/
model-full.py
150 lines (122 loc) · 4.44 KB
/
model-full.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, Merge, Permute
from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras import backend as K
import cv2
import numpy as np
import json
np.random.seed(07) # 0bserver07 for reproducibility
img_w = 480
img_h = 360
n_labels = 12
kernel = 3
pad = 1
pool_size = 2
encoding_layers = [
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
]
decoding_layers = [
UpSampling2D(size=(pool_size,pool_size)),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(size=(pool_size,pool_size)),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(size=(pool_size,pool_size)),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(size=(pool_size,pool_size)),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(size=(pool_size,pool_size)),
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(n_labels, 1, 1, border_mode='valid'),
BatchNormalization(),
]
segnet_basic = models.Sequential()
segnet_basic.add(Layer(input_shape=(3, 360, 480)))
segnet_basic.encoding_layers = encoding_layers
for l in segnet_basic.encoding_layers:
segnet_basic.add(l)
segnet_basic.decoding_layers = decoding_layers
for l in segnet_basic.decoding_layers:
segnet_basic.add(l)
segnet_basic.add(Reshape((n_labels, img_h * img_w), input_shape=(12,img_h, img_w)))
segnet_basic.add(Permute((2, 1)))
segnet_basic.add(Activation('softmax'))
with open('segNet_full_model.json', 'w') as outfile:
outfile.write(json.dumps(json.loads(segnet_basic.to_json()), indent=2))