pyramid pipeline ready
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from utils.convnext_utils import CNBlockConfig
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config = dict()
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config['gpu'] = '5'
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config['name'] = 'pyr_torch_32'
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config['notes'] = ''
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config['tags'] = ['pyr', 'iccv', 'torch']
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config['script'] = 'scripts/cvae_pyramid.py'
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config['batch_size'] = 4
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config['is_skirt'] = False
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config['is_cape'] = False
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config['device'] = 'cuda'
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config['use_skinning'] = True # and deshaping
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config['remove_global_orientation'] = True
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config['use_extra_losses'] = True # edge, collision, 3d mesh
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config['use_offsets'] = False
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config['use_norm_loss'] = True
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config['use_curvature'] = True
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config['pretrained_model'] = ''
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config['pretrained_model_iteration'] = ''
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config['pyramid'] = dict()
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config['pyramid']['resolutions'] = [32]
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config['pyramid']['pretrained_pyramid'] = ''
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config['pyramid']['pretrained_pyramid_iteration'] = ''
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config['pyramid']['final_upscale_enhancer'] = ''
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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['dataset_in_memory'] = False
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config['resolution'] = None
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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['lr'] = 0.0001
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config['starting_balancing_coeff'] = 0.001
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config['balancing_activation_limit'] = 0.015
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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['avg_garment_path'] = ''
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config['bottleneck_scale'] = 2
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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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'mesh_body_posed',
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'mesh_posed',
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'mesh_unposed',
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'uv_unposed',
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'uv_curvature',
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'uv_static',
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'mesh_mask',
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'uv_mask',
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}
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config['pyramid']['is_label'] = False
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# config['pyramid']['add_noise'] = False
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# config['pyramid']['noise_amount'] = 0.0
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config['pyramid']['loss_weights'] = [1,1,1,1,1]
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for res in config['pyramid']['resolutions']:
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if res != 512:
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res_modifier = '_' + str(res)
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config['signature'].add('uv_body_posed' + res_modifier)
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config['signature'].add('uv_unposed' + res_modifier)
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config['signature'].add('uv_curvature' + res_modifier)
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config['signature'].add('uv_static' + res_modifier)
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config['signature'].add('uv_mask' + res_modifier)
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config['static_model'] = 'checkpoints/iccv/static_torch_orig_2'
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config['body_model'] = 'checkpoints/iccv/body_torch_1'
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config['curvature_model'] = 'checkpoints/iccv/curv_torch_1'
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config['freeze_encoders'] = True
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config['pyramid']['levels'] = []
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for res in config['pyramid']['resolutions']:
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if res == 512:
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level = dict()
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level['resolution'] = 512
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level['bottleneck_size'] = 64 * config['bottleneck_scale']
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level['kernel_size'] = 7
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level['upscale_enhancer'] = ''
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level['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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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, 3),
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CNBlockConfig(128, level['bottleneck_size'], 9),
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CNBlockConfig(level['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(level['bottleneck_size'] * 2, 128, 3),
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CNBlockConfig(128, 64, 9),
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CNBlockConfig(64, 32, 3),
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CNBlockConfig(32, 16, 3),
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CNBlockConfig(16, 3, 3),
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]
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level['encoder_block_settings'] = block_setting
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level['decoder_block_settings'] = block_setting_reverse
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config['pyramid']['levels'].append(level)
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elif res == 256:
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level = dict()
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level['resolution'] = 256
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level['kernel_size'] = 5
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level['upscale_enhancer'] = 'checkpoints/iccv/torch/iccv_256'
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level['upscale_enhancer_freeze'] = True
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level['bottleneck_size'] = 64 * config['bottleneck_scale']
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level['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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block_setting = [
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CNBlockConfig(64, 128, 3),
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CNBlockConfig(128, 128, 3),
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CNBlockConfig(128, level['bottleneck_size'], 9),
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CNBlockConfig(level['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(level['bottleneck_size'] * 2, 128, 3),
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CNBlockConfig(128, 128, 9),
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CNBlockConfig(128, 65, 3),
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CNBlockConfig(64, 3, 3),
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]
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level['encoder_block_settings'] = block_setting
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level['decoder_block_settings'] = block_setting_reverse
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config['pyramid']['levels'].append(level)
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elif res == 128:
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level = dict()
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level['resolution'] = 128
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level['kernel_size'] = 5
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level['upscale_enhancer'] = 'checkpoints/iccv/torch/iccv_128'
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level['upscale_enhancer_freeze'] = True
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level['bottleneck_size'] = 64 * config['bottleneck_scale']
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level['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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block_setting = [
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CNBlockConfig(64, 128, 9),
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CNBlockConfig(128, level['bottleneck_size'], 9),
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CNBlockConfig(level['bottleneck_size'], None, 9),
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]
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block_setting_reverse = [
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CNBlockConfig(level['bottleneck_size'] * 2, 128, 9),
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CNBlockConfig(128, 128, 9),
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CNBlockConfig(64, 3, 9),
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]
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level['encoder_block_settings'] = block_setting
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level['decoder_block_settings'] = block_setting_reverse
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config['pyramid']['levels'].append(level)
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elif res == 64:
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level = dict()
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level['resolution'] = 64
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level['kernel_size'] = 3
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level['upscale_enhancer'] = 'checkpoints/iccv/torch/iccv_64'
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level['upscale_enhancer_freeze'] = True
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level['bottleneck_size'] = 64 * config['bottleneck_scale']
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level['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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block_setting = [
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CNBlockConfig(64, level['bottleneck_size'], 9),
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CNBlockConfig(level['bottleneck_size'], None, 9),
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]
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block_setting_reverse = [
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CNBlockConfig(level['bottleneck_size'] * 2, 64, 9),
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CNBlockConfig(64, 3, 9),
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]
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level['encoder_block_settings'] = block_setting
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level['decoder_block_settings'] = block_setting_reverse
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config['pyramid']['levels'].append(level)
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elif res == 32:
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level = dict()
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level['resolution'] = 32
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level['kernel_size'] = 3
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level['upscale_enhancer'] = 'checkpoints/iccv/torch/iccv_32'
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level['upscale_enhancer_freeze'] = True
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level['bottleneck_size'] = 64 * config['bottleneck_scale']
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level['input_features'] = 3+3+3+7 if config['use_curvature'] else 3+3+3
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block_setting = [
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CNBlockConfig(level['bottleneck_size'], None, 16),
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]
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block_setting_reverse = [
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CNBlockConfig(level['bottleneck_size'] * 2, 3, 16),
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]
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level['encoder_block_settings'] = block_setting
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level['decoder_block_settings'] = block_setting_reverse
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config['pyramid']['levels'].append(level)
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else:
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raise NotImplementedError
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####################### cond_ae_curv ############################
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config['cond_ae_curv'] = dict()
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config['cond_ae_curv']['device'] = config['device']
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config['cond_ae_curv']['is_label'] = True
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# config['cond_ae_curv']['latent_dim'] = config['latent_dim']
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config['cond_ae_curv']['kernel_size'] = 7
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config['cond_ae_curv']['bottleneck_size'] = 40 * 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_curv']['bottleneck_size'], 9),
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CNBlockConfig(config['cond_ae_curv']['bottleneck_size'], None, 3),
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]
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block_setting_reverse = [
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CNBlockConfig(config['cond_ae_curv']['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_curv']['encoder_block_settings'] = block_setting
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config['cond_ae_curv']['decoder_block_settings'] = block_setting_reverse
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config['cond_ae_curv']['activation'] = 'tanh'
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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'
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