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models.py
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models.py
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import tensorflow as tf
from tensorflow.keras.utils import plot_model
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer
class MaxPoolingWithArgmax2D(Layer):
def __init__(self,**kwargs):
super(MaxPoolingWithArgmax2D,self).__init__(**kwargs)
def call(self,inputs):
output,argmax = tf.nn.max_pool_with_argmax(inputs,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
argmax = K.cast(argmax,K.floatx())
return [output,argmax]
def compute_output_shape(self,input_shape):
ratio = (1,2,2,1)
output_shape = [dim//ratio[idx] if dim is not None else None for idx, dim in enumerate(input_shape)]
output_shape = tuple(output_shape)
return [output_shape,output_shape]
class MaxUnpooling2D(Layer):
def __init__(self,**kwargs):
super(MaxUnpooling2D,self).__init__(**kwargs)
def call(self,inputs,output_shape = None):
updates, mask = inputs[0],inputs[1]
with tf.compat.v1.variable_scope(self.name):
mask = K.cast(mask, 'int32')
input_shape = tf.shape(updates, out_type='int32')
# calculation new shape
if output_shape is None:
output_shape = (input_shape[0],input_shape[1]*2,input_shape[2]*2,input_shape[3])
self.output_shape1 = output_shape
# calculation indices for batch, height, width and feature maps
one_like_mask = K.ones_like(mask, dtype='int32')
batch_shape = K.concatenate([[input_shape[0]], [1 ], [1], [1]],axis=0)
batch_range = K.reshape(tf.range(output_shape[0], dtype='int32'),shape=batch_shape)
b = one_like_mask * batch_range
y = mask // (output_shape[2] * output_shape[3])
x = (mask // output_shape[3]) % output_shape[2]
feature_range = tf.range(output_shape[3], dtype='int32')
f = one_like_mask * feature_range
# transpose indices & reshape update values to one dimension
updates_size = tf.size(updates)
indices = K.transpose(K.reshape(
K.stack([b, y, x, f]),
[4, updates_size]))
values = K.reshape(updates, [updates_size])
ret = tf.scatter_nd(indices, values, output_shape)
return ret
def compute_output_shape(self,input_shape):
shape = input_shape[1]
return (shape[0],shape[1]*2,shape[2]*2,shape[3])
class MaxPoolingWithArgmax2DA(Layer):
def __init__(
self,
pool_size=(2, 2),
strides=(2, 2),
padding='same',
**kwargs):
super(MaxPoolingWithArgmax2DA, self).__init__(**kwargs)
self.padding = padding
self.pool_size = pool_size
self.strides = strides
def get_config(self):
config = super().get_config()
config.update({
"pool_size":self.pool_size,
"strides":self.strides,
"padding":self.padding
})
return config
def call(self, inputs, **kwargs):
padding = self.padding
pool_size = self.pool_size
strides = self.strides
ksize = [1, *pool_size, 1]
padding = padding.upper()
strides = [1, *strides, 1]
output, argmax = tf.nn.max_pool_with_argmax(
inputs,
ksize=ksize,
strides=strides,
padding=padding)
argmax = K.cast(argmax, K.floatx())
return [output, argmax]
def compute_output_shape(self, input_shape):
ratio = (1, 2, 2, 1)
output_shape = [
dim // ratio[idx]
if dim is not None else None
for idx, dim in enumerate(input_shape)]
output_shape = tuple(output_shape)
return [output_shape, output_shape]
def compute_mask(self, inputs, mask=None):
return 2 * [None]
class MaxUnpooling2DA(Layer):
def __init__(self, size=(2, 2), **kwargs):
super(MaxUnpooling2DA, self).__init__(**kwargs)
