82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '0'
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config['name'] = 'direct_3'
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config['notes'] = 'with normals input'
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config['tags'] = ['torch', 'direct']
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config['script'] = 'scripts/direct_train.py'
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config['pretrained_model'] = ''
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config['use_curvature'] = False
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config['use_normals'] = True
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# assert that only use curvature or normals
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assert not (config['use_curvature'] and config['use_normals'])
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config['batch_size'] = 4
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config['train_dir'] = '/data-local/127/train'
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config['valid_dir'] = '/data-local/127/valid'
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config['test_dir'] = '/data-net/dataset/test_ready'
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config['train_frames'] = None
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config['valid_frames'] = None
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config['resolution'] = 512
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config['is_skirt'] = False
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config['dataset_in_memory'] = False
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config['is_cape'] = False
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config['remove_global_orientation'] = True
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config['device'] = 'cuda'
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config['input_features'] = 9
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config['smpl_res'] = 14475
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if config['is_cape']:
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config['smpl_res'] = 3638
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config['latent_dim'] = 128
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config['bottleneck_scale'] = 2
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config['is_label'] = False
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config['kernel_size'] = 7
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config['bottleneck_size'] = 64 * config['bottleneck_scale']
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block_setting = [
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CNBlockConfig(16, 32, 3),
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CNBlockConfig(32, 128, 3),
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CNBlockConfig(128, 256, 9),
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CNBlockConfig(256, config['bottleneck_size'], 9),
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CNBlockConfig(config['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['bottleneck_size'], 256, 3),
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CNBlockConfig(256, 128, 9),
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CNBlockConfig(128, 32, 9),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 3, 3),
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# CNBlockConfig(3, 3, 3),
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]
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config['encoder_block_settings'] = block_setting
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config['decoder_block_settings'] = block_setting_reverse
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config['activation'] = 'tanh'
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config['lr'] = 0.0001
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config['reg_loss_coeff'] = 0.1
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config['signature'] = { 'id',
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'frame_nr',
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'pose_shape',
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'uv_normals',
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'mesh_mask',
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'mesh_body_posed',
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'mesh_posed',
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'uv_unposed',
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'mesh_unposed',
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'uv_static',
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'uv_mask',
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'uv_body_posed',
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} |