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generate_test_data_real_array.py
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generate_test_data_real_array.py
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import numpy as np
import tqdm
import argparse
import sfs
import time
import os
import pyroomacoustics as pra
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
def main():
# Arguments parse
parser = argparse.ArgumentParser(description='Generate data for circular array setup')
parser.add_argument('--dataset_path', type=str, help="Base Data Directory", default='/nas/home/lcomanducci/soundfield_synthesis_RQ/dataset/test/real_array')
parser.add_argument('--models_path', type=str, help='Deep learning models folder',
default='/nas/home/lcomanducci/soundfield_synthesis_RQ/models/real_array/')
parser.add_argument('--gt_soundfield', type=bool, help='compute ground truth soundfield', default=True)
parser.add_argument('--pm', type=bool, help='compute pressure matching', default=True)
parser.add_argument('--pwd', type=bool, help='compute model-based acoustic rendering', default=True)
parser.add_argument('--pwd_cnn', type=bool, help='compute model-based acoustic rendering + CNN', default=True)
parser.add_argument('--n_missing', type=int, help='number missing loudspeakers', default=28)
parser.add_argument('--wfs', type=bool, help='compute Wave Field Synthesis', default=True)
parser.add_argument('--awfs', type=bool, help='compute Adaptive Wave Field Synthesis', default=True)
parser.add_argument('--pwd_apwd', type=bool,
help='compute Adaptive model-based acoustic rendering + adaptive wfs-like optimization',
default=True)
parser.add_argument('--gpu', type=str, help='GPU NUMBER', default='0')
# Load missing packages here to be able to select GPU
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import tensorflow as tf
from data_lib import params_real
from data_lib import soundfield_generation_real as sg, results_utils
eval_points = True
args = parser.parse_args()
dataset_path = args.dataset_path
# Grid of points where we actually compute the soundfield
point = params_real.points
N_pts = len(point)
# Load green function secondary sources --> eval points (it is in train directory since it is the same)
dataset_path_train = '/nas/home/lcomanducci/soundfield_synthesis_RQ/dataset/real_array'
green_function_sec_sources_path = 'green_function_sec_sources_nl_60_real.npy'
G = np.load(os.path.join(dataset_path_train, green_function_sec_sources_path)).astype(np.complex64)
# Load Missing loudspeakers configuration
lspk_config_path = 'lspk_config_nl_' + str(params_real.N_ldspk_array) + '_missing_' + str(args.n_missing) + '.npy'
lspk_config_path_global = os.path.join(dataset_path_train, 'setup', lspk_config_path)
idx_missing = np.load(lspk_config_path_global)
N_lspks = params_real.N_ldspk_array - args.n_missing
theta_l = np.delete(params_real.theta_l, idx_missing)
G = np.delete(G, idx_missing, axis=1)
array_wfs = sfs.array.as_secondary_source_distribution(
[np.delete(params_real.array.x, idx_missing, axis=0),
np.delete(params_real.array.n, idx_missing, axis=0),
np.delete(params_real.array.a, idx_missing, axis=0)])
# Let's precompute what we need in order to apply the selected models
# Precompute for pwd and cnn
N, h, theta_n, trunc_mod_exp_idx = sg.model_based_synthesis_circular_array(N_lspks, theta_l)
# Regularization params as in Ueno, N., Koyama, S., & Saruwatari, H. (2019). Three-dimensional sound field reproduction based on weighted mode-matching method. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(12), 1852-1867.
