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liver_seg.py
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#!/usr/bin/env python
# coding: utf-8
"""
Created on Sat Mar 21 2020
@author: abramo, modified by ngyenge
"""
import numpy as np
import os
import random
import skimage.io as io
import skimage.transform as trans
from keras.layers import Layer, InputSpec
from keras import initializers, regularizers, constraints
from keras import backend as K
import tensorflow as tf
from keras.activations import softmax
from keras.layers import Dense, Input, Conv2D, Conv2DTranspose, UpSampling2D, MaxPooling2D, Dropout, Flatten, BatchNormalization, Concatenate, Lambda, ZeroPadding2D, Activation, Reshape, Add
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, Callback
from keras.utils import plot_model, Sequence
def unet():
inputs=Input(shape=(432,432,3))
weight_matrix=Lambda(lambda z: z[:,:,:,1])(inputs)
weight_matrix=Reshape((432,432,1))(weight_matrix)
reshape=Lambda(lambda z : z[:,:,:,0])(inputs)
reshape=Reshape((432,432,1))(reshape)
reg=0.01
#reshape=Dropout(0.2)(reshape) ## Hyperparameter optimization only on visible layer
Level1_l=Conv2D(filters=32,kernel_size=(1,1),strides=1,kernel_regularizer=regularizers.l2(reg))(reshape)
Level1_l=BatchNormalization(axis=-1)(Level1_l)
Level1_l_shortcut=Level1_l#Level1_l#
Level1_l=Activation('relu')(Level1_l)
Level1_l=Conv2D(filters=32,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level1_l)#(Level1_l)# ## kernel_initializer='glorot_uniform' is the default
Level1_l=BatchNormalization(axis=-1)(Level1_l)
#Level1_l=InstanceNormalization(axis=-1)(Level1_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level1_l=Activation('relu')(Level1_l)
#Level1_l=Dropout(0.5)(Level1_l)
Level1_l=Conv2D(filters=32,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level1_l)
Level1_l=BatchNormalization(axis=-1)(Level1_l)
#Level1_l=InstanceNormalization(axis=-1)(Level1_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level1_l=Add()([Level1_l,Level1_l_shortcut])
Level1_l=Activation('relu')(Level1_l)
Level2_l=Conv2D(filters=64,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level1_l)
Level2_l=BatchNormalization(axis=-1)(Level2_l)
Level2_l_shortcut=Level2_l
Level2_l=Activation('relu')(Level2_l)
#Level2_l=BatchNormalization(axis=-1)(Level2_l)
#Level2_l=ZeroPadding2D(padding=(1,1))(Level2_l)
Level2_l=Conv2D(filters=64,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level2_l)
Level2_l=BatchNormalization(axis=-1)(Level2_l)
#Level2_l=InstanceNormalization(axis=-1)(Level2_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level2_l=Activation('relu')(Level2_l)
#Level2_l=Dropout(0.5)(Level2_l)
Level2_l=Conv2D(filters=64,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level2_l)
Level2_l=BatchNormalization(axis=-1)(Level2_l)
#Level2_l=InstanceNormalization(axis=-1)(Level2_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level2_l=Add()([Level2_l,Level2_l_shortcut])
Level2_l=Activation('relu')(Level2_l)
Level3_l=Conv2D(filters=128,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level2_l)
Level3_l=BatchNormalization(axis=-1)(Level3_l)
Level3_l_shortcut=Level3_l
Level3_l=Activation('relu')(Level3_l)
#Level3_l=ZeroPadding2D(padding=(1,1))(Level3_l)
Level3_l=Conv2D(filters=128,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level3_l)
Level3_l=BatchNormalization(axis=-1)(Level3_l)
#Level3_l=InstanceNormalization(axis=-1)(Level3_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level3_l=Activation('relu')(Level3_l)
#Level3_l=Dropout(0.5)(Level3_l)
Level3_l=Conv2D(filters=128,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level3_l)
Level3_l=BatchNormalization(axis=-1)(Level3_l)
#Level3_l=InstanceNormalization(axis=-1)(Level3_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level3_l=Add()([Level3_l,Level3_l_shortcut])
Level3_l=Activation('relu')(Level3_l)
Level4_l=Conv2D(filters=256,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level3_l)
Level4_l=BatchNormalization(axis=-1)(Level4_l)
Level4_l_shortcut=Level4_l
Level4_l=Activation('relu')(Level4_l)
