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Configuration error
Configuration error
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d01f62c | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | from argparse import ArgumentParser
def none_or_default(x, default):
return x if x is not None else default
class Configuration():
def parse(self, unknown_arg_ok=False):
parser = ArgumentParser()
# Enable torch.backends.cudnn.benchmark -- Faster in some cases, test in your own environment
parser.add_argument('--benchmark', action='store_true')
parser.add_argument('--no_amp', action='store_true')
# Data parameters
parser.add_argument('--static_root', help='Static training data root', default='../static')
parser.add_argument('--bl_root', help='Blender training data root', default='../BL30K')
parser.add_argument('--yv_root', help='YouTubeVOS data root', default='../YouTube')
parser.add_argument('--davis_root', help='DAVIS data root', default='/data2/yangyixin/data/DAVIS')
parser.add_argument('--num_workers', help='Total number of dataloader workers across all GPUs processes', type=int, default=16)
parser.add_argument('--key_dim', default=64, type=int)
parser.add_argument('--value_dim', default=512, type=int)
parser.add_argument('--hidden_dim', default=64, help='Set to =0 to disable', type=int)
parser.add_argument('--deep_update_prob', default=0.2, type=float)
parser.add_argument('--stages', help='Training stage (0-static images, 1-Blender dataset, 2-DAVIS+YouTubeVOS)', default='02')
parser.add_argument('--server', help='Training stage (0-pjs-04, 1-A6000Local)', default='0')
parser.add_argument('--savepath', help='Blender training data root', default='/data1/yangyixin/code/xmem')
"""
Stage-specific learning parameters
Batch sizes are effective -- you don't have to scale them when you scale the number processes
"""
# Stage 0, static images
parser.add_argument('--s0_batch_size', default=16, type=int)
parser.add_argument('--s0_iterations', default=150000, type=int)
parser.add_argument('--s0_finetune', default=0, type=int)
parser.add_argument('--s0_steps', nargs="*", default=[], type=int)
parser.add_argument('--s0_lr', help='Initial learning rate', default=1e-5, type=float)
parser.add_argument('--s0_num_ref_frames', default=2, type=int)
parser.add_argument('--s0_num_frames', default=3, type=int)
parser.add_argument('--s0_start_warm', default=20000, type=int)
parser.add_argument('--s0_end_warm', default=70000, type=int)
# Stage 1, BL30K
parser.add_argument('--s1_batch_size', default=8, type=int)
parser.add_argument('--s1_iterations', default=250000, type=int)
# fine-tune means fewer augmentations to train the sensory memory
parser.add_argument('--s1_finetune', default=0, type=int)
parser.add_argument('--s1_steps', nargs="*", default=[200000], type=int)
parser.add_argument('--s1_lr', help='Initial learning rate', default=1e-5, type=float)
parser.add_argument('--s1_num_ref_frames', default=3, type=int)
parser.add_argument('--s1_num_frames', default=8, type=int)
parser.add_argument('--s1_start_warm', default=20000, type=int)
parser.add_argument('--s1_end_warm', default=70000, type=int)
# Stage 2, DAVIS+YoutubeVOS, longer
parser.add_argument('--s2_batch_size', default=2, type=int)
parser.add_argument('--s2_iterations', default=150000, type=int)
# fine-tune means fewer augmentations to train the sensory memory
parser.add_argument('--s2_finetune', default=10000, type=int)
parser.add_argument('--s2_steps', nargs="*", default=[120000], type=int)
# parser.add_argument('--s2_lr', help='Initial learning rate', default=1e-5, type=float)
parser.add_argument('--s2_lr', help='Initial learning rate', default=2e-5, type=float)
parser.add_argument('--s2_num_ref_frames', default=3, type=int)
parser.add_argument('--s2_num_frames', default=8, type=int)
parser.add_argument('--s2_start_warm', default=20000, type=int)
parser.add_argument('--s2_end_warm', default=70000, type=int)
# Stage 3, DAVIS+YoutubeVOS, shorter
parser.add_argument('--s3_batch_size', default=8, type=int)
parser.add_argument('--s3_iterations', default=100000, type=int)
# fine-tune means fewer augmentations to train the sensory memory
parser.add_argument('--s3_finetune', default=10000, type=int)
parser.add_argument('--s3_steps', nargs="*", default=[80000], type=int)
parser.add_argument('--s3_lr', help='Initial learning rate', default=1e-5, type=float)
parser.add_argument('--s3_num_ref_frames', default=3, type=int)
parser.add_argument('--s3_num_frames', default=8, type=int)
parser.add_argument('--s3_start_warm', default=20000, type=int)
parser.add_argument('--s3_end_warm', default=70000, type=int)
parser.add_argument('--gamma', help='LR := LR*gamma at every decay step', default=0.1, type=float)
parser.add_argument('--weight_decay', default=0.05, type=float)
# Loading
parser.add_argument('--load_network', help='Path to pretrained network weight only')
parser.add_argument('--load_checkpoint', help='Path to the checkpoint file, including network, optimizer and such')
# Logging information
parser.add_argument('--log_text_interval', default=100, type=int)
parser.add_argument('--log_image_interval', default=100, type=int)
parser.add_argument('--save_network_interval', default=2500, type=int)
parser.add_argument('--save_checkpoint_interval', default=999999999999999999999, type=int)
parser.add_argument('--exp_id', help='Experiment UNIQUE id, use NULL to disable logging to tensorboard', default='NULL')
parser.add_argument('--debug', help='Debug mode which logs information more often', action='store_true')
# # Multiprocessing parameters, not set by users
# parser.add_argument('--local_rank', default=0, type=int, help='Local rank of this process')
if unknown_arg_ok:
args, _ = parser.parse_known_args()
self.args = vars(args)
else:
self.args = vars(parser.parse_args())
self.args['amp'] = not self.args['no_amp']
# check if the stages are valid
stage_to_perform = list(self.args['stages'])
for s in stage_to_perform:
if s not in ['0', '1', '2', '3']:
raise NotImplementedError
def get_stage_parameters(self, stage):
parameters = {
'batch_size': self.args['s%s_batch_size'%stage],
'iterations': self.args['s%s_iterations'%stage],
'finetune': self.args['s%s_finetune'%stage],
'steps': self.args['s%s_steps'%stage],
'lr': self.args['s%s_lr'%stage],
'num_ref_frames': self.args['s%s_num_ref_frames'%stage],
'num_frames': self.args['s%s_num_frames'%stage],
'start_warm': self.args['s%s_start_warm'%stage],
'end_warm': self.args['s%s_end_warm'%stage],
}
return parameters
def __getitem__(self, key):
return self.args[key]
def __setitem__(self, key, value):
self.args[key] = value
def __str__(self):
return str(self.args)
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