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Merge #41

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d5cb5bb
add diversity metrics
Aug 31, 2021
63b9f73
update fid script
Sep 1, 2021
14b842b
update readme
Sep 1, 2021
46661bc
beat scores
Sep 1, 2021
83d64b9
update beat score script for raw paper data
Nov 10, 2021
d40065a
Merge branch 'google-research:main' into main
liruilong940607 Mar 2, 2022
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -68,7 +68,8 @@ Note you might want to change the `batch_size` in the config file if you meet OU
# caching the generated motions (seed included) to `./outputs`
python evaluator.py --config_path ./configs/fact_v5_deeper_t10_cm12.config --model_dir ./checkpoints
# calculate FIDs
python tools/calculate_scores.py
python tools/extract_aist_features.py
python tools/calculate_fid_scores.py
```


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214 changes: 214 additions & 0 deletions tools/calculate_beat_scores.py
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@@ -0,0 +1,214 @@
from absl import app
from absl import flags
from absl import logging

import os
from librosa import beat
import torch
import numpy as np
import pickle
from scipy.spatial.transform import Rotation as R
import scipy.signal as scisignal
from aist_plusplus.loader import AISTDataset


FLAGS = flags.FLAGS
flags.DEFINE_string(
'anno_dir', '/mnt/data/aist_plusplus_final/',
'Path to the AIST++ annotation files.')
flags.DEFINE_string(
'audio_dir', '/mnt/data/AIST/music/',
'Path to the AIST wav files.')
flags.DEFINE_string(
'audio_cache_dir', './data/aist_audio_feats/',
'Path to cache dictionary for audio features.')
flags.DEFINE_enum(
'split', 'testval', ['train', 'testval'],
'Whether do training set or testval set.')
flags.DEFINE_string(
'result_files', '/mnt/data/aist_paper_results/*.pkl',
'The path pattern of the result files.')
flags.DEFINE_bool(
'legacy', True,
'Whether the result files are the legacy version.')


def eye(n, batch_shape):
iden = np.zeros(np.concatenate([batch_shape, [n, n]]))
iden[..., 0, 0] = 1.0
iden[..., 1, 1] = 1.0
iden[..., 2, 2] = 1.0
return iden


def get_closest_rotmat(rotmats):
"""
Finds the rotation matrix that is closest to the inputs in terms of the Frobenius norm. For each input matrix
it computes the SVD as R = USV' and sets R_closest = UV'. Additionally, it is made sure that det(R_closest) == 1.
Args:
rotmats: np array of shape (..., 3, 3).
Returns:
A numpy array of the same shape as the inputs.
"""
u, s, vh = np.linalg.svd(rotmats)
r_closest = np.matmul(u, vh)

# if the determinant of UV' is -1, we must flip the sign of the last column of u
det = np.linalg.det(r_closest) # (..., )
iden = eye(3, det.shape)
iden[..., 2, 2] = np.sign(det)
r_closest = np.matmul(np.matmul(u, iden), vh)
return r_closest


def recover_to_axis_angles(motion):
batch_size, seq_len, dim = motion.shape
assert dim == 225
transl = motion[:, :, 6:9]
rotmats = get_closest_rotmat(
np.reshape(motion[:, :, 9:], (batch_size, seq_len, 24, 3, 3))
)
axis_angles = R.from_matrix(
rotmats.reshape(-1, 3, 3)
).as_rotvec().reshape(batch_size, seq_len, 24, 3)
return axis_angles, transl


def recover_motion_to_keypoints(motion, smpl_model):
smpl_poses, smpl_trans = recover_to_axis_angles(motion)
smpl_poses = np.squeeze(smpl_poses, axis=0) # (seq_len, 24, 3)
smpl_trans = np.squeeze(smpl_trans, axis=0) # (seq_len, 3)
keypoints3d = smpl_model.forward(
global_orient=torch.from_numpy(smpl_poses[:, 0:1]).float(),
body_pose=torch.from_numpy(smpl_poses[:, 1:]).float(),
transl=torch.from_numpy(smpl_trans).float(),
).joints.detach().numpy()[:, :24, :] # (seq_len, 24, 3)
return keypoints3d


def motion_peak_onehot(joints):
"""Calculate motion beats.
