def SmallNet(input_var, image_size, num_labels):
net = {}
net['input'] = InputLayer(shape=(None, 1, image_size[0], image_size[1]), input_var=input_var)
net['conv1'] = Conv2DLayer(net['input'], num_filters=32, filter_size=(7, 7), stride=(2, 2))
net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(2, 2))
net['conv2'] = Conv2DLayer(net['pool1'], num_filters=64, filter_size=(5, 5), stride=(2, 2))
net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(2, 2))
net['conv3'] = Conv2DLayer(net['pool2'], num_filters=128, filter_size=(3, 3), pad=(1, 1))
net['conv4'] = Conv2DLayer(net['conv3'], num_filters=128, filter_size=(3, 3), pad=(1, 1))
net['conv5_p'] = Conv2DLayer(net['conv4'], num_filters=64, filter_size=(1, 1))
net['conv6_p'] = Conv2DLayer(net['conv5_p'], num_filters=num_labels, filter_size=(1, 1), nonlinearity=linear)
net['average_pool_p'] = GlobalPoolLayer(net['conv6_p'], pool_function=T.mean)
net['softmax_p'] = NonlinearityLayer(net['average_pool_p'], nonlinearity=softmax)
net['output'] = net['softmax_p']
net['feature_maps'] = net['conv6_p']
net['last_activation'] = net['average_pool_p']
return net
def build_nets(input_var, channels=1, do_batchnorm=True, z_dim=100):
def ns(shape):
ret=list(shape)
ret[0]=[0]
return tuple(ret)
ret = {}
bn = batch_norm if do_batchnorm else lambda x:x
ret['ae_in'] = layer = InputLayer(shape=(None,channels,28,28), input_var=input_var)
ret['ae_conv1'] = layer = bn(Conv2DLayer(layer, num_filters=64, filter_size=5))
ret['ae_pool1'] = layer = MaxPool2DLayer(layer, pool_size=2)
ret['ae_conv2'] = layer = bn(Conv2DLayer(layer, num_filters=128, filter_size=3))
ret['ae_pool2'] = layer = MaxPool2DLayer(layer, pool_size=2)
ret['ae_enc'] = layer = DenseLayer(layer, num_units=z_dim,
nonlinearity=nn.nonlinearities.tanh)
ret['ae_unenc'] = layer = bn(nn.layers.DenseLayer(layer,
num_units = np.product(nn.layers.get_output_shape(ret['ae_pool2'])[1:])))
ret['ae_resh'] = layer = ReshapeLayer(layer,
shape=ns(nn.layers.get_output_shape(ret['ae_pool2'])))
ret['ae_depool2'] = layer = Upscale2DLayer(layer, scale_factor=2)
ret['ae_deconv2'] = layer = bn(Conv2DLayer(layer, num_filters=64, filter_size=3,
pad='full'))
ret['ae_depool1'] = layer = Upscale2DLayer(layer, scale_factor=2)
ret['ae_out'] = Conv2DLayer(layer, num_filters=1, filter_size=5, pad='full',
nonlinearity=nn.nonlinearities.sigmoid)
ret['disc_in'] = layer = InputLayer(shape=(None,channels,28,28), input_var=input_var)
ret['disc_conv1'] = layer = bn(Conv2DLayer(layer, num_filters=64, filter_size=5))
ret['disc_pool1'] = layer = MaxPool2DLayer(layer, pool_size=2)
ret['disc_conv2'] = layer = bn(Conv2DLayer(layer, num_filters=128, filter_size=3))
ret['disc_pool2'] = layer = MaxPool2DLayer(layer, pool_size=2)
ret['disc_hid'] = layer = bn(DenseLayer(layer, num_units=100))
ret['disc_out'] = DenseLayer(layer, num_units=1, nonlinearity=nn.nonlinearities.sigmoid)
return ret
def build_discriminator(input_var=None):
from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
DenseLayer, batch_norm, DropoutLayer)
from lasagne.layers.dnn import Conv2DDNNLayer as Conv2DLayer # override
from lasagne.nonlinearities import LeakyRectify, sigmoid
lrelu = LeakyRectify(0.2)
# input: (None, 3, 64, 64)
layer = InputLayer(shape=(None, 3, 64, 64), input_var=input_var)
# 2 convolutions
layer = batch_norm(Conv2DLayer(layer, 128, 3, stride=1, pad=1, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 128, 3, stride=2, pad=1, nonlinearity=lrelu))
# 2 convolutions
layer = batch_norm(Conv2DLayer(layer, 192, 3, stride=1, pad=1, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 192, 3, stride=1, pad=1, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 192, 3, stride=2, pad=1, nonlinearity=lrelu))
