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model-tiramasu-56.py
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model-tiramasu-56.py
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from __future__ import absolute_import
from __future__ import print_function
import os
import keras.models as models
from keras.layers.core import Layer, Dense, Dropout, Activation, Flatten, Reshape, Permute
from keras.layers.convolutional import Conv2D, MaxPooling2D, UpSampling2D, Cropping2D
from keras.layers.normalization import BatchNormalization
from keras.layers import Conv2D, Conv2DTranspose
from keras import backend as K
import cv2
import numpy as np
import json
# weight_decay = 0.0001
from keras.regularizers import l2
class Tiramisu():
def __init__(self):
self.create()
def DenseBlock(self, layers, filters):
model = self.model
for i in range(layers):
model.add(BatchNormalization(mode=0, axis=1,
gamma_regularizer=l2(0.0001),
beta_regularizer=l2(0.0001)))
model.add(Activation('relu'))
model.add(Conv2D(filters, kernel_size=(3, 3), padding='same',
kernel_initializer="he_uniform"))
model.add(Dropout(0.2))
def TransitionDown(self,filters):
model = self.model
model.add(BatchNormalization(mode=0, axis=1,
gamma_regularizer=l2(0.0001),
beta_regularizer=l2(0.0001)))
model.add(Activation('relu'))
model.add(Conv2D(filters, kernel_size=(1, 1), padding='same',
kernel_initializer="he_uniform"))
model.add(Dropout(0.2))
model.add(MaxPooling2D( pool_size=(2, 2),
strides=(2, 2),
data_format='channels_last'))
def TransitionUp(self,filters, input_shape,output_shape):
model = self.model
model.add(Conv2DTranspose(filters, kernel_size=(3, 3), strides=(2, 2),
padding='same',
output_shape=output_shape,
input_shape=input_shape,
kernel_initializer="he_uniform",
data_format='channels_last'))
def create(self):
model = self.model = models.Sequential()
# cropping
# model.add(Cropping2D(cropping=((68, 68), (128, 128)), input_shape=(3, 360,480)))
model.add(Conv2D(48, kernel_size=(3, 3), padding='same',
input_shape=(224,224,3),
kernel_initializer="he_uniform",
kernel_regularizer = l2(0.0001),
data_format='channels_last'))
# (5 * 4)* 2 + 5 + 5 + 1 + 1 +1
# growth_m = 4 * 12
# previous_m = 48
self.DenseBlock(4,96) # 4*12 = 48 + 48 = 96
self.TransitionDown(96)
self.DenseBlock(4,144) # 4*12 = 48 + 96 = 144
self.TransitionDown(144)
self.DenseBlock(4,192) # 4*12 = 48 + 144 = 192
self.TransitionDown(192)
self.DenseBlock(4,240)# 4*12 = 48 + 192 = 240
self.TransitionDown(240)
self.DenseBlock(4,288) # 4*12 = 48 + 288 = 336
self.TransitionDown(288)
self.DenseBlock(15,336) # 4 * 12 = 48 + 288 = 336
self.TransitionUp(384, (384, 7, 7), (None, 384, 14, 14)) # m = 288 + 4x12 + 4x12 = 384.
self.DenseBlock(4,384)
self.TransitionUp(336, (336, 14, 14), (None, 336, 28, 28)) #m = 240 + 4x12 + 4x12 = 336
self.DenseBlock(4,336)
self.TransitionUp(288, (288, 28, 28), (None, 288, 56, 56)) # m = 192 + 4x12 + 4x12 = 288
self.DenseBlock(4,288)
self.TransitionUp(240, (240, 56, 56), (None, 240, 112, 112)) # m = 144 + 4x12 + 4x12 = 240
self.DenseBlock(4,240)
self.TransitionUp(192, (192, 112, 112), (None, 192, 224, 224)) # m = 96 + 4x12 + 4x12 = 192
self.DenseBlock(4,192)
model.add(Conv2D(12, kernel_size=(1,1),
padding='same',
kernel_initializer="he_uniform",
kernel_regularizer = l2(0.0001),
data_format='channels_last'))
model.add(Reshape((12, 224 * 224)))
model.add(Permute((2, 1)))
model.add(Activation('softmax'))
model.summary()
with open('tiramisu_fc_dense56_model.json', 'w') as outfile:
outfile.write(json.dumps(json.loads(model.to_json()), indent=3))
Tiramisu()