pyramid pipeline ready
This commit is contained in:
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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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+116
-124
@@ -1,176 +1,168 @@
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import os
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import time
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import random
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import tensorflow as tf
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import torch
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from torch import nn
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import numpy as np
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import scipy as sp
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# import plotly.graph_objects as go
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# import plotly.express as ex
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from custom_utils.custom_utils import load_model
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from models.base import AEEncoder, VariationalEncoder, Decoder, VariationalEncoderNew, DecoderNew
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from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew
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from utils.checkpoints import load_model
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from models.base import AEEncoder, VariationalEncoder, Decoder
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from models.upscaler import Upscaler
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class PyramidLevel(tf.keras.Model):
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# class PyramidLevel(nn.Module):
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# def __init__(self, config):
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# super(PyramidLevel, self).__init__()
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# self.latent_dim = config['latent_dim']
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# self.size = config['resolution']
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# self.encoder: VariationalEncoder = VariationalEncoder(config['encoder_block_settings'], config['input_features'], kernel_size=config['kernel_size'])
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# self.decoder: Decoder = Decoder(config['decoder_block_settings'], config['kernel_size'])
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# self.upscale_enhancer = None
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# if config['upscale_enhancer'] != '':
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# k_size = config['upscale_enhancer_kernel_size']
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# self.upscale_enhancer = Upscaler(config['resolution'], kernel_size=k_size)
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# load_model(self.upscale_enhancer, config['upscale_enhancer'])
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# for param in self.upscale_enhancer.parameters(): param.requires_grad = False
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# if config['avg_garment_path'] != '':
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# self.avg_garment = np.load(config['avg_garment_path'])
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# else:
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# self.avg_garment = None
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# def enhance(self, low, mask, training=None):
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# x = self.upscale_enhancer([low, mask], training=training)
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# return x
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# def sample(self, z_cond, uv_mask, noise=None):
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# if noise is None:
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# eps = torch.normal(shape=(uv_mask.shape[0], self.latent_dim))
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# else:
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# eps = noise
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# z = torch.concat([eps, z_cond], dim=-1)
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# x = self.decoder(z)
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# if self.avg_garment is not None:
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# x = x + self.avg_garment[None,:,:,:]
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# x = x * uv_mask[:,None,:,:]
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# return x
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# def reparameterize(self, mean, logvar, training, fix_noise=False):
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# if training == True:
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# if not fix_noise:
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# eps = torch.normal(mean=0.0, std=1.0, size=mean.shape)
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# else:
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# eps = torch.normal(mean=0.0, std=1.0, size=mean.shape)
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# eps = torch.tile(eps, [mean.shape[0],1])
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# out = eps * torch.exp(logvar * .5) + mean
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# else:
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# out = torch.exp(logvar * .5) + mean
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# return out
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# def call(self, inputs, z_cond, training=None):
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# uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
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# x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
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# mean, logvar = self.encoder(x, training=training)
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# z = self.reparameterize(mean, logvar, training)
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# z = torch.concat([z, z_cond], dim=-1)
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# x = self.decoder(z, training=training)
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# if self.avg_garment is not None:
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# x = x + self.avg_garment[None,:,:,:]
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# x = x * uv_mask[:,None,:,:]
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# # if self.upscale_enhancer is not None:
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# # pred_low = self.enhance(x, uv_mask, training=training)
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# # x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# # x = x * uv_mask_high[:,:,:,tf.newaxis]
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# return x, mean, logvar
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class PyramidLevel(nn.Module):
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def __init__(self, config):
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super(PyramidLevel, self).__init__()
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self.latent_dim = config['latent_dim']
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self.size = config['resolution']
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self.encoder = VariationalEncoder(config['latent_dim'], config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'])
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self.decoder = Decoder(config['decoder_input'], config['decoder_depths'], config['decoder_dims'], config['kernel_size'])
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self.encoder: VariationalEncoder = VariationalEncoder(config['encoder_block_settings'], input_features=config['input_features'], kernel_size=config['kernel_size'])
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self.decoder: Decoder = Decoder(config['decoder_block_settings'], config['kernel_size'])
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self.upscale_enhancer = None
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if config['upscale_enhancer'] != '':
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k_size = config['upscale_enhancer_kernel_size'] if 'upscale_enhancer_kernel_size' in config.keys() else ((3 if config['resolution'] <= 64 else 5))
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self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=k_size)
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k_size = config['kernel_size']
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self.upscale_enhancer = Upscaler(config['resolution'], kernel_size=k_size).to('cuda')
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load_model(self.upscale_enhancer, config['upscale_enhancer'])
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self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']
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if config['avg_garment_path'] != '':
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self.avg_garment = np.load(config['avg_garment_path'])
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else:
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self.avg_garment = None
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for param in self.upscale_enhancer.parameters(): param.requires_grad = False
