def main():
args = parse_args()
print('Called with args:')
print(args)
# Set main gpu
# theano.sandbox.cuda.use(args.gpu_id)
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if not args.randomize:
np.random.seed(cfg.CONST.RNG_SEED)
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.iter is not None:
cfg_from_list(['TRAIN.NUM_ITERATION', args.iter])
if args.save_freq is not None:
cfg_from_list(['TRAIN.SAVE_FREQ', args.save_freq])
if args.valid_freq is not None:
cfg_from_list(['TRAIN.VALIDATION_FREQ', args.valid_freq])
if args.nan_check_freq is not None:
cfg_from_list(['TRAIN.NAN_CHECK_FREQ', args.nan_check_freq])
if args.net_name is not None:
cfg_from_list(['NET_NAME', args.net_name])
if args.model_name is not None:
cfg_from_list(['CONST.NETWORK_CLASS', args.model_name])
if args.dataset is not None:
cfg_from_list(['DATASET', args.dataset])
if args.exp is not None:
cfg_from_list(['TEST.EXP_NAME', args.exp])
if args.out_path is not None:
cfg_from_list(['DIR.OUT_PATH', args.out_path])
if args.weights is not None:
cfg_from_list([
'CONST.WEIGHTS', args.weights, 'TRAIN.RESUME_TRAIN', True,
'TRAIN.INITIAL_ITERATION',
int(args.init_iter)
])
print('Using config:')
pprint.pprint(cfg)
if not args.test:
train_net()
else:
test_net()
def main():
args = parse_args()
print('Called with args:')
print(args)
# Set main gpu
#theano.sandbox.cuda.use(args.gpu_id)
#theano.gpuarray.use(args.gpu_id)
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if not args.randomize:
np.random.seed(cfg.CONST.RNG_SEED)
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.iter is not None:
cfg_from_list(['TRAIN.NUM_ITERATION', args.iter])
if args.net_name is not None:
cfg_from_list(['NET_NAME', args.net_name])
if args.model_name is not None:
cfg_from_list(['CONST.NETWORK_CLASS', args.model_name])
if args.dataset is not None:
cfg_from_list(['DATASET', args.dataset])
if args.exp is not None:
cfg_from_list(['TEST.EXP_NAME', args.exp])
if args.out_path is not None:
cfg_from_list(['DIR.OUT_PATH', args.out_path])
if args.tb_path is not None:
cfg_from_list(['DIR.TB_PATH', args.tb_path])
if args.dyna_dict is not None:
cfg_from_list(['CONST.dynamic_dict', args.dyna_dict])
if args.learn_rate is not None:
cfg_from_list(['TRAIN.DEFAULT_LEARNING_RATE', args.learn_rate])
if args.weights is not None:
cfg_from_list(['CONST.WEIGHTS', args.weights, 'TRAIN.RESUME_TRAIN', True,
'TRAIN.INITIAL_ITERATION', int(args.init_iter)])
print('Using config:')
pprint.pprint(cfg)
if not args.test:
train_net()
else:
test_net()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('dataset',
help='dataset ('
'shapenet'
', '
'primitives'
')')
parser.add_argument('embeddings_path',
help='path to text embeddings pickle file')
parser.add_argument('--metric',
help='path to text embeddings pickle file',
default='minkowski',
type=str)
parser.add_argument('--cfg',
dest='cfg_files',
action='append',
help='optional config file',
default=None,
type=str)
args = parser.parse_args()
# modify default config if requested
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
cfg_from_list(['CONST.DATASET', args.dataset])
with open(args.embeddings_path, 'rb') as f:
embeddings_dict = pickle.load(f)
if os.path.basename(args.embeddings_path) == 'text_embeddings.p':
subdir = 'text'
elif ((os.path.basename(args.embeddings_path) == 'shape_embeddings.p')
or (os.path.basename(args.embeddings_path)
== 'modified_shape_embeddings.p')):
subdir = 'shape'
else:
subdir = 'unspecified'
render_dir = os.path.join(os.path.dirname(args.embeddings_path),
'nearest_neighbor_renderings', subdir)
np.random.seed(1234)
compute_metrics(args.dataset,
embeddings_dict,
metric=args.metric,
concise=render_dir)
def main():
