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generate_test_data_linear_array_USED.py
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#from numba import jit, cuda
import numpy as np
import os
import matplotlib.pyplot as plt
import sfs
import scipy
import tqdm
import argparse
import tensorflow as tf
from data_lib import params_linear_2D
#import jax.numpy as jnp
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'True'
print("Running test set generation")
# /nas/home/lcomanducci/soundfield_synthesis_filterbank/MeshRIR/S32-M441_npy
# /nas/home/lcomanducci/soundfield_synthesis_filterbank/MeshRIR/irutilities.py
from skimage.metrics import structural_similarity as ssim
#from scikit-image.metrics import structural_similarity as _ssim
#plt.rcParams.update({
#"text.usetex": True,
#"font.family": "sans-serif",
#"font.sans-serif": ["Helvetica"],
#'font.size': 20})
#@jit(target_backend='cuda')
def plot_soundfield(cmap, P, n_f, selection, axis_label_size, tick_font_size, save_path, plot_ldspks=True, do_norm=True):
figure = plt.figure(figsize=(20, 20))
if do_norm:
im = sfs.plot2d.amplitude(np.reshape(P[:, n_f], (params_linear_2D.N_sample, params_linear_2D.N_sample)),
params_linear_2D.grid, xnorm=[0, 0, 0], cmap=cmap, vmin=-1.0, vmax=1.0, colorbar=False)
else:
im = sfs.plot2d.amplitude(np.reshape(P[:, n_f], (params_linear_2D.N_sample, params_linear_2D.N_sample)),
params_linear_2D.grid, cmap=cmap, colorbar=False, vmin=P[:, n_f].min(), vmax=P[:, n_f].max(), xnorm=None)
if plot_ldspks:
sfs.plot2d.loudspeakers(params_linear_2D.array.x[selection], params_linear_2D.array.n[selection], a0=1, size=0.18)
plt.xlabel('$x [m]$', fontsize=axis_label_size), plt.ylabel('$y [m]$', fontsize=axis_label_size)
plt.tick_params(axis='both', which='major', labelsize=tick_font_size)
cbar = plt.colorbar(im, fraction=0.046)
cbar.ax.tick_params(labelsize=tick_font_size)
# cbar.set_label('$NRE~[\mathrm{dB}]$',fontsize=tick_font_size))
plt.tight_layout()
plt.savefig(save_path)
plt.show()
#@jit(target_backend='cuda')
def sound_field(d, selection, secondary_source, array, grid, tapering=True):
if tapering:
tapering_window = sfs.tapering.tukey(selection, alpha=0.3)
else:
tapering_window = sfs.tapering.none(selection)
p = sfs.fd.synthesize(d, tapering_window, array, secondary_source, grid=grid)
return p, tapering_window
def normalize(x):
min_x = x.min()
max_x = x.max()
#print("x = {}\nmin_x = {}\nmax_x = {}".format(x, min_x, max_x))
den = (max_x-min_x)
if den == 0 :
den = den + np.finfo(np.float128).eps # 1.084202172485504434e-19
x_norm = (x - min_x)/den
return x_norm
def nmse(P_hat, P_gt,type='freq'):
if type=='freq':
return np.mean((np.power(np.abs(P_hat[ :, :] - P_gt[ :, :]), 2) / np.power(np.abs(P_gt[ :, :]), 2)), axis=0)
else:
return np.power(np.abs(P_hat[ :, :] - P_gt[ :, :]), 2) / np.power(np.abs(P_gt[ :, :]), 2)
def ssim_abs(P_hat, P_gt):
P_hat = normalize(np.abs(P_hat))
P_gt = normalize(np.abs(P_gt))
return ssim(P_gt, P_hat, data_range=1)
def ssim_freq(P_hat, P_gt):
ssim_freq_array = np.zeros(params_linear_2D.N_freqs)
for n_f in range(params_linear_2D.N_freqs):
ssim_freq_array[n_f] = ssim_abs(P_hat[:, n_f], P_gt[:, n_f])
return ssim_freq_array
#@jit(target_backend='cuda')
def main():
# Arguments parse
parser = argparse.ArgumentParser(description='Generate data for linear array setup')
parser.add_argument('--dataset_path', type=str, help="Base Data Directory", default='/nas/home/ralessandri/thesis_project/dataset/test/linear_array')
parser.add_argument('--model_path', type=str, help='Deep learning models folder', default='/nas/home/ralessandri/thesis_project/models/linear_array')
#parser.add_argument('--dataset_path', type=str, help="Base Data Directory", default='C:/Users/rales/OneDrive/Desktop/POLIMI/TESI/pressure_matching_deep_learning/dataset/test/linear_array')
#parser.add_argument('--model_path', type=str, help='Deep learning models folder', default='C:/Users/rales/OneDrive/Desktop/POLIMI/TESI/pressure_matching_deep_learning/models/linear_array')
parser.add_argument('--gt_soundfield', type=bool, help='compute ground truth soundfield', default=True)
#parser.add_argument('--wfs', type=bool, help='compute soundfield synthesis', default=True)
#parser.add_argument('--hoa', type=bool, help='compute higher order ambisonics', default=True)
parser.add_argument('--pm', type=bool, help='compute pressure matching', default=True)
parser.add_argument('--pwd_cnn', type=bool, help='compute model-based acoustic rendering + CNN', default=False) # True?