# 3 convolutions
layer = batch_norm(Conv2DLayer(layer, 256, 5, stride=1, pad=2, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 256, 5, stride=1, pad=2, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 192, 5, stride=2, pad=2, nonlinearity=lrelu))
# 4 convolutions
layer = batch_norm(Conv2DLayer(layer, 384, 5, stride=1, pad=2, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 384, 5, stride=1, pad=2, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 384, 5, stride=1, pad=2, nonlinearity=lrelu))
layer = batch_norm(Conv2DLayer(layer, 384, 5, stride=2, pad=2, nonlinearity=lrelu))
# fully-connected layer
layer = batch_norm(DenseLayer(layer, 4096, nonlinearity=lrelu))
# output layer
layer = batch_norm(DropoutLayer(layer, 0.5))
# After FC layerm addth Global Average Pooling layer
#layer = lasagne.layers.GlobalPoolLayer(layer)
layer = DenseLayer(layer, 1, nonlinearity=sigmoid)
print ("Discriminator output:", layer.output_shape)
return layer
def define_net(input_var):
net = {}
net['data'] = InputLayer(shape=(None, 3, IMAGE_SHAPE[0], IMAGE_SHAPE[1]),
input_var=input_var)
net['patch'] = sample_layer.Sample2DLayer(net['data'],
5, (227, 227),
pad=False)
# conv1
net['conv1'] = Conv2DLayer(net['patch'],
num_filters=96,
filter_size=(11, 11),
stride=4,
nonlinearity=lasagne.nonlinearities.rectify)
# pool1
net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(3, 3), stride=2)
# norm1
net['norm1'] = LocalResponseNormalization2DLayer(net['pool1'],
n=5,
alpha=0.0001 / 5.0,
beta=0.75,
k=1)
# before conv2 split the data
net['conv2_data1'] = SliceLayer(net['norm1'], indices=slice(0, 48), axis=1)
net['conv2_data2'] = SliceLayer(net['norm1'],
indices=slice(48, 96),
axis=1)
# now do the convolutions
net['conv2_part1'] = Conv2DLayer(net['conv2_data1'],
num_filters=128,
filter_size=(5, 5),
pad=2)
net['conv2_part2'] = Conv2DLayer(net['conv2_data2'],
num_filters=128,
filter_size=(5, 5),
pad=2)
# now combine
net['conv2'] = concat((net['conv2_part1'], net['conv2_part2']), axis=1)
# pool2
net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(3, 3), stride=2)
# norm2
net['norm2'] = LocalResponseNormalization2DLayer(net['pool2'],
n=5,
alpha=0.0001 / 5.0,
beta=0.75,
k=1)
# conv3
# no group
net['conv3'] = Conv2DLayer(net['norm2'],
num_filters=384,
filter_size=(3, 3),
pad=1)
# conv4
# group = 2
net['conv4_data1'] = SliceLayer(net['conv3'],
indices=slice(0, 192),
axis=1)
net['conv4_data2'] = SliceLayer(net['conv3'],
indices=slice(192, 384),
axis=1)
net['conv4_part1'] = Conv2DLayer(net['conv4_data1'],
num_filters=192,
filter_size=(3, 3),
pad=1)
net['conv4_part2'] = Conv2DLayer(net['conv4_data2'],
num_filters=192,
filter_size=(3, 3),
pad=1)
net['conv4'] = concat((net['conv4_part1'], net['conv4_part2']), axis=1)
# conv5
# group 2
net['conv5_data1'] = SliceLayer(net['conv4'],
indices=slice(0, 192),
axis=1)
net['conv5_data2'] = SliceLayer(net['conv4'],
indices=slice(192, 384),
axis=1)
net['conv5_part1'] = Conv2DLayer(net['conv5_data1'],
num_filters=128,
filter_size=(3, 3),
pad=1)
net['conv5_part2'] = Conv2DLayer(net['conv5_data2'],
num_filters=128,
filter_size=(3, 3),
pad=1)
net['conv5'] = concat((net['conv5_part1'], net['conv5_part2']), axis=1)
# pool 5
net['pool5'] = MaxPool2DLayer(net['conv5'], pool_size=(3, 3), stride=2)
# fc6
net['fc6'] = DenseLayer(net['pool5'],
num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
# fc7
net['fc7'] = DenseLayer(net['fc6'],
num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
