from lasagne.layers import Conv2DLayer as ConvLayer from lasagne.layers import Pool2DLayer as PoolLayer from lasagne.layers import Upscale2DLayer from lasagne.nonlinearities import sigmoid import lasagne import cPickle import vgg16 from constants import PATH_TO_VGG16_WEIGHTS def set_pretrained_weights(net, path_to_model_weights=PATH_TO_VGG16_WEIGHTS): # Set out weights vgg16 = cPickle.load(open(path_to_model_weights)) num_elements_to_set = 26 # Number of W and b elements for the first convolutional layers lasagne.layers.set_all_param_values(net['conv5_3'], vgg16['param values'][:num_elements_to_set]) def build_encoder(input_height, input_width, input_var): encoder = vgg16.build(input_height, input_width, input_var) set_pretrained_weights(encoder) return encoder def build_decoder(net): net['uconv5_3']= ConvLayer(net['conv5_3'], 512, 3, pad=1) print "uconv5_3: {}".format(net['uconv5_3'].output_shape[1:]) net['uconv5_2'] = ConvLayer(net['uconv5_3'], 512, 3, pad=1) print "uconv5_2: {}".format(net['uconv5_2'].output_shape[1:]) net['uconv5_1'] = ConvLayer(net['uconv5_2'], 512, 3, pad=1) print "uconv5_1: {}".format(net['uconv5_1'].output_shape[1:]) net['upool4'] = Upscale2DLayer(net['uconv5_1'], scale_factor=2) print "upool4: {}".format(net['upool4'].output_shape[1:]) net['uconv4_3'] = ConvLayer(net['upool4'], 512, 3, pad=1) print "uconv4_3: {}".format(net['uconv4_3'].output_shape[1:]) net['uconv4_2'] = ConvLayer(net['uconv4_3'], 512, 3, pad=1) print "uconv4_2: {}".format(net['uconv4_2'].output_shape[1:]) net['uconv4_1'] = ConvLayer(net['uconv4_2'], 512, 3, pad=1) print "uconv4_1: {}".format(net['uconv4_1'].output_shape[1:]) net['upool3'] = Upscale2DLayer(net['uconv4_1'], scale_factor=2) print "upool3: {}".format(net['upool3'].output_shape[1:]) net['uconv3_3'] = ConvLayer(net['upool3'], 256, 3, pad=1) print "uconv3_3: {}".format(net['uconv3_3'].output_shape[1:]) net['uconv3_2'] = ConvLayer(net['uconv3_3'], 256, 3, pad=1) print "uconv3_2: {}".format(net['uconv3_2'].output_shape[1:]) net['uconv3_1'] = ConvLayer(net['uconv3_2'], 256, 3, pad=1) print "uconv3_1: {}".format(net['uconv3_1'].output_shape[1:]) net['upool2'] = Upscale2DLayer(net['uconv3_1'], scale_factor=2) print "upool2: {}".format(net['upool2'].output_shape[1:]) net['uconv2_2'] = ConvLayer(net['upool2'], 128, 3, pad=1) print "uconv2_2: {}".format(net['uconv2_2'].output_shape[1:]) net['uconv2_1'] = ConvLayer(net['uconv2_2'], 128, 3, pad=1) print "uconv2_1: {}".format(net['uconv2_1'].output_shape[1:]) net['upool1'] = Upscale2DLayer(net['uconv2_1'], scale_factor=2) print "upool1: {}".format(net['upool1'].output_shape[1:]) net['uconv1_2'] = ConvLayer(net['upool1'], 64, 3, pad=1,) print "uconv1_2: {}".format(net['uconv1_2'].output_shape[1:]) net['uconv1_1'] = ConvLayer(net['uconv1_2'], 64, 3, pad=1) print "uconv1_1: {}".format(net['uconv1_1'].output_shape[1:]) net['output'] = ConvLayer(net['uconv1_1'], 1, 1, pad=0,nonlinearity=sigmoid) print "output: {}".format(net['output'].output_shape[1:]) return net def build(input_height, input_width, input_var): encoder = build_encoder(input_height, input_width, input_var) generator = build_decoder(encoder) return generator