self.size = size
def get_config(self):
config = super().get_config()
config.update({
"size":self.size,
})
return config
def call(self, inputs, output_shape=None):
updates, mask = inputs[0], inputs[1]
mask = K.cast(mask, 'int32')
input_shape = tf.shape(updates, out_type='int32')
if output_shape is None:
output_shape = (
input_shape[0],
input_shape[1] * self.size[0],
input_shape[2] * self.size[1],
input_shape[3])
ret = tf.scatter_nd(K.expand_dims(K.flatten(mask)),
K.flatten(updates),
[K.prod(output_shape)])
input_shape = updates.shape
out_shape = [-1,
input_shape[1] * self.size[0],
input_shape[2] * self.size[1],
input_shape[3]]
return K.reshape(ret, out_shape)
def compute_output_shape(self, input_shape):
mask_shape = input_shape[1]
return (
mask_shape[0],
mask_shape[1] * self.size[0],
mask_shape[2] * self.size[1],
mask_shape[3]
)
class CAM(Layer):
def __init__(self,previous_layer,shape,mode="thr",thr=0,**kwargs):
super(CAM,self).__init__(**kwargs)
self.previous_layer = previous_layer
self.cam_weights = tf.reshape(previous_layer.get_weights()[0],[-1])
self.input_size = shape
self.mode = mode
self.thr = thr
def get_config(self):
config = super().get_config()
config.update({
"previous_layer":self.previous_layer,
"shape":self.input_size,
"mode":self.mode,
"thr":self.thr
})
return config
def get_weights(self):
return [self.cam_weights]
def call(self,inputs,output_shape=None):
conv_output = inputs[0]
output = inputs[1]
if output_shape is None:
output_shape = [self.input_size,conv_output]
cam = tf.reduce_sum(conv_output * self.cam_weights,axis=3)
return cam,output
def compute_output_shape(self,input_shape):
return [self.input_size,input_shape]
def simple_model(shape):
"""define the most simple fcnn model
Args:
shape (tuple(int,int,int)): the shape of the input data
"""
input = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(4, (3, 3), padding="same", activation="relu")(input)
maxpool1 = keras.layers.MaxPool2D((2, 2))(conv1)
upsampling1 = keras.layers.UpSampling2D((2, 2))(maxpool1)
conv2 = keras.layers.Conv2D(3, (3, 3), padding="same", activation="softmax")(
upsampling1
)
model = keras.Model(inputs=input, outputs=conv2)
return model
def simple_model_multi(shape):
"""define the most simple fcnn model
Args:
shape (tuple(Int,Int,Int)): the shape of the input data
"""
input = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(4, (3, 3), padding="same", activation="relu")(input)
maxpool1 = keras.layers.MaxPool2D((2, 2))(conv1)
upsampling1 = keras.layers.UpSampling2D((2, 2))(maxpool1)
conv2 = keras.layers.Conv2D(3, (3, 3), padding="same", activation="softmax")(
upsampling1
)
model = keras.Model(inputs=input, outputs=conv2)
return model
def simple_model_classification(shape):
"""define the most simple cnn classification model
Args:
shape (tuple(Int,Int,Int)): the shape of the input data
"""
inputs = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(4,(3,3),padding="same", activation="relu")(inputs)
conv2 = keras.layers.Conv2D(4,(3,3),padding="same",activation="relu")(conv1)
maxpool1 = keras.layers.MaxPool2D((2,2))(conv2)
conv3 = keras.layers.Conv2D(4,(3,3),padding="same", activation="relu")(maxpool1)
conv4 = keras.layers.Conv2D(4,(3,3),padding="same",activation="relu")(conv3)
up1 = keras.layers.UpSampling2D((2,2))(conv4)
gap1 = keras.layers.GlobalAveragePooling2D()(up1)
dense1 = keras.layers.Dense(1,activation="sigmoid")(gap1)
