cvpr/cvae_evaluate.py

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()