import os import string from configs.config_cvae 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 time import argparse import wandb import random import numpy as np import scipy as sp from custom_utils.custom_utils import UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces import plotly.graph_objects as go import plotly.express as ex from PIL import Image if config['is_skirt']: from losses import edge_loss_skirt as edge_loss else: from losses import edge_loss from utils import str_to_bool from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing from load_models import load_generator, load_discriminator from losses import collision_ratio, uv_mask_loss, bend_loss, kl_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 from dataset import DatasetFactory, StaticDataset from custom_utils.smpl import SMPL from custom_utils.supersmpl_io import writeOBJ from models.cvae_model import CVAE def init_wandb(): run = wandb.init(project='cvae', name=config['name'], notes=config['notes']) artifact = wandb.Artifact('code', type='code') artifact.add_file('cvae.py') artifact.add_file('cvae_evaluate.py') artifact.add_file('models/cvae_model.py') artifact.add_file('losses.py') artifact.add_file('configs/config_base.py') artifact.add_file('configs/config_cvae.py') artifact.add_file('models/base.py') artifact.add_file('models/cond_encoder.py') artifact.add_file('dataset.py') artifact.add_file('convnext/models/convnext_tf.py') run.log_artifact(artifact) uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt']) @tf.function def inference_step(batch): pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']) mesh_pred = uv2mesh(pred) mesh_orig = batch['mesh_unposed'] 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) # is the output already masked? e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])) # c_ratio = tf.reduce_mean(collision_ratio(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])) l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed'] - pred) ** 2), axis=-1), axis=[1, 2])) ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) euclid_error = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) return { 'test/l2_loss': l2_loss, 'test/edge_loss': e_loss, 'test/collision_loss': c_loss, 'test/euclid_error': euclid_error, 'test/ms_ssim': ms_ssim, } def plot_everything(batch: dict, type: str): pred = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']) mesh_pred = uv2mesh(pred).numpy() mesh_orig = batch['mesh_unposed'] orig_img = batch['uv_unposed'] uv_static_img = batch['uv_static'] uv_body_posed_img = batch['uv_body_posed'] pred_img = pred img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img), axis=2) imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)] 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])]) F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy") mask = batch['mesh_mask'].numpy().astype(bool) garment_unposed, faces = maskFacesBatch(mesh_pred, F, mask) garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])] gt = [mesh_orig[i].numpy()[mask[i], :] + shape_offsets.numpy()[i, mask[i], :] for i in range(pred.shape[0])] body = batch['mesh_body_posed'].numpy() for i in range(body.shape[0]): mesh_pred[i, mask[i], :] += shape_offsets.numpy()[i, mask[i], :] mesh_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred).numpy() garment_posed = [mesh_posed[i, mask[i], :] for i in range(body.shape[0])] collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))] collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))] collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))] figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) } for i in range(4): temp_file = 'temp/' + config['name'] + '_' + batch['id'][i].numpy().decode('UTF-8') + '_' + ''.join(random.choice(string.ascii_lowercase) for _ in range(4)) writeOBJ(temp_file + '_garment.obj', garment_posed[i], faces[i].astype(np.int32)) writeOBJ(temp_file + '_body.obj', body[i], F[i].astype(np.int32)) return { **figs, **{ type + '/images': imgs } } def test(): load_model(model, 'checkpoints/' + config['name'].split('_')[0]) dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False, do_repeat=False) static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False) step = 0 losses = [] for batch in dataset_test: batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic'])) inference_loss = inference_step(batch) losses.append(inference_loss) to_log = { **{ 'current_step': step }, **inference_loss } if step % 100 == 0: figs = plot_everything(batch, 'test') to_log = { **to_log, **figs } wandb.log(to_log, step = step) if step > 300: break print(step) step += 1 # averages = dict() path_to_plotly_html = "temp/plotly_figure.html" histograms = dict() for key in inference_loss.keys(): accumulated = [losses[i][key].numpy() for i in range(len(losses))] fig = ex.histogram(accumulated, title='Average: ' + str(np.asarray(accumulated).mean())) # fig.write_html(path_to_plotly_html, auto_play = False) histograms['all/' + key.split('/')[-1] + '_hist'] = fig # wandb.Html(path_to_plotly_html) # averages['all/' + key.split('/')[-1] + '_avg'] = np.asarray(accumulated).mean() wandb.log({ **histograms }, step = step) wandb.finish() pass if __name__ == '__main__': # init_wandb() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str) args = vars(parser.parse_args()) smpl = SMPL() skirt_modifier = '_skirt' if config['is_skirt'] else '' factory = DatasetFactory() model = CVAE(config) g_balancing_coeff = config['starting_balancing_coeff'] test()