def main(argv=None):
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
print("-- evaluating: on {} pairs from {}/{}".format(
FLAGS.num, FLAGS.dataset, FLAGS.variant))
default_config = config_dict()
dirs = default_config['dirs']
if FLAGS.dataset == 'kitti':
data = KITTIData(dirs['data'], development=True)
data_input = KITTIInput(data,
batch_size=1,
normalize=False,
dims=(384, 1280))
inputs = getattr(data_input, 'input_' + FLAGS.variant)()
elif FLAGS.dataset == 'nao':
data = NaoData(dirs['data'], development=True)
data_input = NaoInput(data,
batch_size=1,
normalize=False,
dir_name=FLAGS.directory,
dims=(192, 256))
elif FLAGS.dataset == 'chairs':
data = ChairsData(dirs['data'], development=True)
data_input = ChairsInput(data,
batch_size=1,
normalize=False,
dims=(384, 512))
if FLAGS.variant == 'test_2015' and FLAGS.num == -1:
FLAGS.num = 200
elif FLAGS.variant == 'test_2012' and FLAGS.num == -1:
FLAGS.num = 195
elif FLAGS.dataset == 'sintel':
data = SintelData(dirs['data'], development=True)
data_input = SintelInput(data,
batch_size=1,
normalize=False,
dims=(512, 1024))
if FLAGS.variant in ['test_clean', 'test_final'] and FLAGS.num == -1:
FLAGS.num = 552
elif FLAGS.dataset == 'mdb':
data = MiddleburyData(dirs['data'], development=True)
data_input = MiddleburyInput(data,
batch_size=1,
normalize=False,
dims=(512, 640))
if FLAGS.variant == 'test' and FLAGS.num == -1:
FLAGS.num = 12
input_fn = getattr(data_input, 'input_' + FLAGS.variant)
results = []
for name in FLAGS.ex.split(','):
result, image_names = _evaluate_experiment(name, input_fn, data_input)
results.append(result)
display(results, image_names)
def main(argv=None):
experiment = Experiment(
name=FLAGS.ex,
overwrite=FLAGS.ow)
dirs = experiment.config['dirs']
run_config = experiment.config['run']
gpu_list_param = run_config['gpu_list']
if isinstance(gpu_list_param, int):
gpu_list = [gpu_list_param]
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list_param)
else:
gpu_list = list(range(len(gpu_list_param.split(','))))
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list_param
gpu_batch_size = int(run_config['batch_size'] / max(len(gpu_list), 1))
devices = ['/gpu:' + str(gpu_num) for gpu_num in gpu_list]
train_dataset = run_config.get('dataset', 'kitti')
kdata = KITTIData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
einput = KITTIInput(data=kdata,
batch_size=1,
normalize=False,
dims=(384, 1280))
epinput = KITTIInput(data=kdata,
batch_size=1,
normalize=False,
dims=(384, 1280))
if train_dataset == 'chairs':
cconfig = copy.deepcopy(experiment.config['train'])
cconfig.update(experiment.config['train_chairs'])
convert_input_strings(cconfig, dirs)
citers = cconfig.get('num_iters', 0)
cdata = ChairsData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
cinput = ChairsInput(data=cdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(cconfig['height'], cconfig['width']))
tr = Trainer(
lambda shift: cinput.input_raw(swap_images=False,
shift=shift * run_config['batch_size']),
lambda: einput.input_train_2012(),
params=cconfig,
normalization=cinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, citers)
elif train_dataset == 'kitti':
kconfig = copy.deepcopy(experiment.config['train'])
kconfig.update(experiment.config['train_kitti'])
convert_input_strings(kconfig, dirs)
kiters = kconfig.get('num_iters', 0)
kinput = KITTIInput(data=kdata,
batch_size=gpu_batch_size,
normalize=False,
skipped_frames=True,
dims=(kconfig['height'], kconfig['width']))
tr = Trainer(
lambda shift: kinput.input_raw(swap_images=False,
center_crop=True,
shift=shift * run_config['batch_size'],
epipolar_weight=kconfig.get('epipolar_weight', None)),
lambda: einput.input_train_2012(),
params=kconfig,