parser = argparse.ArgumentParser(description="SMDL: SubModular Dataloader")
parser.add_argument("--cfg",
dest='cfg_file',
default='./config/smdl.yml',
type=str,
help="An optional config file"
" to be loaded")
args = parser.parse_args()
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if not os.path.exists('datasets'):
os.makedirs('datasets')
if not os.path.exists('output'):
os.makedirs('output')
timestamp = time.strftime("%m%d_%H%M%S")
cfg.timestamp = timestamp
output_dir = './output/' + cfg.run_label + '_' + cfg.timestamp
cfg.output_dir = output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
os.makedirs(output_dir + '/models')
os.makedirs(output_dir + '/plots')
os.makedirs(output_dir + '/logs')
os.makedirs(output_dir + '/accuracies')
logging.basicConfig(filename=output_dir + '/logs/smdl_' + timestamp +
'.log',
level=logging.DEBUG,
format='%(levelname)s:\t%(message)s')
log(pprint.pformat(cfg))
gpu_list = cfg.gpu_ids.split(',')
gpus = [int(iter) for iter in gpu_list]
torch.cuda.set_device(gpus[0])
torch.backends.cudnn.benchmark = True
if cfg.seed != 0:
np.random.seed(cfg.seed)
torch.backends.cudnn.deterministic = True
torch.manual_seed(cfg.seed)
submodular_training(gpus)
def __init__(self):
# dnn
cfg_from_file(cfg_file)
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = cfg.RPN.FIXED = True
cfg.RPN.LOC_XZ_FINE = False
self.pc_roi = [[-25, 25], [-3, 2], [-25, 25]]
self.down_sample = {'axis': int(0), 'depth': self.pc_roi[0][1] / 2} # [axis,depth]
self.mode = 'TEST'
with torch.no_grad():
self.model = PointRCNN(num_classes=2, use_xyz=True, mode=self.mode)
self.model.cuda()
self.model.eval()
load_checkpoint(model=self.model, optimizer=None, filename=pointrcnn_weight)
# ros
self.pc_sub = rospy.Subscriber(pc_topic, PointCloud2, self.pc_cb, queue_size=1, buff_size=2 ** 24)
self.pc_pub = rospy.Publisher(pack_name + "/networks_input", PointCloud2, queue_size=1) if is_viz else None
self.mk_pub = rospy.Publisher(pack_name + "/networks_output", MarkerArray, queue_size=1) if is_viz else None
self.Tr_velo_kitti_cam = np.array([0.0, - 1.0, 0.0, 0.0,
0.0, 0.0, -1.0, 1.5,
1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0]).reshape(4, 4) if is_tf else np.identity(4)
logger=logger)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers,
collate_fn=test_set.collate_batch)
return test_loader
if __name__ == "__main__":
# merge config and log to file
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.TAG = os.path.splitext(os.path.basename(args.cfg_file))[0]
if args.eval_mode == 'rpn':
cfg.RPN.ENABLED = True
cfg.RCNN.ENABLED = False
root_result_dir = os.path.join('../', 'output', 'rpn', cfg.TAG)
ckpt_dir = os.path.join('../', 'output', 'rpn', cfg.TAG, 'ckpt')
elif args.eval_mode == 'rcnn':
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = cfg.RPN.FIXED = True
root_result_dir = os.path.join('../', 'output', 'rcnn', cfg.TAG)
ckpt_dir = os.path.join('../', 'output', 'rcnn', cfg.TAG, 'ckpt')
elif args.eval_mode == 'rcnn_offline':
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".pth")
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Image Captioning')
parser.add_argument('--folder', dest='folder', default=None, type=str)
parser.add_argument("--resume", type=int, default=-1)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.folder is not None:
cfg_from_file(os.path.join(args.folder, 'config.yml'))
cfg.ROOT_DIR = args.folder
tester = Tester(args)
tester.eval(args.resume)
def __init__(self, cfg_filepath=".\\cfgs\\easypr.yml"):
cfg_from_file(cfg_filepath)
self._cfg = cfg
parser.add_argument("--test_bs", type=int, default=-1)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
args.resume = -1
