-0.5, vmax=0.5, vox_bs=bs,
im_bs=ims_per_model, grid_size=args.nvox,
im_h=args.im_h, im_w=args.im_w,
norm=args.norm, mode="TEST")
net = model_vlsm(net, im_nets[args.im_net], grid_nets[args.grid_net], conv_rnns[args.rnn])
vars_restore = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='MVNet')
sess = tf.InteractiveSession(config=get_session_config())
saver = tf.train.Saver(var_list=vars_restore)
saver.restore(sess, os.path.join(log_dir, ckpt))
#print net.im_batch
#sys.exit()
from shapenet import ShapeNet
# Read data
dset = ShapeNet(im_dir=im_dir, split_file=os.path.join(SAMPLE_DIR, 'splits_sample.json'), rng_seed=1)
test_mids = dset.get_smids('test')
print test_mids[0]