model = keras.Model(inputs=inputs,outputs=dense1)
return model
def brain_classification(shape):
"""define the most simple cnn classification model
Args:
shape (tuple(Int,Int,Int)): the shape of the input data
"""
inputs = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(8,(3,3),padding="same", activation="relu")(inputs)
conv2 = keras.layers.Conv2D(8,(3,3),padding="same",activation="relu")(conv1)
conv3 = keras.layers.Conv2D(8,(3,3),padding="same", activation="relu")(conv2)
maxpool1 = keras.layers.MaxPool2D((2,2))(conv3)
conv4 = keras.layers.Conv2D(16,(3,3),padding="same", activation="relu")(maxpool1)
conv5 = keras.layers.Conv2D(16,(3,3),padding="same",activation="relu")(conv4)
conv6 = keras.layers.Conv2D(16,(3,3),padding="same", activation="relu")(conv5)
maxpool2 = keras.layers.MaxPool2D((2,2))(conv6)
up1 = keras.layers.UpSampling2D((4,4))(maxpool2)
gap1 = keras.layers.GlobalAveragePooling2D()(up1)
dense1 = keras.layers.Dense(1,activation="sigmoid")(gap1)
model = keras.Model(inputs=inputs,outputs=dense1)
return model
def brain_simple_classification(shape):
"""define the most simple cnn classification model
Args:
shape (tuple(Int,Int,Int)): the shape of the input data
"""
inputs = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(8,(3,3),padding="same", activation="relu")(inputs)
conv2 = keras.layers.Conv2D(8,(3,3),padding="same",activation="relu")(conv1)
conv3 = keras.layers.Conv2D(8,(3,3),padding="same", activation="relu")(conv2)
maxpool1 = keras.layers.MaxPool2D((2,2))(conv3)
up1 = keras.layers.UpSampling2D((2,2))(maxpool1)
gap1 = keras.layers.GlobalAveragePooling2D()(up1)
dense1 = keras.layers.Dense(1,activation="sigmoid")(gap1)
model = keras.Model(inputs=inputs,outputs=dense1)
return model
def concatenate_simple(shape):
inputs = keras.layers.Input(shape=shape)
x1 = keras.layers.Conv2D(4, (3, 3), padding="same", activation="relu")(inputs)
x2 = keras.layers.Conv2D(4, (3, 3), padding="same", activation="relu")(inputs)
x3 = keras.layers.Concatenate(axis=3)([x1, x2])
x4 = keras.layers.MaxPool2D((2, 2))(x3)
x5 = keras.layers.UpSampling2D((2, 2))(x4)
output = keras.layers.Conv2D(1, (3, 3), padding="same", activation="sigmoid")(x5)
model = keras.Model(inputs=inputs, outputs=output)
return model
def fcn_vgg16(shape, version=8):
input = keras.layers.Input(shape=shape)
# first layer
conv1 = keras.layers.Conv2D(64, (3, 3), padding="same", activation="relu")(input)
conv2 = keras.layers.Conv2D(64, (3, 3), padding="same", activation="relu")(conv1)
mp1 = keras.layers.MaxPool2D((2, 2))(conv2)
# second layer
conv3 = keras.layers.Conv2D(128, (3, 3), padding="same", activation="relu")(mp1)
conv4 = keras.layers.Conv2D(128, (3, 3), padding="same", activation="relu")(conv3)
mp2 = keras.layers.MaxPool2D((2, 2))(conv4)
# third layer
conv5 = keras.layers.Conv2D(256, (3, 3), padding="same", activation="relu")(mp2)
conv6 = keras.layers.Conv2D(256, (3, 3), padding="same", activation="relu")(conv5)
conv7 = keras.layers.Conv2D(256, (3, 3), padding="same", activation="relu")(conv6)
mp3 = keras.layers.MaxPool2D((2, 2))(conv7)
# fourth layer
conv8 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(mp3)
conv9 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(conv8)
conv10 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(conv9)
mp4 = keras.layers.MaxPool2D((2, 2))(conv10)
