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# coding=utf-8
# Copyright 2020 The Google AI Perception Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Estimate AIST++ SMPL-format Motion."""
import os
import pickle
from absl import app
from absl import flags
from absl import logging
from aist_plusplus.loader import AISTDataset
import numpy as np
from smplx import SMPL
import torch
FLAGS = flags.FLAGS
flags.DEFINE_string(
'anno_dir',
'/usr/local/google/home/ruilongli/data/public/aist_plusplus_final/',
'input local dictionary for AIST++ annotations.')
flags.DEFINE_string(
'smpl_dir',
'/usr/local/google/home/ruilongli/data/SMPL/',
'input local dictionary that stores SMPL data.')
flags.DEFINE_string(
'save_dir',
'/usr/local/google/home/ruilongli/data/public/aist_plusplus_final/motions/',
'output local dictionary that stores AIST++ SMPL-format motion data.')
flags.DEFINE_list(
'sequence_names',
None,
'list of sequence names to be processed. None means to process all.')
flags.DEFINE_string(
'save_dir_gcs',
None,
'output GCS directory.')
np.random.seed(0)
torch.manual_seed(0)
def unify_joint_mappings(dataset='openpose25'):
"""Unify different joint definations.
Output unified defination:
['Nose',
'RShoulder', 'RElbow', 'RWrist',
'LShoulder', 'LElbow', 'LWrist',
'RHip', 'RKnee', 'RAnkle',
'LHip', 'LKnee', 'LAnkle',
'REye', 'LEye',
'REar', 'LEar',
'LBigToe', 'LHeel',
'RBigToe', 'RHeel',]
Args:
dataset: `openpose25`, `coco`(17) and `smpl`.
Returns:
a list of indexs that maps the joints to a unified defination.
"""
if dataset == 'openpose25':
return np.array([
0,
2, 3, 4,
5, 6, 7,
9, 10, 11,
12, 13, 14,
15, 16,
17, 18,
19, 21,
22, 24,
], dtype=np.int32)
elif dataset == 'smpl':
return np.array([
24,
17, 19, 21,
16, 18, 20,
2, 5, 8,
1, 4, 7,
25, 26,
27, 28,
29, 31,
32, 34,
], dtype=np.int32)
elif dataset == 'coco':
return np.array([
0,
5, 7, 9,
6, 8, 10,
11, 13, 15,
12, 14, 16,
1, 2,
3, 4,
], dtype=np.int32)
else:
raise ValueError(f'{dataset} is not supported')
class SMPLRegressor:
"""SMPL fitting based on 3D keypoints."""
def __init__(self, smpl_model_path, smpl_model_gener='MALE'):
# Fitting hyper-parameters
self.base_lr = 100.0
self.niter = 10000
self.metric = torch.nn.MSELoss()
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.smpl_model_path = smpl_model_path
self.smpl_model_gender = smpl_model_gener
# Mapping to unify joint definations
self.joints_mapping_smpl = unify_joint_mappings(dataset='smpl')
def get_optimizer(self, smpl, step, base_lr):
"""Setup opimizer with a warm up learning rate."""
if step < 100:
optimizer = torch.optim.SGD([
{'params': [smpl.transl], 'lr': base_lr},
{'params': [smpl.scaling], 'lr': base_lr * 0.01},
{'params': [smpl.global_orient], 'lr': 0.0},
{'params': [smpl.body_pose], 'lr': 0.0},
{'params': [smpl.betas], 'lr': 0.0},
])
elif step < 400:
optimizer = torch.optim.SGD([
{'params': [smpl.transl], 'lr': base_lr},
{'params': [smpl.scaling], 'lr': base_lr * 0.01},
{'params': [smpl.global_orient], 'lr': base_lr * 0.001},
{'params': [smpl.body_pose], 'lr': 0.0},
{'params': [smpl.betas], 'lr': 0.0},
])
else:
optimizer = torch.optim.SGD([
{'params': [smpl.transl], 'lr': base_lr},
{'params': [smpl.scaling], 'lr': base_lr * 0.01},
{'params': [smpl.global_orient], 'lr': base_lr * 0.001},
{'params': [smpl.body_pose], 'lr': base_lr * 0.001},
{'params': [smpl.betas], 'lr': 0.0},
])
return optimizer
def fit(self, keypoints3d, dtype='coco', verbose=True):
"""Run fitting to optimize the SMPL parameters."""
assert dtype == 'coco', 'only support coco format for now.'
assert len(keypoints3d.shape) == 3, 'input shape should be [N, njoints, 3]'
mapping_target = unify_joint_mappings(dataset=dtype)
keypoints3d = keypoints3d[:, mapping_target, :]
keypoints3d = torch.from_numpy(keypoints3d).float().to(self.device)
batch_size, njoints = keypoints3d.shape[0:2]
# Init learnable smpl model
smpl = SMPL(
model_path=self.smpl_model_path,
gender=self.smpl_model_gender,
batch_size=batch_size).to(self.device)
# Start fitting
for step in range(self.niter):
optimizer = self.get_optimizer(smpl, step, self.base_lr)
output = smpl.forward()
joints = output.joints[:, self.joints_mapping_smpl[:njoints], :]
loss = self.metric(joints, keypoints3d)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if verbose and step % 10 == 0:
logging.info(f'step {step:03d}; loss {loss.item():.3f};')
# Return results
return smpl, loss.item()
def main(_):
aist_dataset = AISTDataset(FLAGS.anno_dir)
smpl_regressor = SMPLRegressor(FLAGS.smpl_dir, 'MALE')
if FLAGS.sequence_names:
seq_names = FLAGS.sequence_names
else:
seq_names = aist_dataset.mapping_seq2env.keys()
for seq_name in seq_names:
logging.info('processing %s', seq_name)
# load 3D keypoints
keypoints3d = AISTDataset.load_keypoint3d(
aist_dataset.keypoint3d_dir, seq_name, use_optim=True)
# SMPL fitting
smpl, loss = smpl_regressor.fit(keypoints3d, dtype='coco', verbose=True)
# One last time forward
with torch.no_grad():
_ = smpl.forward()
body_pose = smpl.body_pose.detach().cpu().numpy()
global_orient = smpl.global_orient.detach().cpu().numpy()
smpl_poses = np.concatenate([global_orient, body_pose], axis=1)
smpl_scaling = smpl.scaling.detach().cpu().numpy()
smpl_trans = smpl.transl.detach().cpu().numpy()
os.makedirs(FLAGS.save_dir, exist_ok=True)
motion_file = os.path.join(FLAGS.save_dir, f'{seq_name}.pkl')
with open(motion_file, 'wb') as f:
pickle.dump({
'smpl_poses': smpl_poses,
'smpl_scaling': smpl_scaling,
'smpl_trans': smpl_trans,
'smpl_loss': loss,
}, f, protocol=pickle.HIGHEST_PROTOCOL)
# upload results to GCS
if FLAGS.save_dir_gcs:
import gcs_utils
gcs_utils.upload_files_to_gcs(
local_folder=FLAGS.save_dir,
gcs_path=FLAGS.save_dir_gcs)
if __name__ == '__main__':
app.run(main)
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