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Update_dataset
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3 files changed

+205
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lines changed

‎config.py

+197
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,197 @@
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import collections
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import functools
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import os
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import re
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import yaml
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from util.distributed import master_only_print as print
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class AttrDict(dict):
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"""Dict as attribute trick."""
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def __init__(self, *args, **kwargs):
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super(AttrDict, self).__init__(*args, **kwargs)
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self.__dict__ = self
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for key, value in self.__dict__.items():
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if isinstance(value, dict):
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self.__dict__[key] = AttrDict(value)
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elif isinstance(value, (list, tuple)):
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if isinstance(value[0], dict):
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self.__dict__[key] = [AttrDict(item) for item in value]
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else:
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self.__dict__[key] = value
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def yaml(self):
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"""Convert object to yaml dict and return."""
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yaml_dict = {}
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for key, value in self.__dict__.items():
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if isinstance(value, AttrDict):
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yaml_dict[key] = value.yaml()
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elif isinstance(value, list):
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if isinstance(value[0], AttrDict):
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new_l = []
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for item in value:
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new_l.append(item.yaml())
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yaml_dict[key] = new_l
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else:
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yaml_dict[key] = value
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else:
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yaml_dict[key] = value
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return yaml_dict
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def __repr__(self):
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"""Print all variables."""
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ret_str = []
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for key, value in self.__dict__.items():
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if isinstance(value, AttrDict):
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ret_str.append('{}:'.format(key))
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child_ret_str = value.__repr__().split('\n')
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for item in child_ret_str:
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ret_str.append(' ' + item)
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elif isinstance(value, list):
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if isinstance(value[0], AttrDict):
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ret_str.append('{}:'.format(key))
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for item in value:
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# Treat as AttrDict above.
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child_ret_str = item.__repr__().split('\n')
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for item in child_ret_str:
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ret_str.append(' ' + item)
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else:
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ret_str.append('{}: {}'.format(key, value))
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else:
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ret_str.append('{}: {}'.format(key, value))
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return '\n'.join(ret_str)
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class Config(AttrDict):
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r"""Configuration class. This should include every human specifiable
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hyperparameter values for your training."""
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def __init__(self, filename=None, args=None, verbose=False, is_train=True):
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super(Config, self).__init__()
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# Set default parameters.
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# Logging.
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large_number = 1000000000
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self.snapshot_save_iter = large_number
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self.snapshot_save_epoch = large_number
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self.snapshot_save_start_iter = 0
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self.snapshot_save_start_epoch = 0
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self.image_save_iter = large_number
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self.eval_epoch = large_number
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self.start_eval_epoch = large_number
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self.eval_epoch = large_number
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self.max_epoch = large_number
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self.max_iter = large_number
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self.logging_iter = 100
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self.image_to_tensorboard=False
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self.which_iter = args.which_iter
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self.resume = not args.no_resume
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self.checkpoints_dir = args.checkpoints_dir
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self.name = args.name
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self.phase = 'train' if is_train else 'test'
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# Networks.
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self.gen = AttrDict(type='generators.dummy')
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self.dis = AttrDict(type='discriminators.dummy')
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# Optimizers.
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# Data.
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self.data = AttrDict(name='dummy',
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type='datasets.images',
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num_workers=0)
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self.test_data = AttrDict(name='dummy',
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type='datasets.images',
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num_workers=0,
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test=AttrDict(is_lmdb=False,
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roots='',
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batch_size=1))
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self.trainer = AttrDict(
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model_average=False,
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model_average_beta=0.9999,
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model_average_start_iteration=1000,
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model_average_batch_norm_estimation_iteration=30,
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model_average_remove_sn=True,
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image_to_tensorboard=False,
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hparam_to_tensorboard=False,
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distributed_data_parallel='pytorch',
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delay_allreduce=True,
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gan_relativistic=False,
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gen_step=1,
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dis_step=1)
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# # Cudnn.
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self.cudnn = AttrDict(deterministic=False,
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benchmark=True)
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# Others.
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self.pretrained_weight = ''
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self.inference_args = AttrDict()
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# Update with given configurations.
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assert os.path.exists(filename), 'File {} not exist.'.format(filename)
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loader = yaml.SafeLoader
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loader.add_implicit_resolver(
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u'tag:yaml.org,2002:float',
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re.compile(u'''^(?:
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[-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
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|[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
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|\\.[0-9_]+(?:[eE][-+][0-9]+)?
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|[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
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|[-+]?\\.(?:inf|Inf|INF)
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|\\.(?:nan|NaN|NAN))$''', re.X),
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list(u'-+0123456789.'))
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try:
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with open(filename, 'r') as f:
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cfg_dict = yaml.load(f, Loader=loader)
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except EnvironmentError:
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print('Please check the file with name of "%s"', filename)
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recursive_update(self, cfg_dict)
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# Put common opts in both gen and dis.
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if 'common' in cfg_dict:
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self.common = AttrDict(**cfg_dict['common'])
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self.gen.common = self.common
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self.dis.common = self.common
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if verbose:
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print(' config '.center(80, '-'))
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print(self.__repr__())
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print(''.center(80, '-'))
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def rsetattr(obj, attr, val):
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"""Recursively find object and set value"""
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pre, _, post = attr.rpartition('.')
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return setattr(rgetattr(obj, pre) if pre else obj, post, val)
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def rgetattr(obj, attr, *args):
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"""Recursively find object and return value"""
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def _getattr(obj, attr):
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r"""Get attribute."""
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return getattr(obj, attr, *args)
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return functools.reduce(_getattr, [obj] + attr.split('.'))
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def recursive_update(d, u):
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"""Recursively update AttrDict d with AttrDict u"""
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for key, value in u.items():
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if isinstance(value, collections.abc.Mapping):
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d.__dict__[key] = recursive_update(d.get(key, AttrDict({})), value)
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elif isinstance(value, (list, tuple)):
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if isinstance(value[0], dict):
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d.__dict__[key] = [AttrDict(item) for item in value]
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else:
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d.__dict__[key] = value
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else:
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d.__dict__[key] = value
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return d