# fifth layer
conv11 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(mp4)
conv12 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(conv11)
conv13 = keras.layers.Conv2D(512, (3, 3), padding="same", activation="relu")(conv12)
mp5 = keras.layers.MaxPool2D((2, 2))(conv13)
# last layer
conv14 = keras.layers.Conv2D(1, (1, 1), padding="same")(mp5)
# replarce sigmoid layer
output32 = tf.math.sigmoid(conv14)
output32 = keras.layers.UpSampling2D((32, 32))(output32)
_mp4 = keras.layers.Conv2D(1, (1, 1), padding="same")(mp4)
us2 = keras.layers.UpSampling2D((2, 2))(conv14)
add16 = keras.layers.Add()([us2, _mp4])
# replarce sigmoid layer
output16 = tf.math.sigmoid(add16)
output16 = keras.layers.UpSampling2D((16, 16))
_mp3 = keras.layers.Conv2D(1, (1, 1), padding="same")(mp3)
us3 = keras.layers.UpSampling2D((2, 2))(add16)
add8 = keras.layers.Add()([us3, mp3])
# replarce sigmoid layer
output8 = tf.math.sigmoid(add8)
output8 = keras.layers.UpSampling2D((8, 8))(output8)
if version == 32:
return keras.Model(inputs=input, outputs=output32)
elif version == 16:
return keras.Model(inputs=input, outputs=output16)
elif version == 8:
return keras.Model(inputs=input, outputs=output8)
else:
print("please confirme versions arguments")
assert False
def test_model(shape):
"""define the most simple fcnn model
Args:
shape (tuple(Int,Int,Int)): the shape of the input data
"""
print(shape)
input = keras.layers.Input(shape=shape)
conv1 = keras.layers.Conv2D(4, (3, 3), padding="same", activation="relu")(input)
maxpool1 = keras.layers.MaxPool2D((2, 2))(conv1)
upsampling1 = keras.layers.UpSampling2D((2, 2))(maxpool1)
conv2 = keras.layers.Conv2D(1, (3, 3), padding="same",activation="sigmoid")(upsampling1)
model = keras.Model(inputs=input, outputs=conv2)
return model
def U_Net(shape):
input = keras.layers.Input(shape=shape)
# Encoder
# block1
b1c1 = tf.keras.layers.Conv2D(
64, (3, 3), name="block1_conv1", activation="relu", padding="same"
)(input)
b1c2 = tf.keras.layers.Conv2D(64, (3, 3), name="block1_conv2", padding="same")(b1c1)
b1bn1 = tf.keras.layers.BatchNormalization()(b1c2)
b1act = tf.keras.layers.ReLU()(b1bn1)
b1p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp1")(b1act)
# block2
b2c1 = tf.keras.layers.Conv2D(
128, (3, 3), name="block2_conv1", activation="relu", padding="same"
)(b1p)
b2c2 = tf.keras.layers.Conv2D(128, (3, 3), name="block2_conv2", padding="same")(
b2c1
)
b2bn1 = tf.keras.layers.BatchNormalization()(b2c2)
b2act = tf.keras.layers.ReLU()(b2bn1)
b2p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp2")(b2act)
# block3
b3c1 = tf.keras.layers.Conv2D(
256, (3, 3), name="block3_conv1", activation="relu", padding="same"
)(b2p)
b3c2 = tf.keras.layers.Conv2D(256, (3, 3), name="block3_conv2", padding="same")(
b3c1
)
b3bn1 = tf.keras.layers.BatchNormalization()(b3c2)
b3act = tf.keras.layers.ReLU()(b3bn1)
b3p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp3")(b3act)
# block4
b4c1 = tf.keras.layers.Conv2D(
512, (3, 3), name="block4_conv1", activation="relu", padding="same"
)(b3p)
b4c2 = tf.keras.layers.Conv2D(512, (3, 3), name="block4_conv2", padding="same")(
b4c1
)
b4bn1 = tf.keras.layers.BatchNormalization()(b4c2)
b4act = tf.keras.layers.ReLU()(b4bn1)
b4p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp4")(b4act)
# block5
b5c1 = tf.keras.layers.Conv2D(
1024, (3, 3), name="block5_conv1", activation="relu", padding="same"
)(b4p)
