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CS-CsiNet_onlytest.py
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CS-CsiNet_onlytest.py
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import tensorflow as tf
from keras.layers import Input, Dense, BatchNormalization, Reshape, Conv2D, add, LeakyReLU
from keras.models import Model, model_from_json
from keras.callbacks import TensorBoard, Callback
import scipy.io as sio
import numpy as np
import math
import time
tf.reset_default_graph()
envir = 'indoor' #'indoor' or 'outdoor'
# image params
img_height = 32
img_width = 32
img_channels = 2
img_total = img_height*img_width*img_channels
# network params
residual_num = 2
encoded_dim = 512 #compress rate=1/4->dim.=512, compress rate=1/16->dim.=128, compress rate=1/32->dim.=64, compress rate=1/64->dim.=32
file = 'CS-CsiNet_'+(envir)+'_dim'+str(encoded_dim)
# load json and create model
outfile = "saved_model/model_%s.json"%file
json_file = open(outfile, 'r')
loaded_model_json = json_file.read()
json_file.close()
decoder = model_from_json(loaded_model_json)
# load weights outto new model
outfile = "saved_model/model_%s.h5"%file
decoder.load_weights(outfile)
# Data loading
if envir == 'indoor':
mat = sio.loadmat('data/DATA_Htestin.mat')
x_test = mat['HT'] # array
elif envir == 'outdoor':
mat = sio.loadmat('data/DATA_Htestout.mat')
x_test = mat['HT'] # array
x_test = x_test.astype('float32')
# encoder with random projection
#A = np.random.uniform(low=-0.5, high=0.5, size = (x_train.shape[1], encoded_dim))
mat = sio.loadmat('data/A%d.mat'%(encoded_dim))
A = mat['A'] # array
y_test = np.dot(x_test, A.T)
x_test = np.reshape(x_test, (len(x_test), img_channels, img_height, img_width)) # adapt this if using `channels_first` image data format
tStart = time.time()
x_hat = decoder.predict(y_test)
tEnd = time.time()
print ("It cost %f sec" % ((tEnd - tStart)/x_test.shape[0]))
# Calcaulating the NMSE and rho
if envir == 'indoor':
mat = sio.loadmat('data/DATA_HtestFin_all.mat')
X_test = mat['HF_all']# array
elif envir == 'outdoor':
mat = sio.loadmat('data/DATA_HtestFout_all.mat')
X_test = mat['HF_all']# array
X_test = np.reshape(X_test, (len(X_test), img_height, 125))
x_test_real = np.reshape(x_test[:, 0, :, :], (len(x_test), -1))
x_test_imag = np.reshape(x_test[:, 1, :, :], (len(x_test), -1))
x_test_C = x_test_real-0.5 + 1j*(x_test_imag-0.5)
x_hat_real = np.reshape(x_hat[:, 0, :, :], (len(x_hat), -1))
x_hat_imag = np.reshape(x_hat[:, 1, :, :], (len(x_hat), -1))
x_hat_C = x_hat_real-0.5 + 1j*(x_hat_imag-0.5)
x_hat_F = np.reshape(x_hat_C, (len(x_hat_C), img_height, img_width))
X_hat = np.fft.fft(np.concatenate((x_hat_F, np.zeros((len(x_hat_C), img_height, 257-img_width))), axis=2), axis=2)
X_hat = X_hat[:, :, 0:125]
n1 = np.sqrt(np.sum(np.conj(X_test)*X_test, axis=1))
n1 = n1.astype('float64')
n2 = np.sqrt(np.sum(np.conj(X_hat)*X_hat, axis=1))
n2 = n2.astype('float64')
aa = abs(np.sum(np.conj(X_test)*X_hat, axis=1))
rho = np.mean(aa/(n1*n2), axis=1)
X_hat = np.reshape(X_hat, (len(X_hat), -1))
X_test = np.reshape(X_test, (len(X_test), -1))
power = np.sum(abs(x_test_C)**2, axis=1)
power_d = np.sum(abs(X_hat)**2, axis=1)
mse = np.sum(abs(x_test_C-x_hat_C)**2, axis=1)
print("In "+envir+" environment")
print("When dimension is", encoded_dim)
print("NMSE is ", 10*math.log10(np.mean(mse/power)))
print("Correlation is ", np.mean(rho))
import matplotlib.pyplot as plt
'''abs'''
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display origoutal
ax = plt.subplot(2, n, i + 1 )
x_testplo = abs(x_test[i, 0, :, :]-0.5 + 1j*(x_test[i, 1, :, :]-0.5))
plt.imshow(np.max(np.max(x_testplo))-x_testplo.T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.invert_yaxis()
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
decoded_imgsplo = abs(x_hat[i, 0, :, :]-0.5
+ 1j*(x_hat[i, 1, :, :]-0.5))
plt.imshow(np.max(np.max(decoded_imgsplo))-decoded_imgsplo.T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.invert_yaxis()
plt.show()