Kwargs:
joints: [nframes, njoints, 3]
Returns:
- peak_onhot: motion beats.
"""
# Calculate velocity.
velocity = np.zeros_like(joints, dtype=np.float32)
velocity[1:] = joints[1:] - joints[:-1]
velocity_norms = np.linalg.norm(velocity, axis=2)
envelope = np.sum(velocity_norms, axis=1) # (seq_len,)

# Find local minima in velocity -- beats
peak_idxs = scisignal.argrelextrema(envelope, np.less, axis=0, order=10) # 10 for 60FPS
peak_onehot = np.zeros_like(envelope, dtype=bool)
peak_onehot[peak_idxs] = 1

# # Second-derivative of the velocity shows the energy of the beats
# peak_energy = np.gradient(np.gradient(envelope)) # (seq_len,)
# # optimize peaks
# peak_onehot[peak_energy<0.001] = 0
return peak_onehot


def alignment_score(music_beats, motion_beats, sigma=3):
"""Calculate alignment score between music and motion."""
if motion_beats.sum() == 0:
return 0.0
music_beat_idxs = np.where(music_beats)[0]
motion_beat_idxs = np.where(motion_beats)[0]
score_all = []
for motion_beat_idx in motion_beat_idxs:
dists = np.abs(music_beat_idxs - motion_beat_idx).astype(np.float32)
ind = np.argmin(dists)
score = np.exp(- dists[ind]**2 / 2 / sigma**2)
score_all.append(score)
return sum(score_all) / len(score_all)


def main(_):
import glob
import tqdm
from smplx import SMPL

# set smpl
smpl = SMPL(model_path="/mnt/data/smpl/", gender='MALE', batch_size=1)

# create list
seq_names = []
if "train" in FLAGS.split:
seq_names += np.loadtxt(
os.path.join(FLAGS.anno_dir, "splits/crossmodal_train.txt"), dtype=str
).tolist()
if "val" in FLAGS.split:
seq_names += np.loadtxt(
os.path.join(FLAGS.anno_dir, "splits/crossmodal_val.txt"), dtype=str
).tolist()
if "test" in FLAGS.split:
seq_names += np.loadtxt(
os.path.join(FLAGS.anno_dir, "splits/crossmodal_test.txt"), dtype=str
).tolist()
ignore_list = np.loadtxt(
os.path.join(FLAGS.anno_dir, "ignore_list.txt"), dtype=str
).tolist()
seq_names = [name for name in seq_names if name not in ignore_list]

# calculate score on real data
dataset = AISTDataset(FLAGS.anno_dir)
n_samples = len(seq_names)
beat_scores = []
for i, seq_name in enumerate(seq_names):
logging.info("processing %d / %d" % (i + 1, n_samples))
# get real data motion beats
smpl_poses, smpl_scaling, smpl_trans = AISTDataset.load_motion(
dataset.motion_dir, seq_name)
smpl_trans /= smpl_scaling
keypoints3d = smpl.forward(
global_orient=torch.from_numpy(smpl_poses[:, 0:1]).float(),
body_pose=torch.from_numpy(smpl_poses[:, 1:]).float(),
transl=torch.from_numpy(smpl_trans).float(),
).joints.detach().numpy()[:, :24, :] # (seq_len, 24, 3)
motion_beats = motion_peak_onehot(keypoints3d)
# get real data music beats
audio_name = seq_name.split("_")[4]
audio_feature = np.load(os.path.join(FLAGS.audio_cache_dir, f"{audio_name}.npy"))
audio_beats = audio_feature[:keypoints3d.shape[0], -1] # last dim is the music beats
# get beat alignment scores
beat_score = alignment_score(audio_beats, motion_beats, sigma=3)
beat_scores.append(beat_score)
print ("\nBeat score on real data: %.3f\n" % (sum(beat_scores) / n_samples))

# calculate score on generated motion data
result_files = sorted(glob.glob(FLAGS.result_files))
result_files = [f for f in result_files if f[-8:-4] in f[:-8]]