#Level4_l=ZeroPadding2D(padding=(1,1))(Level4_l)
Level4_l=Conv2D(filters=256,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level4_l)
Level4_l=BatchNormalization(axis=-1)(Level4_l)
#Level4_l=InstanceNormalization(axis=-1)(Level4_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level4_l=Activation('relu')(Level4_l)
#Level4_l=Dropout(0.5)(Level4_l)
Level4_l=Conv2D(filters=256,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level4_l)
Level4_l=BatchNormalization(axis=-1)(Level4_l)
#Level4_l=InstanceNormalization(axis=-1)(Level4_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level4_l=Add()([Level4_l,Level4_l_shortcut])
Level4_l=Activation('relu')(Level4_l)
Level5_l=Conv2D(filters=512,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level4_l)
Level5_l=BatchNormalization(axis=-1)(Level5_l)
Level5_l_shortcut=Level5_l
Level5_l=Activation('relu')(Level5_l)
#Level5_l=BatchNormalization(axis=-1)(Level5_l)
#Level5_l=ZeroPadding2D(padding=(1,1))(Level5_l)
Level5_l=Conv2D(filters=512,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level5_l)
Level5_l=BatchNormalization(axis=-1)(Level5_l)
#Level5_l=InstanceNormalization(axis=-1)(Level5_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level5_l=Activation('relu')(Level5_l)
#Level5_l=Dropout(0.5)(Level5_l)
Level5_l=Conv2D(filters=512,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level5_l)
Level5_l=BatchNormalization(axis=-1)(Level5_l)
#Level5_l=InstanceNormalization(axis=-1)(Level5_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level5_l=Add()([Level5_l,Level5_l_shortcut])
Level5_l=Activation('relu')(Level5_l)
Level6_l=Conv2D(filters=1024,kernel_size=(3,3),strides=3,kernel_regularizer=regularizers.l2(reg))(Level5_l)
Level6_l=BatchNormalization(axis=-1)(Level6_l)
Level6_l_shortcut=Level6_l
Level6_l=Activation('relu')(Level6_l)
#Level5_l=BatchNormalization(axis=-1)(Level5_l)
#Level5_l=ZeroPadding2D(padding=(1,1))(Level5_l)
Level6_l=Conv2D(filters=1024,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level6_l)
Level6_l=BatchNormalization(axis=-1)(Level6_l)
#Level5_l=InstanceNormalization(axis=-1)(Level5_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level6_l=Activation('relu')(Level6_l)
#Level5_l=Dropout(0.5)(Level5_l)
Level6_l=Conv2D(filters=1024,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level6_l)
Level6_l=BatchNormalization(axis=-1)(Level6_l)
#Level5_l=InstanceNormalization(axis=-1)(Level5_l) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level6_l=Add()([Level6_l,Level6_l_shortcut])
Level6_l=Activation('relu')(Level6_l)
Level5_r=Conv2DTranspose(filters=512,kernel_size=(3,3),strides=3,kernel_regularizer=regularizers.l2(reg))(Level6_l)
#Level4_r=UpSampling2D(size=(2, 2),interpolation='nearest')(Level5_l)
#Level4_r=Conv2D(filters=256,kernel_size=(2,2),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level4_r)
Level5_r=BatchNormalization(axis=-1)(Level5_r)
Level5_r_shortcut=Level5_r
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level5_r=Activation('relu')(Level5_r)
merge5=Concatenate(axis=-1)([Level5_l,Level5_r])
Level5_r=Conv2D(filters=512,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(merge5)
Level5_r=BatchNormalization(axis=-1)(Level5_r)
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level5_r=Activation('relu')(Level5_r)
#Level4_r=Dropout(0.5)(Level4_r)
Level5_r=Conv2D(filters=512,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level5_r)
Level5_r=BatchNormalization(axis=-1)(Level5_r)
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level5_r=Add()([Level5_r,Level5_r_shortcut])
Level5_r=Activation('relu')(Level5_r)
Level4_r=Conv2DTranspose(filters=256,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level5_r)
#Level4_r=UpSampling2D(size=(2, 2),interpolation='nearest')(Level5_l)
#Level4_r=Conv2D(filters=256,kernel_size=(2,2),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level4_r)
Level4_r=BatchNormalization(axis=-1)(Level4_r)
Level4_r_shortcut=Level4_r
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level4_r=Activation('relu')(Level4_r)
merge4=Concatenate(axis=-1)([Level4_l,Level4_r])
