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DL_CFAR_test.py
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DL_CFAR_test.py
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# DL_CFAR Architecture
from functools import wraps, reduce
from keras import Input
from keras.layers import Conv2D, Add, Flatten, Dense
from keras.layers.advanced_activations import PReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
def compose(*funcs): # model
"""Compose arbitrarily many functions, evaluated left to right.
Reference: https://mathieularose.com/function-composition-in-python/
"""
# return lambda x: reduce(lambda v, f: f(v), funcs, x)
if funcs:
return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
else:
raise ValueError('Composition of empty sequence not supported.')
# parameters, same settings as the paper below
# C. -H. Lin, Y. -C. Lin, Y. Bai, W. -H. Chung, T. -S. Lee and H. Huttunen,
# "DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning,"
# 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, p.1-6
EPOCHS = 500
BATCH_SIZE = 128
NUM_SAMPLE_train = 200000
NUM_SAMPLE_val = 60000
@wraps(Conv2D)
def DL_Conv2D(*args, **kwargs):
conv_kwargs = {'padding': 'same'}
conv_kwargs.update(kwargs)
return Conv2D(*args, **conv_kwargs)
def Conv2D_BN_PReLU(*args, **kwargs):
no_bias_kwargs = {'use_bias': False}
no_bias_kwargs.update(kwargs)
return compose(
DL_Conv2D(*args, **no_bias_kwargs),
BatchNormalization(),
PReLU())
def residual_block(x, num_filters):
y = compose(
Conv2D_BN_PReLU(num_filters * 32, (3, 3)),
Conv2D_BN_PReLU(num_filters * 16, (3, 3)),
Conv2D_BN_PReLU(num_filters * 8, (3, 3)),
Conv2D_BN_PReLU(num_filters, (3, 3)),
BatchNormalization())(x)
x = Add()([x, y])
return x
def DLCFAR_body(x):
x = residual_block(x, 1)
x = PReLU()(x)
x = residual_block(x, 1)
x = PReLU()(x)
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
return x
input_ = Input(shape=(16, 16, 1))
output_ = DLCFAR_body(input_)
model = Model(inputs=input_, outputs=output_)
model.compile(optimizer=Adam(lr=5 * 1e-5), loss='mean_squared_error') # Compile the model (determine optimizer and loss)
model.summary()
# Using TensorFlow backend.
# 2022-05-18 13:47:09.658251: I C:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:140]
# Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
# __________________________________________________________________________________________________
# Layer (type) Output Shape Param # Connected to
# ==================================================================================================
# input_1 (InputLayer) (None, 16, 16, 1) 0
# __________________________________________________________________________________________________
# conv2d_1 (Conv2D) (None, 16, 16, 32) 288 input_1[0][0]
# __________________________________________________________________________________________________
# batch_normalization_1 (BatchNor (None, 16, 16, 32) 128 conv2d_1[0][0]
# __________________________________________________________________________________________________
# p_re_lu_1 (PReLU) (None, 16, 16, 32) 8192 batch_normalization_1[0][0]
# __________________________________________________________________________________________________
# conv2d_2 (Conv2D) (None, 16, 16, 16) 4608 p_re_lu_1[0][0]
# __________________________________________________________________________________________________
# batch_normalization_2 (BatchNor (None, 16, 16, 16) 64 conv2d_2[0][0]
# __________________________________________________________________________________________________
# p_re_lu_2 (PReLU) (None, 16, 16, 16) 4096 batch_normalization_2[0][0]
# __________________________________________________________________________________________________
# conv2d_3 (Conv2D) (None, 16, 16, 8) 1152 p_re_lu_2[0][0]
# __________________________________________________________________________________________________
# batch_normalization_3 (BatchNor (None, 16, 16, 8) 32 conv2d_3[0][0]
# __________________________________________________________________________________________________
# p_re_lu_3 (PReLU) (None, 16, 16, 8) 2048 batch_normalization_3[0][0]
# __________________________________________________________________________________________________
# conv2d_4 (Conv2D) (None, 16, 16, 1) 72 p_re_lu_3[0][0]
# __________________________________________________________________________________________________
# batch_normalization_4 (BatchNor (None, 16, 16, 1) 4 conv2d_4[0][0]
# __________________________________________________________________________________________________
# p_re_lu_4 (PReLU) (None, 16, 16, 1) 256 batch_normalization_4[0][0]
# __________________________________________________________________________________________________
# batch_normalization_5 (BatchNor (None, 16, 16, 1) 4 p_re_lu_4[0][0]
# __________________________________________________________________________________________________
# add_1 (Add) (None, 16, 16, 1) 0 input_1[0][0]
# batch_normalization_5[0][0]
# __________________________________________________________________________________________________
# p_re_lu_5 (PReLU) (None, 16, 16, 1) 256 add_1[0][0]
# __________________________________________________________________________________________________
# conv2d_5 (Conv2D) (None, 16, 16, 32) 288 p_re_lu_5[0][0]
# __________________________________________________________________________________________________
# batch_normalization_6 (BatchNor (None, 16, 16, 32) 128 conv2d_5[0][0]
# __________________________________________________________________________________________________
# p_re_lu_6 (PReLU) (None, 16, 16, 32) 8192 batch_normalization_6[0][0]
# __________________________________________________________________________________________________
# conv2d_6 (Conv2D) (None, 16, 16, 16) 4608 p_re_lu_6[0][0]
# __________________________________________________________________________________________________
# batch_normalization_7 (BatchNor (None, 16, 16, 16) 64 conv2d_6[0][0]
# __________________________________________________________________________________________________
# p_re_lu_7 (PReLU) (None, 16, 16, 16) 4096 batch_normalization_7[0][0]
# __________________________________________________________________________________________________
# conv2d_7 (Conv2D) (None, 16, 16, 8) 1152 p_re_lu_7[0][0]
# __________________________________________________________________________________________________
# batch_normalization_8 (BatchNor (None, 16, 16, 8) 32 conv2d_7[0][0]
# __________________________________________________________________________________________________
# p_re_lu_8 (PReLU) (None, 16, 16, 8) 2048 batch_normalization_8[0][0]
# __________________________________________________________________________________________________
# conv2d_8 (Conv2D) (None, 16, 16, 1) 72 p_re_lu_8[0][0]
# __________________________________________________________________________________________________
# batch_normalization_9 (BatchNor (None, 16, 16, 1) 4 conv2d_8[0][0]
# __________________________________________________________________________________________________
# p_re_lu_9 (PReLU) (None, 16, 16, 1) 256 batch_normalization_9[0][0]
# __________________________________________________________________________________________________
# batch_normalization_10 (BatchNo (None, 16, 16, 1) 4 p_re_lu_9[0][0]
# __________________________________________________________________________________________________
# add_2 (Add) (None, 16, 16, 1) 0 p_re_lu_5[0][0]
# batch_normalization_10[0][0]
# __________________________________________________________________________________________________
# p_re_lu_10 (PReLU) (None, 16, 16, 1) 256 add_2[0][0]
# __________________________________________________________________________________________________
# flatten_1 (Flatten) (None, 256) 0 p_re_lu_10[0][0]
# __________________________________________________________________________________________________
# dense_1 (Dense) (None, 512) 131584 flatten_1[0][0]
# __________________________________________________________________________________________________
# dense_2 (Dense) (None, 256) 131328 dense_1[0][0]
# ==================================================================================================
# Total params: 305,312
# Trainable params: 305,080
# Non-trainable params: 232
# __________________________________________________________________________________________________