# fc8
net['out'] = DenseLayer(net['fc7'],
num_units=1,
nonlinearity=lasagne.nonlinearities.linear)
# print ('Objective layer shapes:')
# print (lasagne.layers.get_output_shape(net['pool5']))
# # fc6
# net['fc6'] = Conv2DLayer(
# net['pool5'], num_filters=4096, filter_size=(6, 6),
# nonlinearity=lasagne.nonlinearities.rectify, flip_filters=False)
# print (lasagne.layers.get_output_shape(net['fc6']))
# # fc7
# net['fc7'] = Conv2DLayer(
# net['fc6'],
# num_filters=4096, filter_size=(1, 1),
# nonlinearity=lasagne.nonlinearities.rectify)
# print (lasagne.layers.get_output_shape(net['fc7']))
# # fc8
# net['out'] = Conv2DLayer(
# net['fc7'],
# num_filters=1, filter_size=(1, 1),
# nonlinearity=lasagne.nonlinearities.linear)
# print (lasagne.layers.get_output_shape(net['out']))
return net
def Encoder(input_var, use_batch_norm=False):
input_var = input_var.dimshuffle(0, 'x', 1, 2)
net = InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(3, 3),
stride=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(3, 3),
stride=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(1, 1),
nonlinearity=lasagne.nonlinearities.elu)
net = Conv2DLayer(net,
num_filters=128,
filter_size=(1, 1),
nonlinearity=lasagne.nonlinearities.elu)
net = FlattenLayer(net, outdim=2)
net = DenseLayer(net,
num_units=128,
nonlinearity=lasagne.nonlinearities.rectify)
net = DenseLayer(net, num_units=40, nonlinearity=None)
return net
def D_mnist_mode_recovery(
num_channels=1,
resolution=32,
fmap_base=64,
fmap_decay=1.0,
fmap_max=256,
mbstat_func='Tstdeps',
mbstat_avg=None, #'all',
label_size=0,
use_wscale=False,
use_gdrop=False,
use_layernorm=False,
use_batchnorm=True,
X=2,
progressive=False,
**kwargs):
R = int(np.log2(resolution))
assert resolution == 2**R and resolution >= 4
cur_lod = theano.shared(np.float32(0.0))
gdrop_strength = theano.shared(np.float32(0.0))
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))) // X, fmap_max)
def GD(layer):
return GDropLayer(layer,
name=layer.name + 'gd',
mode='prop',
strength=gdrop_strength) if use_gdrop else layer
def LN(layer):
return LayerNormLayer(layer, name=layer.name +
'ln') if use_layernorm else layer
def WS(layer):
return WScaleLayer(layer, name=layer.name +
'ws') if use_wscale else layer
def BN(layer):
return lasagne.layers.batch_norm(layer) if use_batchnorm else layer
net = input_layer = InputLayer(name='Dimages',
shape=[None, num_channels, 2**R, 2**R])
for I in xrange(R - 1, 1, -1): # I = R-1, R-2, ..., 2 (i.e. 4,3,2)
net = BN(
LN(
WS(
Conv2DLayer(GD(net),
name='D%da' % I,
num_filters=nf(I - 1),
filter_size=3,
pad=1,
nonlinearity=lrelu,
W=ilrelu))))
net = Downscale2DLayer(net, name='D%ddn' % I, scale_factor=2)
if progressive:
lod = Downscale2DLayer(input_layer,
name='D%dxs' % (I - 1),
scale_factor=2**(R - I))
lod = WS(
NINLayer(lod,
name='D%dx' % (I - 1),
num_units=nf(I - 1),
nonlinearity=lrelu,
W=ilrelu))
net = LODSelectLayer(name='D%dlod' % (I - 1),
incomings=[net, lod],
cur_lod=cur_lod,
first_incoming_lod=R - I - 1)
if mbstat_avg is not None:
net = MinibatchStatConcatLayer(net,
name='Dstat',
func=globals()[mbstat_func],
averaging=mbstat_avg)
net = FlattenLayer(GD(net), name='Dflatten')
output_layers = [
WS(
DenseLayer(net,
name='Dscores',
num_units=1,
nonlinearity=linear,
W=ilinear))
]
if label_size:
output_layers += [
WS(
DenseLayer(net,
name='Dlabels',
num_units=label_size,
nonlinearity=linear,
W=ilinear))
]
return dict(input_layers=[input_layer],
output_layers=output_layers,
cur_lod=cur_lod,
gdrop_strength=gdrop_strength)
def D_paper(
num_channels=1, # Overridden based on dataset.