parser.add_argument('--n_loudspeakers', type=int, help='Numbr of loudspeakers in array', default=16)
parser.add_argument('--n_missing', type=int, help='number missing loudspeakers', default=0)
eval_points = True
PLOT = False
args = parser.parse_args()
dataset_path =args.dataset_path
# Grid of points where we actually compute the soundfield
point = params_linear_2D.point
#N_pts = len(point) #params_circular.radius_sources_test
N_pts = int((len(params_linear_2D._grid_[0][0])))
grid = params_linear_2D._grid_
# Load green function secondary sources --> eval points (it is in train directory since it is the same)
dataset_path_train = '/nas/home/ralessandri/thesis_project/dataset/linear_array'
#dataset_path_train = 'C:/Users/rales/OneDrive/Desktop/POLIMI/TESI/pressure_matching_deep_learning/dataset/linear_array'
#green_function_sec_sources_path = 'green_function_sec_sources_nl_' + str(args.n_loudspeakers) + '_r_' + str(params_linear_2D.rangeX[0]) + '.npy'
green_function_sec_sources_path = 'green_function_sec_sources_nl_' + str(params_linear_2D.N_lspks) + '_r_' + str(params_linear_2D.rangeX[0]) + '.npy'
g_path_joined = os.path.join(dataset_path_train, green_function_sec_sources_path).replace('\\','/')
print("problem = ", g_path_joined)
#G = np.load(os.path.join(dataset_path_train, green_function_sec_sources_path))
G = np.load(g_path_joined)
# Load Missing loudspeakers configuration
lspk_config_path = 'lspk_config_nl_' + str(params_linear_2D.N_lspks) + '_missing_' + str(args.n_missing) + '.npy'
lspk_config_path_global = os.path.join(dataset_path_train, 'setup', lspk_config_path)
# Let's precompute what we need in order to apply the selected models
# Load pwd_cnn deep learning model
if args.pwd_cnn:
model_name = 'model_linear_config_nl_'+str(args.n_loudspeakers)
network_model = tf.keras.models.load_model(os.path.join(args.model_path, model_name).replace('\\', '/'))
if args.pm:
lambda_ = 1e-2
G_cp = G[params_linear_2D.idx_lr[params_linear_2D.idx_cp]] # Green function at control points
# points_cp = params_circular.point[params_circular.idx_lr[params_circular.idx_cp]]
C_pm = np.zeros((args.n_loudspeakers, len(params_linear_2D.idx_cp), params_linear_2D.N_freqs ),dtype=complex)
for n_f in tqdm.tqdm(range(len(params_linear_2D.wc))):
# INSTALLARE JAXLIB
C_pm[:, :, n_f] = np.matmul(np.linalg.pinv(np.matmul(G_cp[:, :, n_f].transpose(), G_cp[:, :, n_f]) + lambda_ * np.eye(args.n_loudspeakers)),G_cp[:, :, n_f].transpose())
#C_pm[:, :, n_f] = np.matmul(jnp.linalg.pinv(np.matmul(G_cp[:, :, n_f].transpose(), G_cp[:, :, n_f]) + lambda_ * np.eye(args.n_loudspeakers)),G_cp[:, :, n_f].transpose())
#if eval_points:
#N_pts = len(params_linear_2D.idx_lr)
#G = G[params_linear_2D.idx_lr]
#point = params_linear_2D.point_lr