if args.config is not None:
cfg_from_file(args.config)
os.environ["TORCH_HOME"] = cfg.MODEL.PRETRAINED_PATH
if args.multi_crop:
cfg.AUG.MULTI_CROP_TEST = True
if args.test_crop_size > 0:
cfg.AUG.TEST_CROP = [args.test_crop_size, args.test_crop_size]
if args.test_resize_size > 0:
cfg.AUG.RESIZE = [args.test_resize_size, args.test_resize_size]
if args.test_bs > 0:
cfg.TEST.BATCH_SIZE = args.test_bs
trainer = Trainer(args)
netG_dict = torch.load(args.netG_model_path,
map_location=lambda storage, loc: storage)
current_state = trainer.netG.state_dict()
keys = list(current_state.keys())
for key in keys:
list(cfg.TRAIN.MOMS), cfg.TRAIN.DIV_FACTOR,
cfg.TRAIN.PCT_START)
else:
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lbmd,
last_epoch=last_iter)
bnm_scheduler = train_utils.BNMomentumScheduler(model,
bnm_lmbd,
last_epoch=last_iter)
return lr_scheduler, bnm_scheduler
if __name__ == "__main__":
if args.cfg_file is not None:
cfg_from_file(args.cfg_file + 'weaklyRPN.yaml')
cfg_from_file(args.cfg_file + 'weaklyRCNN.yaml')
cfg_from_file(args.cfg_file + 'weaklyIOUN.yaml')
cfg.TAG = os.path.splitext(os.path.basename(args.cfg_file))[0]
cfg.RCNN.ENABLED = False
cfg.IOUN.ENABLED = True
cfg.RPN.ENABLED = cfg.RPN.FIXED = False
root_result_dir = os.path.join('../', 'output', 'ioun', cfg.TAG + exp_id)
if args.output_dir is not None:
root_result_dir = args.output_dir
os.makedirs(root_result_dir, exist_ok=True)
log_file = os.path.join(root_result_dir, 'log_train.txt')
logger = create_logger(log_file)
snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".pth")
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Image Captioning')
parser.add_argument('--folder', dest='folder', default=None, type=str)
parser.add_argument("--resume", type=int, default=-1)
parser.add_argument('--config', default='config.yml')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.folder is not None:
cfg_from_file(os.path.join(args.folder, args.config))
cfg.ROOT_DIR = args.folder
tester = Tester(args)
tester.eval(args.resume)
'rpn_cls': rpn_cls,
'rpn_reg': rpn_reg,
'backbone_xyz': backbone_xyz,
'backbone_features': backbone_features
}
return ret_dict
if __name__ == '__main__':
import ipdb
from lib.config import cfg, cfg_from_file, save_config_to_file, cfg_from_list
import torch
from lib.net.rcnn_net import RCNNNet
cfg_file = 'tools/cfgs/default.yaml'
cfg_from_file(cfg_file)
cfg.TAG = os.path.splitext(os.path.basename(cfg_file))[0]
train_mode = 'rcnn'
if train_mode == 'rpn':
cfg.RPN.ENABLED = True
cfg.RCNN.ENABLED = False
root_result_dir = os.path.join('../', 'output', 'rpn', cfg.TAG)
elif train_mode == 'rcnn':
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = cfg.RPN.FIXED = True
root_result_dir = os.path.join('../', 'output', 'rcnn', cfg.TAG)
elif train_mode == 'rcnn_offline':
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = False
root_result_dir = os.path.join('../', 'output', 'rcnn', cfg.TAG)
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.folder is not None:
if not os.path.exists(args.config):
config_path = os.path.join(args.folder, args.config)
else:
config_path = args.config
cfg_from_file(config_path)
cfg.ROOT_DIR = args.folder
if args.test_raw_image:
cfg.RAW_DATA_LOADER.TEST_IMG_DIR = os.path.join(
args.test_dir, 'images')
cfg.RAW_DATA_LOADER.TEST_ATT_FEATS = os.path.join(
args.test_dir, 'vg/features')
cfg.RAW_DATA_LOADER.TEST_PROCESSEDIMG_DIR = os.path.join(
args.test_dir, 'vg/images')
tester = Tester(args)
tester.eval(args.resume)
def main():
"""
This function:
- Creates a random class-list that will be used by all the methods,
- Saves it to file,
- Creates yaml files with run configurations,
- Executes each of the runs.