if FLAGS.legacy:
# for some reason there are repetitive results. Skip them
result_files = {f[-34:]: f for f in result_files}
result_files = result_files.values()
n_samples = len(result_files)
beat_scores = []
for result_file in tqdm.tqdm(result_files):
if FLAGS.legacy:
with open(result_file, "rb") as f:
data = pickle.load(f)
result_motion = np.concatenate([
np.pad(data["pred_trans"], ((0, 0), (0, 0), (6, 0))),
data["pred_motion"].reshape(1, -1, 24 * 9)
], axis=-1) # [1, 120 + 1200, 225]
else:
result_motion = np.load(result_file)[None, ...] # [1, 120 + 1200, 225]
keypoints3d = recover_motion_to_keypoints(result_motion, smpl)
motion_beats = motion_peak_onehot(keypoints3d)
if FLAGS.legacy:
audio_beats = data["audio_beats"][0] > 0.5
else:
audio_name = result_file[-8:-4]
audio_feature = np.load(os.path.join(FLAGS.audio_cache_dir, f"{audio_name}.npy"))
audio_beats = audio_feature[:, -1] # last dim is the music beats
beat_score = alignment_score(audio_beats[120:], motion_beats[120:], sigma=3)
beat_scores.append(beat_score)
print ("\nBeat score on generated data: %.3f\n" % (sum(beat_scores) / n_samples))


if __name__ == '__main__':
app.run(main)
47 changes: 34 additions & 13 deletions tools/calculate_scores.py → tools/calculate_fid_scores.py
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Expand Up @@ -151,7 +151,7 @@ def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
+ np.trace(sigma2) - 2 * tr_covmean)


def extract_feature(motion, smpl_model, mode="kinetic"):
def recover_motion_to_keypoints(motion, smpl_model):
smpl_poses, smpl_trans = recover_to_axis_angles(motion)
smpl_poses = np.squeeze(smpl_poses, axis=0) # (seq_len, 24, 3)
smpl_trans = np.squeeze(smpl_trans, axis=0) # (seq_len, 3)
Expand All @@ -160,7 +160,10 @@ def extract_feature(motion, smpl_model, mode="kinetic"):
body_pose=torch.from_numpy(smpl_poses[:, 1:]).float(),
transl=torch.from_numpy(smpl_trans).float(),
).joints.detach().numpy()[:, :24, :] # (seq_len, 24, 3)
return keypoints3d


def extract_feature(keypoints3d, mode="kinetic"):
if mode == "kinetic":
feature = extract_kinetic_features(keypoints3d)
elif mode == "manual":
Expand All @@ -170,6 +173,20 @@ def extract_feature(motion, smpl_model, mode="kinetic"):
return feature # (f_dim,)


def calculate_avg_distance(feature_list, mean=None, std=None):
feature_list = np.stack(feature_list)
n = feature_list.shape[0]
# normalize the scale
if (mean is not None) and (std is not None):
feature_list = (feature_list - mean) / std
dist = 0
for i in range(n):
for j in range(i + 1, n):
dist += np.linalg.norm(feature_list[i] - feature_list[j])
dist /= (n * n - n) / 2
return dist


def calculate_frechet_feature_distance(feature_list1, feature_list2):
feature_list1 = np.stack(feature_list1)
feature_list2 = np.stack(feature_list2)
Expand All @@ -180,13 +197,14 @@ def calculate_frechet_feature_distance(feature_list1, feature_list2):
feature_list1 = (feature_list1 - mean) / std
feature_list2 = (feature_list2 - mean) / std

dist = calculate_frechet_distance(
frechet_dist = calculate_frechet_distance(
mu1=np.mean(feature_list1, axis=0),
sigma1=np.cov(feature_list1, rowvar=False),
mu2=np.mean(feature_list2, axis=0),
