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light_network.py
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light_network.py
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#!/usr/bin/env python3
# -*- coding = utf-8 -*-
import os
import sys
import tensorflow as tf
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Conv2D, SeparableConv2D, DepthwiseConv2D, AveragePooling2D
from tensorflow.keras.layers import BatchNormalization, MaxPooling2D, UpSampling2D
from tensorflow.keras.layers import InputLayer, Input, Add, SpatialDropout2D, Concatenate, Dropout
from tensorflow.keras.regularizers import l2
__all__ = ['light_model', 'light_model_v2']
def light_model(input_shape = (512, 512, 4), classes = 8):
"""Construct the encoder-decoder model architecture."""
input = Input(input_shape)
# First Encoder stage 1.
enc = Conv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(input)
enc = BatchNormalization()(enc)
enc = MaxPooling2D(pool_size = (2, 2), padding = 'same')(enc)
enc = Dropout(0.1)(enc)
# First Encoder stage 2.
enc = Conv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', kernel_regularizer = l2(0.01))(enc)
enc = BatchNormalization()(enc)
enc = MaxPooling2D(pool_size = (2, 2), padding = 'same')(enc)
enc = Dropout(0.1)(enc)
# First Encoder stage 3.
enc = Conv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', kernel_regularizer = l2(0.01))(enc)
enc = BatchNormalization()(enc)
enc = MaxPooling2D(pool_size = (2, 2), padding = 'same')(enc)
enc = Dropout(0.1)(enc)
# First Encoder stage 4.
enc = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', kernel_regularizer = l2(0.01))(enc)
enc = BatchNormalization()(enc)
enc = MaxPooling2D(pool_size = (2, 2), padding = 'same')(enc)
enc = Dropout(0.1)(enc)
# Second Encoder stage 1.
enc2 = SeparableConv2D(256, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(input)
enc2 = BatchNormalization()(enc2)
enc2 = AveragePooling2D(pool_size = (2, 2), padding = 'same')(enc2)
enc2 = Dropout(0.1)(enc2)
# Second Encoder stage 2.
enc2 = SeparableConv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(enc2)
enc2 = BatchNormalization()(enc2)
enc2 = AveragePooling2D(pool_size = (2, 2), padding = 'same')(enc2)
enc2 = Dropout(0.1)(enc2)
# Second Encoder stage 3.
enc2 = SeparableConv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(enc2)
enc2 = BatchNormalization()(enc2)
enc2 = AveragePooling2D(pool_size = (2, 2), padding = 'same')(enc2)
enc2 = Dropout(0.1)(enc2)
# Second Encoder stage 4.
enc2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(enc2)
enc2 = BatchNormalization()(enc2)
enc2 = AveragePooling2D(pool_size = (2, 2), padding = 'same')(enc2)
enc2 = Dropout(0.1)(enc2)
# Concatenate encoders into a single output.
encoder_output = Concatenate()([enc, enc2])
# Decoder stage 1.
dec = Conv2D(32, kernel_size = (5, 5), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(encoder_output)
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
dec = Dropout(0.1)(dec)
# Decoder stage 2.
dec = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
dec = Dropout(0.1)(dec)
# Decoder Stage 3.
dec = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
dec = Dropout(0.1)(dec)
# Decoder Stage 4.
dec = Conv2D(32, kernel_size = (1, 1), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
decoder_output = Dropout(0.1)(dec)
# Model output stage.
output = Conv2D(classes, kernel_size = (3, 3), activation = 'sigmoid', padding = 'same',
kernel_initializer = 'he_normal')(decoder_output)
return Model(input, output)
def light_model_v2(input_shape = (512, 512, 4), classes = 8):
"""Construct the encoder-decoder model architecture."""
input = Input(input_shape)
# First Encoder stage 1.
enc = Conv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(input)
enc = BatchNormalization()(enc)
# First Encoder stage 2.
enc = Conv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc)
enc = BatchNormalization()(enc)
# First Encoder stage 3.
enc = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc)
enc = BatchNormalization()(enc)
# First Encoder stage 4.
enc = Conv2D(16, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc)
enc = BatchNormalization()(enc)
# Second Encoder stage 1.
enc2 = SeparableConv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(input)
enc2 = BatchNormalization()(enc2)
# Second Encoder stage 2.
enc2 = SeparableConv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc2)
enc2 = BatchNormalization()(enc2)
