diff --git a/configs/ae_curv_cape_m.py b/configs/ae_curv_cape_m.py new file mode 100644 index 0000000..f275928 --- /dev/null +++ b/configs/ae_curv_cape_m.py @@ -0,0 +1,72 @@ +from utils.convnext_utils import CNBlockConfig + +config = dict() + +config['gpu'] = '5' +config['name'] = 'curv_torch_cape_m' +config['notes'] = ' ' +config['tags'] = ['torch', 'curv'] +config['script'] = 'scripts/ae_curv_train.py' + +config['pretrained_model'] = '' + +config['batch_size'] = 4 +config['train_dir'] = '/data-local/127/cape_processed/train/male' +config['valid_dir'] = '/data-local/127/cape_processed/test/male' +config['test_dir'] = '/data-local/127/cape_processed/test/male' +config['train_frames'] = None +config['valid_frames'] = None + +config['resolution'] = 512 +config['is_skirt'] = False +config['dataset_in_memory'] = False +config['is_cape'] = True +config['remove_global_orientation'] = True +config['device'] = 'cuda' + +config['smpl_res'] = 14475 +if config['is_cape']: + config['smpl_res'] = 3638 + +config['latent_dim'] = 128 + +config['bottleneck_scale'] = 2 + +config['cond_ae'] = dict() +config['cond_ae']['device'] = config['device'] +config['cond_ae']['is_label'] = True +config['cond_ae']['latent_dim'] = config['latent_dim'] +config['cond_ae']['kernel_size'] = 7 +config['cond_ae']['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']['bottleneck_size'], 9), + CNBlockConfig(config['cond_ae']['bottleneck_size'], None, 3), +] + +block_setting_reverse = [ + CNBlockConfig(config['cond_ae']['bottleneck_size'], 128, 3), + CNBlockConfig(128, 64, 9), + CNBlockConfig(64, 32, 9), + CNBlockConfig(32, 16, 3), + CNBlockConfig(16, 3, 3), +] + +config['cond_ae']['encoder_block_settings'] = block_setting +config['cond_ae']['decoder_block_settings'] = block_setting_reverse +config['cond_ae']['activation'] = 'tanh' + + +config['lr'] = 0.001 +config['cond_ae']['upscale_output'] = False + +config['signature'] = { 'id', + 'frame_nr', + 'uv_curvature', + 'mesh_curvature', + 'mesh_mask', + 'uv_mask', + } \ No newline at end of file diff --git a/scripts/ae_curv_train.py b/scripts/ae_curv_train.py index 858b3c7..528e15b 100644 --- a/scripts/ae_curv_train.py +++ b/scripts/ae_curv_train.py @@ -8,14 +8,17 @@ import importlib 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 -# from configs.ae_curv_1 import config +if args['debug']: + from configs.ae_curv_cape_m import config os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] - +if args['debug']: + os.environ["CUDA_LAUNCH_BLOCKING"] = '1' import time import wandb @@ -160,37 +163,31 @@ def train(): if step == 0: to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } } - wandb.log(to_log, step=step) + if not args['debug']: + wandb.log(to_log, step=step) train_accumulator.reset() valid_accumulator.reset() print(step) if step % 10000 == 0: - ckpt_saver_enc(model.encoder, step, optimizer) - ckpt_saver_dec(model.decoder, step, optimizer) + ckpt_saver_enc(model.encoder, step, optimizer, None) + ckpt_saver_dec(model.decoder, step, optimizer, None) step += 1 if __name__ == '__main__': - init_wandb() - # device = "cuda" if torch.cuda.is_available() else "cpu" + if not args['debug']: + init_wandb() - # parser = argparse.ArgumentParser(description='') - # parser.add_argument('--name', type=str) - # args = vars(parser.parse_args()) - - resolution_modifier = '' - if config['resolution'] != 512: - resolution_modifier = '_' + str(config['resolution']) skirt_modifier = '_skirt' if config['is_skirt'] else '' uv_mask_body = np.load('custom_utils/mask_256x512' + ('_skirt' if config['is_skirt'] else '') + '.npy') uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2) uv_mask_body = torch.from_numpy(uv_mask_body).float().to(config['device']) - dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory']) - dataset_valid = Dataset(config['valid_dir'], config['signature'], device=config['device'], frames_file=config['valid_frames'], use_cache=config['dataset_in_memory']) + dataset_train = Dataset(config['train_dir'], config['signature'], device=config['device'], frames_file=config['train_frames'], use_cache=config['dataset_in_memory'], 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'], 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() @@ -210,11 +207,16 @@ if __name__ == '__main__': load_model(model.encoder, config['pretrained_model'] + '_enc') load_model(model.decoder, config['pretrained_model'] + '_dec') - ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/' + config['name'] + '_enc', top_n=5) - ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/' + config['name'] + '_dec', top_n=5) + ckpt_saver_enc = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_enc', top_n=5) + ckpt_saver_dec = CheckpointSaver('checkpoints/iccv/torch/' + config['name'] + '_dec', top_n=5) run_name = config['name'] print(f'started training: {run_name}') + + if not args['debug']: + # model = torch.compile(model) + wandb.watch(model, log='all', log_freq=1000, log_graph=True) + train()