resolution=32, # Overridden based on dataset.
label_size=0, # Overridden based on dataset.
fmap_base=4096,
fmap_decay=1.0,
fmap_max=256,
mbstat_func='Tstdeps',
mbstat_avg='all',
mbdisc_kernels=None,
use_wscale=True,
use_gdrop=True,
use_layernorm=False,
**kwargs):
R = int(np.log2(resolution))
assert resolution == 2**R and resolution >= 4
cur_lod = theano.shared(np.float32(0.0))
gdrop_strength = theano.shared(np.float32(0.0))
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_max)
def GD(layer):
return GDropLayer(layer,
name=layer.name + 'gd',
mode='prop',
strength=gdrop_strength) if use_gdrop else layer
def LN(layer):
return LayerNormLayer(layer, name=layer.name +
'ln') if use_layernorm else layer
def WS(layer):
return WScaleLayer(layer, name=layer.name +
'ws') if use_wscale else layer
input_layer = InputLayer(name='Dimages',
shape=[None, num_channels, 2**R, 2**R])
net = WS(
NINLayer(input_layer,
name='D%dx' % (R - 1),
num_units=nf(R - 1),
nonlinearity=lrelu,
W=ilrelu))
for I in xrange(R - 1, 1, -1): # I = R-1, R-2, ..., 2
net = LN(
WS(
Conv2DLayer(GD(net),
name='D%db' % I,
num_filters=nf(I),
filter_size=3,
pad=1,
nonlinearity=lrelu,
W=ilrelu)))
net = LN(
WS(
Conv2DLayer(GD(net),
name='D%da' % I,
num_filters=nf(I - 1),
filter_size=3,
pad=1,
nonlinearity=lrelu,
W=ilrelu)))
net = Downscale2DLayer(net, name='D%ddn' % I, scale_factor=2)
lod = Downscale2DLayer(input_layer,
name='D%dxs' % (I - 1),
scale_factor=2**(R - I))
lod = WS(
NINLayer(lod,
name='D%dx' % (I - 1),
num_units=nf(I - 1),
nonlinearity=lrelu,
W=ilrelu))
net = LODSelectLayer(name='D%dlod' % (I - 1),
incomings=[net, lod],
cur_lod=cur_lod,
first_incoming_lod=R - I - 1)
if mbstat_avg is not None:
net = MinibatchStatConcatLayer(net,
name='Dstat',
func=globals()[mbstat_func],
averaging=mbstat_avg)
net = LN(
WS(
Conv2DLayer(GD(net),
name='D1b',
num_filters=nf(1),
filter_size=3,
pad=1,
nonlinearity=lrelu,
W=ilrelu)))
net = LN(
WS(
Conv2DLayer(GD(net),
name='D1a',
num_filters=nf(0),
filter_size=4,
pad=0,
nonlinearity=lrelu,
W=ilrelu)))
if mbdisc_kernels:
import minibatch_discrimination
net = minibatch_discrimination.MinibatchLayer(
net, name='Dmd', num_kernels=mbdisc_kernels)
output_layers = [
WS(
DenseLayer(net,
name='Dscores',
num_units=1,
nonlinearity=linear,
W=ilinear))
]
if label_size:
output_layers += [
WS(
DenseLayer(net,
name='Dlabels',
num_units=label_size,
nonlinearity=linear,
W=ilinear))
]
return dict(input_layers=[input_layer],
output_layers=output_layers,
cur_lod=cur_lod,
gdrop_strength=gdrop_strength)
def conv_block(net,no_f_base,f_size,pad,flip,nonlinearity,depth):
for i in xrange(depth):
net = Conv2DLayer(net,no_f_base,f_size,pad=pad,flip_filters=flip,nonlinearity=nonlinearity)
net = Pool2DLayer(net,pool_size=2)
return net
def input_fused_convnets(self,
fusion_type,
input_var1=None,
input_var2=None,
bottleneck_W=None):
net = OrderedDict()
net['input_rgb'] = InputLayer((None, 4, 128, 128),
input_var=input_var1)
layer = 0
net['input_depth'] = InputLayer((None, 1, 128, 128),
input_var=input_var2)
layer += 1
if fusion_type == self.CONCAT:
net['merge'] = concat([net['input_rgb'], net['input_depth']])
layer += 1
elif fusion_type == self.CONCATCONV:
net['concat'] = concat([net['input_rgb'], net['input_depth']])
layer += 1
net['merge'] = Conv2DLayer(net['concat'],
num_filters=1,
filter_size=(1, 1),
nonlinearity=None)
layer += 1
for i in range(self._net_specs_dict['num_conv_layers']):
