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config.py
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# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu). All Rights Reserved.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part of
# the code.
import argparse
def str2opt(arg):
assert arg in ['SGD', 'Adagrad', 'Adam', 'RMSProp', 'Rprop', 'SGDLars']
return arg
def str2scheduler(arg):
assert arg in ['StepLR', 'PolyLR', 'ExpLR', 'SquaredLR']
return arg
def str2bool(v):
return v.lower() in ('true', '1')
def str2list(l):
return [int(i) for i in l.split(',')]
def add_argument_group(name):
arg = parser.add_argument_group(name)
arg_lists.append(arg)
return arg
arg_lists = []
parser = argparse.ArgumentParser()
# Network
net_arg = add_argument_group('Network')
net_arg.add_argument(
'--model', type=str, default='ResUNet14', help='Model name')
net_arg.add_argument(
'--conv1_kernel_size', type=int, default=3, help='First layer conv kernel size')
net_arg.add_argument('--weights', type=str, default='None', help='Saved weights to load')
net_arg.add_argument(
'--dilations', type=str2list, default='1,1,1,1', help='Dilations used for ResNet or DenseNet')
net_arg.add_argument('--nonlinearity', default='ReLU', type=str)
net_arg.add_argument('--xyz_input', default=True, type=bool)
net_arg.add_argument('--ks', default=3, type=int)
# Optimizer arguments
opt_arg = add_argument_group('Optimizer')
# opt_arg.add_argument('--optimizer', type=str, default='SGD')
opt_arg.add_argument('--optimizer', type=str, default='Adam')
opt_arg.add_argument('--lr', type=float, default=1e-2)
opt_arg.add_argument('--lr_warmup', type=float, default=None)
opt_arg.add_argument('--sgd_momentum', type=float, default=0.9)
opt_arg.add_argument('--sgd_dampening', type=float, default=0.1)
opt_arg.add_argument('--adam_beta1', type=float, default=0.9)
opt_arg.add_argument('--adam_beta2', type=float, default=0.999)
#opt_arg.add_argument('--weight_decay', type=float, default=0)
opt_arg.add_argument('--weight_decay', type=float, default=1e-4)
opt_arg.add_argument('--param_histogram_freq', type=int, default=100)
opt_arg.add_argument('--save_param_histogram', type=str2bool, default=False)
opt_arg.add_argument('--iter_size', type=int, default=1, help='accumulate gradient')
opt_arg.add_argument('--bn_momentum', type=float, default=0.02)
# Scheduler
opt_arg.add_argument('--scheduler', type=str2scheduler, default='StepLR')
opt_arg.add_argument('--max_iter', type=int, default=6e4)
# opt_arg.add_argument('--epochs', type=int, default=100)
opt_arg.add_argument('--step_size', type=int, default=2e4)
opt_arg.add_argument('--step_gamma', type=float, default=0.1)
opt_arg.add_argument('--poly_power', type=float, default=0.9)
opt_arg.add_argument('--exp_gamma', type=float, default=0.95)
opt_arg.add_argument('--exp_step_size', type=float, default=445)
# Directories
dir_arg = add_argument_group('Directories')
dir_arg.add_argument('--log_dir', type=str, default='outputs/default')
dir_arg.add_argument('--data_dir', type=str, default='data')
# Data
data_arg = add_argument_group('Data')
data_arg.add_argument('--dataset', type=str, default='ScannetSparseVoxelizationDataset')
data_arg.add_argument('--load_whole', type=bool, default=False) # only used when scannetSparseVoxelizationDataset
data_arg.add_argument('--point_lim', type=int, default=-1)
data_arg.add_argument('--pre_point_lim', type=int, default=-1)
data_arg.add_argument('--batch_size', type=int, default=16)
data_arg.add_argument('--val_batch_size', type=int, default=8)
data_arg.add_argument('--test_batch_size', type=int, default=1)
data_arg.add_argument('--cache_data', type=str2bool, default=False)
data_arg.add_argument('--sample_stride', type=int, default=1)
data_arg.add_argument(
'--threads', type=int, default=0, help='num threads for train/test dataloader')
data_arg.add_argument('--val_threads', type=int, default=0, help='num threads for val dataloader')
data_arg.add_argument('--ignore_label', type=int, default=-1)
data_arg.add_argument('--train_elastic_distortion', type=str2bool, default=True)
data_arg.add_argument('--test_elastic_distortion', type=str2bool, default=False)
data_arg.add_argument('--return_transformation', type=str2bool, default=False)
data_arg.add_argument('--ignore_duplicate_class', type=str2bool, default=False)
data_arg.add_argument('--partial_crop', type=float, default=0.)
