File size: 4,668 Bytes
cda88e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import argparse
class params():
""" Singleton class for doing experiments """
class __params():
def __init__(self):
self.norm = 'group_norm'
self.prelu = False
self.weight_decay = 5e-4
self.small_ds = False
self.multi_gpu = False
self.log = False
self.input_channel = 1
self.vis_port = 8002
self.cpu = False
self.pred_touch = False
self.tbaseline = False
self.touch_loss = False
self.input_channel = 1
def set_params(self, options):
self.options = options
self.norm = options.norm
self.prelu = options.prelu
self.weight_decay = options.weight_decay
self.small_ds = options.small_ds
self.multi_gpu = options.multi_gpu
self.log = options.log
self.input_channel = options.input_channel
self.vis_port = options.vis_port
self.cpu = options.cpu
self.ds_folder = options.ds_folder
self.pred_touch = options.pred_touch
self.tbaseline = options.tbaseline
self.touch_loss = options.touch_loss
def __str__(self):
return 'norm: {} prelu: {} weight decay: {} small ds: {}'.format(self.norm, self.prelu, self.weight_decay, self.small_ds)
# private static variable
param_instance = None
def __init__(self):
if not params.param_instance:
params.param_instance = params.__params()
def get_params(self):
return params.param_instance
def set_params(self, options):
params.param_instance.set_params(options)
def parse_params():
parser = argparse.ArgumentParser()
parser.add_argument('--workers', type=int, help='number of data loading workers', default=16)
parser.add_argument('--batch_size', type=int, default=28, help='input batch size during training')
parser.add_argument('--epochs', type=int, default=10000, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.003, help='learning rate, default=0.005')
parser.add_argument('--beta1', type=float, default=0.9, help='momentum for SGD, default=0.9')
parser.add_argument('--resume', action='store_true', help='resume training')
parser.add_argument('--relearn', action='store_true', help='forget previous best validation loss')
parser.add_argument('--weight_file',type=str, help='weight file')
parser.add_argument('--multi_gpu', action='store_true', help='use multiple GPU training')
parser.add_argument('--timers', type=int, default=1, help='number of epochs to train for')
parser.add_argument('--use_schedule', action='store_true',help='use automatic schedule')
parser.add_argument('--patience', type=int, default=2, help='use automatic schedule')
parser.add_argument('--exp_name', type=str, default='l1 loss',help='experiment name')
parser.add_argument('--norm', type=str, default='group_norm', help='use group norm')
parser.add_argument('--ds_folder', type=str, default='./dataset/general_dataset', help='Dataset folder')
parser.add_argument('--hd_dir', type=str, default='/mnt/yifan/data/Adobe/HD_styleshadow/', help='Dataset folder')
parser.add_argument('--prelu', action='store_true', help='use prelu')
parser.add_argument('--small_ds', action='store_true', help='small dataset')
parser.add_argument('--log', action='store_true', help='log information')
parser.add_argument('--vis_port', default=8002,type=int, help='visdom port')
parser.add_argument('--weight_decay', type=float, default=4e-5, help='weight decay for model weight')
parser.add_argument('--save', action='store_true', help='save batch results?')
parser.add_argument('--cpu', action='store_true', help='Force training on CPU')
parser.add_argument('--pred_touch', action='store_true', help='Use touching surface')
parser.add_argument('--input_channel', type=int, default=1, help='how many input channels')
# based on baseline method, for fine tuning
parser.add_argument('--from_baseline', action='store_true', help='training from baseline')
parser.add_argument('--tbaseline', action='store_true', help='T-baseline, input two channels')
parser.add_argument('--touch_loss', action='store_true', help='Use touching loss')
arguments = parser.parse_args()
parameter = params()
parameter.set_params(arguments)
return arguments
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