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solarPV_identification/CNN/model_main.py
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model_main.py
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import os
import tensorflow
import numpy as np
import scipy
import data_loaders
import datetime
import losses
import sys
#import cv2
from scipy import ndimage
#from matplotlib import pyplot as plt
from PIL import Image
from keras.models import *
from keras.layers import Reshape
from keras.layers.core import *
from keras.layers import *
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam , SGD
from keras.utils import np_utils
from keras import backend as K
directory_path = '/home/rcastell/solar_deployment/segmentation/CNN/models/'
def unet(nClasses, loss, optimizer=None, input_width=360, input_height=480, nChannels=1):
ker = 3
pad = 3
dropout_rate = 0.2
inputs = Input((input_height, input_width, nChannels))
inputs_padded = ZeroPadding2D(padding=pad, input_shape=(input_width, input_height, 3))(inputs)
conv1 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(inputs_padded)
conv1 = Dropout(dropout_rate)(conv1)
conv1 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(padding='same')(conv1)
conv2 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(pool1)
conv2 = Dropout(dropout_rate)(conv2)
conv2 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(padding='same')(conv2)
conv3 = Conv2D(128, kernel_size=ker, activation='relu', padding='same')(pool2)
conv3 = Dropout(dropout_rate)(conv3)
conv3 = Conv2D(128, kernel_size=ker, activation='relu', padding='same')(conv3)
up1 = concatenate([UpSampling2D(size=(2, 2))(conv3), conv2], axis=3)
conv4 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(up1)
conv4 = Dropout(dropout_rate)(conv4)
conv4 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(conv4)
up2 = concatenate([UpSampling2D(size=(2, 2))(conv4), conv1], axis=3)
conv5 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(up2)
conv5 = Dropout(dropout_rate)(conv5)
conv5 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(conv5)
conv6 = Conv2D(nClasses, kernel_size=1, activation='relu', padding='same')(conv5)
cropped_conv6 = Cropping2D(cropping=pad)(conv6)
conv6 = core.Reshape((input_height * input_width, nClasses))(cropped_conv6)
conv7 = core.Activation('softmax')(conv6)
model = Model(input=inputs, output=conv7)
if not optimizer is None:
model.compile(loss=loss, optimizer=optimizer, metrics=[losses.dice_coef]) ## metric: here using IoU
print("Model was compiled.")
return model
def run(rotations, lights, dice, weight1, weight2, epochs):
original_images, labeled_images = data_loaders.load_images(lights=lights, rotations=rotations)
X_train = np.array(original_images)
Y_train_flat = np.array(labeled_images).reshape(len(labeled_images), -1)
# One dimension for each channel (one channel PV, one channel non-PV)
Y_train_2d = np.stack((np.where(Y_train_flat == 0, 1, 0), Y_train_flat), axis=-1).reshape(len(X_train), -1, 2)
inp_shape = X_train[0].shape
out_shape_flat = Y_train_flat[0].shape
out_shape_2d = Y_train_2d[0].shape
if not dice:
model_u = unet(2, loss=losses.weighted_categorical_crossentropy([weight1, weight2]),
optimizer='adam', input_width=250, input_height=250, nChannels=3)
else:
model_u = unet(2, loss=losses.dice_coef_loss,
optimizer='adam', input_width=250, input_height=250, nChannels=3)
model_u.summary()
model_name = str(datetime.date.today()) + \
"dice_{:}_{:}_epochs_{:}_rotlight:{:}".format(dice, (weight1, weight2), epochs, (rotations, lights))
print(model_name)
model_u.fit(X_train, Y_train_2d, validation_split=0.1, epochs=epochs)
# Save the weights
model_u.save(directory_path + '{:}.h5'.format(model_name))
# Save the model architecture
# Save the weights
model_u.save_weights(directory_path + '{:}weights.h5'.format(model_name))
# Save the model architecture
with open(directory_path +'{:}architecture.json'.format(model_name), 'w') as f:
f.write(model_u.to_json())
if __name__ == "__main__":
rotations = int(sys.argv[1])
lights = int(sys.argv[2])
dice = int(sys.argv[3])
weight1 = float(sys.argv[4])
weight2 = float(sys.argv[5])
epochs = int(sys.argv[6])
run(rotations, lights, dice, weight1, weight2, epochs)
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