Level4_r=Conv2D(filters=256,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(merge4)
Level4_r=BatchNormalization(axis=-1)(Level4_r)
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level4_r=Activation('relu')(Level4_r)
#Level4_r=Dropout(0.5)(Level4_r)
Level4_r=Conv2D(filters=256,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level4_r)
Level4_r=BatchNormalization(axis=-1)(Level4_r)
#Level4_r=InstanceNormalization(axis=-1)(Level4_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level4_r=Add()([Level4_r,Level4_r_shortcut])
Level4_r=Activation('relu')(Level4_r)
Level3_r=Conv2DTranspose(filters=128,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level4_r)
#Level3_r=UpSampling2D(size=(2, 2),interpolation='nearest')(Level4_r)
#Level3_r=Conv2D(filters=128,kernel_size=(2,2),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level3_r)
Level3_r=BatchNormalization(axis=-1)(Level3_r)
Level3_r_shortcut=Level3_r
#Level3_r=InstanceNormalization(axis=-1)(Level3_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level3_r=Activation('relu')(Level3_r)
merge3=Concatenate(axis=-1)([Level3_l,Level3_r])
Level3_r=Conv2D(filters=128,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(merge3)
Level3_r=BatchNormalization(axis=-1)(Level3_r)
#Level3_r=InstanceNormalization(axis=-1)(Level3_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level3_r=Activation('relu')(Level3_r)
#Level3_r=Dropout(0.5)(Level3_r)
Level3_r=Conv2D(filters=128,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level3_r)
Level3_r=BatchNormalization(axis=-1)(Level3_r)
#Level3_r=InstanceNormalization(axis=-1)(Level3_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level3_r=Add()([Level3_r,Level3_r_shortcut])
Level3_r=Activation('relu')(Level3_r)
Level2_r=Conv2DTranspose(filters=64,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level3_r)
#Level2_r=UpSampling2D(size=(2, 2),interpolation='nearest')(Level3_r)
#Level2_r=Conv2D(filters=64,kernel_size=(2,2),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level2_r)
Level2_r=BatchNormalization(axis=-1)(Level2_r)
Level2_r_shortcut=Level2_r
#Level2_r=InstanceNormalization(axis=-1)(Level2_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level2_r=Activation('relu')(Level2_r)
merge2=Concatenate(axis=-1)([Level2_l,Level2_r])
Level2_r=Conv2D(filters=64,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(merge2)
Level2_r=BatchNormalization(axis=-1)(Level2_r)
#Level2_r=InstanceNormalization(axis=-1)(Level2_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level2_r=Activation('relu')(Level2_r)
#Level2_r=Dropout(0.5)(Level2_r)
Level2_r=Conv2D(filters=64,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level2_r)
Level2_r=BatchNormalization(axis=-1)(Level2_r)
#Level2_r=InstanceNormalization(axis=-1)(Level2_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level2_r=Add()([Level2_r,Level2_r_shortcut])
Level2_r=Activation('relu')(Level2_r)
Level1_r=Conv2DTranspose(filters=32,kernel_size=(2,2),strides=2,kernel_regularizer=regularizers.l2(reg))(Level2_r)
#Level1_r=UpSampling2D(size=(2, 2),interpolation='nearest')(Level2_r)
#Level1_r=Conv2D(filters=32,kernel_size=(2,2),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level1_r)
Level1_r=BatchNormalization(axis=-1)(Level1_r)
Level1_r_shortcut=Level1_r
#Level1_r=InstanceNormalization(axis=-1)(Level1_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level1_r=Activation('relu')(Level1_r)
merge1=Concatenate(axis=-1)([Level1_l,Level1_r])
Level1_r=Conv2D(filters=32,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(merge1)
Level1_r=BatchNormalization(axis=-1)(Level1_r)
#Level1_r=InstanceNormalization(axis=-1)(Level1_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level1_r=Activation('relu')(Level1_r)
#Level1_r=Dropout(0.5)(Level1_r)
Level1_r=Conv2D(filters=32,kernel_size=(3,3),strides=1,padding='same',kernel_regularizer=regularizers.l2(reg))(Level1_r)
Level1_r=BatchNormalization()(Level1_r)
#Level1_r=InstanceNormalization(axis=-1)(Level1_r) ## Instance Normalization. Use InstanceNormalization() for Layer Normalization.
