def build_autoencoder_network():
input_var = T.tensor4('input_var');
layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool1 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool1, 240, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Conv2DLayer(layer, 320, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
pool2 = layers.MaxPool2DLayer(layer, (2, 2), 2);
layer = batch_norm(layers.Conv2DLayer(pool2, 640, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
prely = batch_norm(layers.Conv2DLayer(layer, 1024, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
featm = batch_norm(layers.Conv2DLayer(prely, 640, filter_size=(1,1), nonlinearity=leaky_rectify));
feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
maskm = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
mask_rep = batch_norm(layers.Conv2DLayer(maskm, 1, filter_size=(1,1), nonlinearity=None), beta=None, gamma=None);
mask_map = SoftThresPerc(mask_rep, perc=90.0, alpha=0.1, beta=init.Constant(0.5), tight=100.0, name="mask_map");
layer = ChInnerProdMerge(feat_map, mask_map, name="encoder");
layer = batch_norm(layers.Deconv2DLayer(layer, 1024, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 640, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool2);
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 320, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(1,1), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.InverseLayer(layer, pool1);
layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
layer = layers.Deconv2DLayer(layer, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
glblf = batch_norm(layers.Conv2DLayer(prely, 128, filter_size=(1,1), nonlinearity=leaky_rectify));
glblf = layers.Pool2DLayer(glblf, pool_size=(5,5), stride=5, mode='average_inc_pad');
glblf = batch_norm(layers.Conv2DLayer(glblf, 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Conv2DLayer(glblf, 5, filter_size=(1,1), nonlinearity=rectify), name="global_feature");
glblf = batch_norm(layers.Deconv2DLayer(glblf, 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(9,9), stride=5, crop=(2,2), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(4,4), stride=2, crop=(1,1), nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
glblf = layers.Deconv2DLayer(glblf, 3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);
layer = layers.ElemwiseSumLayer([layer, glblf]);
network = ReshapeLayer(layer, ([0], -1));
mask_var = lasagne.layers.get_output(mask_map);
output_var = lasagne.layers.get_output(network);
return network, input_var, mask_var, output_var;
def _forward(self):
net = {}
net['input'] = layers.InputLayer(shape=(None, 1, 28, 28),
input_var=self.X)
net['dense1'] = layers.DenseLayer(net['input'],
num_units=1024,
W=init.GlorotUniform(),
b=init.Constant(0.),
nonlinearity=nonlinearities.rectify)
net['dense2'] = layers.DenseLayer(net['dense1'],
num_units=512,
W=init.GlorotUniform(),
b=None,
nonlinearity=nonlinearities.rectify)
net['out'] = layers.DenseLayer(net['dense2'],
num_units=10,
W=init.GlorotUniform(),
b=None,
nonlinearity=nonlinearities.softmax)
net['outinv'] = layers.InverseLayer(net['out'], net['out'])
return net
def build_deconv_network():
input_var = theano.tensor.tensor4('input_var')
net = {}
net['input'] = layers.InputLayer(shape=(None, 3, PS, PS),
input_var=input_var)
# Encoding part
net['conv1_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['input'],
64,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv1_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv1_1'],
64,
filter_size=(3, 3),
stride=1,
pad=1)))
net['pool1'] = layers.Pool2DLayer(net['conv1_2'],
pool_size=(2, 2),
stride=2,
mode='max')
net['conv2_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['pool1'],
128,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv2_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv2_1'],
128,
filter_size=(3, 3),
stride=1,
pad=1)))
net['pool2'] = layers.Pool2DLayer(net['conv2_2'],
pool_size=(2, 2),
stride=2,
mode='max')
net['conv3_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['pool2'],
256,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv3_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv3_1'],
256,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv3_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv3_2'],
256,
filter_size=(3, 3),
stride=1,
pad=1)))
net['pool3'] = layers.Pool2DLayer(net['conv3_3'],
pool_size=(2, 2),
stride=2,
mode='max')