nmse_pwd_cnn = np.zeros((len(params_linear_2D.src_pos_train), params_linear_2D.num_sources, params_linear_2D.N_freqs))
nmse_pwd_pm = np.zeros_like(nmse_pwd_cnn)
ssim_pwd_cnn = np.zeros_like(nmse_pwd_cnn)
ssim_pwd_pm = np.zeros_like(nmse_pwd_cnn)
print("n_r lenght = ", len(params_linear_2D.src_pos_train))
print("n_s length = ", params_linear_2D.num_sources)
for n_r in tqdm.tqdm(range(len(params_linear_2D.src_pos_train))):
P_gt = np.zeros((N_pts, params_linear_2D.N_freqs), dtype=complex)
P_pwd_cnn = np.zeros((N_pts, params_linear_2D.N_freqs), dtype=complex)
P_pwd_pm = np.zeros((N_pts, params_linear_2D.N_freqs), dtype=complex)
for n_s in range(params_linear_2D.num_sources):
if PLOT:
n_s = 41
if (n_r < len(params_linear_2D.src_pos_test[0]) & n_s < len(params_linear_2D.src_pos_test[1])):
#print("src_pos_test[0 -> {}, 1 -> {}]".format(len(params_linear_2D.src_pos_test[0]), len(params_linear_2D.src_pos_test[1])))
xs = params_linear_2D.src_pos_test[n_r, n_s]
if args.gt_soundfield:
for n_f in range(params_linear_2D.N_freqs):
hankel_arg = (params_linear_2D.wc[n_f] / params_linear_2D.c) * np.linalg.norm(point[:, :2] - xs, axis=1)
P_gt[:, n_f] = (1j / 4) * scipy.special.hankel2(0, hankel_arg)
if args.pwd_cnn:
if eval_points:
P_input = P_gt[params_linear_2D.idx_cp]
else:
P_input = P_gt[params_linear_2D.idx_lr[params_linear_2D.idx_cp]]
d_array_cnn = network_model.predict(
np.expand_dims(np.concatenate([np.real(P_input), np.imag(P_input)], axis=0), axis=[0, -1]).astype('float32'))[0, :, :, 0].astype('float64')
d_array_cnn_complex = d_array_cnn[:int(d_array_cnn.shape[0] / 2)] + (1j * d_array_cnn[int(d_array_cnn.shape[0] / 2):])
plt.figure(figsize=(10,10))
plt.imshow(d_array_cnn.T, aspect='auto',cmap='RdBu')
plt.xlabel('$l$',fontsize=120),plt.ylabel('$k$',fontsize=120)
plt.tick_params(axis='both', which='major', labelsize=120)
plt.gca().invert_yaxis()
plt.show()
for n_p in range(N_pts):
P_pwd_cnn[ n_p, :] = np.sum(G[n_p] * d_array_cnn_complex, axis=0)
if args.pm:
d_pm = np.zeros((args.n_loudspeakers, params_linear_2D.N_freqs),dtype=complex)
for n_f in range(params_linear_2D.N_freqs):
if eval_points:
d_pm[:, n_f] = np.matmul(C_pm[:, :, n_f], P_gt[params_linear_2D.idx_cp, n_f]) # CHECK idx_cp is USING EVAL POINTSSSSS
else:
d_pm[:, n_f] = np.matmul(C_pm[:, :, n_f], P_gt[params_linear_2D.idx_lr[params_linear_2D.idx_cp], n_f]) # CHECK idx_cp is USING EVAL POINTSSSSS
for n_p in range(N_pts):
P_pwd_pm[n_p, :] = np.sum(G[n_p] * d_pm, axis=0)
#nmse_pwd_cnn[n_r, n_s], nmse_pwd_pm[n_r, n_s] = #nmse(P_pwd_cnn, P_gt), nmse(P_pwd_pm, P_gt)
#ssim_pwd_cnn[n_r, n_s], ssim_pwd_pm[n_r, n_s] = ssim_freq(P_pwd_cnn, P_gt), ssim_freq(P_pwd_pm, P_gt)
if PLOT:
plt.figure(), plt.plot(params_linear_2D.array_pos[:4, 0], params_linear_2D.array_pos[:4, 1], 'r*'), plt.show()