normalization=kinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices,
eval_pose_batch_fn=lambda: epinput.input_odometry(),
eval_pose_summaries_dir=experiment.eval_pose_dir)
tr.run(0, kiters)
elif train_dataset == 'cityscapes':
kconfig = copy.deepcopy(experiment.config['train'])
kconfig.update(experiment.config['train_cityscapes'])
convert_input_strings(kconfig, dirs)
kiters = kconfig.get('num_iters', 0)
cdata = CityscapesData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
kinput = KITTIInput(data=cdata,
batch_size=gpu_batch_size,
normalize=False,
skipped_frames=False,
dims=(kconfig['height'], kconfig['width']))
tr = Trainer(
lambda shift: kinput.input_raw(swap_images=False,
center_crop=True,
skip=[0, 1],
shift=shift * run_config['batch_size']),
lambda: einput.input_train_2012(),
params=kconfig,
normalization=kinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, kiters)
elif train_dataset == 'synthia':
sconfig = copy.deepcopy(experiment.config['train'])
sconfig.update(experiment.config['train_synthia'])
convert_input_strings(sconfig, dirs)
siters = sconfig.get('num_iters', 0)
sdata = SynthiaData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
sinput = KITTIInput(data=sdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(sconfig['height'], sconfig['width']))
tr = Trainer(
lambda shift: sinput.input_raw(swap_images=False,
center_crop=True,
shift=shift * run_config['batch_size'],
epipolar_weight=sconfig.get('epipolar_weight', None)),
lambda: einput.input_train_2012(),
params=sconfig,
normalization=sinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, siters)
elif train_dataset == 'kitti_ft':
ftconfig = copy.deepcopy(experiment.config['train'])
ftconfig.update(experiment.config['train_kitti_ft'])
convert_input_strings(ftconfig, dirs)
ftiters = ftconfig.get('num_iters', 0)
ftinput = KITTIInput(data=kdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(ftconfig['height'], ftconfig['width']))
tr = Trainer(
lambda shift: ftinput.input_train_gt(40),
lambda: einput.input_train_2015(40),
supervised=True,
params=ftconfig,
normalization=ftinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, ftiters)
else:
raise ValueError(
"Invalid dataset. Dataset must be one of "
"{synthia, kitti, kitti_ft, cityscapes, chairs}")
if not FLAGS.debug:
experiment.conclude()
def main(argv=None):
experiment = Experiment(name=FLAGS.ex, overwrite=FLAGS.ow)
dirs = experiment.config['dirs']
run_config = experiment.config['run']
gpu_list_param = run_config['gpu_list']
if isinstance(gpu_list_param, int):
gpu_list = [gpu_list_param]
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list_param)
else:
gpu_list = list(range(len(gpu_list_param.split(','))))
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list_param
gpu_batch_size = int(run_config['batch_size'] / max(len(gpu_list), 1))
devices = ['/gpu:' + str(gpu_num) for gpu_num in gpu_list]
train_dataset = run_config.get('dataset', 'kitti')
print("train_dataset:", train_dataset)
kdata = KITTIData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
einput = KITTIInput(data=kdata,
batch_size=1,
normalize=False,
dims=(384, 1280))
if train_dataset == 'chairs':
cconfig = copy.deepcopy(experiment.config['train'])
cconfig.update(experiment.config['train_chairs'])
convert_input_strings(cconfig, dirs)
citers = cconfig.get('num_iters', 0)
cdata = ChairsData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
cinput = ChairsInput(data=cdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(cconfig['height'], cconfig['width']))
tr = Trainer(lambda shift: cinput.input_raw(
swap_images=False, shift=shift * run_config['batch_size']),
lambda: einput.input_train_2012(),
params=cconfig,
