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from pprint import pprint
# Default Configs for training
# NOTE that, config items could be overwriten by passing argument through command line.
# e.g. --voc-data-dir='./data/'
class Config:
# data
voc_data_dir = '/dataset/PASCAL2007/VOC2007/'
min_size = 600 # image resize
max_size = 1000 # image resize
num_workers = 8
test_num_workers = 8
# sigma for l1_smooth_loss
rpn_sigma = 3.
roi_sigma = 1.
# param for optimizer
# 0.0005 in origin paper but 0.0001 in tf-faster-rcnn
weight_decay = 0.0005
lr_decay = 0.1 # 1e-3 -> 1e-4
lr = 1e-3
# visualization
env = 'faster-rcnn' # visdom env
port = 8097
plot_every = 40 # vis every N iter
# preset
data = 'voc'
pretrained_model = 'vgg16'
# training
epoch = 14
use_adam = False # Use Adam optimizer
use_chainer = False # try match everything as chainer
use_drop = False # use dropout in RoIHead
# debug
debug_file = '/tmp/debugf'
test_num = 10000
# model
load_path = None
caffe_pretrain = False # use caffe pretrained model instead of torchvision
caffe_pretrain_path = 'checkpoints/vgg16_caffe.pth'
def _parse(self, kwargs):
state_dict = self._state_dict()
for k, v in kwargs.items():
if k not in state_dict:
raise ValueError('UnKnown Option: "--%s"' % k)
setattr(self, k, v)
print('======user config========')
pprint(self._state_dict())
print('==========end============')
def _state_dict(self):
return {k: getattr(self, k) for k, _ in Config.__dict__.items() \
if not k.startswith('_')}
opt = Config()
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