# Add convolution layers
net['conv{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
# Add fc-layers
net['fc1'] = DenseLayer(net.values()[layer],
self._net_specs_dict['num_fc_units'][0])
# Add dropout layer
net['dropout1'] = dropout(net['fc1'], p=self._model_hp_dict['p'])
net['fc2'] = DenseLayer(net['dropout1'],
self._net_specs_dict['num_fc_units'][1])
# Add dropout layer
net['dropout2'] = dropout(net['fc2'], p=self._model_hp_dict['p'])
if bottleneck_W is not None:
# Add bottleneck layer
net['bottleneck'] = DenseLayer(net['dropout2'], 30)
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['bottleneck'],
3 * self._num_joints,
W=bottleneck_W[0:30],
nonlinearity=lasagne.nonlinearities.tanh)
else:
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['dropout2'],
3 * self._num_joints,
nonlinearity=lasagne.nonlinearities.tanh)
return net
return image
infilename = sys.argv[1]
outfilename = sys.argv[2]
if len(sys.argv) > 3:
scale = int(sys.argv[3])
else:
scale = 3
net = {}
# once a channel among 'R','G','B',
# if all channels are fed to laplacian conv at the same time,
# the output will be sum of the outputs from all input channels
net['img'] = InputLayer((None, 1, None, None))
net['laplacian'] = Conv2DLayer(net['img'], 1, 3, pad=1)
laplacian = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], dtype=np.float32)
W = np.zeros((1, 1, 3, 3), dtype=np.float32)
W[0, 0] = laplacian
net['laplacian'].W.set_value(W)
orig_img = scipy.ndimage.imread(infilename, mode='RGB')
img = prepare_image(orig_img)
output = []
for i in xrange(3):
img_chan = img[:, [i], :, :]
tensor_input = {net['img']: img_chan}
out_chan = lasagne.layers.get_output(net['laplacian'], tensor_input)
output.append(out_chan.eval()[0, 0])
def build_network(cls):
net = {}
# Input layer: shape is of the form (sample, channel, height, width).
# We are using 3 channel images of size 224 x 224.
# We leave the sample dimension with no size (`None`) so that the
# minibatch size is whatever we need it to be when we use it
net['input'] = InputLayer((None, 3, 224, 224))
# First two convolutional layers: 64 filters, 3x3 convolution, 1 pixel padding
net['conv1_1'] = Conv2DLayer(net['input'],
64,
3,
pad=1,
flip_filters=False)
net['conv1_2'] = Conv2DLayer(net['conv1_1'],
64,
3,
pad=1,
flip_filters=False)
# 2x2 max-pooling; will reduce size from 224x224 to 112x112
net['pool1'] = Pool2DLayer(net['conv1_2'], 2)
# Two convolutional layers, 128 filters
net['conv2_1'] = Conv2DLayer(net['pool1'],
128,
3,
pad=1,
flip_filters=False)
net['conv2_2'] = Conv2DLayer(net['conv2_1'],
128,
3,
pad=1,
flip_filters=False)
# 2x2 max-pooling; will reduce size from 112x112 to 56x56
net['pool2'] = Pool2DLayer(net['conv2_2'], 2)
# Four convolutional layers, 256 filters
net['conv3_1'] = Conv2DLayer(net['pool2'],
256,
3,
pad=1,
flip_filters=False)
net['conv3_2'] = Conv2DLayer(net['conv3_1'],
256,
3,
pad=1,
flip_filters=False)
net['conv3_3'] = Conv2DLayer(net['conv3_2'],
256,
3,
pad=1,
flip_filters=False)
net['conv3_4'] = Conv2DLayer(net['conv3_3'],
256,
3,
pad=1,
flip_filters=False)
# 2x2 max-pooling; will reduce size from 56x56 to 28x28
net['pool3'] = Pool2DLayer(net['conv3_4'], 2)
# Four convolutional layers, 512 filters
net['conv4_1'] = Conv2DLayer(net['pool3'],
512,
3,
pad=1,
flip_filters=False)
net['conv4_2'] = Conv2DLayer(net['conv4_1'],
512,
3,
pad=1,
flip_filters=False)
net['conv4_3'] = Conv2DLayer(net['conv4_2'],
512,
3,
pad=1,
flip_filters=False)
net['conv4_4'] = Conv2DLayer(net['conv4_3'],