|
||||
|
||||
|
||||
def enhance(self, low, mask, training=None):
|
||||
x = self.upscale_enhancer([low, mask], training=training)
|
||||
def enhance(self, low, mask):
|
||||
x = self.upscale_enhancer([low, mask])
|
||||
return x
|
||||
|
||||
|
||||
def sample(self, z_cond, uv_mask, noise=None):
|
||||
if noise is None:
|
||||
eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
|
||||
eps = torch.normal(shape=(uv_mask.shape[0], self.latent_dim))
|
||||
else:
|
||||
eps = noise
|
||||
|
||||
z = tf.concat([eps, z_cond], axis=-1)
|
||||
z = torch.concat([eps, z_cond], dim=-1)
|
||||
|
||||
x = self.decoder(z)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
x = x + self.avg_garment[None,:,:,:]
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
x = x * uv_mask[:,None:,:]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def reparameterize(self, mean, logvar, training, fix_noise=False):
|
||||
if training == True:
|
||||
def reparameterize(self, mean, logvar, fix_noise=False):
|
||||
if self.training == True:
|
||||
if not fix_noise:
|
||||
eps = tf.random.normal(shape=mean.shape)
|
||||
eps = torch.normal(mean=0.0, std=1.0, size=mean.shape).to(mean.device)
|
||||
else:
|
||||
eps = tf.random.normal(shape=(1, mean.shape[1]))
|
||||
eps = tf.tile(eps, [mean.shape[0],1])
|
||||
out = eps * tf.exp(logvar * .5) + mean
|
||||
eps = torch.normal(mean=0.0, std=1.0, size=mean.shape).to(mean.device)
|
||||
eps = torch.tile(eps, [mean.shape[0],1])
|
||||
out = eps * torch.exp(logvar * .5) + mean
|
||||
else:
|
||||
out = tf.exp(logvar * .5) + mean
|
||||
out = torch.exp(logvar * .5) + mean
|
||||
return out
|
||||
|
||||
|
||||
def call(self, inputs, z_cond, training=None):
|
||||
def forward(self, inputs, z_cond):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
|
||||
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
mean, logvar = self.encoder(x, training=training)
|
||||
z = self.reparameterize(mean, logvar, training)
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
x = self.decoder(z, training=training)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
# if self.upscale_enhancer is not None:
|
||||
# pred_low = self.enhance(x, uv_mask, training=training)
|
||||
# x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
|
||||
# x = x * uv_mask_high[:,:,:,tf.newaxis]
|
||||
|
||||
return x, mean, logvar
|
||||
|
||||
|
||||
|
||||
class PyramidLevelNew(tf.keras.Model):
|
||||
|
||||
def __init__(self, config):
|
||||
super(PyramidLevelNew, self).__init__()
|
||||
self.size = config['resolution']
|
||||
|
||||
self.encoder = VariationalEncoderNew(config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'])
|
||||
self.decoder = DecoderNew(config['decoder_depths'], config['decoder_dims'], config['kernel_size'])
|
||||
|
||||
self.upscale_enhancer = None
|
||||
if config['upscale_enhancer'] != '':
|
||||
k_size = config['upscale_enhancer_kernel_size'] if 'upscale_enhancer_kernel_size' in config.keys() else ((3 if config['resolution'] <= 64 else 5))
|
||||
self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=k_size)
|
||||
load_model(self.upscale_enhancer, config['upscale_enhancer'])
|
||||
self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']
|
||||
|
||||
if config['avg_garment_path'] != '':
|
||||
self.avg_garment = np.load(config['avg_garment_path'])
|
||||
else:
|
||||
self.avg_garment = None
|
||||
|
||||
self.reshape = tf.keras.layers.Reshape((8, 4, 64))
|
||||
|
||||
|
||||
def enhance(self, low, mask, training=None):
|
||||
x = self.upscale_enhancer([low, mask], training=training)
|
||||
return x
|
||||
|
||||
|
||||
def sample(self, z_cond, uv_mask, noise=None):
|
||||
if noise is None:
|
||||
eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
|
||||
else:
|
||||
eps = noise
|
||||
|
||||
z = tf.concat([eps, z_cond], axis=-1)
|
||||
x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
|
||||
mean, logvar = self.encoder(x)
|
||||
z = self.reparameterize(mean, logvar)
|
||||
z = torch.reshape(z, (mean.shape[0], -1, 8, 4))
|
||||
|
||||
z = torch.concat([z, z_cond], dim=1)
|
||||
x = self.decoder(z)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def reparameterize(self, mean, logvar, training, fix_noise=False):
|
||||
if training == True:
|
||||
if not fix_noise:
|
||||
eps = tf.random.normal(shape=mean.shape)
|
||||
else:
|
||||
eps = tf.random.normal(shape=(1, mean.shape[1]))
|
||||
eps = tf.tile(eps, [mean.shape[0],1])
|
||||
out = eps * tf.exp(logvar * .5) + mean
|
||||
else:
|
||||
out = tf.exp(logvar * .5) + mean
|
||||
return out
|
||||
|
||||
|
||||
def call(self, inputs, z_cond, training=None):
|
||||
uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
|
||||
|
||||
x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
|
||||
mean, logvar = self.encoder(x, training=training)
|
||||
z = self.reparameterize(mean, logvar, training)
|
||||
z = self.reshape(z)
|
||||
|
||||
z = tf.concat([z, z_cond], axis=-1)
|
||||
x = self.decoder(z, training=training)
|
||||
|
||||
if self.avg_garment is not None:
|
||||
x = x + self.avg_garment[tf.newaxis,:,:,:]
|
||||
|
||||
x = x * uv_mask[:,:,:,tf.newaxis]
|
||||
x = x * uv_mask[:,None,:,:]
|
||||
|
||||
# if self.upscale_enhancer is not None:
|
||||
# pred_low = self.enhance(x, uv_mask, training=training)
|
||||
|
||||
@@ -0,0 +1,246 @@
|
||||
import os
|
||||
import time
|
||||
import random
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
# import numpy as np
|
||||
# import scipy as sp
|
||||
# import plotly.graph_objects as go
|
||||
# import plotly.express as ex
|
||||
|
||||
from utils.checkpoints import load_model
|
||||
from models.base import AEEncoder
|
||||
from models.pyramid_level import PyramidLevel
|
||||
|
||||
|
||||
# class Pyramid(nn.Module):
|
||||
|
||||
# def __init__(self, config):
|
||||
# super(Pyramid, self).__init__()
|
||||
|
||||
# self.static_enc : AEEncoder = AEEncoder(config['cond_ae_static']['encoder_block_settings'], kernel_size=config['cond_ae_static']['kernel_size'], activation=config['cond_ae_static']['activation'])
|
||||
# if config['pyramid']['static_model'] != '':
|
||||
# load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
|
||||
|
||||
# self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], kernel_size=config['cond_ae_body']['kernel_size'], activation=config['cond_ae_body']['activation'])
|
||||
# if config['pyramid']['body_model'] != '':
|
||||
# load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
|
||||
|
||||
# self.curv_enc : AEEncoder = AEEncoder(config['cond_ae_curv']['encoder_block_settings'], kernel_size=config['cond_ae_curv']['kernel_size'], activation=config['cond_ae_curv']['activation'])
|
||||
# if config['pyramid']['curvature_model'] != '':
|
||||
# load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
# if config['freeze_encoders']:
|
||||
# for param in self.static_enc.parameters(): param.requires_grad = False
|
||||
# for param in self.body_enc.parameters(): param.requires_grad = False
|
||||
# for param in self.curv_enc.parameters(): param.requires_grad = False
|
||||
|
||||
|
||||
# self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae_static': config['cond_ae_static'], 'cond_ae_body': config['cond_ae_body'], 'cond_ae_curv': config['cond_ae_curv'], } }) for i in range(len(config['pyramid']['levels']))]
|
||||
|
||||
# # if config['is_cape']:
|
||||
# # if len(config['pyramid']['resolutions']) == 2:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # if len(config['pyramid']['resolutions']) == 3:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # if len(config['pyramid']['resolutions']) == 4:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # else:
|
||||
|
||||
# # if len(config['pyramid']['resolutions']) == 2:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # if len(config['pyramid']['resolutions']) == 3:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# # self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # if len(config['pyramid']['resolutions']) == 4:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# # self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# # self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# # if len(config['pyramid']['resolutions']) == 5:
|
||||
# # self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
|
||||
# # self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# # self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# # self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
|
||||
# # self.add_noise = config['pyramid']['add_noise']
|
||||
# # self.noise_amount = config['pyramid']['noise_amount']
|
||||
# self.use_offsets = config['use_offsets']
|
||||
|
||||
# # self.upscale_enhancer = None
|
||||
# # if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
# # k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
# # self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
# # load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# # self.upscale_enhancer.trainable = False
|
||||
|
||||
# def enhance(self, low, mask):
|
||||
# x = self.upscale_enhancer([low, mask])
|
||||
# return x
|
||||
|
||||
# def encode_conds(self, uv_template, uv_body_posed, uv_curv):
|
||||
# z_static = self.static_enc(uv_template)
|
||||
# z_body = self.body_enc(uv_body_posed)
|
||||
# z_curv = self.curv_enc(uv_curv)
|
||||
|
||||
# z_cond = torch.concat([z_static, z_body, z_curv], dim=-1)
|
||||
|
||||
# return z_cond
|
||||
|
||||
# def call(self, inputs):
|
||||
# levels_input, conds = inputs
|
||||
# uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
|
||||
|
||||