sigma2=np.cov(feature_list2, rowvar=False),
)
return dist
avg_dist = calculate_avg_distance(feature_list2)
return frechet_dist, avg_dist


if __name__ == "__main__":
Expand All @@ -199,31 +217,34 @@ def calculate_frechet_feature_distance(feature_list1, feature_list2):
"kinetic": [np.load(f) for f in glob.glob("./data/aist_features/*_kinetic.npy")],
"manual": [np.load(f) for f in glob.glob("./data/aist_features/*_manual.npy")],
}

# set smpl
smpl = SMPL(model_path="/mnt/data/smpl/", gender='MALE', batch_size=1)

# get motion features for the results
result_features = {"kinetic": [], "manual": []}
result_files = glob.glob("outputs/*.npy")
# result_files = [f for f in result_files if f[-8:-4] in f[:-8]]
beat_alignment_score = 0
for result_file in tqdm.tqdm(result_files):
result_motion = np.load(result_file)[None, ...] # [1, 120 + 1200, 225]
# visualize(result_motion, smpl)
result_features["kinetic"].append(
extract_feature(result_motion[:, 120:], smpl, "kinetic"))
result_features["manual"].append(
extract_feature(result_motion[:, 120:], smpl, "manual"))

keypoints3d = recover_motion_to_keypoints(result_motion[:, 120:], smpl)
result_features["kinetic"].append(extract_feature(keypoints3d, "kinetic"))
result_features["manual"].append(extract_feature(keypoints3d, "manual"))

# FID metrics
FID_k = calculate_frechet_feature_distance(
FID_k, Dist_k = calculate_frechet_feature_distance(
real_features["kinetic"], result_features["kinetic"])
FID_g = calculate_frechet_feature_distance(
FID_g, Dist_g = calculate_frechet_feature_distance(
real_features["manual"], result_features["manual"])

# Evaluation: FID_k: ~38, FID_g: ~27
# Evaluation: FID_k: ~32, FID_g: ~17
# Evaluation: Dist_k: ~6, Dist_g: ~6
# The AIChoreo paper used a bugged version of manual feature extractor from
# fairmotion (see here: https://github.com/facebookresearch/fairmotion/issues/50)
# So the FID_g here does not match with the paper. But this value should be correct.
# In this aistplusplus_api repo the feature extractor bug has been fixed.
# (see here: https://github.com/google/aistplusplus_api/blob/main/aist_plusplus/features/manual.py#L50)
print('\nEvaluation: FID_k: {:.4f}, FID_g: {:.4f}\n'.format(FID_k, FID_g))
print('\nEvaluation: FID_k: {:.4f}, FID_g: {:.4f}'.format(FID_k, FID_g))
print('Evaluation: Dist_k: {:.4f}, Dist_g: {:.4f}\n'.format(Dist_k, Dist_g))
2 changes: 1 addition & 1 deletion tools/extract_aist_features.py
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Expand Up @@ -65,5 +65,5 @@ def main(seq_name, motion_dir):

# processing
process = functools.partial(main, motion_dir=aist_dataset.motion_dir)
pool = multiprocessing.Pool(12)
pool = multiprocessing.Pool(8)
pool.map(process, seq_names)
2 changes: 1 addition & 1 deletion tools/preprocessing.py
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Expand Up @@ -20,7 +20,7 @@
'audio_dir', '/mnt/data/AIST/music/',
'Path to the AIST wav files.')
flags.DEFINE_string(
'audio_cache_dir', '/tmp/aist_audio_feats/',
'audio_cache_dir', './data/aist_audio_feats/',
'Path to cache dictionary for audio features.')
flags.DEFINE_enum(
'split', 'train', ['train', 'testval'],
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