def pretraining_clf(self):
enc_conv1_1 = self.conv_bn_act(x=self.input, k=3, s=1, f=self.ch, p="same", n="enc_conv1_1")
enc_conv1_2 = self.conv_bn_act(x=enc_conv1_1, k=4, s=2, f=self.ch, p="same", n="enc_conv1_2")
enc_conv2_1 = self.conv_bn_act(x=enc_conv1_2, k=3, s=1, f=self.ch * 2, p="same", n="enc_conv2_1")
enc_conv2_2 = self.conv_bn_act(x=enc_conv2_1, k=4, s=2, f=self.ch * 2, p="same", n="enc_conv2_2")
enc_conv3_1 = self.conv_bn_act(x=enc_conv2_2, k=3, s=1, f=self.ch * 4, p="same", n="enc_conv3_1")
enc_conv3_2 = self.conv_bn_act(x=enc_conv3_1, k=4, s=2, f=self.ch * 4, p="same", n="enc_conv3_2")
enc_conv4_1 = self.conv_bn_act(x=enc_conv3_2, k=3, s=1, f=self.ch * 8, p="same", n="enc_conv4_1")
enc_conv4_2 = self.conv_bn_act(x=enc_conv4_1, k=4, s=2, f=self.ch * 8, p="same", n="enc_conv4_2")
fc1 = self.flatten_layer(x=enc_conv4_2, n="enc_flatten")
drop5 = M_layers.dropout(rate=0.5, name="enc_drop5")(fc1)
dense_1 = self.dense_layer(x=drop5, f=128, n="dense_1")
drop6 = M_layers.dropout(rate=0.25, name="enc_drop6")(dense_1)
dense_2 = self.dense_layer(x=drop6, f=10, act=None, n="dense_3")
cls_out = M_layers.softmax(x=dense_2, name="softmax")
self.enc_model = keras.Model({"enc_in": self.input}, {"enc_out": enc_conv4_2}, name="enc_model")
self.cls_model = keras.Model({"cls_in": self.input}, {"cls_out": cls_out}, name="cls_model")
return self.enc_model, self.cls_model
def dense_layer(self, x, f, act="leakyrelu", n=None):
dense_l = M_layers.dense(f, act=None, name=n + "_dense")
out = dense_l(x)
if act:
act_l = M_layers.leakyrelu(n + "_leakyrelu")
out = act_l(out)
return out
def dense_layer(self, x, f, act="relu", n=None):
dense_l = M_layers.dense(f, act=None, name=n + "_dense")
out = dense_l(x)
self.layers[n + "_dense"] = dense_l
if act:
act_l = M_layers.relu(n + "_relu")
self.layers[n + "_relu"] = act_l
out = act_l(out)
return out
def conv_bn_act(self, x, f, n, s=1, k=None, p="same", batch=True, act=True):
c_layer = M_layers.conv
if k:
conv_l = c_layer(f=f, p=p, k=k, s=s, name=n + "_conv")
else:
conv_l = c_layer(f=f, p=p, name=n + "_conv")
out = conv_l(x)
if batch:
batch_l = M_layers.batch_norm(name=n + "_batchnorm")
out = batch_l(out)
if act:
act_l = M_layers.leakyrelu(name=n + "_leakyrelu")
out = act_l(out)
return out
def conv_bn_act(self, x, f, n, s=1, k=None, p="same", out_p=None, act=True, trans=False):
if trans:
c_layer = M_layers.conv_transpose
else:
c_layer = M_layers.conv
if k:
conv_l = c_layer(f=f, p=p, k=k, s=s, out_p=out_p, name=n + "_conv")
else:
conv_l = c_layer(f=f, p=p, name=n + "_conv")
out = conv_l(x)
norm_l = M_layers.batch_norm(name=n + "_norm")
out = norm_l(out)
self.layers[n + "_conv"] = conv_l
self.layers[n + "_norm"] = norm_l
if act:
act_l = M_layers.relu(name=n + "_relu")
self.layers[n + "_relu"] = act_l
out = act_l(out)
return out
def __init__(self, ch=conf.cfmap_ch):
self.ch = ch
self.layers = {}
self.input = M_layers.input_layer(name="input")
self.c1, self.c2, self.c3 = M_layers.c1(name="c1"), M_layers.c2(name="c2"), M_layers.c3(name="c3")
self.pretraining_clf(), self.CFmap_generator()
def flatten_layer(self, x, n=None):
flatten_l = M_layers.flatten(n + "_flatten")(x)
self.layers[n + "_flatten"] = flatten_l
return flatten_l
def concat(self, x, y, n):
concat_l = M_layers.concat(name=n + "_concat")
self.layers[n + "_concat"] = concat_l
return concat_l([x, y])
def flatten_layer(self, x, n=None):
flatten_l = M_layers.flatten(n + "_flatten")(x)
return flatten_l
def __init__(self, ch=conf.disc_ch):
self.ch = ch
self.discri_input = M_layers.input_layer(name="discri_input")
self.build_model()
def CFmap_generator(self):