b5c2 = tf.keras.layers.Conv2D(1024, (3, 3), name="block5_conv2", padding="same")(
b5c1
)
b5bn1 = tf.keras.layers.BatchNormalization()(b5c2)
b5act = tf.keras.layers.ReLU()(b5bn1)
# Decoder
# block6
b6up = tf.keras.layers.UpSampling2D((2, 2))(b5act)
b6c1 = tf.keras.layers.Conv2D(
512, (2, 2), name="block6_conv1", activation="relu", padding="same"
)(b6up)
b7conc = tf.keras.layers.Concatenate(axis=3)([b4act, b6c1])
b6c2 = tf.keras.layers.Conv2D(
512, (3, 3), name="block6_conv2", activation="relu", padding="same"
)(b6c1)
b6c3 = tf.keras.layers.Conv2D(
512, (3, 3), name="block6_conv3", activation="relu", padding="same"
)(b6c2)
b6bn = tf.keras.layers.BatchNormalization()(b6c3)
b6act = tf.keras.layers.ReLU()(b6bn)
# block7
b7up = tf.keras.layers.UpSampling2D((2, 2))(b6act)
b7c1 = tf.keras.layers.Conv2D(
256, (2, 2), name="block7_conv1", activation="relu", padding="same"
)(b7up)
b7conc = tf.keras.layers.Concatenate(axis=3)([b3act, b7c1])
b7c2 = tf.keras.layers.Conv2D(
256, (3, 3), name="block7_conv2", activation="relu", padding="same"
)(b7conc)
b7c3 = tf.keras.layers.Conv2D(
256, (3, 3), name="bloc7_conv3", activation="relu", padding="same"
)(b7c2)
b7bn = tf.keras.layers.BatchNormalization()(b7c3)
b7act = tf.keras.layers.ReLU()(b7bn)
# block8
b8up = tf.keras.layers.UpSampling2D((2, 2))(b7act)
b8c1 = tf.keras.layers.Conv2D(
128, (2, 2), name="block8_conv1", activation="relu", padding="same"
)(b8up)
b8conc = tf.keras.layers.Concatenate(axis=3)([b2act, b8c1])
b8c2 = tf.keras.layers.Conv2D(
128, (3, 3), name="block8_conv2", activation="relu", padding="same"
)(b8conc)
b8c3 = tf.keras.layers.Conv2D(
128, (3, 3), name="block8_conv3", activation="relu", padding="same"
)(b8c2)
b8bn = tf.keras.layers.BatchNormalization()(b8c3)
b8relu = tf.keras.layers.ReLU()(b8bn)
# block9
b9up = tf.keras.layers.UpSampling2D((2, 2))(b8relu)
b9c1 = tf.keras.layers.Conv2D(
64, (2, 2), name="block9_conv1", activation="relu", padding="same"
)(b9up)
b9conc = tf.keras.layers.Concatenate(axis=3)([b1act, b9c1])
b9c2 = tf.keras.layers.Conv2D(
64, (3, 3), name="block9_conv2", activation="relu", padding="same"
)(b9conc)
b9c3 = tf.keras.layers.Conv2D(
64, (3, 3), name="block9_conv3", activation="relu", padding="same"
)(b9c2)
b9bn = tf.keras.layers.BatchNormalization()(b9c3)
b9relu = tf.keras.layers.ReLU()(b9bn)
# block10
b10up = tf.keras.layers.UpSampling2D((2, 2))(b9relu)
output = tf.keras.layers.Conv2D(1, (2, 2), activation="sigmoid", padding="same")(
b10up
)
return keras.Model(inputs=input, outputs=output)
def mini_U_Net(input_shape):
input_layer = keras.layers.Input(shape=input_shape)
# Encoder
# block1
b1c1 = tf.keras.layers.Conv2D(
16, (2, 2), name="block1_conv1", activation="relu", padding="same"
)(input_layer)
b1p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp1")(b1c1)
# block2
b2c1 = tf.keras.layers.Conv2D(
32, (2, 2), name="block2_conv1", activation="relu", padding="same"
)(b1p)
b2p = tf.keras.layers.MaxPool2D((2, 2), name="block2_mp1")(b2c1)
# block3
b3c1 = tf.keras.layers.Conv2D(
64, (2, 2), name="block3_conv1", activation="relu", padding="same"
)(b2p)
b3p = tf.keras.layers.MaxPool2D((2, 2), name="block3_mp1")(b3c1)
# block4
b4c1 = tf.keras.layers.Conv2D(
128, (2, 2), name="block4_conv1", activation="relu", padding="same"
)(b3p)
b4p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp4")(b4c1)
# block5
b5c1 = tf.keras.layers.Conv2D(
