111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
import os
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import sys
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from configs.config_abl_all import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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# Currently, memory growth needs to be the same across GPUs
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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# Memory growth must be set before GPUs have been initialized
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print(e)
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import glob
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import time
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import argparse
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import wandb
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import tqdm
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import random
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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from postprocessing import fix_collision
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if config['is_skirt']:
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from losses import edge_loss_skirt as edge_loss
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else:
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from losses import edge_loss
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from losses import collision_ratio, kl_loss #, concept_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.smpl import SMPL
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from models.cvae_model import CVAE
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from custom_utils.supersmpl_io import writeOBJ
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if __name__ == '__main__':
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# init_wandb()
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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args = vars(parser.parse_args())
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smpl = SMPL(use_one_hot=config['use_one_hot'])
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skirt_modifier = '_skirt' if config['is_skirt'] else ''
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folder = config['test_dir']
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# str_split = config['name'].split('_')
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# test_idx = str_split.index('test')
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# checkpoint_name = '_'.join(str_split[:test_idx])
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checkpoint_name = config['name']
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F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
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output_folder = '/home/workspace/data/results/support'
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os.makedirs(output_folder + '/bodies', exist_ok=True)
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os.makedirs(output_folder + '/faces', exist_ok=True)
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config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
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'mesh_body_posed': (14475, 3,),
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}
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config['signature_static'] = {
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'mesh_mask': (14475,),
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}
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factory = DatasetFactory()
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dataset_test = factory.get_dataset(folder=config['test_dir'],
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batch_size=config['batch_size'],
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signature=config['signature_dynamic'],
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resolutions=[config['resolution']],
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in_memory=False,
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do_repeat=False,
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use_one_hot=config['use_one_hot'],
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use_no_orient=config['remove_global_orientation'])
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static_dataset_test = StaticDataset(folder=config['test_dir'],
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signature=config['signature_static'],
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resolutions=[config['resolution']],
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in_memory=False)
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for batch in tqdm.tqdm(dataset_test):
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batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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# mask = batch['mesh_mask'].numpy().astype(bool)
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body = batch['mesh_body_posed'].numpy()
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# _, faces = maskFacesBatch(np.zeros((body.shape[0],14475,3)), F, mask)
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for i in range(body.shape[0]):
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id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
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writeOBJ(os.path.join(output_folder, 'bodies', id + '_' + frame_nr + '_body_no_orient.obj'), body[i], F)
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# np.save(os.path.join(output_folder, 'faces', id + '_' + frame_nr + '.npy'), faces[i].astype(np.int32))
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