# Encoder
enc_conv1_1 = self.conv_bn_act(x=self.input, k=3, s=1, f=self.ch, p="same", n="enc_conv1_1")
enc_conv1_2 = self.conv_bn_act(x=enc_conv1_1, k=4, s=2, f=self.ch, p="same", n="enc_conv1_2")
enc_conv2_1 = self.conv_bn_act(x=enc_conv1_2, k=3, s=1, f=self.ch * 2, p="same", n="enc_conv2_1")
enc_conv2_2 = self.conv_bn_act(x=enc_conv2_1, k=4, s=2, f=self.ch * 2, p="same", n="enc_conv2_2")
enc_conv3_1 = self.conv_bn_act(x=enc_conv2_2, k=3, s=1, f=self.ch * 4, p="same", n="enc_conv3_1")
enc_conv3_2 = self.conv_bn_act(x=enc_conv3_1, k=4, s=2, f=self.ch * 4, p="same", n="enc_conv3_2")
enc_conv4_1 = self.conv_bn_act(x=enc_conv3_2, k=3, s=1, f=self.ch * 8, p="same", n="enc_conv4_1")
enc_conv4_2 = self.conv_bn_act(x=enc_conv4_1, k=4, s=2, f=self.ch * 8, p="same", n="enc_conv4_2")
# Decoder
dec_up3 = M_layers.upsample(rank=2, name="dec_up3")(enc_conv4_2)
dec_upconv3 = self.conv_bn_act(dec_up3, f=self.ch * 4, k=3, s=1, p="same", n="dec_upconv3")
dec_code_concat3 = self.concat(enc_conv3_2, self.c3, n="dec_code_concat3")
dec_code_conv3 = self.conv_bn_act(x=dec_code_concat3, f=self.ch * 4, k=3, s=1, p="same",
n="dec_code_conv3")
dec_concat3 = self.concat(dec_code_conv3, dec_upconv3, n="dec_concat3")
dec_conv3 = self.conv_bn_act(dec_concat3, f=self.ch * 4, k=3, s=1, p="same", n="dec_conv3")
dec_up2 = M_layers.upsample(rank=2, name="dec_up2")(dec_conv3)
dec_upconv2 = self.conv_bn_act(dec_up2, f=self.ch * 2, k=2, s=1, p="valid", n="dec_upconv2")
dec_code_concat2 = self.concat(enc_conv2_2, self.c2, n="dec_code_concat2")
dec_code_conv2 = self.conv_bn_act(x=dec_code_concat2, f=self.ch * 2, k=3, s=1, p="same",
n="dec_code_conv2")
dec_concat2 = self.concat(dec_code_conv2, dec_upconv2, n="dec_concat2")
dec_conv2 = self.conv_bn_act(dec_concat2, f=self.ch * 2, k=3, s=1, p="same", n="dec_conv2")
dec_up1 = M_layers.upsample(rank=2, name="dec_up1")(dec_conv2)
dec_upconv1 = self.conv_bn_act(dec_up1, f=self.ch, k=3, s=1, p="same", n="dec_upconv1")
dec_code_concat1 = self.concat(enc_conv1_2, self.c1, n="dec_code_concat1")
dec_code_conv1 = self.conv_bn_act(x=dec_code_concat1, f=self.ch, k=3, s=1, p="same", n="dec_code_conv1")
dec_concat1 = self.concat(dec_code_conv1, dec_upconv1, n="dec_concat1")
dec_conv1 = self.conv_bn_act(dec_concat1, f=self.ch, k=3, s=1, p="same", n="dec_conv1")
dec_up = self.conv_bn_act(dec_conv1, f=1, k=4, s=2, p="same", act=False, trans=True, n="dec_up")
dec_out = M_layers.tanh(x=dec_up, name="dec_out_tanh")
self.dec_model = keras.Model({"dec_in": self.input, "c1": self.c1, "c2": self.c2, "c3": self.c3},
{"dec_out": dec_out}, name="dec_model")
# Classifier
enc_conv1_1 = self.conv_bn_act_reuse(x=self.input, n="enc_conv1_1")
enc_conv1_2 = self.conv_bn_act_reuse(x=enc_conv1_1, n="enc_conv1_2")
enc_conv2_1 = self.conv_bn_act_reuse(x=enc_conv1_2, n="enc_conv2_1")
enc_conv2_2 = self.conv_bn_act_reuse(x=enc_conv2_1, n="enc_conv2_2")
enc_conv3_1 = self.conv_bn_act_reuse(x=enc_conv2_2, n="enc_conv3_1")
enc_conv3_2 = self.conv_bn_act_reuse(x=enc_conv3_1, n="enc_conv3_2")
enc_conv4_1 = self.conv_bn_act_reuse(x=enc_conv3_2, n="enc_conv4_1")
enc_conv4_2 = self.conv_bn_act_reuse(x=enc_conv4_1, n='enc_conv4_2')
fc1 = self.flatten_layer(x=enc_conv4_2, n="enc_flatten")
drop5 = M_layers.dropout(rate=0.5, name="enc_drop5")(fc1)
dense_1 = self.dense_layer(x=drop5, f=128, n="dense_1")
drop6 = M_layers.dropout(rate=0.25, name="enc_drop6")(dense_1)
dense_2 = self.dense_layer(x=drop6, f=10, act=None, n="dense_3")
cls_out = M_layers.softmax(x=dense_2, name="softmax")
self.cls_model = keras.Model({"cls_in": self.input}, {"cls_out": cls_out}, name="cls_model")
return self.cls_model, self.dec_model