# z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
|
||||
|
||||
# # if self.add_noise == True and training == True:
|
||||
# # z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
# out_levels = []
|
||||
# pred, mean, logvar = self.levels[0](levels_input[0], z_cond)
|
||||
# if self.use_offsets:
|
||||
# pred = pred + levels_input[0][2] # uv_body_posed_32
|
||||
# if 1 != len(self.levels):
|
||||
# pred = self.levels[0].enhance(pred, levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
|
||||
# pred = F.interpolate(pred, size=(self.levels[1].size, self.levels[1].size//2), mode='bilinear')
|
||||
# out_levels.append([pred, mean, logvar])
|
||||
# pred = pred * levels_input[1][-2][:,None,:,:]
|
||||
# else:
|
||||
# pred = self.enhance(pred, levels_input[0][-2])
|
||||
# pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
|
||||
# pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
|
||||
# out_levels.append([pred, mean, logvar])
|
||||
|
||||
# for i in range(1, len(self.levels)):
|
||||
# pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond)
|
||||
# pred = pred + pred_level
|
||||
# if i != len(self.levels) - 1:
|
||||
# pred = self.levels[i].enhance(pred, levels_input[i][-2])
|
||||
# out_levels.append([pred, mean, logvar])
|
||||
# pred = F.interpolate(pred, size=(self.levels[i+1].size, self.levels[i+1].size//2), mode='bilinear')
|
||||
# pred = pred * levels_input[i+1][-2][:,None,:,:]
|
||||
# else:
|
||||
# pred = self.enhance(pred, levels_input[i][-1])
|
||||
# pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
|
||||
# pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
|
||||
# out_levels.append([pred, mean, logvar])
|
||||
|
||||
# return out_levels
|
||||
|
||||
|
||||
|
||||
class Pyramid(nn.Module):
|
||||
|
||||
def __init__(self, config):
|
||||
super(Pyramid, self).__init__()
|
||||
|
||||
self.static_enc : AEEncoder = AEEncoder(config['cond_ae_static']['encoder_block_settings'], kernel_size=config['cond_ae_static']['kernel_size'], activation=config['cond_ae_static']['activation'])
|
||||
if config['static_model'] != '':
|
||||
load_model(self.static_enc, config['static_model'] + '_enc')
|
||||
|
||||
self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], kernel_size=config['cond_ae_body']['kernel_size'], activation=config['cond_ae_body']['activation'])
|
||||
if config['body_model'] != '':
|
||||
load_model(self.body_enc, config['body_model'] + '_enc')
|
||||
|
||||
self.curv_enc : AEEncoder = AEEncoder(config['cond_ae_curv']['encoder_block_settings'], kernel_size=config['cond_ae_curv']['kernel_size'], activation=config['cond_ae_curv']['activation'], is_label=True, nr_of_labels=7)
|
||||
if config['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['curvature_model'] + '_enc')
|
||||
|
||||
if config['freeze_encoders']:
|
||||
for param in self.static_enc.parameters(): param.requires_grad = False
|
||||
for param in self.body_enc.parameters(): param.requires_grad = False
|
||||
for param in self.curv_enc.parameters(): param.requires_grad = False
|
||||
|
||||
self.levels = [PyramidLevel({ **config['pyramid']['levels'][i] }) for i in range(len(config['pyramid']['levels']))]
|
||||
|
||||
|
||||
# if len(config['pyramid']['resolutions']) == 2:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 3:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 4:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 5:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
|
||||
# self.add_noise = config['pyramid']['add_noise']
|
||||
# self.noise_amount = config['pyramid']['noise_amount']
|
||||
# self.use_offsets = config['use_offsets']
|
||||
|
||||
# self.upscale_enhancer = None
|
||||
# if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
# k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
# self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
# load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# self.upscale_enhancer.trainable = False
|
||||
|
||||
# def enhance(self, low, mask, training):
|
||||
# x = self.upscale_enhancer([low, mask], training=training)
|
||||
# return x
|
||||
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv):
|
||||
z_static = self.static_enc(uv_template)
|
||||
z_body = self.body_enc(uv_body_posed)
|
||||
z_curv = self.curv_enc(uv_curv)
|
||||
|
||||
z_cond = torch.concat([z_static, z_body, z_curv], dim=1)
|
||||
|
||||
return z_cond
|
||||
|
||||
def forward(self, inputs):
|
||||
levels_input, conds = inputs
|
||||
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
|
||||
|
||||
z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
|
||||
|
||||
# if self.add_noise == True and training == True:
|
||||
# z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
out_levels = []
|
||||
pred, mean, logvar = self.levels[0](levels_input[0], z_cond)
|
||||
# if self.use_offsets:
|
||||
# pred = pred + levels_input[0][2] # uv_body_posed_32
|
||||
if 1 != len(self.levels):
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
|
||||
pred = F.interpolate(pred, size=(self.levels[1].size, self.levels[1].size//2), mode='bilinear')
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = pred * levels_input[1][-2][:,None,:,:]
|
||||
else:
|
||||
# pred = self.enhance(pred, levels_input[0][-2], training=training)
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2]) # levels_input[i][-2]=uv_mask_res
|
||||
pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
|
||||
pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
for i in range(1, len(self.levels)):
|
||||
input = levels_input[i]
|
||||
# input[0] = input[0] - pred
|
||||
# input_min, input_max = tf.reduce_min(input[0], axis=[1,2,3]), tf.reduce_max(input[0], axis=[1,2,3])
|
||||
# input[0] = (input[0] - input_min[:,None,None,None]) / (input_max - input_min)[:,None,None,None]
|
||||
# input[0] = input[0] * input[-2][:,None,:,:] # uv_mask_res
|
||||
pred_level, mean, logvar = self.levels[i](input, z_cond)
|
||||
# pred_level = ( (pred_level * (input_max - input_min)[:,None,None,None]) + input_min[:,None,None,None] ) * input[-2][:,:,:,None] # uv_mask_res
|
||||
pred = pred + pred_level
|
||||
if i != len(self.levels) - 1:
|
||||
pred = self.levels[i].enhance(pred, levels_input[i][-2])
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = F.interpolate(pred, size=(self.levels[i+1].size, self.levels[i+1].size//2), mode='bilinear')
|
||||
pred = pred * levels_input[i+1][-2][:,None,:,:]
|
||||
else:
|
||||
# if self.upscale_enhancer is not None:
|
||||
# pred = self.enhance(pred, levels_input[-1][-2], training=training)
|
||||
pred = self.levels[-1].enhance(pred, levels_input[-1][-2])
|
||||
pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
|
||||
pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
return out_levels
|
||||
@@ -1,365 +0,0 @@
|
||||
import os
|
||||
import time
|
||||
import random
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
# import plotly.graph_objects as go
|
||||
# import plotly.express as ex
|
||||
|
||||
from custom_utils.custom_utils import load_model
|
||||
from models.base import AEEncoder, AEEncoderNew
|
||||
from models.pyramid_level import PyramidLevel, PyramidLevelNew
|
||||
from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew
|
||||
|
||||
|
||||
class Pyramid(tf.keras.Model):
|
||||
|
||||
def __init__(self, config):
|
||||
super(Pyramid, self).__init__()
|
||||
|
||||
self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
if config['pyramid']['static_model'] != '':
|
||||
load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
|
||||
|
||||
self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
|
||||
if config['pyramid']['body_model'] != '':
|
||||
load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
|
||||
|
||||
self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
if config['pyramid']['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
if config['pyramid']['freeze_encoders']:
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
tf.keras.layers.Dense(256, activation='relu'),
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Dense(config['pyramid']['latent_dim'], activation='tanh'),])
|
||||
|
||||
self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
|
||||
|
||||
# if config['is_cape']:
|
||||
# if len(config['pyramid']['resolutions']) == 2:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 3:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 4:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# else:
|
||||
if len(config['pyramid']['resolutions']) == 2:
|
||||
self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
if len(config['pyramid']['resolutions']) == 3:
|
||||
self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
if len(config['pyramid']['resolutions']) == 4:
|
||||
self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
if len(config['pyramid']['resolutions']) == 5:
|
||||
self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
|
||||
self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
|
||||
self.add_noise = config['pyramid']['add_noise']
|
||||
self.noise_amount = config['pyramid']['noise_amount']
|
||||
self.use_offsets = config['use_offsets']
|
||||
|
||||
self.upscale_enhancer = None
|
||||
if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
self.upscale_enhancer.trainable = False
|
||||
|
||||
def enhance(self, low, mask, training):
|
||||
x = self.upscale_enhancer([low, mask], training=training)
|
||||
return x
|
||||
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
z_static = self.static_enc(uv_template, training)
|
||||
z_body = self.body_enc(uv_body_posed, training)