G_cp = G[params_real.idx_cp] # Green function at control points
reg_array = np.zeros(params_real.N_freqs)
for n_f in range(params_real.N_freqs):
_, s, _ = np.linalg.svd(np.matmul(np.conj(G_cp[:, :, n_f]).transpose(), G_cp[:, :, n_f]))
reg_array[n_f] = np.max(s) * 1e-3
# Load pwd_cnn deep learning model
if args.pwd_cnn:
model_name ='model_real_config_nl_60_missing_'+ str(args.n_missing) + '_COMPLEX_CP_' + str(len(params_real.idx_cp)) + '_lr_0.0001PReLU_earlyStop_10'
network_model = tf.keras.models.load_model(os.path.join(args.models_path, model_name))
# Driving signals - Model-based acoustic rendering
if args.pm:
C_pm = np.zeros((N_lspks, len(params_real.idx_cp), params_real.N_freqs), dtype=complex)
for n_f in tqdm.tqdm(range(len(params_real.wc))):
C_pm[:, :, n_f] = np.matmul(np.linalg.pinv(
np.matmul(G_cp[:, :, n_f].transpose(), G_cp[:, :, n_f]) + reg_array[n_f] * np.eye(N_lspks)),
G_cp[:, :, n_f].transpose())
#N_pts = len(params_circular.idx_lr)
nmse_pwd = np.zeros((params_real.N_sources_test, params_real.N_freqs), dtype=complex)
nmse_pwd_cnn, nmse_pwd_pm = np.zeros_like(nmse_pwd), np.zeros_like(nmse_pwd)
nmse_pwd_apwd = np.zeros_like(nmse_pwd, dtype=np.complex64)
nmse_wfs = np.zeros_like(nmse_pwd, dtype=np.complex64)
nmse_awfs = np.zeros_like(nmse_pwd, dtype=np.complex64)
ssim_pwd, ssim_pwd_cnn, ssim_pwd_pm = np.zeros_like(nmse_pwd), np.zeros_like(nmse_pwd), np.zeros_like(nmse_pwd)
ssim_pwd_apwd = np.zeros_like(nmse_pwd, dtype=np.complex64)
ssim_wfs = np.zeros_like(nmse_pwd, dtype=np.complex64)
ssim_awfs = np.zeros_like(nmse_pwd, dtype=np.complex64)
P_gt = np.zeros((params_real.N_sources_test, N_pts, params_real.N_freqs), dtype=np.complex64)
print('COMPUTE GROUND TRUTH SOUNDFIELD')
if args.gt_soundfield:
path_soundfield = os.path.join(dataset_path, 'gt_soundfield_test' + '_cp' + str(len(params_real.idx_cp)) + '.npy')
if os.path.exists(path_soundfield):
tic = time.time()
P_gt = np.load(path_soundfield)
print(str(time.time()-tic)+'s for loading gt soundfield....')
else:
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
xs = params_real.src_pos_test[n_s]
xs = np.concatenate([xs, np.array([0])])
room = pra.AnechoicRoom(fs=params_real.f_s)
mic_locs_pra = np.array(
[params_real.x_mic_array, params_real.y_mic_array, np.zeros_like(params_real.x_mic_array)])
room.add_microphone(mic_locs_pra)
room.add_source(xs)
room.compute_rir()
for n_p in range(params_real.N_mic_array):
P_gt[n_s, n_p, :] = np.fft.rfft(room.rir[n_p][0], n=params_real.nfft)[params_real.f_axis_mapping]
np.save(path_soundfield,P_gt)
path_pwd = os.path.join(dataset_path, 'filters_test_config_nl_' + str(params_real.N_ldspk_array) + '_missing_' + str(
args.n_missing) + '_cp' + str(len(params_real.idx_cp)) + '.npy')
if args.pwd:
print('COMPUTE PWD') ###########################################################################################
if os.path.exists(path_pwd):
d_array = np.load(path_pwd)
else:
d_array = np.zeros((params_real.N_sources_test, N_lspks, params_real.N_freqs), dtype=np.complex64)
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
xs = [params_real.src_pos_test[n_s][0], params_real.src_pos_test[n_s][1] ]
Phi = sg.herglotz_density_point_source(xs, theta_n, trunc_mod_exp_idx, N, A=1)
# Multiply filters for herglotz density function
for n_f in range(params_real.N_freqs):
d_array[ n_s, :, n_f] = (1 / N[n_f]) * np.matmul(np.expand_dims(Phi[n_f], axis=0), h[n_f].transpose())