normalization=cinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, citers)
elif train_dataset == 'kitti':
kconfig = copy.deepcopy(experiment.config['train'])
kconfig.update(experiment.config['train_kitti'])
convert_input_strings(kconfig, dirs)
kiters = kconfig.get('num_iters', 0)
kinput = KITTIInput(data=kdata,
batch_size=gpu_batch_size,
normalize=False,
skipped_frames=True,
dims=(kconfig['height'], kconfig['width']))
tr = Trainer(lambda shift: kinput.input_raw(swap_images=False,
center_crop=True,
shift=shift * run_config[
'batch_size']),
lambda: einput.input_train_2012(),
params=kconfig,
normalization=kinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, kiters)
elif train_dataset == 'cityscapes':
kconfig = copy.deepcopy(experiment.config['train'])
kconfig.update(experiment.config['train_cityscapes'])
convert_input_strings(kconfig, dirs)
kiters = kconfig.get('num_iters', 0)
cdata = CityscapesData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
kinput = KITTIInput(data=cdata,
batch_size=gpu_batch_size,
normalize=False,
skipped_frames=False,
dims=(kconfig['height'], kconfig['width']))
tr = Trainer(lambda shift: kinput.input_raw(swap_images=False,
center_crop=True,
skip=[0, 1],
shift=shift * run_config[
'batch_size']),
lambda: einput.input_train_2012(),
params=kconfig,
normalization=kinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, kiters)
elif train_dataset == 'synthia':
sconfig = copy.deepcopy(experiment.config['train'])
sconfig.update(experiment.config['train_synthia'])
convert_input_strings(sconfig, dirs)
siters = sconfig.get('num_iters', 0)
sdata = SynthiaData(data_dir=dirs['data'],
fast_dir=dirs.get('fast'),
stat_log_dir=None,
development=run_config['development'])
sinput = KITTIInput(data=sdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(sconfig['height'], sconfig['width']))
tr = Trainer(lambda shift: sinput.input_raw(
swap_images=False, shift=shift * run_config['batch_size']),
lambda: einput.input_train_2012(),
params=sconfig,
normalization=sinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, siters)
elif train_dataset == 'nao':
# c&p and adjusted from synthia
nconfig = copy.deepcopy(experiment.config['train'])
nconfig.update(experiment.config['train_nao'])
convert_input_strings(nconfig, dirs)
niters = nconfig.get('num_iters', 0)
ndata = NaoData(dirs['data'], development=False)
ninput = NaoInput(ndata,
batch_size=1,
normalize=False,
dir_name='grey400',
dims=(192, 256))
tr = Trainer(
lambda shift: ninput.input_consecutive(return_shape=False),
# lambda: einput.input_train_2012(), # todo: is this appropriate for nao data? what does it do?
lambda: None,
params=nconfig,
normalization=ninput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, niters)
elif train_dataset == 'kitti_ft':
ftconfig = copy.deepcopy(experiment.config['train'])
ftconfig.update(experiment.config['train_kitti_ft'])
convert_input_strings(ftconfig, dirs)
ftiters = ftconfig.get('num_iters', 0)
ftinput = KITTIInput(data=kdata,
batch_size=gpu_batch_size,
normalize=False,
dims=(ftconfig['height'], ftconfig['width']))
tr = Trainer(lambda shift: ftinput.input_train_gt(40),
lambda: einput.input_train_2015(40),
supervised=True,
params=ftconfig,
normalization=ftinput.get_normalization(),
train_summaries_dir=experiment.train_dir,
eval_summaries_dir=experiment.eval_dir,
experiment=FLAGS.ex,
ckpt_dir=experiment.save_dir,
debug=FLAGS.debug,
interactive_plot=run_config.get('interactive_plot'),
devices=devices)
tr.run(0, ftiters)
else:
raise ValueError("Invalid dataset. Dataset must be one of "
"{synthia, kitti, kitti_ft, cityscapes, chairs, nao}")
if not FLAGS.debug:
experiment.conclude()
print('done with training at:')
print(str(datetime.now()))