:return: None
"""
# Retrieving the arguments
parser = argparse.ArgumentParser(
description="SMILe: SubModular Incremental Learning")
parser.add_argument("--cfg",
dest='cfg_file',
default='./config/smile.yml',
type=str,
help="An optional config file"
" to be loaded")
args = parser.parse_args()
# Updating the configuration object
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
# Creating the class list
class_list = []
classes = range(0, cfg.dataset.total_num_classes)
for i in range(cfg.repeat_rounds):
classes = np.random.permutation(classes)
class_list.append(classes)
# Saving the class list
output_dir = './run_sandbox/'
cfg.output_dir = output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(output_dir + '/class_list.pkl', 'w') as f:
pickle.dump(class_list, f)
# Start Smiling :)
gpus = [5, 6, 7]
current_run_label = cfg['run_label']
cfg['load_class_list_from_file'] = True
# YAML for Random Run
cfg['gpu_ids'] = str(gpus[1])
cfg['run_label'] = 'random_' + current_run_label
cfg['sampling_strategy'] = 'random'
with open(output_dir + '/random.yml', 'w') as outfile:
yaml.dump(dict(cfg), outfile, default_flow_style=False)
# YAML for SubModular Run
cfg['gpu_ids'] = str(gpus[0])
cfg['run_label'] = 'submodular_' + current_run_label
cfg['sampling_strategy'] = 'submodular'
with open(output_dir + '/submodular.yml', 'w') as outfile:
yaml.dump(dict(cfg), outfile, default_flow_style=False)
# YAML for Full-Dataset Run
cfg['gpu_ids'] = str(gpus[2])
cfg['run_label'] = 'full_dataset_' + current_run_label
cfg['use_all_exemplars'] = True
with open(output_dir + '/full_dataset.yml', 'w') as outfile:
yaml.dump(dict(cfg), outfile, default_flow_style=False)
p1 = subprocess.Popen([
'/raid/joseph/il/opy27/bin/python', 'smile.py', '--cfg',
'./run_sandbox/full_dataset.yml'
],
stdout=subprocess.PIPE)
p2 = subprocess.Popen([
'/raid/joseph/il/opy27/bin/python', 'smile.py', '--cfg',
'./run_sandbox/random.yml'
],
stdout=subprocess.PIPE)
p3 = subprocess.Popen([
'/raid/joseph/il/opy27/bin/python', 'smile.py', '--cfg',
'./run_sandbox/submodular.yml'
],
stdout=subprocess.PIPE)
print 'All processes started normally.'
p1.communicate()
p2.communicate()
p3.communicate()
print 'Finishing Launcher.'