net['conv4_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['pool3'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv4_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv4_1'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv4_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv4_2'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['pool4'] = layers.Pool2DLayer(net['conv4_3'],
pool_size=(2, 2),
stride=2,
mode='max')
net['conv5_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['pool4'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv5_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv5_1'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['conv5_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['conv5_2'],
512,
filter_size=(3, 3),
stride=1,
pad=1)))
net['pool5'] = layers.Pool2DLayer(net['conv5_3'],
pool_size=(2, 2),
stride=2,
mode='max')
net['fc6'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['pool5'],
4096,
filter_size=(7, 7),
stride=1,
pad='same')))
# fc7 is the encoding layer
net['fc7'] = layers.NonlinearityLayer(
batch_norm(
layers.Conv2DLayer(net['fc6'],
4096,
filter_size=(1, 1),
stride=1,
pad='same')))
# Decoding part
net['fc6_deconv'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['fc7'],
512,
filter_size=(7, 7),
stride=1,
crop='same')))
net['unpool5'] = layers.InverseLayer(net['fc6_deconv'], net['pool5'])
net['deconv5_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['unpool5'],
512,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv5_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv5_1'],
512,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv5_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv5_2'],
512,
filter_size=(3, 3),
stride=1,
crop='same')))
net['unpool4'] = layers.InverseLayer(net['deconv5_3'], net['pool4'])
net['deconv4_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['unpool4'],
512,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv4_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv4_1'],
512,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv4_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv4_2'],
256,
filter_size=(3, 3),
stride=1,
crop='same')))
net['unpool3'] = layers.InverseLayer(net['deconv4_3'], net['pool3'])
net['deconv3_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['unpool3'],
256,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv3_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv3_1'],
256,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv3_3'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv3_2'],
128,
filter_size=(3, 3),
stride=1,
crop='same')))
net['unpool2'] = layers.InverseLayer(net['deconv3_3'], net['pool2'])
net['deconv2_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['unpool2'],
128,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv2_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv2_1'],
64,
filter_size=(3, 3),
stride=1,
crop='same')))
net['unpool1'] = layers.InverseLayer(net['deconv2_2'], net['pool1'])
net['deconv1_1'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['unpool1'],
64,
filter_size=(3, 3),
stride=1,
crop='same')))
net['deconv1_2'] = layers.NonlinearityLayer(
batch_norm(
layers.Deconv2DLayer(net['deconv1_1'],
64,
filter_size=(3, 3),
stride=1,
crop='same')))
# Segmentation layer
net['seg_score'] = layers.Deconv2DLayer(
net['deconv1_2'],
1,
filter_size=(1, 1),
stride=1,
crop='same',
nonlinearity=lasagne.nonlinearities.sigmoid)
network = ReshapeLayer(net['seg_score'], ([0], -1))
output_var = lasagne.layers.get_output(network)
all_param = lasagne.layers.get_all_params(network, trainable=True)
return network, input_var, output_var, all_param
def build_network_from_ae_old(classn):
input_var = T.tensor4('input_var');
net = {}
net['input'] = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
net['conv1_1'] = batch_norm(layers.Conv2DLayer(net['input'], 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv1_2'] = batch_norm(layers.Conv2DLayer(net['conv1_1'], 64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['pool1'] = layers.Pool2DLayer(net['conv1_2'], pool_size=(2,2), stride=2, mode='max');
net['conv2_1'] = batch_norm(layers.Conv2DLayer(net['pool1'], 128, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv2_2'] = batch_norm(layers.Conv2DLayer(net['conv2_1'], 128, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['pool2'] = layers.Pool2DLayer(net['conv2_2'], pool_size=(2,2), stride=2, mode='max');