# Second Encoder stage 3.
enc2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc2)
enc2 = BatchNormalization()(enc2)
# Second Encoder stage 4.
enc2 = SeparableConv2D(16, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal', strides = (2, 2))(enc2)
enc2 = BatchNormalization()(enc2)
# Concatenate encoders into a single output.
encoder_output = Concatenate()([enc, enc2])
# Decoder stage 1.
dec_branch_1 = Conv2D(32, kernel_size = (5, 5), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(encoder_output)
dec_branch_2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(encoder_output)
dec = Add()([dec_branch_1, dec_branch_2])
dec = UpSampling2D(size = (2, 2))(dec)
# Decoder stage 2.
dec_branch_1 = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec_branch_2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = Add()([dec_branch_1, dec_branch_2])
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
# Decoder Stage 3.
dec_branch_1 = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec_branch_2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = Add()([dec_branch_1, dec_branch_2])
dec = BatchNormalization()(dec)
dec = UpSampling2D(size = (2, 2))(dec)
# Decoder Stage 4.
dec_branch_1 = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec_branch_2 = SeparableConv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(dec)
dec = Add()([dec_branch_1, dec_branch_2])
dec = BatchNormalization()(dec)
decoder_output = UpSampling2D(size = (2, 2))(dec)
# Mini-encoder-decoder to learn higher-level features.
# MIni-encoder-decoder encoding branch 1.
mini_model_branch1 = DepthwiseConv2D(kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(input)
mini_model_branch1 = Conv2D(128, kernel_size = (1, 1), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch1)
mini_model_branch1 = BatchNormalization()(mini_model_branch1)
mini_model_branch1 = MaxPooling2D(pool_size = (2, 2))(mini_model_branch1)
mini_model_branch1 = DepthwiseConv2D(kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch1)
mini_model_branch1 = Conv2D(64, kernel_size = (1, 1), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch1)
mini_model_branch1 = BatchNormalization()(mini_model_branch1)
mini_model_branch1 = MaxPooling2D(pool_size = (2, 2))(mini_model_branch1)
mini_model_branch1 = DepthwiseConv2D(kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch1)
mini_model_branch1 = Conv2D(16, kernel_size = (1, 1), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch1)
mini_model_branch1 = BatchNormalization()(mini_model_branch1)
mini_model_branch1 = MaxPooling2D(pool_size = (2, 2))(mini_model_branch1)
# Mini encoder-decoder encoding branch 2.
mini_model_branch2 = Conv2D(128, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(input)
mini_model_branch2 = BatchNormalization()(mini_model_branch2)
mini_model_branch2 = AveragePooling2D(pool_size = (2, 2))(mini_model_branch2)
mini_model_branch2 = Conv2D(64, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch2)
mini_model_branch2 = BatchNormalization()(mini_model_branch2)
mini_model_branch2 = AveragePooling2D(pool_size = (2, 2))(mini_model_branch2)
mini_model_branch2 = Conv2D(16, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_branch2)
mini_model_branch2 = BatchNormalization()(mini_model_branch2)
mini_model_branch2 = AveragePooling2D(pool_size = (2, 2))(mini_model_branch2)
# Concatenate encoding branches.
mini_model_encoding = Concatenate()([mini_model_branch1, mini_model_branch2])
# Mini encoder-decoder decoder segment.
mini_model_decoding = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_encoding)
mini_model_decoding = BatchNormalization()(mini_model_decoding)
mini_model_decoding = UpSampling2D(size = (2, 2))(mini_model_decoding)
mini_model_decoding = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_decoding)
mini_model_decoding = BatchNormalization()(mini_model_decoding)
mini_model_decoding = UpSampling2D(size = (2, 2))(mini_model_decoding)
mini_model_decoding = Conv2D(32, kernel_size = (3, 3), activation = 'relu', padding = 'same',
kernel_initializer = 'he_normal')(mini_model_decoding)
mini_model_decoding = BatchNormalization()(mini_model_decoding)
mini_model_decoding = UpSampling2D(size = (2, 2))(mini_model_decoding)
# Concatenate primary model and mini-model.
decoder_output = Add()([mini_model_decoding, decoder_output])
# Model output stage.
output = Conv2D(classes, kernel_size = (3, 3), activation = 'sigmoid', padding = 'same',
kernel_initializer = 'he_normal')(decoder_output)
return Model(input, output)