|
||||
z_curv = self.curv_enc(uv_curv, training)
|
||||
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
z_cond = self.cond_encoder(z_cond, training=training)
|
||||
|
||||
return z_cond
|
||||
|
||||
def call(self, inputs, training):
|
||||
levels_input, conds = inputs
|
||||
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
|
||||
|
||||
z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training)
|
||||
|
||||
if self.add_noise == True and training == True:
|
||||
z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
out_levels = []
|
||||
pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
|
||||
if self.use_offsets:
|
||||
pred = pred + levels_input[0][2] # uv_body_posed_32
|
||||
if 1 != len(self.levels):
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
|
||||
else:
|
||||
pred = self.enhance(pred, levels_input[0][-2], training=training)
|
||||
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
for i in range(1, len(self.levels)):
|
||||
pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
|
||||
pred = pred + pred_level
|
||||
if i != len(self.levels) - 1:
|
||||
pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
|
||||
pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
|
||||
else:
|
||||
if self.upscale_enhancer is not None:
|
||||
pred = self.enhance(pred, levels_input[-1][-2], training=training)
|
||||
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
return out_levels
|
||||
|
||||
# # 32
|
||||
# pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
|
||||
# pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
|
||||
# pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# pred_0 = pred_0 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
# return [ [pred_0, mean_0, logvar_0] ]
|
||||
|
||||
# # 32 64
|
||||
# pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
|
||||
# pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
# pred_0 = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
|
||||
# pred = pred_0 * levels_input[1][-2][:,:,:,tf.newaxis]
|
||||
|
||||
# pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
|
||||
# pred_1 = pred + pred_1
|
||||
# pred_1 = self.enhance(pred_1, levels_input[-1][-2], training=training)
|
||||
# pred_1 = tf.image.resize(pred_1, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# pred_1 = pred_1 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
|
||||
# return [ [pred_0, mean_0, logvar_0], [pred_1, mean_1, logvar_1] ]
|
||||
|
||||
|
||||
# class Pyramid(tf.keras.Model):
|
||||
|
||||
# def __init__(self, config):
|
||||
# super(Pyramid, self).__init__()
|
||||
|
||||
# self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
# if config['pyramid']['static_model'] != '':
|
||||
# load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
|
||||
|
||||
# self.body_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
# if config['pyramid']['body_model'] != '':
|
||||
# load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
|
||||
|
||||
# self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
|
||||
# if config['pyramid']['curvature_model'] != '':
|
||||
# load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
# if config['pyramid']['freeze_encoders']:
|
||||
# self.static_enc.trainable = False
|
||||
# self.body_enc.trainable = False
|
||||
# self.curv_enc.trainable = False
|
||||
|
||||
# self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
|
||||
# tf.keras.layers.Dense(256, activation='relu'),
|
||||
# tf.keras.layers.Dense(128, activation='relu'),
|
||||
# tf.keras.layers.Dense(config['pyramid']['latent_dim']),])
|
||||
|
||||
# self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
|
||||
|
||||
|
||||
# self.upscale_enhancer = None
|
||||
# if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
# self.upscale_enhancer = UpscalerBasicNew(low_res=config['pyramid']['resolutions'][-1])
|
||||
# load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# self.upscale_enhancer.trainable = False
|
||||
|
||||
# def enhance(self, low, mask, training):
|
||||
# x = self.upscale_enhancer([low, mask], training=training)
|
||||
# return x
|
||||
|
||||
# def encode_conds(self, uv_template, uv_body_posed, uv_curv):
|
||||
# z_static = self.static_enc(uv_template)
|
||||
# z_body = self.body_enc(uv_body_posed)
|
||||
# z_curv = self.curv_enc(uv_curv)
|
||||
|
||||
# z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
# z_cond = self.cond_encoder(z_cond)
|
||||
|
||||
# return z_cond
|
||||
|
||||
# def call(self, inputs, training):
|
||||
# levels_input, conds = inputs
|
||||
# uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
|
||||
# z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
|
||||
|
||||
# out_levels = []
|
||||
# pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
|
||||
# pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
# # pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
|
||||
|
||||
# ##################
|
||||
# # if more than 1 level
|
||||
# # x = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
|
||||
# # x = x * levels_input[1][-2][:,:,:,tf.newaxis]
|
||||
|
||||
# # if only 1 level
|
||||
# pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# ##################
|
||||
|
||||
|
||||
# # pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
|
||||
# # pred_1 = pred_0 + pred_1
|
||||
# # pred_1 = self.levels[1].enhance(pred, levels_input[1][-2], training=training)
|
||||
|
||||
# # for i in range(1, len(self.levels)):
|
||||
# # pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
|
||||
# # pred = pred + pred_level
|
||||
# # if i != len(self.levels) - 1:
|
||||
# # pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
|
||||
# # out_levels.append([pred, mean, logvar])
|
||||
# # pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
|
||||
# # pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
|
||||
# # else:
|
||||
# # if self.upscale_enhancer is not None:
|
||||
# # pred = self.enhance(pred, levels_input[-1][-2], training=training)
|
||||
# # pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
|
||||
# # out_levels.append([pred, mean, logvar])
|
||||
# # pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
|
||||
# out_levels = [
|
||||
# [pred_0, mean_0, logvar_0]
|
||||
# ]
|
||||
|
||||
# return out_levels
|
||||
|
||||
|
||||
class PyramidNew(tf.keras.Model):
|
||||
|
||||
def __init__(self, config):
|
||||
super(PyramidNew, self).__init__()
|
||||
|
||||
self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
|
||||
if config['pyramid']['static_model'] != '':
|
||||
load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
|
||||
|
||||
self.body_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['kernel_size'])
|
||||
if config['pyramid']['body_model'] != '':
|
||||
load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
|
||||
|
||||
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
|
||||
if config['pyramid']['curvature_model'] != '':
|
||||
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
|
||||
|
||||
if config['pyramid']['freeze_encoders']:
|
||||
self.static_enc.trainable = False
|
||||
self.body_enc.trainable = False
|
||||
self.curv_enc.trainable = False
|
||||
|
||||
self.levels = [PyramidLevelNew({ **config['pyramid']['levels'][i] }) for i in range(len(config['pyramid']['levels']))]
|
||||
|
||||
|
||||
# if len(config['pyramid']['resolutions']) == 2:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 3:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 4:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
# if len(config['pyramid']['resolutions']) == 5:
|
||||
# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
|
||||
# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
|
||||
# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
|
||||
# self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
|
||||
|
||||
self.add_noise = config['pyramid']['add_noise']
|
||||
self.noise_amount = config['pyramid']['noise_amount']
|
||||
self.use_offsets = config['use_offsets']
|
||||
|
||||
self.upscale_enhancer = None
|
||||
if config['pyramid']['final_upscale_enhancer'] != '':
|
||||
k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
|
||||
self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
|
||||
load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
self.upscale_enhancer.trainable = False
|
||||
|
||||
def enhance(self, low, mask, training):
|
||||
x = self.upscale_enhancer([low, mask], training=training)
|
||||
return x
|
||||
|
||||
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
|
||||
z_static = self.static_enc(uv_template, training)
|
||||
z_body = self.body_enc(uv_body_posed, training)
|
||||
z_curv = self.curv_enc(uv_curv, training)
|
||||
|
||||
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
|
||||
|
||||
return z_cond
|
||||
|
||||
def call(self, inputs, training):
|
||||
levels_input, conds = inputs
|
||||
uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
|
||||
|
||||
z_cond = self.encode_conds(uv_static, uv_body, uv_curv, training)
|
||||
|
||||
if self.add_noise == True and training == True:
|
||||
z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
|
||||
|
||||
out_levels = []
|
||||
pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
|
||||
if self.use_offsets:
|
||||
pred = pred + levels_input[0][2] # uv_body_posed_32
|
||||
if 1 != len(self.levels):
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
|
||||
else:
|
||||
# pred = self.enhance(pred, levels_input[0][-2], training=training)
|
||||
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
|
||||
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
|
||||
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
for i in range(1, len(self.levels)):
|
||||
input = levels_input[i]
|
||||
input[0] = input[0] - pred
|
||||
input_min, input_max = tf.reduce_min(input[0], axis=[1,2,3]), tf.reduce_max(input[0], axis=[1,2,3])
|
||||
input[0] = (input[0] - input_min[:,None,None,None]) / (input_max - input_min)[:,None,None,None]
|
||||
input[0] = input[0] * input[-2][:,:,:,None] # uv_mask_res
|
||||
pred_level, mean, logvar = self.levels[i](input, z_cond, training=training)
|
||||
pred_level = ( (pred_level * (input_max - input_min)[:,None,None,None]) + input_min[:,None,None,None] ) * input[-2][:,:,:,None] # uv_mask_res
|
||||
pred = pred + pred_level
|
||||
if i != len(self.levels) - 1:
|
||||
pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
|
||||
out_levels.append([pred, mean, logvar])
|
||||
pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.BILINEAR)
|
||||
pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
|
||||
else:
|
||||
# if self.upscale_enhancer is not None:
|
||||
# pred = self.enhance(pred, levels_input[-1][-2], training=training)
|
||||
pred = self.levels[-1].enhance(pred, levels_input[-1][-2], training=training)
|
||||
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
|
||||
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
|
||||
out_levels.append([pred, mean, logvar])
|
||||
|
||||
return out_levels
|
||||
+279
-302
@@ -1,65 +1,67 @@
|
||||
import os
|
||||
import sys
|
||||
from configs.config_abl_pyr_32_skirt import config
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
|
||||
|
||||
import importlib
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
parser.add_argument('--config', type=str)
|
||||
parser.add_argument('--debug', action='store_true')
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
config_module = importlib.import_module(args['config'])
|
||||
config = config_module.config
|
||||
if args['debug']:
|
||||
from configs.pyr_32 import config
|
||||
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
|
||||
import tensorflow as tf
|
||||
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
if gpus:
|
||||
try:
|
||||
# Currently, memory growth needs to be the same across GPUs
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
except RuntimeError as e:
|
||||
# Memory growth must be set before GPUs have been initialized
|
||||
print(e)
|
||||
if args['debug']:
|
||||
os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
|
||||
|
||||
import time
|
||||
import tqdm
|
||||
import argparse
|
||||
import wandb
|
||||
import tqdm
|
||||
import random
|
||||
import numpy as np
|
||||
import scipy as sp
|
||||
from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, get_summary_str, plot_3d_collision, group_inputs_for_levels
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as ex
|
||||
from plotly.subplots import make_subplots
|
||||
from PIL import Image
|
||||
|
||||
if config['is_skirt']:
|
||||
from losses import edge_loss_skirt as edge_loss
|
||||
from losses.losses import edge_loss_skirt as edge_loss
|
||||
else:
|
||||
from losses import edge_loss
|
||||
from losses.losses import edge_loss
|
||||
|
||||
from losses import uv_mask_loss, bend_loss, kl_loss, collision_ratio, normal_loss
|
||||
# from losses import collision_penalty as collision_penalty
|
||||
from losses import collision_loss as collision_penalty
|
||||
from custom_utils.custom_utils import batch_to_dict, load_model
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.smpl import SMPL
|
||||
from models.pyramid_model_optimized import Pyramid
|
||||
from postprocessing import fix_collision
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
import torch.nn.functional as F
|
||||
from data.dataset import Dataset
|
||||
from utils.checkpoints import CheckpointSaver, load_model
|
||||
from utils.utils import InfiniteDataLoader as DataLoader
|
||||
from utils.postprocess import fix_collision
|
||||
from losses.losses import collision_loss, normal_loss, kl_loss
|
||||
from pytorch_msssim import MS_SSIM
|
||||
from custom_utils.custom_utils import UV2Mesh, Accumulator, plot_3d, get_summary_html, maskFacesBatch, get_summary_str, plot_3d_collision, group_inputs_for_levels
|
||||
from smpl.smpl_torch import SMPL
|
||||
from plotly.subplots import make_subplots
|
||||
from models.pyramid_model import Pyramid
|
||||
|
||||
|
||||
def init_wandb():
|
||||
run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
|
||||
|
||||
run = wandb.init(project='iccv', name=config['name'], notes=config['notes'], dir='/data-local/127/temp', tags=config['tags'])
|
||||
artifact = wandb.Artifact('code', type='code')
|
||||
artifact.add_file('cvae_pyramid.py')
|
||||
artifact.add_file('models/cvae_model.py')
|
||||
artifact.add_file('models/pyramid_model_optimized.py')
|
||||
artifact.add_file('models/pyramid_level.py')
|
||||
artifact.add_file('models/upscaler.py')
|
||||
artifact.add_file('losses.py')
|
||||
artifact.add_file('scripts/cvae_pyramid.py')
|
||||
artifact.add_file('losses/losses.py')
|
||||
artifact.add_file('models/base.py')
|
||||
artifact.add_file('models/cond_encoder.py')
|
||||
artifact.add_file('dataset.py')
|
||||
artifact.add_file('convnext/models/convnext_tf.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs')]
|
||||
artifact.add_file('models/convnext.py')
|
||||
artifact.add_file('models/pyramid_model.py')
|
||||
artifact.add_file('models/pyramid_level.py')
|
||||
artifact.add_file('data/dataset.py')
|
||||
config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
||||
for config_file_name in config_file_names:
|
||||
file_name = '/'.join(config_file_name.split('.')) + '.py'
|
||||
if os.path.exists(file_name):
|
||||
@@ -210,86 +212,12 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
|
||||
return fig
|
||||
|
||||
|
||||
# @tf.function
|
||||
def train_step(batch):
|
||||
model.train()
|
||||
# with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
with tf.GradientTape() as tape:
|
||||
out_levels = model([levels_input, conds], training=True)
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
output = dict()
|
||||
reconstr_loss_total = 0
|
||||
kl_total = 0
|
||||
|
||||
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
i = -1
|
||||
res = config['pyramid']['resolutions'][-1]
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
# is the output already masked?
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
# if config['use_norm_loss'] and step > 2000:
|
||||
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
|
||||
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
|
||||
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
|
||||
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
gradients = tape.gradient(loss, model.trainable_variables)
|
||||
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
|
||||
return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
|
||||
|
||||
|
||||
@tf.function
|
||||
def valid_step(batch):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
out_levels = model([levels_input, conds], training=False)
|
||||
|
||||
out_levels = model([levels_input, conds])
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
@@ -310,23 +238,24 @@ def valid_step(batch):
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
# is the output already masked?
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
|
||||
# is the output already masked?
|
||||
# ms_ssim = torch.mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = torch.mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
|
||||
e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
|
||||
l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((uv_orig - pred) ** 2), dim=-1), dim=[1, 2]))
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_extra_losses']:
|
||||
@@ -335,35 +264,121 @@ def valid_step(batch):
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
# if config['use_norm_loss'] and step > 2000:
|
||||
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
loss = reconstr_loss_total + g_balancing_coeff * kl_total
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
# scaler.scale(loss).backward()
|
||||
# scaler.step(optimizer)
|
||||
# scaler.update()
|
||||
# optimizer.zero_grad()
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
mean = torch.mean(mean)
|
||||
logvar = torch.mean(logvar)
|
||||
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 1))
|
||||
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
|
||||
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
|
||||
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
|
||||
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss, 'train/ms_ssim' + res_modifier: ms_ssim, } } #
|
||||
|
||||
|
||||
return { **{ 'train/loss': loss, 'train/reconstr_loss_total': reconstr_loss_total, 'train/kl_total': kl_total }, **output }
|
||||
|
||||
|
||||
def valid_step(batch):
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
out_levels = model([levels_input, conds])
|
||||
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
|
||||
|
||||
output = dict()
|
||||
reconstr_loss_total = 0
|
||||
kl_total = 0
|
||||
|
||||
#for i, res in enumerate(config['pyramid']['resolutions']):
|
||||
i = -1
|
||||
res = config['pyramid']['resolutions'][-1]
|
||||
res_modifier = '_' + str(res) if res != 512 else ''
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(dtype=torch.int64)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])])
|
||||
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)[0]
|
||||
else:
|
||||
mesh_pred_posed = mesh_pred
|
||||
|
||||
# is the output already masked?
|
||||
# ms_ssim = torch.mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = torch.mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
|
||||
e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_loss(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
|
||||
l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((uv_orig - pred) ** 2), dim=-1), dim=[1, 2]))
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, dim=-1), dim=-1))
|
||||
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
if config['use_extra_losses']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
if not config['is_cape']:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
|
||||
else:
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
|
||||
|
||||
if config['use_norm_loss'] and step > 2000:
|
||||
reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
|
||||
|
||||
kl_total += kl
|
||||
loss = reconstr_loss_total + g_balancing_coeff * kl_total
|
||||
|
||||
mean = torch.mean(mean)
|
||||
logvar = torch.mean(logvar)
|
||||
|
||||
ms_ssim = torch.mean(_MSSSIM(pred + 1, batch['uv_static'] + 1))
|
||||
metric = torch.sum(torch.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred.detach()) ** 2, axis=-1))) / torch.sum(batch['mesh_mask'])
|
||||
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
|
||||
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss, 'valid/ms_ssim' + res_modifier: ms_ssim, } } #
|
||||
|
||||
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
|
||||
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
|
||||
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
|
||||
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
|
||||
|
||||
loss = reconstr_loss_total + balancing_coeff * kl_total
|
||||
|
||||
return { **{ 'valid/loss': loss, 'valid/reconstr_loss_total': reconstr_loss_total, 'valid/kl_total': kl_total }, **output }
|
||||
|
||||
|
||||
@tf.function
|
||||
def test_step(batch):
|
||||
levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
out_levels = model([levels_input, conds], training=False)
|
||||
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, dim=0) for i in range(shape.shape[0])])
|
||||
mesh_orig = batch['mesh_unposed']
|
||||
|
||||
output = dict()
|
||||
@@ -377,18 +392,18 @@ def test_step(batch):
|
||||
pred, mean, logvar = out_levels[i]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
|
||||
# is the output already masked?
|
||||
|
||||
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
e_loss = torch.mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
|
||||
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
||||
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
|
||||
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2]))
|
||||
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1), axis=-1))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
c_loss, c_ratio = torch.mean(c_loss), torch.mean(c_ratio)
|
||||
l2_loss = torch.mean(1e3 * torch.mean(torch.sum(((batch['uv_unposed'] - pred) ** 2), dim=-1), dim=[1, 2]))
|
||||
l2_mesh_loss = torch.mean(1e3 * torch.mean(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, dim=-1), dim=-1))
|
||||
# ms_ssim = torch.mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
|
||||
# ms_ssim = torch.mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
|
||||
kl = kl_loss(mean, logvar)
|
||||
|
||||
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
|
||||
@@ -397,11 +412,11 @@ def test_step(batch):
|
||||
# reconstr_loss_total += config['pyramid']['loss_weights'][i] * (l2_loss + (e_loss + c_loss) * config['reg_loss_coeff'])
|
||||
kl_total += kl
|
||||
|
||||
mean = tf.reduce_mean(mean)
|
||||
logvar = tf.reduce_mean(logvar)
|
||||
mean = torch.mean(mean)
|
||||
logvar = torch.mean(logvar)
|
||||
|
||||
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
|
||||
euclid_error_per_garment = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1)), axis=1) / tf.reduce_sum(batch['mesh_mask'], axis=1)
|
||||
metric = torch.sum(tf.math.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, dim=-1))) / torch.sum(batch['mesh_mask'])
|
||||
euclid_error_per_garment = torch.sum(tf.math.sqrt(torch.sum(batch['mesh_mask'][:,:,None] * (mesh_orig - mesh_pred) ** 2, dim=-1)), dim=1) / torch.sum(batch['mesh_mask'], dim=1)
|
||||
|
||||
output = { **output, **{ 'test/edge_loss' + res_modifier: e_loss, 'test/collision_loss' + res_modifier: c_loss,'test/euclid_error' + res_modifier: metric,
|
||||
'test/mean' + res_modifier: mean, 'test/logvar' + res_modifier: logvar, 'test/kl_loss' + res_modifier: kl,
|
||||
@@ -416,50 +431,52 @@ def test_step(batch):
|
||||
|
||||
def plot_everything(batch: dict, type: str):
|
||||
if config['use_skinning']:
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'].numpy(), batch['uv_unposed'].numpy()
|
||||
mesh_orig, uv_orig = batch['mesh_unposed'].cpu().numpy(), batch['uv_unposed']
|
||||
else:
|
||||
mesh_orig, uv_orig = batch['mesh_posed'].numpy(), batch['uv_posed'].numpy()
|
||||
mesh_orig, uv_orig = batch['mesh_posed'].cpu().numpy(), batch['uv_posed']
|
||||
|
||||
levels_input = group_inputs_for_levels(batch, config)
|
||||
output_levels = model(levels_input, training=False)
|
||||
model.eval()
|
||||
with torch.inference_mode():
|
||||
levels_input = group_inputs_for_levels(batch, config)
|
||||
output_levels = model(levels_input)
|
||||
|
||||
mesh_pred_levels = [uv2mesh(output_levels[i][0]).numpy() for i in range(len(output_levels))]
|
||||
mesh_orig_posed = batch['mesh_posed'].numpy()
|
||||
body = batch['mesh_body_posed'].numpy()
|
||||
mesh_pred_levels = [uv2mesh(output_levels[i][0]).detach().cpu().numpy() for i in range(len(output_levels))]
|
||||
mesh_orig_posed = batch['mesh_posed'].cpu().numpy()
|
||||
body = batch['mesh_body_posed'].cpu().numpy()
|
||||
|
||||
orig_img = (uv_orig + 1) / 2
|
||||
uv_static_img = (batch['uv_static'].numpy() + 1) / 2
|
||||
uv_body_posed_img = (batch['uv_body_posed'].numpy() + 1) / 2
|
||||
uv_curvature = (np.tile(batch['uv_curvature'].numpy().argmax(-1)[:,:,:,np.newaxis], [1,1,1,3]) / 6)
|
||||
pred_img = np.concatenate([tf.image.resize(output_levels[i][0], (512, 256), tf.image.ResizeMethod.LANCZOS5) for i in range(len(output_levels))], axis=2)
|
||||
pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
|
||||
img = tf.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image(img[i]) for i in range(4)]
|
||||
orig_img = (torch.permute(uv_orig, [0,2,3,1]) + 1) / 2
|
||||
uv_static_img = (torch.permute(batch['uv_static'], [0,2,3,1]) + 1) / 2
|
||||
uv_body_posed_img = (torch.permute(batch['uv_body_posed'], [0,2,3,1]) + 1) / 2
|
||||
uv_curvature = (torch.tile(torch.argmax(torch.permute(batch['uv_curvature'], [0,2,3,1]), dim=-1)[:,:,:,None], [1,1,1,3]) / 6)
|
||||
pred_img = torch.concatenate([torch.permute(F.interpolate(output_levels[i][0], size=(512, 256), mode='bilinear'), [0,2,3,1]) for i in range(len(output_levels))], dim=3)
|
||||
# pred_img = (pred_img - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis]) / (pred_img.max(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis] - pred_img.min(axis=(1,2,3))[:,np.newaxis,np.newaxis,np.newaxis])
|
||||