dataset = KittiDataset('/raid/meng/Dataset/Kitti/object',
split=split,
noise='label_noise')
else:
dataset = KittiDataset('/raid/meng/Dataset/Kitti/object', split=split)
data_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
pin_memory=True,
num_workers=1)
save_dir = '/raid/meng/Dataset/Kitti/object/training/boxes_dataset'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
ckpt_file = '/raid/meng/Pointcloud_Detection/PointRCNN4_weak/output/rpn/weaklyRPN0500/410_floss03_8000/ckpt/checkpoint_iter_07620.pth'
cfg_from_file(
'/raid/meng/Pointcloud_Detection/PointRCNN1.1_weak/tools/cfgs/weaklyRPN.yaml'
)
cfg.RPN.SCORE_THRESH = 0.1
PROP_DIST = 0.3
BACKGROUND_ADDING = False
BACK_THRESH = 0.3
COSINE_DISTANCE = False
COS_THRESH = 0.3
from lib.net.point_rcnn import PointRCNN
model = PointRCNN(num_classes=data_loader.dataset.num_class,
use_xyz=True,
mode='TEST')
model.cuda()
checkpoint = torch.load(ckpt_file)
def main():
### set divice
if not args.cuda:
cfg.TRAIN.DEVICE = torch.device('cpu')
else :
assert torch.cuda.is_available(), "Not enough GPU"
#assert d < torch.cuda.device_count(), "Not enough GPU"
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(ids) for ids in args.device_ids])
torch.backends.cudnn.benchmark=True
cfg.CUDA = True
cfg.TRAIN.DEVICE = torch.device('cuda:0')
print("Let's use", torch.cuda.device_count(), "GPUs!")
### set config
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg_from_args(args)
print_cfg(cfg)
# assert_and_infer_cfg()
if not cfg.TRAIN.NO_SAVE:
run_folder = create_folder_for_run(cfg.TRAIN.RUNS_FOLDER)
logging.basicConfig(level=logging.INFO,
format='%(message)s',
handlers=[
logging.FileHandler(os.path.join(run_folder, f'{cfg.TRAIN.NAME}.log')),
logging.StreamHandler(sys.stdout)
])
with open(os.path.join(run_folder, 'config_and_args.pkl'), 'wb') as f:
blob = {'cfg': yaml.dump(cfg), 'args': args}
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(run_folder, 'args.txt'), 'w') as f:
for item in vars(args):
f.write(item+":"+str(getattr(args,item))+'\n')
logging.info('×' * 40)
shutil.copy(args.cfg_file, os.path.join(run_folder, cfg.TRAIN.NAME) + '_cfg')
logging.info('save config and args in runs folder:\n %s' % run_folder)
# if args.use_tfboard:
# tblogger = SummaryWriter(run_folder)
else:
logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)
# print('args:')
# logging.info(pprint.pformat(vars(args)))
# print('cfg:')
# logging.info(yaml.dump(cfg.TRAIN))
loader = Random_DataLoader()
model = get_Model()
model = nn.DataParallel(model)
model.to(cfg.TRAIN.DEVICE)
logging.info(model)
Trainer(loader, model)
def modify_args(args):
"""Modify the default config based on the command line arguments.
"""
# modify default config if requested
if args.cfg_files is not None:
for cfg_file in args.cfg_files:
cfg_from_file(cfg_file)
randomize = args.randomize
if args.test: # Always randomize in test phase
randomize = True
if not randomize:
np.random.seed(cfg.CONST.RNG_SEED)
# NOTE: Unfortunately order matters here
if args.lba_only is True:
cfg_from_list(['LBA.COSINE_DIST', False])
if args.metric_learning_only is True:
cfg_from_list(['LBA.NO_LBA', True])
if args.non_inverted_loss is True:
cfg_from_list(['LBA.INVERTED_LOSS', False])
if args.dataset is not None:
cfg_from_list(['CONST.DATASET', args.dataset])
if args.lba_mode is not None:
cfg_from_list(['LBA.MODEL_TYPE', args.lba_mode])
if args.lba_test_mode is not None:
cfg_from_list(['LBA.TEST_MODE', args.lba_test_mode])