data_arg.add_argument('--train_limit_numpoints', type=int, default=180000)
data_arg.add_argument('--enable_point_branch', type=str2bool, default=False)
# data_arg.add_argument('--points', type=bool, default=False)
data_arg.add_argument('--voxel_size', type=float, default=0.1)
# data_arg.add_argument('--voxel_size', type=float, default=0.075)
# data_arg.add_argument('--voxel_size', type=float, default=0.05)
data_arg.add_argument('--num_points', type=int, default=8192)
data_arg.add_argument('--pure_point', type=bool, default=False)
# data_arg.add_argument('--class_reweight_lambda', type=float, default=1.e4)
# Point Cloud Dataset
data_arg.add_argument(
'--scannet_path',
type=str,
default='',
help='Scannet online voxelization dataset root dir')
data_arg.add_argument(
"--semantic_kitti_path",
type=str,
default='',
help='Semantic KITTI dataset root dir'
)
# Training / test parameters
train_arg = add_argument_group('Training')
train_arg.add_argument('--is_train', type=str2bool, default=True)
train_arg.add_argument('--is_export', type=str2bool, default=False)
train_arg.add_argument('--is_debug', type=str2bool, default=False)
train_arg.add_argument('--multiprocess', type=str2bool, default=False) # DEBUG: use the multiprocess training
# train_arg.add_argument('--stat_freq', type=int, default=50, help='print frequency')
train_arg.add_argument('--stat_freq', type=int, default=50, help='print frequency')
train_arg.add_argument('--test_stat_freq', type=int, default=100, help='print frequency')
train_arg.add_argument('--save_freq', type=int, default=1000, help='save frequency')
# train_arg.add_argument('--val_freq', type=int, default=1000, help='validation frequency')
train_arg.add_argument('--val_freq', type=int, default=200, help='validation frequency')
train_arg.add_argument('--train_phase', type=str, default='train', help='Dataset for training')
train_arg.add_argument('--val_phase', type=str, default='val', help='Dataset for validation')
train_arg.add_argument(
'--overwrite_weights', type=str2bool, default=True, help='Overwrite checkpoint during training')
train_arg.add_argument(
'--resume', default=None, type=str, help='path to latest checkpoint (default: none)')
train_arg.add_argument(
'--resume_optimizer',
default=True,
type=str2bool,
help='Use checkpoint optimizer states when resume training')
train_arg.add_argument('--eval_upsample', type=str2bool, default=False)
train_arg.add_argument('--use_sam', type=str2bool, default=False)
train_arg.add_argument('--distill', type=str2bool, default=False)
# train_arg.add_argument('--tch_model', type=str, default="Res16UNet18A") # speciffy this in train_distill func
# some about use the aux-info
# train_arg.add_argument('--save_pred', type=str2bool, default=False)
train_arg.add_argument('--use_aux', type=str2bool, default=False)
# Data augmentation
data_aug_arg = add_argument_group('DataAugmentation')
data_aug_arg.add_argument(
'--use_feat_aug', type=str2bool, default=True, help='Simple feat augmentation')
data_aug_arg.add_argument(
'--data_aug_color_trans_ratio', type=float, default=0.10, help='Color translation range')
data_aug_arg.add_argument(
'--data_aug_color_jitter_std', type=float, default=0.05, help='STD of color jitter')
data_aug_arg.add_argument(
'--data_aug_height_trans_std', type=float, default=1, help='STD of height translation')
data_aug_arg.add_argument(
'--data_aug_height_jitter_std', type=float, default=0.1, help='STD of height jitter')
data_aug_arg.add_argument(
'--data_aug_normal_jitter_std', type=float, default=0.01, help='STD of normal jitter')
data_aug_arg.add_argument('--normalize_color', type=str2bool, default=True)
data_aug_arg.add_argument('--data_aug_scale_min', type=float, default=0.8)
data_aug_arg.add_argument('--data_aug_scale_max', type=float, default=1.2)
data_aug_arg.add_argument(
'--data_aug_hue_max', type=float, default=0.5, help='Hue translation range. [0, 1]')
data_aug_arg.add_argument(
'--data_aug_saturation_max', type=float, default=0.20, help='Saturation translation range, [0, 1]')
data_aug_arg.add_argument('--temporal_rand_dilation', type=str2bool, default=False)
data_aug_arg.add_argument('--temporal_rand_numseq', type=str2bool, default=False)
# Test
test_arg = add_argument_group('Test')
test_arg.add_argument(
'--test_config', default=None, type=str, help='path to the json config file for testing.')
test_arg.add_argument('--test_phase', type=str, default='test', help='Dataset for test')
test_arg.add_argument('--weights_for_inner_model', type=bool, default=False, help='Dataset for test')
test_arg.add_argument('--submit', type=str2bool, default=False, help='SemanticKITTI submit to test server')
# Misc
misc_arg = add_argument_group('Misc')
misc_arg.add_argument('--is_cuda', type=str2bool, default=True)
misc_arg.add_argument('--load_path', type=str, default='')
misc_arg.add_argument('--log_step', type=int, default=50)
misc_arg.add_argument('--log_level', type=str, default='INFO', choices=['INFO', 'DEBUG', 'WARN'])
misc_arg.add_argument('--num_gpu', type=str2bool, default=1)
misc_arg.add_argument('--seed', type=int, default=123)
misc_arg.add_argument(
'--debug', type=str2bool, default=True, help='print out detailed results for debugging')
data_aug_arg.add_argument(
'--lenient_weight_loading',
type=str2bool,
default=False,
help='Weights with the same size will be loaded')
def get_config():
config = parser.parse_args()
return config # Training settings