256, (2, 2), name="block5_conv1", activation="relu", padding="same"
)(b4p)
# Decoder
# block6
b6up = tf.keras.layers.UpSampling2D((2, 2))(b5c1)
# b6conc = tf.keras.layers.Concatenate(axis=3)([b4c1,b6up])
b6c1 = tf.keras.layers.Conv2D(
128, (3, 3), name="block6_conv1", activation="relu", padding="same"
)(b6up)
# block7
b7up = tf.keras.layers.UpSampling2D((2, 2))(b6c1)
b7conc = tf.keras.layers.Concatenate(axis=3)([b3c1, b7up])
b7c1 = tf.keras.layers.Conv2D(
64, (3, 3), name="block7_conv1", activation="relu", padding="same"
)(b7conc)
# block8
b8up = tf.keras.layers.UpSampling2D((2, 2))(b7c1)
# b8conc = tf.keras.layers.Concatenate(axis=3)([b2c1,b8up])
b8c1 = tf.keras.layers.Conv2D(
32, (3, 3), name="block8_conv1", activation="relu", padding="same"
)(b8up)
# block9
b9up = tf.keras.layers.UpSampling2D((2, 2))(b8c1)
b9conc = tf.keras.layers.Concatenate(axis=3)([b1c1,b9up])
b9c1 = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(b9conc)
return keras.Model(inputs=input_layer, outputs=b9c1)
def mini_U_Net_v2(input_shape):
input_layer = keras.layers.Input(shape=input_shape)
# Encoder
# block1
b1c1 = tf.keras.layers.Conv2D(
32, (2, 2), name="block1_conv1", activation="relu", padding="same"
)(input_layer)
b1p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp1")(b1c1)
# block2
b2c1 = tf.keras.layers.Conv2D(
64, (2, 2), name="block2_conv1", activation="relu", padding="same"
)(b1p)
b2p = tf.keras.layers.MaxPool2D((2, 2), name="block2_mp1")(b2c1)
# block3
b3c1 = tf.keras.layers.Conv2D(
128, (2, 2), name="block3_conv1", activation="relu", padding="same"
)(b2p)
b3p = tf.keras.layers.MaxPool2D((2, 2), name="block3_mp1")(b3c1)
# block4
b4c1 = tf.keras.layers.Conv2D(
256, (2, 2), name="block4_conv1", activation="relu", padding="same"
)(b3p)
b4p = tf.keras.layers.MaxPool2D((2, 2), name="block1_mp4")(b4c1)
# Decoder
# block5
b5c1 = tf.keras.layers.Conv2D(
512, (2, 2), name="block5_conv1", activation="relu", padding="same"
)(b4p)
# block6
b6ct= tf.keras.layers.Conv2DTranspose(256,(2, 2),strides=(2,2), name="block6_ct" ,activation="relu",padding="same")(b5c1)
b6c1 = tf.keras.layers.Conv2D(256,(2,2), name="blcok6_c1" , activation="relu",padding="same")(b6ct)
# block7
b7ct = tf.keras.layers.Conv2DTranspose(128,(2, 2),strides=(2,2), name="block7_ct",activation="relu",padding="same")(b6c1)
b7conc = tf.keras.layers.Concatenate(axis=3)([b3c1, b7ct])
b7c1 = tf.keras.layers.Conv2D(
128, (3, 3), name="block7_conv1", activation="relu", padding="same"
)(b7conc)
# block8
b8ct = tf.keras.layers.Conv2DTranspose(64,(2, 2),strides=(2,2), name="block8_ct",activation="relu",padding="same")(b7c1)
b8conc = tf.keras.layers.Concatenate(axis=3)([b8ct,b2c1])
b8c1 = tf.keras.layers.Conv2D(
64, (3, 3), name="block8_conv1", activation="relu", padding="same"
)(b8conc)
# block9
b9ct = tf.keras.layers.Conv2DTranspose(32,(2, 2),strides=(2,2), name="block9_ct",activation="relu",padding="same")(b8c1)
b9conc = tf.keras.layers.Concatenate(axis=3)([b9ct, b1c1])
b9c1 = tf.keras.layers.Conv2D(1, (1, 1), activation="sigmoid")(b9conc)
return keras.Model(inputs=input_layer, outputs=b9c1)
def u_net_encoder_block(input,channel):
conv = tf.keras.layers.Conv2D(
channel, (2, 2), activation="relu", padding="same"
)(input)
output = tf.keras.layers.MaxPool2D((2, 2))(conv)
return conv,output,channel*2
def u_net_decoder_block(input,skip_input,channel,last=False):