np.save(path_pwd,d_array)
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
# PWD
P_pwd=np.einsum('ijk,jk->ik', G, d_array[n_s])
nmse_pwd[n_s] = results_utils.nmse(P_pwd, P_gt[n_s])
ssim_pwd[n_s] = results_utils.ssim_freq(P_pwd, P_gt[n_s])
# APWD
if args.pwd_apwd:
e_pwd = P_gt[ n_s, params_real.idx_cp] - P_pwd[params_real.idx_cp]
d_array_opt = np.zeros_like(d_array[n_s], dtype=np.complex64)
for n_f in range(params_real.N_freqs):
d_array_opt[:, n_f] = np.matmul(np.linalg.inv(
np.matmul(np.conj(G_cp[:, :, n_f].T), G_cp[:, :, n_f]) + reg_array[n_f] * np.eye(N_lspks)),
np.matmul(np.conj(G_cp[:, :, n_f].T), e_pwd[:, n_f])) + d_array[ n_s,:, n_f]
P_pwd_apwd = np.einsum('ijk,jk->ik', G, d_array_opt)
nmse_pwd_apwd[n_s], ssim_pwd_apwd[n_s] = results_utils.nmse(P_pwd_apwd, P_gt[ n_s]), results_utils.ssim_freq(P_pwd_apwd, P_gt[ n_s])
np.save(os.path.join(dataset_path, 'nmse_pwd_missing_' + str(args.n_missing) + '.npy'),nmse_pwd)
np.save(os.path.join(dataset_path, 'ssim_pwd_missing_' + str(args.n_missing) + '.npy'),ssim_pwd)
np.save(os.path.join(dataset_path, 'nmse_pwd_apwd_missing_' + str(args.n_missing) + '.npy'),nmse_pwd_apwd)
np.save(os.path.join(dataset_path, 'ssim_pwd_apwd_missing_' + str(args.n_missing) + '.npy'),ssim_pwd_apwd)
if args.wfs:
print('COMPUTE WFS') #########################################################################################
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
xs = params_real.src_pos_test[ n_s]
# Simulation parameters
### WFS
d_array_wfs = np.zeros((N_lspks, params_real.N_freqs), dtype=np.complex64)
for n_f in range(params_real.N_freqs):
frequency = params_real.f_axis[n_f] # in Hz
# grid = sfs.util.xyz_grid([-2, 2], [-2, 2], 0, spacing=0.02)
omega = 2 * np.pi * frequency # angular frequency
d, selection, secondary_source = sfs.fd.wfs.point_25d(omega, array_wfs.x, array_wfs.n,
[xs[0], xs[1], 0])
d_array_wfs[:, n_f] = d
P_wfs = np.einsum('ijk,jk->ik', G[:,selection], d_array_wfs[selection])
nmse_wfs[ n_s], ssim_wfs[ n_s] = results_utils.nmse(P_wfs, P_gt[ n_s]), results_utils.ssim_freq(P_wfs, P_gt[ n_s])
### AWFS
if args.awfs:
e_pwfs = P_gt[ n_s,params_real.idx_cp] - P_wfs[params_real.idx_cp]
d_array_awfs = np.zeros_like(d_array_wfs[selection], dtype=np.complex64)
for n_f in range(params_real.N_freqs):
d_array_awfs[:, n_f] = np.matmul(np.linalg.inv(
np.matmul(np.conj(G_cp[:, selection, n_f].T), G_cp[:, selection, n_f]) + reg_array[
n_f] * np.eye(np.sum(selection))),
np.matmul(np.conj(G_cp[:, selection, n_f].T), e_pwfs[:, n_f])) + d_array_wfs[selection, n_f]
P_awfs = np.einsum('ijk,jk->ik', G[:,selection], d_array_awfs)
nmse_awfs[ n_s], ssim_awfs[ n_s] = results_utils.nmse(P_awfs, P_gt[ n_s]), results_utils.ssim_freq(P_awfs, P_gt[ n_s])
np.save(os.path.join(dataset_path, 'nmse_awfs_missing_' + str(args.n_missing) + '.npy'), nmse_awfs)
np.save(os.path.join(dataset_path, 'ssim_awfs_missing_' + str(args.n_missing) + '.npy'), ssim_awfs)
np.save(os.path.join(dataset_path, 'nmse_wfs_missing_' + str(args.n_missing) + '.npy'), nmse_wfs)
np.save(os.path.join(dataset_path, 'ssim_wfs_missing_' + str(args.n_missing) + '.npy'), ssim_wfs)
if args.pm:
print('COMPUTE PM') ###########################################################################################
print(P_gt.shape)
d_pm = np.zeros((N_lspks, params_real.N_freqs), dtype=np.complex64)