net['conv3_1'] = batch_norm(layers.Conv2DLayer(net['pool2'], 256, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv3_2'] = batch_norm(layers.Conv2DLayer(net['conv3_1'], 256, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv3_3'] = batch_norm(layers.Conv2DLayer(net['conv3_2'], 256, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['pool3'] = layers.Pool2DLayer(net['conv3_3'], pool_size=(2,2), stride=2, mode='max');
net['conv4_1'] = batch_norm(layers.Conv2DLayer(net['pool3'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv4_2'] = batch_norm(layers.Conv2DLayer(net['conv4_1'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv4_3'] = batch_norm(layers.Conv2DLayer(net['conv4_2'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['pool4'] = layers.Pool2DLayer(net['conv4_3'], pool_size=(2,2), stride=2, mode='max');
net['conv5_1'] = batch_norm(layers.Conv2DLayer(net['pool4'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv5_2'] = batch_norm(layers.Conv2DLayer(net['conv5_1'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['conv5_3'] = batch_norm(layers.Conv2DLayer(net['conv5_2'], 512, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
net['pool5'] = layers.Pool2DLayer(net['conv5_3'], pool_size=(2,2), stride=2, mode='max');
net['fc6'] = batch_norm(layers.Conv2DLayer(net['pool5'], 4096, filter_size=(7,7), stride=1, pad='valid', nonlinearity=leaky_rectify));
net['fc7'] = batch_norm(layers.Conv2DLayer(net['fc6'], 4096, filter_size=(1,1), stride=1, pad='valid', nonlinearity=leaky_rectify));
#net['fc6'] = batch_norm(layers.DenseLayer(net['pool5'], 4096, nonlinearity=leaky_rectify));
#net['fc7'] = batch_norm(layers.DenseLayer(net['fc6'], 4096, nonlinearity=leaky_rectify));
net['fc6_deconv'] = batch_norm(layers.Deconv2DLayer(net['fc7'], 512, filter_size=(7,7), stride=1, crop='valid', nonlinearity=leaky_rectify));
net['unpool5'] = layers.InverseLayer(net['fc6_deconv'], net['pool5']);
net['deconv5_1'] = batch_norm(layers.Deconv2DLayer(net['unpool5'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv5_2'] = batch_norm(layers.Deconv2DLayer(net['deconv5_1'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv5_3'] = batch_norm(layers.Deconv2DLayer(net['deconv5_2'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool4'] = layers.InverseLayer(net['deconv5_3'], net['pool4']);
net['deconv4_1'] = batch_norm(layers.Deconv2DLayer(net['unpool4'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv4_2'] = batch_norm(layers.Deconv2DLayer(net['deconv4_1'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv4_3'] = batch_norm(layers.Deconv2DLayer(net['deconv4_2'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool3'] = layers.InverseLayer(net['deconv4_3'], net['pool3']);
net['deconv3_1'] = batch_norm(layers.Deconv2DLayer(net['unpool3'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv3_2'] = batch_norm(layers.Deconv2DLayer(net['deconv3_1'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv3_3'] = batch_norm(layers.Deconv2DLayer(net['deconv3_2'], 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool2'] = layers.InverseLayer(net['deconv3_3'], net['pool2']);
net['deconv2_1'] = batch_norm(layers.Deconv2DLayer(net['unpool2'], 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv2_2'] = batch_norm(layers.Deconv2DLayer(net['deconv2_1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool1'] = layers.InverseLayer(net['deconv2_2'], net['pool1']);
net['deconv1_1'] = batch_norm(layers.Deconv2DLayer(net['unpool1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv1_2'] = batch_norm(layers.Deconv2DLayer(net['deconv1_1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['score'] = layers.Deconv2DLayer(net['deconv1_2'], 1, filter_size=(1,1), stride=1, crop='same', nonlinearity=sigmoid);
net['score_flat'] = ReshapeLayer(net['score'], ([0], -1));
all_params = layers.get_all_params(net['score_flat'], trainable=True);
target_var = T.fmatrix('targets');
return net, all_params, input_var, target_var;
def build_network_from_ae(classn):
input_var = theano.tensor.tensor4('input_var');
net = {};
net['input'] = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
# Encoding part
net['conv1_1'] = batch_norm(layers.Conv2DLayer(net['input'], 64, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv1_2'] = batch_norm(layers.Conv2DLayer(net['conv1_1'], 64, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['pool1'] = layers.Pool2DLayer(net['conv1_2'], pool_size=(2,2), stride=2, mode='max');