# cfg_from_list(['LBA.N_CAPTIONS_PER_MODEL', 1]) # NOTE: Important!
if args.shapenet_ct_classifier is True:
cfg_from_list(
['CONST.SHAPENET_CT_CLASSIFIER', args.shapenet_ct_classifier])
if args.visit_weight is not None:
cfg_from_list(['LBA.VISIT_WEIGHT', args.visit_weight])
if args.lba_unnormalize is True:
cfg_from_list(['LBA.NORMALIZE', False])
if args.improved_wgan is True:
cfg_from_list(['CONST.IMPROVED_WGAN', args.improved_wgan])
if args.synth_embedding is True:
cfg_from_list(['CONST.SYNTH_EMBEDDING', args.synth_embedding])
if args.all_tuples is True:
cfg_from_list(['CONST.TEST_ALL_TUPLES', args.all_tuples])
if args.reed_classifier is True:
cfg_from_list(['CONST.REED_CLASSIFIER', args.reed_classifier])
if args.noise_dist is not None:
cfg_from_list(['GAN.NOISE_DIST', args.noise_dist])
if args.uniform_max is not None:
cfg_from_list(['GAN.NOISE_UNIF_ABS_MAX', args.uniform_max])
if args.num_critic_steps is not None:
cfg_from_list(['WGAN.NUM_CRITIC_STEPS', args.num_critic_steps])
if args.intense_training_freq is not None:
cfg_from_list(
['WGAN.INTENSE_TRAINING_FREQ', args.intense_training_freq])
if args.match_loss_coeff is not None:
cfg_from_list(['WGAN.MATCH_LOSS_COEFF', args.match_loss_coeff])
if args.fake_match_loss_coeff is not None:
cfg_from_list(
['WGAN.FAKE_MATCH_LOSS_COEFF', args.fake_match_loss_coeff])
if args.fake_mismatch_loss_coeff is not None:
cfg_from_list(
['WGAN.FAKE_MISMATCH_LOSS_COEFF', args.fake_mismatch_loss_coeff])
if args.gp_weight is not None:
cfg_from_list(['WGAN.GP_COEFF', args.gp_weight])
if args.text2text_weight is not None:
cfg_from_list(['WGAN.TEXT2TEXT_WEIGHT', args.text2text_weight])
if args.shape2shape_weight is not None:
cfg_from_list(['WGAN.SHAPE2SHAPE_WEIGHT', args.shape2shape_weight])
if args.learning_rate is not None:
cfg_from_list(['TRAIN.LEARNING_RATE', args.learning_rate])
if args.critic_lr_multiplier is not None:
cfg_from_list(
['GAN.D_LEARNING_RATE_MULTIPLIER', args.critic_lr_multiplier])
if args.decay_steps is not None:
cfg_from_list(['TRAIN.DECAY_STEPS', args.decay_steps])
if args.queue_capacity is not None:
cfg_from_list(['CONST.QUEUE_CAPACITY', args.queue_capacity])
if args.n_minibatch_test is not None:
cfg_from_list(['CONST.N_MINIBATCH_TEST', args.n_minibatch_test])
if args.noise_size is not None:
cfg_from_list(['GAN.NOISE_SIZE', args.noise_size])
if args.batch_size is not None:
cfg_from_list(['CONST.BATCH_SIZE', args.batch_size])
if args.summary_freq is not None:
cfg_from_list(['TRAIN.SUMMARY_FREQ', args.summary_freq])
if args.num_epochs is not None:
cfg_from_list(['TRAIN.NUM_EPOCHS', args.num_epochs])
if args.model is not None:
cfg_from_list(['NETWORK', args.model])
if args.optimizer is not None:
cfg_from_list(['TRAIN.OPTIMIZER', args.optimizer])
if args.critic_optimizer is not None:
cfg_from_list(['GAN.D_OPTIMIZER', args.critic_optimizer])
if args.ckpt_path is not None:
cfg_from_list(['DIR.CKPT_PATH', args.ckpt_path])
if args.lba_ckpt_path is not None:
cfg_from_list(['END2END.LBA_CKPT_PATH', args.lba_ckpt_path])
if args.val_ckpt_path is not None:
cfg_from_list(['DIR.VAL_CKPT_PATH', args.val_ckpt_path])
if args.log_path is not None:
cfg_from_list(['DIR.LOG_PATH', args.log_path])
if args.augment_max is not None:
cfg_from_list(['TRAIN.AUGMENT_MAX', args.augment_max])
if args.test:
cfg_from_list(['TRAIN.AUGMENT_MAX', 0])
cfg_from_list(['CONST.BATCH_SIZE', 1])
cfg_from_list(['LBA.N_CAPTIONS_PER_MODEL', 1]) # NOTE: Important!