Level1_r=Add()([Level1_r,Level1_r_shortcut])
Level1_r=Activation('relu')(Level1_r)
output=Conv2D(filters=7,kernel_size=(1,1),strides=1,kernel_regularizer=regularizers.l2(reg))(Level1_r)
#output=BatchNormalization(axis=-1)(output)
output=Lambda(lambda x : softmax(x,axis=-1))(output)
output=Concatenate(axis=-1)([output,weight_matrix])
model=Model(inputs=inputs,outputs=output)
return model
def l1_reg(weight_matrix):
return 0.01 * K.sum(K.abs(weight_matrix)) ## l1 regularization. Implement per-layer as kernel_regularizer=l1_reg
class InstanceNormalization(Layer):
"""Instance normalization layer.
Normalize the activations of the previous layer at each step,
i.e. applies a transformation that maintains the mean activation
close to 0 and the activation standard deviation close to 1.
# Arguments
axis: Integer, the axis that should be normalized
(typically the features axis).
For instance, after a `Conv2D` layer with
`data_format="channels_first"`,
set `axis=1` in `InstanceNormalization`.
Setting `axis=None` will normalize all values in each
instance of the batch.
Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid errors.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of `beta` to normalized tensor.
If False, `beta` is ignored.
scale: If True, multiply by `gamma`.
If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`),
this can be disabled since the scaling
will be done by the next layer.
beta_initializer: Initializer for the beta weight.
gamma_initializer: Initializer for the gamma weight.
beta_regularizer: Optional regularizer for the beta weight.
gamma_regularizer: Optional regularizer for the gamma weight.
beta_constraint: Optional constraint for the beta weight.
gamma_constraint: Optional constraint for the gamma weight.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a Sequential model.
# Output shape
Same shape as input.
# References
- [Layer Normalization](https://arxiv.org/abs/1607.06450)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](
https://arxiv.org/abs/1607.08022)
"""
def __init__(self,
axis=None,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
**kwargs):
super(InstanceNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.axis = axis
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
def build(self, input_shape):
ndim = len(input_shape)
if self.axis == 0:
raise ValueError('Axis cannot be zero')
if (self.axis is not None) and (ndim == 2):
raise ValueError('Cannot specify axis for rank 1 tensor')
self.input_spec = InputSpec(ndim=ndim)
if self.axis is None:
shape = (1,)
else:
shape = (input_shape[self.axis],)
if self.scale:
self.gamma = self.add_weight(shape=shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(shape=shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
self.built = True
def call(self, inputs, training=None):
input_shape = K.int_shape(inputs)
reduction_axes = list(range(0, len(input_shape)))
if self.axis is not None:
del reduction_axes[self.axis]
del reduction_axes[0]
mean = K.mean(inputs, reduction_axes, keepdims=True)
stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilon
normed = (inputs - mean) / stddev
broadcast_shape = [1] * len(input_shape)
if self.axis is not None:
broadcast_shape[self.axis] = input_shape[self.axis]
if self.scale:
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
normed = normed * broadcast_gamma
if self.center:
broadcast_beta = K.reshape(self.beta, broadcast_shape)
normed = normed + broadcast_beta
return normed
def get_config(self):
config = {
'axis': self.axis,
'epsilon': self.epsilon,
'center': self.center,
'scale': self.scale,
'beta_initializer': initializers.serialize(self.beta_initializer),
'gamma_initializer': initializers.serialize(self.gamma_initializer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer),
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_constraint': constraints.serialize(self.beta_constraint),
'gamma_constraint': constraints.serialize(self.gamma_constraint)
}
base_config = super(InstanceNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def weighted_loss(y_true,y_pred):