net['conv2_1'] = batch_norm(layers.Conv2DLayer(net['pool1'], 128, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv2_2'] = batch_norm(layers.Conv2DLayer(net['conv2_1'], 128, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['pool2'] = layers.Pool2DLayer(net['conv2_2'], pool_size=(2,2), stride=2, mode='max');
net['conv3_1'] = batch_norm(layers.Conv2DLayer(net['pool2'], 256, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv3_2'] = batch_norm(layers.Conv2DLayer(net['conv3_1'], 256, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv3_3'] = batch_norm(layers.Conv2DLayer(net['conv3_2'], 256, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['pool3'] = layers.Pool2DLayer(net['conv3_3'], pool_size=(2,2), stride=2, mode='max');
net['conv4_1'] = batch_norm(layers.Conv2DLayer(net['pool3'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv4_2'] = batch_norm(layers.Conv2DLayer(net['conv4_1'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv4_3'] = batch_norm(layers.Conv2DLayer(net['conv4_2'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['pool4'] = layers.Pool2DLayer(net['conv4_3'], pool_size=(2,2), stride=2, mode='max');
net['conv5_1'] = batch_norm(layers.Conv2DLayer(net['pool4'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv5_2'] = batch_norm(layers.Conv2DLayer(net['conv5_1'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['conv5_3'] = batch_norm(layers.Conv2DLayer(net['conv5_2'], 512, filter_size=(3,3), stride=1, pad=1, nonlinearity=leaky_rectify));
net['pool5'] = layers.Pool2DLayer(net['conv5_3'], pool_size=(2,2), stride=2, mode='max');
net['fc6'] = batch_norm(layers.Conv2DLayer(net['pool5'], 4096, filter_size=(7,7), stride=1, pad='same', nonlinearity=leaky_rectify));
# fc7 is the encoding layer
net['fc7'] = batch_norm(layers.Conv2DLayer(net['fc6'], 4096, filter_size=(1,1), stride=1, pad='same', nonlinearity=leaky_rectify));
# Decoding part
net['fc6_deconv'] = batch_norm(layers.Deconv2DLayer(net['fc7'], 512, filter_size=(7,7), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool5'] = layers.InverseLayer(net['fc6_deconv'], net['pool5']);
net['deconv5_1'] = batch_norm(layers.Deconv2DLayer(net['unpool5'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv5_2'] = batch_norm(layers.Deconv2DLayer(net['deconv5_1'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv5_3'] = batch_norm(layers.Deconv2DLayer(net['deconv5_2'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool4'] = layers.InverseLayer(net['deconv5_3'], net['pool4']);
net['deconv4_1'] = batch_norm(layers.Deconv2DLayer(net['unpool4'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv4_2'] = batch_norm(layers.Deconv2DLayer(net['deconv4_1'], 512, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv4_3'] = batch_norm(layers.Deconv2DLayer(net['deconv4_2'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool3'] = layers.InverseLayer(net['deconv4_3'], net['pool3']);
net['deconv3_1'] = batch_norm(layers.Deconv2DLayer(net['unpool3'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv3_2'] = batch_norm(layers.Deconv2DLayer(net['deconv3_1'], 256, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv3_3'] = batch_norm(layers.Deconv2DLayer(net['deconv3_2'], 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool2'] = layers.InverseLayer(net['deconv3_3'], net['pool2']);
net['deconv2_1'] = batch_norm(layers.Deconv2DLayer(net['unpool2'], 128, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv2_2'] = batch_norm(layers.Deconv2DLayer(net['deconv2_1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['unpool1'] = layers.InverseLayer(net['deconv2_2'], net['pool1']);
net['deconv1_1'] = batch_norm(layers.Deconv2DLayer(net['unpool1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
net['deconv1_2'] = batch_norm(layers.Deconv2DLayer(net['deconv1_1'], 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
# Segmentation layer
net['seg_score'] = layers.Deconv2DLayer(net['deconv1_2'], 1, filter_size=(1,1), stride=1, crop='same', nonlinearity=lasagne.nonlinearities.sigmoid);
net['score_flat'] = ReshapeLayer(net['seg_score'], ([0], -1));
output_var = lasagne.layers.get_output(net['score_flat']);
all_param = lasagne.layers.get_all_params(net['score_flat'], trainable=True);
target_var = T.fmatrix('targets');
#return network, input_var, output_var, all_param;
return net, all_param, input_var, target_var;
def _invert_MaxPoolingLayer(self, layer, feeder):
assert type(layer) in [L.MaxPool2DLayer, L.MaxPool1DLayer]
return L.InverseLayer(feeder, layer)
def _invert_LocalResponseNormalisation2DLayer(self, layer, feeder):
return L.InverseLayer(feeder, layer)