cfg_from_list(['LBA.N_PRIMITIVE_SHAPES_PER_CATEGORY',
1]) # NOTE: Important!
if args.test_npy:
cfg_from_list(['CONST.BATCH_SIZE', 1])
# To overwrite default variables, put the set_cfgs after all argument initializations
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
def main():
args = parse_args()
cfg.DATA_DIR = args.data_dir
cfg.CONTEXT_FUSION = args.context_fusion
# c_time = time.strftime('%m%d_%H%M', time.localtime())
# if not os.path.exists(cfg.LOG_DIR):
# os.makedirs(cfg.LOG_DIR)
# file_handler = logging.FileHandler(pjoin(cfg.LOG_DIR,
# args.network_name + '_%s.txt' % c_time))
# logging.getLogger().addHandler(file_handler)
print('------ called with args: -------')
pprint.pprint(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if cfg.INIT_BY_GLOVE and cfg.KEEP_AS_GLOVE_DIM:
cfg.EMBED_DIM = cfg.GLOVE_DIM
else:
cfg.EMBED_DIM = args.embed_dim
print("Using config:")
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
tf.set_random_seed(cfg.RNG_SEED)
imdb, roidb = GetRoidb(args.imdb_name)
output_dir = get_output_dir(imdb, args.tag)
print("output will be saved to `{:s}`".format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add validation set, but with no flipping image
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = GetRoidb(args.imdbval_name)
cfg.TRAIN.USE_FLIPPED = orgflip
# load network
if args.net == 'vgg16':
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
else:
raise NotImplementedError
if args.weights and not args.weights.endswith('.ckpt'):
try:
ckpt = tf.train.get_checkpoint_state(args.weights)
pretrained_model = ckpt.model_checkpoint_path
except:
raise ValueError("NO checkpoint found in {}".format(args.weights))
else:
pretrained_model = args.weights
# TODO: "imdb" may not be useful during training
train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=pretrained_model,
max_iters=args.max_iters)
def main():
args = parse_args()
# c_time = time.strftime('%m%d_%H%M', time.localtime())
# if not os.path.exists(cfg.LOG_DIR):
# os.makedirs(cfg.LOG_DIR)
# file_handler = logging.FileHandler(pjoin(cfg.LOG_DIR,
# args.network_name + '_%s.txt' % c_time))
# logging.getLogger().addHandler(file_handler)
print('------ called with args: -------')
pprint.pprint(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print("runing with LIMIT_RAM: {}".format(cfg.LIMIT_RAM))
print("Using config:")
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
tf.set_random_seed(cfg.RNG_SEED)
if not cfg.LIMIT_RAM:
imdb, roidb = combined_roidb(args.imdb_name)
else:
imdb, roidb = get_roidb_limit_ram(args.imdb_name)
output_dir = get_output_dir(imdb, args.tag)
print("output will be saved to `{:s}`".format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add validation set, but with no flipping image
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
if not cfg.LIMIT_RAM:
_, valroidb = combined_roidb(args.imdbval_name)
else:
_, valroidb = get_roidb_limit_ram(args.imdbval_name)
cfg.TRAIN.USE_FLIPPED = orgflip
# load network
if args.net == 'vgg16':
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
else:
raise NotImplementedError
# TODO: "imdb" may not be useful during training
train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=args.pretrained_model,
max_iters=args.max_iters)