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model.py
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import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate
from tensorflow.keras.models import Model
# Model code borrowed and adapted from: https://github.com/zhixuhao/unet
# Inputs need to be divisible? or power? of 2 -- due to correct sizing with concatentation and 2x2 downsampling and 2x2 upsampling
def unet(input_size, ds=1):
'''
ds: int, some multiple of 2, cuts number of filters uniformly
across entire network for quick prototype of Unet for
GPU RAM purposes
'''
inp = Input(input_size)
conv_0 = Conv2D(filters = 64//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(inp)
conv_0 = Conv2D(filters = 64//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_0)
downsample_0 = MaxPooling2D(pool_size=2)(conv_0)
conv_1 = Conv2D(filters = 128//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(downsample_0)
conv_1 = Conv2D(filters = 128//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_1)
downsample_1 = MaxPooling2D(pool_size=2)(conv_1)
conv_2 = Conv2D(filters = 256//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(downsample_1)
conv_2 = Conv2D(filters = 256//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_2)
downsample_2 = MaxPooling2D(pool_size=2)(conv_2)
conv_3 = Conv2D(filters = 512//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(downsample_2)
conv_3 = Conv2D(filters = 512//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_3)
downsample_3 = MaxPooling2D(pool_size=2)(conv_3)
conv_4 = Conv2D(filters = 1024//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(downsample_3)
conv_4 = Conv2D(filters = 1024//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_4)
upsample_5 = Conv2D(filters=512//ds,
kernel_size=2,
activation = 'relu',
padding = 'same')(UpSampling2D(size = 2)(conv_4))
merge_5 = concatenate([conv_3, upsample_5], axis = 3)
conv_5 = Conv2D(filters = 512//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(merge_5)
conv_5 = Conv2D(filters = 512//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_5)
upsample_6 = Conv2D(filters = 256//ds,
kernel_size = 2,
activation = 'relu',
padding = 'same')(UpSampling2D(size = 2)(conv_5))
merge_6 = concatenate([conv_2, upsample_6], axis = 3)
conv_6 = Conv2D(filters = 256//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(merge_6)
conv_6 = Conv2D(filters = 256//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_6)
upsample_7 = Conv2D(filters = 128//ds,
kernel_size = 2,
activation = 'relu',
padding = 'same')(UpSampling2D(size = 2)(conv_6))
merge_7 = concatenate([conv_1, upsample_7], axis = 3)
conv_7 = Conv2D(filters = 128//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(merge_7)
conv_7 = Conv2D(filters = 128//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_7)
upsample_8 = Conv2D(filters = 64//ds,
kernel_size = 2,
activation = 'relu',
padding = 'same')(UpSampling2D(size = 2)(conv_7))
merge_8 = concatenate([conv_0, upsample_8], axis = 3)
conv_8 = Conv2D(filters = 64//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(merge_8)
conv_8 = Conv2D(filters = 64//ds,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_8)
conv_8 = Conv2D(filters = 2,
kernel_size = 3,
activation = 'relu',
padding = 'same')(conv_8)
conv_9 = Conv2D(filters = 1,
kernel_size = 1,
activation = 'sigmoid',
dtype="float32")(conv_8)
model = Model(inputs=inp, outputs=conv_9)
return model