152 lines
4.8 KiB
Python
152 lines
4.8 KiB
Python
from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '2'
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config['name'] = 'cvae_norm_3'
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config['notes'] = ''
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config['tags'] = ['iccv', 'cvae_norm', 'torch']
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config['script'] = 'scripts/cvae_norm.py'
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config['pretrained_model'] = 'checkpoints/iccvw/cvae_norm_2'
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config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
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config['batch_size'] = 4
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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['device'] = 'cuda'
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config['train_dir'] = '/data-local/127/cloth3d_processed'
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config['valid_dir'] = '/data-local/127/cloth3d_processed'
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config['test_dir'] = '/data-local/127_2/cloth3d_processed_test'
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if config['is_skirt']:
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config['train_frames'] = 'frames_train_skirt.txt'
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config['valid_frames'] = 'frames_valid_skirt.txt'
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config['test_frames'] = 'frames_test_skirt.txt'
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else:
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config['train_frames'] = 'frames_train.txt'
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config['valid_frames'] = 'frames_valid.txt'
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config['test_frames'] = 'frames_test.txt'
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config['static_model'] = 'checkpoints/iccvw/static_iccvw_4'
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config['body_model'] = 'checkpoints/iccvw/body_iccvw_1'
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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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###############
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config['lr'] = 0.0001# 0.0001
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###############
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config['starting_balancing_coeff'] = 1
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config['balancing_activation_limit'] = 0.1
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config['ignore_saved_optimizer'] = True
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config['signature']: set = { 'id',
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'frame_nr',
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'pose_shape',
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'uv_body_posed',
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'uv_normals',
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'uv_static',
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'mesh_mask',
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'uv_mask',
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'mesh_normals',
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}
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config['upscale_enhancer'] = ''
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config['freeze_encoders'] = True
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config['use_one_hot'] = False
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config['bottleneck_scale'] = 2
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config['cvae'] = dict()
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config['cvae']['device'] = config['device']
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config['cvae']['is_label'] = False
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config['cvae']['input_features'] = 3+3+3
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config['cvae']['kernel_size'] = 7
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config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale']
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config['cvae']['latent_dim'] = config['cvae']['bottleneck_size']
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block_setting = [
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, 128, 3),
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CNBlockConfig(128, 256, 3),
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CNBlockConfig(256, config['cvae']['bottleneck_size'], 9),
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CNBlockConfig(config['cvae']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(176, 256, 3),
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CNBlockConfig(256, 128, 9),
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CNBlockConfig(128, 64, 9),
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CNBlockConfig(64, 32, 3),
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CNBlockConfig(32, 3, 3),
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]
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config['cvae']['encoder_block_settings'] = block_setting
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config['cvae']['decoder_block_settings'] = block_setting_reverse
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####################### cond_ae_body ############################
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config['cond_ae_body'] = dict()
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config['cond_ae_body']['device'] = config['device']
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config['cond_ae_body']['is_label'] = False
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config['cond_ae_body']['latent_dim'] = config['latent_dim']
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config['cond_ae_body']['kernel_size'] = 7
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config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale']
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block_setting = [
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CNBlockConfig(16, 32, 3),
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, 128, 9),
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CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3),
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CNBlockConfig(128, 64, 9),
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CNBlockConfig(64, 32, 9),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 3, 3),
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]
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config['cond_ae_body']['encoder_block_settings'] = block_setting
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config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_body']['activation'] = 'tanh'
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####################### cond_ae_static ############################
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config['cond_ae_static'] = dict()
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config['cond_ae_static']['device'] = config['device']
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config['cond_ae_static']['is_label'] = False
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config['cond_ae_static']['latent_dim'] = config['latent_dim']
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config['cond_ae_static']['kernel_size'] = 7
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config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale']
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block_setting = [
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CNBlockConfig(8, 16, 3),
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CNBlockConfig(16, 32, 3),
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CNBlockConfig(32, 64, 3),
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CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3),
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CNBlockConfig(64, 32, 9),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 8, 3),
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CNBlockConfig(8, 3, 3),
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]
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config['cond_ae_static']['encoder_block_settings'] = block_setting
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config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_static']['activation'] = 'tanh' |