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a segnet-like architecture for building detection in the spacenet dataset
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| #from __future__ import absolute_import | |
| from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img | |
| from keras.callbacks import ModelCheckpoint | |
| from keras.models import Sequential | |
| from keras.layers import Convolution2D, MaxPooling2D | |
| from keras.layers import Layer, Dense, Dropout, Activation, Flatten, Reshape, Merge, Permute | |
| from keras.layers import ZeroPadding2D, UpSampling2D | |
| from keras.layers.normalization import BatchNormalization | |
| import sys | |
| import os | |
| import numpy as np | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| #from preprocessing.visualize_prepro import shiftedColorMap | |
| import itertools | |
| import tensorflow as tf | |
| path = sys.argv[1] | |
| # input image dimensions | |
| img_rows, img_cols = 400, 400 | |
| # output image dimensions | |
| label_rows, label_cols = 400, 400 | |
| with tf.device('/gpu:1'): | |
| # we create two instances with the same arguments | |
| img_data_gen_args = dict( | |
| # featurewise_center=True, | |
| # featurewise_std_normalization=True, | |
| rescale=1. / 255, | |
| rotation_range=90., | |
| width_shift_range=0.1, | |
| height_shift_range=0.1, | |
| zoom_range=0.2, | |
| fill_mode="constant", | |
| cval=0 | |
| ) | |
| label_data_gen_args = dict( | |
| rotation_range=90., | |
| width_shift_range=0.1, | |
| height_shift_range=0.1, | |
| zoom_range=0.2, | |
| fill_mode="constant", | |
| cval=1 | |
| ) | |
| image_datagen = ImageDataGenerator(**img_data_gen_args) | |
| mask_datagen = ImageDataGenerator(**label_data_gen_args) | |
| # Provide the same seed and keyword arguments to the fit and flow methods | |
| seed = 1 | |
| # image_datagen.fit(images, augment=True, seed=seed) | |
| # mask_datagen.fit(masks, augment=True, seed=seed) | |
| image_generator = image_datagen.flow_from_directory( | |
| os.path.join(path, '3band/'), | |
| target_size=(img_rows, img_cols), | |
| class_mode=None, | |
| batch_size=8, | |
| shuffle=False, | |
| seed=seed) | |
| mask_generator = mask_datagen.flow_from_directory( | |
| os.path.join(path, 'labels'), | |
| target_size=(label_rows, label_cols), | |
| class_mode=None, | |
| batch_size=8, | |
| shuffle=False, | |
| color_mode='grayscale', | |
| seed=seed) | |
| # combine generators into one which yields image and masks | |
| train_generator = itertools.izip(image_generator, mask_generator) | |
| kernel = 3 | |
| filter_size = 64 | |
| pad = 1 | |
| pool_size = 2 | |
| model = Sequential() | |
| model.add(Layer(input_shape=(img_rows, img_cols, 3))) | |
| # encoding layers | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(filter_size, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(MaxPooling2D(pool_size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(128, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(256, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(512, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Activation('relu')) | |
| # decoding layers | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(512, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(UpSampling2D(size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(256, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(UpSampling2D(size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(128, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(UpSampling2D(size=(pool_size, pool_size))) | |
| model.add(ZeroPadding2D(padding=(pad, pad))) | |
| model.add(Convolution2D(filter_size, kernel, kernel, border_mode='valid')) | |
| model.add(BatchNormalization()) | |
| model.add(Convolution2D(1, 1, 1, border_mode='valid',)) | |
| print model.output_shape | |
| model.add(Reshape((label_rows * label_cols,))) | |
| model.add(Activation('sigmoid')) | |
| model.add(Reshape((label_rows, label_cols, 1))) | |
| model.compile(loss="binary_crossentropy", optimizer='rmsprop', | |
| metrics=['binary_accuracy']) | |
| model.summary() | |
| checkpointer = ModelCheckpoint(filepath="weights.hdf5", verbose=1, save_best_only=False) | |
| model.fit_generator( | |
| train_generator, | |
| samples_per_epoch=1000, | |
| nb_epoch=20, | |
| callbacks=[checkpointer]) | |
| model.save('spacenetmodel2.h5') |
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