saver.restore(sess, args.load)
batch_idx_train = 0
batch_idx_test = 0
epoch_idx = 0
iteration = 0
maxl_array = np.zeros((2))
maxl_array[0] = args.voxel_size
maxl_array[1] = args.voxel_size
epCount = dataLoad.fileCount(args.datapath)
stepFactor = 9
epochs = dataLoad.gen_epochs(args.epochs, args.datapath, args.batch_size,
args.velocity_multiplier, True, args.output_dim)
sess.graph.finalize()
while True:
batch_train, batch_validate = next(epochs, [None, None])
epoch_idx += 1
if batch_train == None:
break
print(colored("Epoch %03d" % (epoch_idx), 'yellow'))
# Training loop
while True:
batch_idx_train = 0
batch_idx_test = 0
epoch_idx = 0
iteration = 0
maxl_array = np.zeros((2))
maxl_array[0] = args.voxel_size
maxl_array[1] = args.voxel_size
epCount = dataLoad.fileCount(args.datapath)
stepFactor = 9
for epoch_train, epoch_validate in dataLoad.gen_epochs(
args.epochs, args.datapath, args.batch_size, args.velocity_multiplier,
True, args.output_dim):
epoch_idx += 1
print(colored("Epoch %03d" % (epoch_idx), 'yellow'))
# Train
for _x, _x_size in epoch_train:
if batch_idx_train == 10 and args.profile:
print(colored("Profiling in progress...", 'yellow'))
with tf.contrib.tfprof.ProfileContext('prof/%s' % args.name,
trace_steps=[],
dump_steps=[]) as pctx:
if args.dosim and args.doloop: