From 2fc0177d5a4d0fce25decfe0ba9c3c77b774832f Mon Sep 17 00:00:00 2001 From: Hunor Laczko Date: Sun, 2 Jul 2023 23:22:34 +0200 Subject: [PATCH] update baseline --- configs/iccvw/abl_all.py | 8 +- configs/iccvw/abl_all_3.py | 194 +++++++++++++++++++++++++++++++++++++ scripts/cvae.py | 7 +- 3 files changed, 201 insertions(+), 8 deletions(-) create mode 100644 configs/iccvw/abl_all_3.py diff --git a/configs/iccvw/abl_all.py b/configs/iccvw/abl_all.py index b9d9639..97ee2ab 100644 --- a/configs/iccvw/abl_all.py +++ b/configs/iccvw/abl_all.py @@ -3,15 +3,15 @@ from utils.convnext_utils import CNBlockConfig config = dict() config['gpu'] = '2' -config['name'] = 'abl_iccvw_all_1' -config['notes'] = '' +config['name'] = 'abl_iccvw_all_2' +config['notes'] = 'fixed collision loss' config['tags'] = ['iccv', 'abl_all', 'torch'] config['script'] = 'scripts/cvae.py' -config['pretrained_model'] = '' +config['pretrained_model'] = 'checkpoints/iccvw/abl_iccvw_all_1' config['use_skinning'] = True # and deshaping -config['use_normals'] = True +config['use_normals'] = True config['use_extra_losses'] = True # edge, collision, 3d mesh config['use_norm_loss'] = True diff --git a/configs/iccvw/abl_all_3.py b/configs/iccvw/abl_all_3.py new file mode 100644 index 0000000..efc17f3 --- /dev/null +++ b/configs/iccvw/abl_all_3.py @@ -0,0 +1,194 @@ +from utils.convnext_utils import CNBlockConfig + +config = dict() + +config['gpu'] = '4' +config['name'] = 'abl_iccvw_all_3' +config['notes'] = 'fixed collision loss, train from zero' +config['tags'] = ['iccv', 'abl_all', 'torch'] +config['script'] = 'scripts/cvae.py' + +config['pretrained_model'] = '' + +config['use_skinning'] = True # and deshaping +config['use_normals'] = True +config['use_extra_losses'] = True # edge, collision, 3d mesh +config['use_norm_loss'] = True + +ablation_count = [config['use_skinning'], config['use_normals'], config['use_extra_losses']].count(False) +assert ablation_count == 1 or ablation_count == 0 + +config['avg_garment_path'] = 'custom_data/avg/train_no_shape_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv train_no_shape_one_hot_avg_uv +if config['use_skinning']: + assert (config['avg_garment_path'] != '') + +config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision + +config['batch_size'] = 4 +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = False +config['device'] = 'cuda' + +config['train_dir'] = '/data-local/127/cloth3d_processed' +config['valid_dir'] = '/data-local/127/cloth3d_processed' +config['test_dir'] = '/data-net/dataset/test_ready' + +if config['is_skirt']: + config['train_frames'] = 'frames_train_skirt.txt' + config['valid_frames'] = 'frames_valid_skirt.txt' +else: + config['train_frames'] = 'frames_train.txt' + config['valid_frames'] = 'frames_valid.txt' + +config['static_model'] = 'checkpoints/iccvw/static_iccvw_4' +config['body_model'] = 'checkpoints/iccvw/body_iccvw_1' +config['normals_model'] = 'checkpoints/iccvw/norm_iccvw_1' + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +############### +config['lr'] = 0.0001# 0.0001 +############### +config['starting_balancing_coeff'] = 0.0001 +config['balancing_activation_limit'] = 0.01 + +config['signature']: set = { 'id', + 'frame_nr', + 'pose_shape', + 'uv_body_posed', + 'mesh_body_posed', + 'mesh_posed', + 'uv_normals', + 'uv_static', + 'mesh_mask', + 'uv_mask', + } + +if config['use_skinning']: + config['signature'].add('uv_unposed') + config['signature'].add('mesh_unposed') +else: + config['signature'].add('uv_posed') + +config['upscale_enhancer'] = '' +config['freeze_encoders'] = True +config['use_one_hot'] = False +config['bottleneck_scale'] = 2 + +config['cvae'] = dict() +config['cvae']['device'] = config['device'] +config['cvae']['is_label'] = False +config['cvae']['input_features'] = 3+3+3+3 if config['use_normals'] else 3+3+3 +config['cvae']['kernel_size'] = 7 +config['cvae']['bottleneck_size'] = 64 * config['bottleneck_scale'] +config['cvae']['latent_dim'] = config['cvae']['bottleneck_size'] + +block_setting = [ + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 3), + CNBlockConfig(128, 256, 3), + CNBlockConfig(256, config['cvae']['bottleneck_size'], 9), + CNBlockConfig(config['cvae']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cvae']['bottleneck_size'] * 2, 256, 3), + CNBlockConfig(256, 128, 9), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 3), + CNBlockConfig(32, 3, 3), +] + +config['cvae']['encoder_block_settings'] = block_setting +config['cvae']['decoder_block_settings'] = block_setting_reverse + + + +####################### cond_ae_norm ############################ +config['cond_ae_norm'] = dict() +config['cond_ae_norm']['device'] = config['device'] +config['cond_ae_norm']['latent_dim'] = config['latent_dim'] +config['cond_ae_norm']['kernel_size'] = 7 +config['cond_ae_norm']['bottleneck_size'] = 40 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 9), + CNBlockConfig(128, config['cond_ae_norm']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_norm']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_norm']['bottleneck_size'], 128, 3), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), +] + +config['cond_ae_norm']['encoder_block_settings'] = block_setting +config['cond_ae_norm']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_norm']['activation'] = 'tanh' + +####################### cond_ae_body ############################ +config['cond_ae_body'] = dict() +config['cond_ae_body']['device'] = config['device'] +config['cond_ae_body']['is_label'] = False +config['cond_ae_body']['latent_dim'] = config['latent_dim'] +config['cond_ae_body']['kernel_size'] = 7 +config['cond_ae_body']['bottleneck_size'] = 16 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, 128, 9), + CNBlockConfig(128, config['cond_ae_body']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_body']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_body']['bottleneck_size'], 128, 3), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), +] + +config['cond_ae_body']['encoder_block_settings'] = block_setting +config['cond_ae_body']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_body']['activation'] = 'tanh' + +####################### cond_ae_static ############################ +config['cond_ae_static'] = dict() +config['cond_ae_static']['device'] = config['device'] +config['cond_ae_static']['is_label'] = False +config['cond_ae_static']['latent_dim'] = config['latent_dim'] +config['cond_ae_static']['kernel_size'] = 7 +config['cond_ae_static']['bottleneck_size'] = 8 * config['bottleneck_scale'] + +block_setting = [ + CNBlockConfig(8, 16, 3), + CNBlockConfig(16, 32, 3), + CNBlockConfig(32, 64, 3), + CNBlockConfig(64, config['cond_ae_static']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae_static']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae_static']['bottleneck_size'], 64, 3), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 8, 3), + CNBlockConfig(8, 3, 3), +] + +config['cond_ae_static']['encoder_block_settings'] = block_setting +config['cond_ae_static']['decoder_block_settings'] = block_setting_reverse +config['cond_ae_static']['activation'] = 'tanh' \ No newline at end of file diff --git a/scripts/cvae.py b/scripts/cvae.py index 2ee71e9..6cb1654 100644 --- a/scripts/cvae.py +++ b/scripts/cvae.py @@ -10,7 +10,6 @@ parser.add_argument('--config', type=str) parser.add_argument('--debug', action='store_true') args = vars(parser.parse_args()) - if args['debug']: from configs.iccvw.abl_all import config else: @@ -253,8 +252,8 @@ def plot_everything(batch: dict, type: str): collision_fixed = [np.concatenate((body[i], garment_posed_fixed[i]), axis=0) for i in range(len(garment_posed))] collision_gt = [np.concatenate((body[i], gt_posed[i]), axis=0) for i in range(len(garment_posed))] - figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) } - figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'] and config['remove_global_orientation'])) for i in range(4) } + figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'])) for i in range(4) } + figs_coll = { type + "/garment_" + str(i) + '_collision': plot_3d_collision(collision_gt[i], collision[i], collision_fixed[i], collision_faces[i], collision_color[i], flip_xy=(not config['use_skinning'])) for i in range(4) } mean_hist = wandb.Histogram(sequence=mean.cpu().numpy().mean(axis=0)) logvar_hist = wandb.Histogram(sequence=logvar.cpu().numpy().mean(axis=0)) @@ -469,7 +468,7 @@ def train(): if train_loss['train/euclid_error'] < config['balancing_activation_limit']: g_balancing_coeff += 1 / 10000 - if step % 25000 == 0: + if step % 25000 == 0 and not args['debug']: ckpt_saver(model, step, optimizer, None) step += 1