d = np.linalg.norm(np.array([params_linear_2D.array_pos[1, 0], params_linear_2D.array_pos[1, 1]])-np.array([params_linear_2D.array_pos[2, 0], params_linear_2D.array_pos[2, 1]]))
aliasing_freq = params_linear_2D.c/(2*d)
# Plot params
selection = np.ones_like(params_linear_2D.array_pos[:, 0])
selection = selection == 1
n_f = 63 # 63
print(str(params_linear_2D.f_axis[n_f]))
cmap = 'RdBu_r'
tick_font_size = 70
axis_label_size = 90
# Ground truth
plot_paths = os.path.join('plots', 'linear')
save_path = os.path.join(plot_paths, 'sf_real_source_' + str(n_s) + '_f_' + str(params_linear_2D.f_axis[n_f]) + '_nl'+str(args.n_loudspeakers)+'.pdf')
plot_soundfield(cmap, P_gt, n_f, selection, axis_label_size, tick_font_size, save_path, plot_ldspks=False)
# PWD-CNN
save_path = os.path.join(plot_paths, 'sf_pwd_cnn_' + str(n_s) + '_f_' + str(params_linear_2D.f_axis[n_f]) + '_nl'+str(args.n_loudspeakers)+'.pdf')
plot_soundfield(cmap, P_pwd_cnn, n_f, selection, axis_label_size, tick_font_size, save_path)
# Error
#nmse_pwd_cnn = 10*np.log10(nmse(P_pwd_cnn, P_gt, type='full'))
#save_path = os.path.join(plot_paths, 'nmse_pwd_cnn_' + str(n_s) + '_f_' + str(params_linear_2D.f_axis[n_f]) + '_nl'+str(args.n_loudspeakers)+'.pdf')
plot_soundfield(cmap, nmse_pwd_cnn, n_f, selection, axis_label_size, tick_font_size, save_path, do_norm=False)
# PM
save_path = os.path.join(plot_paths, 'sf_pm_' + str(n_s) + '_f_' + str(params_linear_2D.f_axis[n_f]) + '_nl'+str(args.n_loudspeakers)+'.pdf')
plot_soundfield(cmap, P_pwd_pm, n_f, selection, axis_label_size, tick_font_size, save_path)
# Error
#nmse_pm = 10*np.log10(nmse(P_pwd_pm, P_gt, type='full'))
#save_path = os.path.join(plot_paths, 'nmse_pm_' + str(n_s) + '_f_' + str(params_linear_2D.f_axis[n_f]) +'_nl'+str(args.n_loudspeakers)+ '.pdf')
#plot_soundfield(cmap, nmse_pm, n_f, selection, axis_label_size, tick_font_size, save_path, do_norm=False)
plt.figure()
#plt.plot(params_linear_2D.f_axis, 10*np.log10(np.mean(nmse(P_pwd_cnn, P_gt, type='full'), axis=0)),'k-*')
#plt.plot(params_linear_2D.f_axis, 10*np.log10(np.mean(nmse(P_pwd_pm, P_gt, type='full'), axis=0)),'r-*')
plt.tick_params(axis='both', which='major', labelsize=10)
plt.legend(['CNN', 'PM'])
plt.show()
print('pause')
# If we are plotting it means we are just computing data for the paper --> no need to save anything
if not PLOT:
# Save arrays
np.savez(os.path.join(dataset_path, 'nmse_nl_' + str(args.n_loudspeakers)+'.npz'), nmse_pwd_cnn=nmse_pwd_cnn, nmse_pwd_pm=nmse_pwd_pm)
np.savez(os.path.join(dataset_path, 'ssim_nl_' + str(args.n_loudspeakers)+'.npz'), ssim_pwd_cnn=ssim_pwd_cnn, ssim_pwd_pm=ssim_pwd_pm)
if __name__ == '__main__':
main()
print("Ended Generate linear testing data")