cvpr/evaluate_ae.py

import os
import sys
from configs.config_abl_all_10p_curv_25_r2 import config

os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        # Currently, memory growth needs to be the same across GPUs
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        # Memory growth must be set before GPUs have been initialized
        print(e)

import glob
import time
import argparse
import wandb
import tqdm
import random
import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
from PIL import Image
from postprocessing import fix_collision

if config['is_skirt']:
    from losses import edge_loss_skirt as edge_loss
else:
    from losses import edge_loss



from losses import collision_ratio, kl_loss #, concept_loss
# from losses import collision_penalty as collision_penalty
from losses import collision_loss as collision_penalty
from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
from dataset import DatasetFactory, StaticDataset
from custom_utils.smpl import SMPL
from models.cvae_model import CVAE
from custom_utils.supersmpl_io import writeOBJ
# from unif_metric import chamfer_and_score


uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])

@tf.function
def step(batch):
    if config['use_skinning']:
        uv_orig = batch['uv_unposed']
    else:
        uv_orig =  batch['uv_posed']
    
    pred, _, _ = model([uv_orig, batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)

    mesh_pred = uv2mesh(pred)

    if config['use_skinning']:
        shape = batch['pose_shape'][:, 72:]
        gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
        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])])

        mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
        mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
    else:
        mesh_pred_posed = mesh_pred
        
    # mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])

    return mesh_pred_posed, None #mesh_pred_posed_fixed


if __name__ == '__main__':
    # init_wandb()
    parser = argparse.ArgumentParser(description='')
    parser.add_argument('--name', type=str)
    args = vars(parser.parse_args())

    skirt_modifier = '_skirt' if config['is_skirt'] else ''

    folder = config['test_dir']
    smpl = SMPL(use_one_hot=False, orig_smpl=config['is_cape'])
    model = CVAE(config)

    # str_split = config['name'].split('_')
    # test_idx = str_split.index('test')
    # checkpoint_name = '_'.join(str_split[:test_idx])
    checkpoint_name = config['name']

    if config['is_cape']:
        F = np.load("custom_utils/smpl_faces_orig.npy").astype(np.int32)
    else:
        F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)


    ids = os.listdir(os.path.join(folder, 'pose_shape'))
    ids = [id.split('_')[0] for id in ids]
    ids = set(ids)

    iteration = '70000'

    load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
    # load_model(model, 'checkpoints/' + 'abl_all_120k')

    output_folder = '/home/workspace/data/results/post/' + checkpoint_name + '-' + iteration


    os.makedirs(output_folder, exist_ok=True)

    if config['use_skinning']:
        config['signature_dynamic'].pop('mesh_posed')
        config['signature_dynamic'].pop('mesh_unposed')
    config['signature_dynamic'].pop('mesh_body_posed')

    factory = DatasetFactory()
    dataset_test = factory.get_dataset(folder=config['test_dir'], 
                                       batch_size=config['batch_size'], 
                                       signature=config['signature_dynamic'], 
                                       resolutions=[config['resolution']], 
                                       in_memory=False, 
                                       do_repeat=False,
                                       use_no_orient=config['remove_global_orientation'],
                                       frames_file=config['frames_file_test'] if 'frames_file_test' in config.keys() else None, 
                                       is_cape=config['is_cape'])
    static_dataset_test = StaticDataset(folder=config['test_dir'], 
                                        signature=config['signature_static'], 
                                        resolutions=[config['resolution']], 
                                        in_memory=True,
                                        is_cape=config['is_cape'])

    # faces_folder = '/home/workspace/data/results/support/faces' + ('_skirt' if config['is_skirt'] else '')
    # ids = os.listdir(faces_folder)
    # ids = [id.split('.')[0] for id in ids]
    # faces = {id : np.load(faces_folder + '/' + id + '.npy') for id in ids }

    for batch in tqdm.tqdm(dataset_test):
        batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))

        mesh_pred_posed, mesh_pred_posed_fixed = step(batch)
        # mesh_pred_posed, mesh_pred_posed_fixed = mesh_pred_posed.numpy(), mesh_pred_posed_fixed.numpy()
        mesh_pred_posed = mesh_pred_posed.numpy()

        mask = batch['mesh_mask'].numpy().astype(bool)

        for i in range(mask.shape[0]):
            id, frame_nr = batch['id'][i].numpy().decode("utf-8"), batch['frame_nr'][i].numpy().decode("utf-8")
            # writeOBJ(os.path.join(output_folder, id + '_' + frame_nr + '.obj'), mesh_pred_posed[i, mask[i], :], faces[id])
            np.save(os.path.join(output_folder, id + '_' + frame_nr + '.npy'), mesh_pred_posed[i, mask[i], :])
            # np.save(os.path.join(output_folder, id + '_' + frame_nr + '_fixed_coll.npy'), mesh_pred_posed_fixed[i, mask[i], :])
            # writeOBJ(os.path.join(output_folder, id + '_' + frame_nr + '_fixed_coll.obj'), mesh_pred_posed_fixed[i, mask[i], :], faces[id])