pred_img = (pred_img - pred_img.flatten().reshape(pred_img.shape[0], -1).min(1)[0][:,None,None,None]) / (pred_img.flatten().reshape(pred_img.shape[0], -1).max(1)[0][:,None,None,None] - pred_img.flatten().reshape(pred_img.shape[0], -1).min(1)[0][:,None,None,None])
|
||||
|
||||
F = np.load("custom_utils/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
mask = batch['mesh_mask'].numpy().astype(bool)
|
||||
_, faces = maskFacesBatch(np.ones_like(mesh_orig), F, mask)
|
||||
img = torch.concat((uv_static_img, uv_body_posed_img, uv_curvature, orig_img, pred_img), axis=2)
|
||||
imgs = [wandb.Image(img[i].cpu().numpy()) for i in range(4)]
|
||||
|
||||
_F = np.load("smpl/smpl_faces" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
|
||||
mask = batch['mesh_mask'].cpu().numpy().astype(bool)
|
||||
_, faces = maskFacesBatch(np.ones_like(mesh_orig), _F, mask)
|
||||
|
||||
if config['use_skinning']:
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]).numpy()
|
||||
|
||||
gender = torch.greater_equal(shape[:, 10] , 0.5).to(torch.long)
|
||||
shape_offsets = torch.stack([torch.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], dims=[[1], [2]])[0,smpl.to_keep,:] for i in range(shape.shape[0])]).cpu().numpy()
|
||||
|
||||
gt = [mesh_orig[i][mask[i], :] + shape_offsets[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
|
||||
|
||||
mesh_unposed_shaped_levels = [[] for _ in range(mask.shape[0])]
|
||||
for i in range(len(output_levels)):
|
||||
mesh_unposed_shaped = [mesh_pred_levels[i][j, mask[j], :] + shape_offsets[j, mask[j]] for j in range(mask.shape[0])]
|
||||
for k in range(mask.shape[0]):
|
||||
mesh_unposed_shaped_levels[k].append(mesh_unposed_shaped[k])
|
||||
|
||||
|
||||
# full res version
|
||||
mesh_unposed_shaped_full = np.zeros((mask.shape[0], config['smpl_res'], 3), dtype=np.float32)
|
||||
for i in range(mask.shape[0]):
|
||||
mesh_unposed_shaped_full[i, mask[i], :] = mesh_unposed_shaped[i]
|
||||
|
||||
mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_unposed_shaped_full).numpy()
|
||||
mesh_posed = smpl(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], torch.tensor(mesh_unposed_shaped_full, device=config['device']))[0].cpu().numpy()
|
||||
else:
|
||||
mesh_posed = mesh_pred_levels[-1]
|
||||
gt = [mesh_orig[i][mask[i], :] for i in range(mesh_posed.shape[0])]
|
||||
@@ -467,7 +484,7 @@ def plot_everything(batch: dict, type: str):
|
||||
|
||||
|
||||
gt_posed = [mesh_orig_posed[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
mesh_posed_fixed = fix_collision(mesh_posed, body, mask).numpy()
|
||||
mesh_posed_fixed = fix_collision(torch.Tensor(mesh_posed).to('cuda'), torch.Tensor(body).to('cuda'), torch.Tensor(mask).to('cuda')).cpu().numpy()
|
||||
|
||||
mesh_posed = [mesh_posed[j, mask[j], :] for j in range(mask.shape[0])]
|
||||
|
||||
@@ -477,7 +494,7 @@ def plot_everything(batch: dict, type: str):
|
||||
collision_color = [np.ones_like(mesh_posed[i], dtype=np.int32)*230 for i in range(len(mesh_posed))]
|
||||
else:
|
||||
collision = [np.concatenate((body[i], mesh_posed[i]), axis=0) for i in range(len(mesh_posed))]
|
||||
collision_faces = [np.concatenate((F, faces[i] + config['smpl_res']), axis=0) for i in range(len(mesh_posed))]
|
||||
collision_faces = [np.concatenate((_F, faces[i] + config['smpl_res']), axis=0) for i in range(len(mesh_posed))]
|
||||
collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(mesh_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(mesh_posed))]
|
||||
|
||||
figs = { type + "/garment_" + str(i): plot_3d(gt[i], mesh_unposed_shaped_levels[i], faces[i], gt_posed[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
@@ -487,7 +504,7 @@ def plot_everything(batch: dict, type: str):
|
||||
else:
|
||||
garment_posed_fixed = [mesh_posed_fixed[i, mask[i], :] for i in range(body.shape[0])]
|
||||
collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(mesh_posed))]
|
||||
gt_posed = [batch['mesh_posed'].numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
gt_posed = [batch['mesh_posed'].cpu().numpy()[i, mask[i], :] for i in range(mask.shape[0])]
|
||||
collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(gt_posed))]
|
||||
figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i]) for i in range(4) }
|
||||
|
||||
@@ -505,7 +522,7 @@ def sample(folder):
|
||||
|
||||
pred = model.sample(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
|
||||
pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:]
|
||||
pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1])
|
||||
pose, shape = tf.tile(pose[None,:], [nr_of_samples,1]), tf.tile(shape[None,:], [nr_of_samples,1])
|
||||
|
||||
mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2]))
|
||||
mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy()
|
||||
@@ -706,13 +723,13 @@ def generate_results():
|
||||
|
||||
shape = batch['pose_shape'][:, 72:]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][None, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
i = -1
|
||||
pred = out_levels[i][0]
|
||||
|
||||
mesh_pred = uv2mesh(pred)
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
|
||||
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,None]
|
||||
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
||||
|
||||
for i in range(batch['id'].shape[0]):
|
||||
@@ -725,157 +742,117 @@ def generate_results():
|
||||
|
||||
pass
|
||||
|
||||
@tf.function
|
||||
def speed_step(levels_input, conds):
|
||||
|
||||
return model.sample([levels_input, conds], training=False)
|
||||
|
||||
|
||||
|
||||
def train():
|
||||
global g_balancing_coeff
|
||||
global step
|
||||
valid_iterator = iter(valid_dataloader)
|
||||
|
||||
for batch in train_dataloader:
|
||||
train_loss = train_step(batch)
|
||||
train_accumulator.update(train_loss)
|
||||
|
||||
if step % 25 == 0:
|
||||
data_valid = next(valid_iterator)
|
||||
valid_loss = valid_step(data_valid)
|
||||
valid_accumulator.update(valid_loss)
|
||||
|
||||
if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
|
||||
to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
|
||||
if step % 5000 == 0 or (step < 5000 and step % 1000 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
if not args['debug']:
|
||||
wandb.log(to_log, step=step)
|
||||
train_accumulator.reset()
|
||||
valid_accumulator.reset()
|
||||
print(step)
|
||||
|
||||
if train_loss['train/euclid_error'] < config['balancing_activation_limit']:
|
||||
g_balancing_coeff += 1 / 10000
|
||||
|
||||
if step % 10000 == 0:
|
||||
ckpt_saver(model, step, optimizer, None)
|
||||
|
||||
step += 1
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# init_wandb()
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str, default='lhunor')
|
||||
args = vars(parser.parse_args())
|
||||
if not args['debug']:
|
||||
init_wandb()
|
||||
|
||||
# config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
|
||||
# assert len(config_file_names) == 1
|
||||
# config_file_name = config_file_names[0]
|
||||
# # assert config_file_name.split('.')[1].endswith(args.name)
|
||||
# assert config_file_name.split('.')[1].endswith(config['name'])
|
||||
|
||||
smpl = SMPL(orig_smpl=config['is_cape'])
|
||||
use_amp = True
|
||||
smpl = SMPL(orig_smpl=config['is_cape'], path='smpl', device=config['device'])
|
||||
skirt_modifier = '_skirt' if config['is_skirt'] else ''
|
||||
dataset_resolutions = config['pyramid']['resolutions'].copy()
|
||||
