188 lines
7.4 KiB
Python
188 lines
7.4 KiB
Python
import os
|
|
import sys
|
|
from configs.iccv.config_abl_pyr_32_64_128_256_512_skirt 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 models.pyramid_model_optimized import PyramidNew as Pyramid
|
|
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, group_inputs_for_levels
|
|
from dataset import DatasetFactory, StaticDataset
|
|
from custom_utils.smpl import SMPL
|
|
from models.cvae_model import CVAE
|
|
from custom_utils.supersmpl_io import writeOBJ
|
|
|
|
|
|
uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False)
|
|
|
|
@tf.function
|
|
def step(batch):
|
|
levels_input, conds = group_inputs_for_levels(batch, config)
|
|
|
|
out_levels = model([levels_input, conds], training=True)
|
|
|
|
pred_final = out_levels[-1][0]
|
|
mesh_pred = uv2mesh(pred_final)
|
|
|
|
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 = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
|
|
|
|
# mesh_pred_posed_fixed = fix_collision(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
|
|
|
|
return mesh_pred_posed, None
|
|
|
|
|
|
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)
|
|
model: Pyramid = Pyramid(config)
|
|
|
|
# str_split = config['name'].split('_')
|
|
# test_idx = str_split.index('test')
|
|
# checkpoint_name = '_'.join(str_split[:test_idx])
|
|
checkpoint_name = config['name']
|
|
|
|
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 = ''
|
|
if iteration == '':
|
|
iteration = load_model(model, 'checkpoints/iccv/' + checkpoint_name, return_iteration=True)
|
|
else:
|
|
load_model(model, 'checkpoints/iccv/' + checkpoint_name, iteration=iteration)
|
|
# load_model(model, 'checkpoints/' + 'abl_all_120k')
|
|
|
|
output_folder = '/home/workspace/local-data_128/results/iccv/' + checkpoint_name + '-' + iteration
|
|
|
|
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
dataset_resolutions = config['pyramid']['resolutions'].copy()
|
|
if 512 not in dataset_resolutions:
|
|
dataset_resolutions.append(512)
|
|
|
|
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=dataset_resolutions,
|
|
in_memory=False,
|
|
do_repeat=False,
|
|
use_no_orient=True)
|
|
static_dataset_test = StaticDataset(folder=config['test_dir'],
|
|
signature=config['signature_static'],
|
|
resolutions=dataset_resolutions,
|
|
in_memory=True)
|
|
|
|
# faces_folder = '/home/workspace/local-data_128/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, _ = step(batch)
|
|
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])
|
|
|
|
# x = batch['mesh_posed'].numpy()[i, mask[i], :]
|
|
# y = mesh_pred_posed[i, mask[i], :]
|
|
# fig = go.Figure(data=[
|
|
# go.Mesh3d(
|
|
# x=x[:,0],
|
|
# y=x[:,1],
|
|
# z=x[:,2],
|
|
# i = faces[id][:,0],
|
|
# j = faces[id][:,1],
|
|
# k = faces[id][:,2],
|
|
# ),
|
|
# go.Mesh3d(
|
|
# x=y[:,0],
|
|
# y=y[:,1],
|
|
# z=y[:,2],
|
|
# i = faces[id][:,0],
|
|
# j = faces[id][:,1],
|
|
# k = faces[id][:,2],
|
|
# ),
|
|
# ])
|
|
|
|
# fig.update_layout(scene_aspectmode='manual',
|
|
# scene_aspectratio=dict(x=1, y=1, z=1))
|
|
# fig.update_layout(
|
|
# scene = dict(
|
|
# xaxis = dict(nticks=4, range=[-1,1],),
|
|
# yaxis = dict(nticks=4, range=[-1,1],),
|
|
# zaxis = dict(nticks=4, range=[-1,1],),)
|
|
# )
|
|
|
|
# fig.show()
|
|
|
|
# print()
|
|
|
|
|