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import os
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import sys
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from configs.config_cape_m_00032_SL_2 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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from unif_metric import chamfer_and_score
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uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
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@tf.function
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def step(batch):
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print('test')
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if config['use_skinning']:
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uv_orig = batch['uv_unposed']
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else:
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uv_orig = batch['uv_posed']
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pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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# if config['use_skinning']:
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# shape = batch['pose_shape'][:, 72:]
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# gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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# 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])])
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# mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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# mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
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# else:
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# mesh_pred_posed = mesh_pred
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# mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
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# return mesh_pred_posed, None #mesh_pred_posed_fixed
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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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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, orig_smpl=config['is_cape'])
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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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if config['is_cape']:
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F = np.load("custom_utils/smpl_faces_orig.npy").astype(np.int32)
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else:
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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 = ''
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load_model(model, 'checkpoints/' + checkpoint_name, iteration=iteration)
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# load_model(model, 'checkpoints/' + 'abl_all_120k')
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output_folder = '/home/workspace/data/results/' + checkpoint_name + '-' + iteration
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# os.makedirs(output_folder, exist_ok=True)
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# if config['use_skinning']:
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# config['signature_dynamic'].pop('mesh_posed')
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# config['signature_dynamic'].pop('mesh_unposed')
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# config['signature_dynamic'].pop('mesh_body_posed')
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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_no_orient=config['remove_global_orientation'],
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frames_file=config['frames_file_test'] if 'frames_file_test' in config.keys() else None,
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is_cape=config['is_cape'])
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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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is_cape=config['is_cape'])
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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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errors = []
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euclid = []
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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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mesh_pred = step(batch)
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# mesh_pred_posed, mesh_pred_posed_fixed = mesh_pred_posed.numpy(), mesh_pred_posed_fixed.numpy()
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mask = batch['mesh_mask'].numpy().astype(bool)
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for i in range(mask.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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x = chamfer_and_score(mesh_pred.numpy()[i, mask[i], :], batch['mesh_unposed'].numpy()[i, mask[i], :])
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y = np.mean(np.sqrt(np.sum((mesh_pred.numpy()[i, mask[i], :] - batch['mesh_unposed'].numpy()[i, mask[i], :])**2, axis=-1)))
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errors.append(x[1])
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euclid.append(y)
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# writeOBJ(os.path.join(output_folder, id + '_' + frame_nr + '.obj'), mesh_pred_posed[i, mask[i], :], faces[id])
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# np.save(os.path.join(output_folder, id + '_' + frame_nr + '.npy'), mesh_pred_posed[i, mask[i], :])
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# np.save(os.path.join(output_folder, id + '_' + frame_nr + '_fixed_coll.npy'), mesh_pred_posed_fixed[i, mask[i], :])
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# writeOBJ(os.path.join(output_folder, id + '_' + frame_nr + '_fixed_coll.obj'), mesh_pred_posed_fixed[i, mask[i], :], faces[id])
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print(np.mean(errors))
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print(np.mean(euclid))
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print()
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