weight_matrix=K.flatten(y_pred[:,:,:,-1])
#weight_matrix=K.ones(K.shape(K.flatten(y_pred[:,:,:,-1])))
y_pre=y_pred[:,:,:,:-1]
E=-1/(1)*K.dot(K.transpose(K.expand_dims(weight_matrix,axis=-1)),K.expand_dims(K.log(K.flatten(tf.math.reduce_sum(tf.multiply(y_true,y_pre),-1))),axis=-1))
return E[:,0]
def generate_data(batch_size,path):
l=list(range(1,4501))
while True:
s=random.choice(l)
l.remove(s)
arr=np.load(os.path.join(path,'train_'+str(s)+'.npy'))
X=np.expand_dims(np.stack([arr[:,:,0],arr[:,:,-1]],axis=-1),0)
y=np.expand_dims(arr[:,:,1:-1],0)
for k in range(2,batch_size+1):
r=random.choice(l)
l.remove(r)
arr=np.load(os.path.join(path,'train_'+str(r)+'.npy'))
X_k=np.expand_dims(np.stack([arr[:,:,0],arr[:,:,-1]],axis=-1),0)
y_k=np.expand_dims(arr[:,:,1:-1],0)
X=np.concatenate([X,X_k],axis=0)
y=np.concatenate([y,y_k],axis=0)
if l==[]:
l=list(range(1,4501))
yield X,y
def generate_data_val(batch_size,path):
l=list(range(1,501))
while True:
s=random.choice(l)
l.remove(s)
arr=np.load(os.path.join(path,'train_'+str(s)+'.npy'))
X=np.expand_dims(np.stack([arr[:,:,0],arr[:,:,-1]],axis=-1),0)
y=np.expand_dims(arr[:,:,1:-1],0)
for k in range(2,batch_size+1):
r=random.choice(l)
l.remove(r)
arr=np.load(os.path.join(path,'train_'+str(r)+'.npy'))
X_k=np.expand_dims(np.stack([arr[:,:,0],arr[:,:,-1]],axis=-1),0)
y_k=np.expand_dims(arr[:,:,1:-1],0)
X=np.concatenate([X,X_k],axis=0)
y=np.concatenate([y,y_k],axis=0)
if l==[]:
l=list(range(1,501))
yield X,y
class DataGenerator(Sequence):
def __init__(self, path, list_X=list(range(1,4501)), batch_size=20, dim=(432,432), shuffle=True):
'Initialization'
self.dim=dim
self.batch_size = batch_size
self.list_X = list_X
self.path = path
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int(np.floor(len(self.list_X) / self.batch_size))
def __getitem__(self, index):
'Generate one batch of data'
# Generate indexes of the batch
indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
# Find list of IDs
list_X_temp = [self.list_X[k] for k in indexes]
# Generate data
X, y = self.__data_generation(list_X_temp, self.path)
return X, y
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(len(self.list_X))
if self.shuffle == True:
np.random.shuffle(self.indexes)
def __data_generation(self, list_X_temp, path):
'Generates data containing batch_size samples'
# Initialization
X = np.empty((self.batch_size, *self.dim, 2))
y = np.empty((self.batch_size, *self.dim, 13))
# Generate data
for i, j in enumerate(list_X_temp):
# Store sample
arr=np.load(os.path.join(path,'train_'+str(j)+'.npy'))
X[i,] = np.stack([arr[:,:,0],arr[:,:,-1]],axis=-1)
# Store class
y[i,] = arr[:,:,1:-1]
return X, y
class LossHistory(Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
self.losses.append(logs.get('loss'))
'''
def evaluation_metric(y_true, y_pred):
acc=0
y_pred=y_pred[:,:,:,:-1]
for j in range(1,13):
elements_per_class=tf.math.reduce_sum(y_true[:,:,:,j])
predicted_per_class=tf.math.reduce_sum(y_pred[:,:,:,j])
if elements_per_class!=0 or predicted_per_class!=0:
intersection=tf.math.reduce_sum(np.multiply(y_pred[:,:,:,j],y_true[:,:,:,j]))
union=elements_per_class+predicted_per_class-intersection
acc+=intersection/union
return acc/12
'''
def evaluation_metric_J(y_true, y_pred): ## Probabilistic Jaccard
acc=0
y_pred=y_pred[:,:,:,:-1]
for j in range(13):
elements_per_class=tf.math.reduce_sum(y_true[:,:,:,j])
predicted_per_class=tf.math.reduce_sum(y_pred[:,:,:,j])
intersection=tf.math.reduce_sum(tf.math.multiply(y_pred[:,:,:,j],y_true[:,:,:,j]))
union=elements_per_class+predicted_per_class-intersection
acc+=intersection/(union+0.000001)
return acc/13
def evaluation_metric(y_true, y_pred): ## Probabilistic Dice
acc=0
y_pred=y_pred[:,:,:,:-1]
for j in range(13):
elements_per_class=tf.math.reduce_sum(y_true[:,:,:,j])
predicted_per_class=tf.math.reduce_sum(y_pred[:,:,:,j])
intersection=tf.math.scalar_mul(2.0,tf.math.reduce_sum(tf.math.multiply(y_pred[:,:,:,j],y_true[:,:,:,j])))
union=elements_per_class+predicted_per_class
acc+=intersection/(union+0.000001)
return acc/13