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
xs = params_real.src_pos_test[ n_s]
for n_f in range(params_real.N_freqs):
d_pm[:, n_f] = np.matmul(C_pm[:, :, n_f], P_gt[n_s, params_real.idx_cp, n_f])
P_pwd_pm = np.einsum('ijk,jk->ik', G, d_pm)
nmse_pwd_pm[ n_s], ssim_pwd_pm[ n_s] = results_utils.nmse(P_pwd_pm, P_gt[ n_s]), results_utils.ssim_freq(P_pwd_pm, P_gt[ n_s])
np.save(os.path.join(dataset_path, 'nmse_pwd_pm_missing_' + str(args.n_missing) + '.npy'), nmse_pwd_pm)
np.save(os.path.join(dataset_path, 'ssim_pwd_pm_missing_' + str(args.n_missing) + '.npy'), ssim_pwd_pm)
if args.pwd_cnn:
print('COMPUTE PWD_CNN') ###########################################################################################
d_array = np.load(path_pwd)
d_array_cnn_complex = network_model.predict(np.expand_dims(d_array, axis=[-1]), verbose=0,batch_size=32)[:, :, :, 0]
for n_s in tqdm.tqdm(range(params_real.N_sources_test)):
P_pwd_cnn = np.einsum('ijk,jk->ik', G, d_array_cnn_complex[ n_s])
nmse_pwd_cnn[ n_s], ssim_pwd_cnn[ n_s] = results_utils.nmse(P_pwd_cnn, P_gt[n_s]), results_utils.ssim_freq(P_pwd_cnn, P_gt[n_s])
"""
plt.figure()
plt.subplot(311), plt.imshow(np.real(np.reshape(P_gt[n_s, :, n_f], (8, 8)))),plt.colorbar()
plt.subplot(312), plt.imshow(np.real(np.reshape(P_pwd_cnn[:, n_f], (8, 8)))),plt.colorbar()
plt.show()
"""
np.save(os.path.join(dataset_path, 'nmse_pwd_cnn_missing_' + str(args.n_missing) + '.npy'), nmse_pwd_cnn)
np.save(os.path.join(dataset_path, 'ssim_pwd_cnn_missing_' + str(args.n_missing) + '.npy'), ssim_pwd_cnn)
if not args.pwd:
nmse_pwd = np.load(os.path.join(dataset_path, 'nmse_pwd_missing_' + str(args.n_missing) + '.npy'))
ssim_pwd=np.load(os.path.join(dataset_path, 'ssim_pwd_missing_' + str(args.n_missing) + '.npy'))
if not args.pwd_apwd:
nmse_pwd_apwd=np.load(os.path.join(dataset_path, 'nmse_pwd_apwd_missing_' + str(args.n_missing) + '.npy'))
ssim_pwd_apwd=np.load(os.path.join(dataset_path, 'ssim_pwd_apwd_missing_' + str(args.n_missing) + '.npy'))
if not args.wfs:
nmse_wfs= np.load(os.path.join(dataset_path, 'nmse_wfs_missing_' + str(args.n_missing) + '.npy'))
ssim_wfs=np.load(os.path.join(dataset_path, 'ssim_wfs_missing_' + str(args.n_missing) + '.npy'))
if not args.awfs:
nmse_awfs= np.load(os.path.join(dataset_path, 'nmse_awfs_missing_' + str(args.n_missing) + '.npy'))
ssim_awfs= np.load(os.path.join(dataset_path, 'ssim_awfs_missing_' + str(args.n_missing) + '.npy'))
if not args.pm:
nmse_pwd_pm=np.load(os.path.join(dataset_path, 'nmse_pwd_pm_missing_' + str(args.n_missing) + '.npy'))
ssim_pwd_pm= np.load(os.path.join(dataset_path, 'ssim_pwd_pm_missing_' + str(args.n_missing) + '.npy'))
if not args.pwd_cnn:
nmse_pwd_cnn=np.load(os.path.join(dataset_path, 'nmse_pwd_cnn_missing_' + str(args.n_missing) + '.npy'))
ssim_pwd_cnn=np.load(os.path.join(dataset_path, 'ssim_pwd_cnn_missing_' + str(args.n_missing) + '.npy'))
print('save data')
# Save arrays
np.savez(os.path.join(dataset_path, 'nmse_missing_' + str(args.n_missing) + '.npz'),
nmse_pwd=nmse_pwd, nmse_pwd_cnn=nmse_pwd_cnn, nmse_pwd_pm=nmse_pwd_pm, nmse_wfs=nmse_wfs,
nmse_awfs=nmse_awfs, nmse_pwd_apwd=nmse_pwd_apwd)
np.savez(os.path.join(dataset_path, 'ssim_missing_' + str(args.n_missing) + '.npz'),
ssim_pwd=ssim_pwd, ssim_pwd_cnn=ssim_pwd_cnn, ssim_pwd_pm=ssim_pwd_pm, ssim_wfs=ssim_wfs,
ssim_awfs=ssim_awfs, ssim_pwd_apwd=ssim_pwd_apwd)
if __name__ == '__main__':
main()
""