‎inference_refine_1D_cam.py

+6-5
Original file line numberDiff line numberDiff line change
@@ -90,15 +90,16 @@ def write2video(results_dir, *video_list):
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opt.data.cross_id = args.cross_id
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opt.data.cross_id_target = args.cross_id_target
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if args.multi_view:
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from data.multiface_video_dataset_inv_fix_target import MultifaceVideoDataset
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dataset = MultifaceVideoDataset(opt.data, is_inference=True)
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# opt.trainer.inversion.iterations = 300
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# TODO: add multi-view dataset
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raise NotImplementedError
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# from data.multiface_video_dataset_inv_fix_target import MultifaceVideoDataset
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# dataset = MultifaceVideoDataset(opt.data, is_inference=True)
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else:
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if args.cross_id_target is not None:
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assert args.cross_id
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from data.hdtf_video_dataset_inv_fix_target import HDTFVideoDataset
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from data.hdtf_cross_id import HDTFVideoDataset
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else:
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from data.hdtf_video_dataset_inv import HDTFVideoDataset
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from data.dataset import HDTFVideoDataset
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dataset = HDTFVideoDataset(opt.data, is_inference=True)
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# create a model

‎scripts/inference.sh

+2-2
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@@ -1,7 +1,7 @@
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export CUDA_VISIBLE_DEVICES=1
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python -m torch.distributed.launch --nproc_per_node=1 --master_port 12345 inference_refine_1D_cam.py \
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--config ./config/config/otavatar.yaml \
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--name config/otavatar.yaml \
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--config ./config/otavatar.yaml \
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--name result/otavatar/animation \
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--no_resume \
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--which_iter 2000 \
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--image_size 512 \

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