if 512 not in dataset_resolutions:
|
||||
dataset_resolutions.append(512)
|
||||
|
||||
factory = DatasetFactory()
|
||||
dataset_train = factory.get_dataset(folder=config['train_dir'],
|
||||
batch_size=config['batch_size'],
|
||||
# batch_size=1,
|
||||
signature=config['signature_dynamic'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=config['dataset_in_memory'],
|
||||
is_cape=config['is_cape'],
|
||||
frames_file=config['frames_file'] if 'frames_file' in config.keys() else None)
|
||||
static_dataset_train = StaticDataset(folder=config['train_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
dataset_valid = factory.get_dataset(folder=config['valid_dir'],
|
||||
batch_size=config['batch_size'],
|
||||
signature=config['signature_dynamic'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=config['dataset_in_memory'],
|
||||
is_cape=config['is_cape'],
|
||||
frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None)
|
||||
static_dataset_valid = StaticDataset(folder=config['valid_dir'],
|
||||
signature=config['signature_static'],
|
||||
resolutions=dataset_resolutions,
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], resolutions=dataset_resolutions, is_cape=config['is_cape'])
|
||||
dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory'], resolutions=dataset_resolutions, is_cape=config['is_cape'])
|
||||
train_dataloader = DataLoader(dataset_train, batch_size=4, drop_last=True, shuffle=True, )
|
||||
valid_dataloader = DataLoader(dataset_valid, batch_size=4, drop_last=True, shuffle=True)
|
||||
train_accumulator = Accumulator()
|
||||
valid_accumulator = Accumulator()
|
||||
|
||||
model: Pyramid = Pyramid(config)
|
||||
|
||||
optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
|
||||
balancing_coeff = config['starting_balancing_coeff']
|
||||
optimizer = optim.Adam(model.parameters(), lr=config['lr'])
|
||||
_MSSSIM = MS_SSIM(data_range=2, size_average=True, channel=3)
|
||||
ckpt_saver = CheckpointSaver('checkpoints/iccv/torch/' + config['name'], top_n=5)
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
|
||||
|
||||
g_balancing_coeff = config['starting_balancing_coeff']
|
||||
reg_coeff = 1
|
||||
valid_iterator = iter(dataset_valid)
|
||||
gamma = 1
|
||||
balancing_activated_step = 0
|
||||
|
||||
# sample('checkpoints/cvae_13')
|
||||
# test()
|
||||
|
||||
# generate_results()
|
||||
|
||||
ckpt = tf.train.Checkpoint(model=model)
|
||||
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + config['name'], max_to_keep=20)
|
||||
|
||||
# load_model(model, 'checkpoints/pyr_abl_32_skirt', iteration='180000')
|
||||
|
||||
step = 0
|
||||
if config['pretrained_model'] != '':
|
||||
# assert len(config['pyramid']['resolutions']) == 1
|
||||
load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
|
||||
step = load_model(model, config['pretrained_model'], optimizer=optimizer, scaler=scaler, iteration=config['pretrained_model_iteration'])
|
||||
|
||||
if not args['debug']:
|
||||
model = torch.compile(model)
|
||||
wandb.watch(model, log='all', log_freq=1000, log_graph=True)
|
||||
|
||||
|
||||
if len(config['pyramid']['resolutions']) == 1:
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
model.curv_enc.trainable = False
|
||||
for param in model.static_enc.parameters(): param.requires_grad = False
|
||||
for param in model.body_enc.parameters(): param.requires_grad = False
|
||||
for param in model.curv_enc.parameters(): param.requires_grad = False
|
||||
else:
|
||||
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
|
||||
model.static_enc.trainable = False
|
||||
model.body_enc.trainable = False
|
||||
model.curv_enc.trainable = False
|
||||
model.cond_encoder.trainable = False
|
||||
for param in model.static_enc.parameters(): param.requires_grad = False
|
||||
for param in model.body_enc.parameters(): param.requires_grad = False
|
||||
for param in model.curv_enc.parameters(): param.requires_grad = False
|
||||
|
||||
# load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
|
||||
# model.levels[0].upscale_enhancer.trainable = False
|
||||
|
||||
# for i in range(len(model.levels)-1):
|
||||
# load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
|
||||
# model.levels[i].upscale_enhancer.trainable = False
|
||||
# if config['pyramid']['resolutions'][-1] != 512:
|
||||
# load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
# model.upscale_enhancer.trainable = False
|
||||
|
||||
for i in range(len(model.levels)-1):
|
||||
load_model(model.levels[i].upscale_enhancer, config['pyramid']['levels'][i]['upscale_enhancer'])
|
||||
model.levels[i].upscale_enhancer.trainable = False
|
||||
if config['pyramid']['resolutions'][-1] != 512:
|
||||
load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
|
||||
model.upscale_enhancer.trainable = False
|
||||
|
||||
for i in range(len(model.levels)-1):
|
||||
model.levels[i].trainable = False
|
||||
for param in model.levels[i].parameters(): param.requires_grad = False
|
||||
|
||||
# load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
|
||||
# load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])
|
||||
# load_model(model.levels[-1], 'checkpoints/cvae_19')
|
||||
|
||||
if not args['debug']:
|
||||
model = torch.compile(model)
|
||||
wandb.watch(model, log='all', log_freq=1000, log_graph=True)
|
||||
|
||||
step = 0
|
||||
times = []
|
||||
for batch in dataset_train:
|
||||
batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
# levels_input, conds = group_inputs_for_levels(batch, config)
|
||||
|
||||
# start = time.perf_counter()
|
||||
# out = speed_step(levels_input, conds)
|
||||
# t = time.perf_counter() - start
|
||||
# print(t)
|
||||
# times.append(t)
|
||||
|
||||
# if step > 200:
|
||||
# break
|
||||
|
||||
|
||||
train_loss = train_step(batch)
|
||||
|
||||
data_valid = valid_iterator.get_next()
|
||||
data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
|
||||
valid_loss = valid_step(data_valid)
|
||||
|
||||
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
||||
if step % 5000 == 0 or (step < 5000 and step % 500 == 0):
|
||||
train_figs = plot_everything(batch, 'train')
|
||||
valid_figs = plot_everything(data_valid, 'valid')
|
||||
to_log = { **to_log, **train_figs, **valid_figs }
|
||||
|
||||
if step % 5000 == 0:
|
||||
manager.save(checkpoint_number=step)
|
||||
if step == 0:
|
||||
to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
|
||||
|
||||
if train_loss['train/euclid_error'] < 0.004:
|
||||
if balancing_activated_step == 0:
|
||||
balancing_activated_step = step
|
||||
balancing_coeff = balancing_coeff + 1 / 20000
|
||||
|
||||
|
||||
to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
|
||||
|
||||
wandb.log(to_log, step = step)
|
||||
|
||||
print(step)
|
||||
|
||||
step += 1
|
||||
# print('mean:')
|
||||
# print(np.mean(times[1:]))
|
||||
train()
|
||||
@@ -25,8 +25,7 @@ to_keep = sio.loadmat('smpl/keep_remove.mat')['keep'].reshape(-1)
|
||||
F_fix_coll = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[to_keep,:][None,:,:], [4,1,1]), device='cuda')
|
||||
|
||||
|
||||
|
||||
def fix_collision(V, B, mask, thr=.004):
|
||||
def fix_collision(V: torch.Tensor, B: torch.Tensor, mask: torch.Tensor, thr=.004):
|
||||
mesh = Meshes(B, F_fix_coll)
|
||||
B_vn = torch.stack(mesh.verts_normals_list())
|
||||
|
||||
|
||||
Reference in New Issue
Block a user