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models.py
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models.py
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from keras.models import Sequential, Model
from keras.layers import Convolution2D,Input,BatchNormalization,Conv2D,Activation,Lambda,Subtract,Conv2DTranspose, PReLU
from keras.regularizers import l2
from keras.layers import Reshape,Dense,Flatten
# from keras.layers.advanced_activations import LeakyReLU
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD, Adam
from scipy.io import loadmat
import keras.backend as K
# from keras.layers.advanced_activations import LeakyReLU
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD, Adam
import numpy as np
import math
from scipy import interpolate
#from scipy.misc import imresize
def psnr(target, ref):
# assume RGB image
target_data = np.array(target, dtype=float)
ref_data = np.array(ref, dtype=float)
diff = ref_data - target_data
diff = diff.flatten('C')
rmse = math.sqrt(np.mean(diff ** 2.))
return 20 * math.log10(255. / rmse)
def interpolation(noisy , SNR , Number_of_pilot , interp)
noisy_image = np.zeros((40000,72,14,2))
noisy_image[:,:,:,0] = np.real(noisy)
noisy_image[:,:,:,1] = np.imag(noisy)
if (Number_of_pilot == 48):
idx = [14*i for i in range(1, 72,6)]+[4+14*(i) for i in range(4, 72,6)]+[7+14*(i) for i in range(1, 72,6)]+[11+14*(i) for i in range(4, 72,6)]
elif (Number_of_pilot == 16):
idx= [4+14*(i) for i in range(1, 72,9)]+[9+14*(i) for i in range(4, 72,9)]
elif (Number_of_pilot == 24):
idx = [14*i for i in range(1,72,9)]+ [6+14*i for i in range(4,72,9)]+ [11+14*i for i in range(1,72,9)]
elif (Number_of_pilot == 8):
idx = [4+14*(i) for i in range(5,72,18)]+[9+14*(i) for i in range(8,72,18)]
elif (Number_of_pilot == 36):
idx = [14*(i) for i in range(1,72,6)]+[6+14*(i) for i in range(4,72,6)] + [11+14*i for i in range(1,72,6)]
r = [x//14 for x in idx]
c = [x%14 for x in idx]
interp_noisy = np.zeros((40000,72,14,2))
for i in range(len(noisy)):
z = [noisy_image[i,j,k,0] for j,k in zip(r,c)]
if(interp == 'rbf'):
f = interpolate.Rbf(np.array(r).astype(float), np.array(c).astype(float), z,function='gaussian')
X , Y = np.meshgrid(range(72),range(14))
z_intp = f(X, Y)
interp_noisy[i,:,:,0] = z_intp.T
elif(interp == 'spline'):
tck = interpolate.bisplrep(np.array(r).astype(float), np.array(c).astype(float), z)
z_intp = interpolate.bisplev(range(72),range(14),tck)
interp_noisy[i,:,:,0] = z_intp
z = [noisy_image[i,j,k,1] for j,k in zip(r,c)]
if(interp == 'rbf'):
f = interpolate.Rbf(np.array(r).astype(float), np.array(c).astype(float), z,function='gaussian')
X , Y = np.meshgrid(range(72),range(14))
z_intp = f(X, Y)
interp_noisy[i,:,:,1] = z_intp.T
elif(interp == 'spline'):
tck = interpolate.bisplrep(np.array(r).astype(float), np.array(c).astype(float), z)
z_intp = interpolate.bisplev(range(72),range(14),tck)
interp_noisy[i,:,:,1] = z_intp
interp_noisy = np.concatenate((interp_noisy[:,:,:,0], interp_noisy[:,:,:,1]), axis=0).reshape(80000, 72, 14, 1)
return interp_noisy
def SRCNN_model():
input_shape = (72,14,1)
x = Input(shape = input_shape)
c1 = Convolution2D( 64 , 9 , 9 , activation = 'relu', init = 'he_normal', border_mode='same')(x)
c2 = Convolution2D( 32 , 1 , 1 , activation = 'relu', init = 'he_normal', border_mode='same')(c1)
c3 = Convolution2D( 1 , 5 , 5 , init = 'he_normal', border_mode='same')(c2)
#c4 = Input(shape = input_shape)(c3)
model = Model(input = x, output = c3)
##compile
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
model.compile(optimizer=adam, loss='mean_squared_error', metrics=['mean_squared_error'])
return model
def SRCNN_train(train_data ,train_label, val_data , val_label , channel_model , num_pilots , SNR ):
srcnn_model = SRCNN_model()
print(srcnn_model.summary())
checkpoint = ModelCheckpoint("SRCNN_check.h5", monitor='val_loss', verbose=1, save_best_only=True,
save_weights_only=False, mode='min')
callbacks_list = [checkpoint]
srcnn_model.fit(train_data, train_label, batch_size=128, validation_data=(val_data, val_label),
callbacks=callbacks_list, shuffle=True, epochs= 300 , verbose=0)
#srcnn_model.save_weights("drive/codes/my_srcnn/SRCNN_SUI5_weights/SRCNN_48_12.h5")
srcnn_model.save_weights("SRCNN_" + channel_model +"_"+ str(num_pilots) + "_" + str(SNR) + ".h5")
def SRCNN_predict(input_data , channel_model , num_pilots , SNR):
srcnn_model = SRCNN_model()
srcnn_model.load_weights("SRCNN_" + channel_model +"_"+ str(num_pilots) + "_" + str(SNR) + ".h5")
predicted = srcnn_model.predict(input_data)
return predicted
def DNCNN_model ():
inpt = Input(shape=(None,None,1))
# 1st layer, Conv+relu
x = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same')(inpt)
x = Activation('relu')(x)
# 18 layers, Conv+BN+relu
for i in range(18):
x = Conv2D(filters=64, kernel_size=(3,3), strides=(1,1), padding='same')(x)
x = BatchNormalization(axis=-1, epsilon=1e-3)(x)
x = Activation('relu')(x)
# last layer, Conv
x = Conv2D(filters=1, kernel_size=(3,3), strides=(1,1), padding='same')(x)
x = Subtract()([inpt, x]) # input - noise
model = Model(inputs=inpt, outputs=x)
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
model.compile(optimizer=adam, loss='mean_squared_error', metrics=['mean_squared_error'])
return model
def DNCNN_train(train_data ,train_label, val_data , val_label, channel_model , num_pilots , SNR ):
dncnn_model = DNCNN_model()
print(dncnn_model.summary())
checkpoint = ModelCheckpoint("DNCNN_check.h5", monitor='val_loss', verbose=1, save_best_only=True,
save_weights_only=False, mode='min')
callbacks_list = [checkpoint]
dncnn_model.fit(train_data, train_label, batch_size=128, validation_data=(val_data, val_label),
callbacks=callbacks_list, shuffle=True, epochs= 200 , verbose=0)
dncnn_model.save_weights("DNCNN_" + channel_model +"_"+ str(num_pilots) + "_" + str(SNR) + ".h5")
def DNCNN_predict(input_data, channel_model , num_pilots , SNR):
dncnn_model = DNCNN_model()
dncnn_model.load_weights("DNCNN_" + channel_model +"_"+ str(num_pilots) + "_" + str(SNR) + ".h5")
predicted = dncnn_model.predict(input_data)
return predicted