up = tf.keras.layers.UpSampling2D((2,2))(input)
if last:
channel=1
conc = tf.keras.layers.Concatenate(axis=3)([up,skip_input])
conv = tf.keras.layers.Conv2D(
channel, (2, 2), activation="sigmoid", padding="same"
)(conc)
else :
channel/=2
conc = tf.keras.layers.Concatenate(axis=3)([up,skip_input])
conv = tf.keras.layers.Conv2D(
channel, (2, 2),activation="relu", padding="same"
)(conc)
return conv,channel
def mini_u_net(channel,input_shape,depth):
input_layer = keras.layers.Input(shape=input_shape)
output = input_layer
middle_output = []
for i in range(depth):
conv,output,channel =u_net_encoder_block(output,channel)
middle_output.insert(0,conv)
output = tf.keras.layers.Conv2D(
channel, (2, 2), activation="relu", padding="same"
)(output)
for j in range(depth):
if depth-1==j:
output,channel = u_net_decoder_block(output,middle_output[j],channel,last=True)
else :
output,channel = u_net_decoder_block(output,middle_output[j],channel)
return keras.Model(inputs=input_layer, outputs=output)
class MaxPool2DWithArgmax(tf.keras.layers.Layer):
def __init__(self):
super(MaxPool2DWithArgmax,self).__init__()
def call(self,input):
pool,pool_index = tf.nn.max_pool_with_argmax(input,(2,2),strides=2,padding='SAME')
return [pool,pool_index]
class UnPooling2D(tf.keras.layers.Layer):
def __init__(self):
super(UnPooling2D,self).__init__()
def call(self,input_index):
input,index = input_index[0],input_index[1]
input_shape = tf.shape(input,out_type=tf.int64)
output_shape = [input_shape[0],input_shape[1]*2,input_shape[2]*2,input_shape[3]]
input_vector = tf.reshape(input,[-1])
pool_index_vector = tf.reshape(index,[-1,1])
unpool = tf.scatter_nd(pool_index_vector,input_vector,[output_shape[0]*output_shape[1]*output_shape[2]*output_shape[3]])
output = tf.reshape(unpool,output_shape)
return output
def seg_net_encoder(input,channel):
conv = tf.keras.layers.Conv2D(channel,(2,2),activation="relu",padding="same")(input)
pool,pool_index = MaxPoolingWithArgmax2D()(conv)
return pool,pool_index,channel*2
def seg_net_decoder(input,pool_index,channel):
channel/=2
unpool = MaxUnpooling2D()([input,pool_index])
conv = tf.keras.layers.Conv2D(channel,(2,2),activation="relu",padding="same")(unpool)
return conv,channel
def seg_net(channel,input_shape,depth):
input_layer = keras.layers.Input(shape=input_shape)
output = input_layer
pooling_indexes = []
for i in range(depth):
output,pooling_index,channel = seg_net_encoder(output,channel)
pooling_indexes.insert(0,pooling_index)
channel /=2
for i in range(depth):
output,channel = seg_net_decoder(output,pooling_indexes[i],channel)
output_layer = tf.keras.layers.Conv2D(1,(2,2),activation="sigmoid",padding="same")(output)
return keras.Model(inputs=input_layer,outputs=output_layer)
def model_plot(model):
plot_model(model, show_shapes=True)
def dice_coef(y_true, y_pred, smooth=1.0):
intersection = tf.reduce_sum(y_true * y_pred)
return (2.0 * intersection + smooth) / (
tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth
)
def IU(y_true, y_pred):
"""this functin caliculate IoU metrics between y_true and y_pred
Assuming that y_true and y_pred is consisted of 0 or 1 and their shape is (B,H,W,C)
"""
intersection = tf.reduce_sum(tf.abs(y_true * y_pred), axis=[1, 2, 3])
union = (
tf.reduce_sum(y_true, [1, 2, 3])
+ tf.reduce_sum(y_pred, [1, 2, 3])
- intersection
)
return tf.math.reduce_mean(intersection / union, axis=0)