115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
import os
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import sys
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from configs.config_abl_skin_var_3 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, UV2MeshLabel
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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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uv2mesh = UV2MeshLabel(res=256)
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@tf.function
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def step(batch):
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pred, mean, logvar = model([batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']])
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mesh_pred = tf.math.argmax(uv2mesh(pred), axis=-1)
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return mesh_pred
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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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smpl = SMPL(use_one_hot=False)
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model = CVAE(config)
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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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ids = os.listdir(os.path.join(folder, 'pose_shape'))
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ids = [id.split('_')[0] for id in ids]
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ids = set(ids)
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iteration = '70000'
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load_model(model, 'checkpoints/' + config['name'], iteration=iteration)
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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=1,
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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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frames_file='/home/workspace/data/dataset/test_ready/frames_pose_cluster_20_sample_10.txt')
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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=True)
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# faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
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# ids = os.listdir(faces_folder)
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# ids = [id.split('.')[0] for id in ids]
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# faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }
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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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