512,
3,
pad=1,
flip_filters=False)
# 2x2 max-pooling; will reduce size from 28x28 to 14x14
net['pool4'] = Pool2DLayer(net['conv4_4'], 2)
# Four convolutional layers, 512 filters
net['conv5_1'] = Conv2DLayer(net['pool4'],
512,
3,
pad=1,
flip_filters=False)
net['conv5_2'] = Conv2DLayer(net['conv5_1'],
512,
3,
pad=1,
flip_filters=False)
net['conv5_3'] = Conv2DLayer(net['conv5_2'],
512,
3,
pad=1,
flip_filters=False)
net['conv5_4'] = Conv2DLayer(net['conv5_3'],
512,
3,
pad=1,
flip_filters=False)
# 2x2 max-pooling; will reduce size from 14x14 to 7x7
net['pool5'] = Pool2DLayer(net['conv5_4'], 2)
# Dense layer, 4096 units
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
# 50% dropout (only applied during training, turned off during prediction)
net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
# Dense layer, 4096 units
net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
# 50% dropout (only applied during training, turned off during prediction)
net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
# Final dense layer, 1000 units: 1 for each class
net['fc8'] = DenseLayer(net['fc7_dropout'],
num_units=1000,
nonlinearity=None)
# Softmax non-linearity that will generate probabilities
net['prob'] = NonlinearityLayer(net['fc8'], softmax)
return net, 'prob'
def create_model(incoming, options):
conv_num_filters1 = 100
conv_num_filters2 = 150
conv_num_filters3 = 200
filter_size1 = 5
filter_size2 = 5
filter_size3 = 3
pool_size = 2
encode_size = options['BOTTLENECK']
dense_mid_size = options['DENSE']
pad_in = 'valid'
pad_out = 'full'
scaled_tanh = create_scaled_tanh()
conv2d1 = Conv2DLayer(incoming,
num_filters=conv_num_filters1,
filter_size=filter_size1,
pad=pad_in,
name='conv2d1',
nonlinearity=scaled_tanh)
maxpool2d2 = MaxPool2DLayer(conv2d1,
pool_size=pool_size,
name='maxpool2d2')
conv2d3 = Conv2DLayer(maxpool2d2,
num_filters=conv_num_filters2,
filter_size=filter_size2,
pad=pad_in,
name='conv2d3',
nonlinearity=scaled_tanh)
maxpool2d4 = MaxPool2DLayer(conv2d3,
pool_size=pool_size,
name='maxpool2d4',
pad=(1, 0))
conv2d5 = Conv2DLayer(maxpool2d4,
num_filters=conv_num_filters3,
filter_size=filter_size3,
pad=pad_in,
name='conv2d5',
nonlinearity=scaled_tanh)
reshape6 = ReshapeLayer(conv2d5, shape=([0], -1), name='reshape6') # 3000
reshape6_output = reshape6.output_shape[1]
dense7 = DenseLayer(reshape6,
num_units=dense_mid_size,
name='dense7',
nonlinearity=scaled_tanh)
bottleneck = DenseLayer(dense7,
num_units=encode_size,
name='bottleneck',
nonlinearity=linear)
# print_network(bottleneck)
dense8 = DenseLayer(bottleneck,
num_units=dense_mid_size,
W=bottleneck.W.T,
name='dense8',
nonlinearity=linear)
dense9 = DenseLayer(dense8,
num_units=reshape6_output,
W=dense7.W.T,
nonlinearity=scaled_tanh,
name='dense9')
reshape10 = ReshapeLayer(dense9,
shape=([0], conv_num_filters3, 3, 5),
name='reshape10') # 32 x 4 x 7
deconv2d11 = Deconv2DLayer(reshape10,
conv2d5.input_shape[1],
conv2d5.filter_size,
stride=conv2d5.stride,
W=conv2d5.W,
flip_filters=not conv2d5.flip_filters,
name='deconv2d11',
nonlinearity=scaled_tanh)
upscale2d12 = Upscale2DLayer(deconv2d11,
scale_factor=pool_size,
name='upscale2d12')
deconv2d13 = Deconv2DLayer(upscale2d12,
conv2d3.input_shape[1],
conv2d3.filter_size,
stride=conv2d3.stride,
W=conv2d3.W,
flip_filters=not conv2d3.flip_filters,
name='deconv2d13',
nonlinearity=scaled_tanh)
upscale2d14 = Upscale2DLayer(deconv2d13,
scale_factor=pool_size,
name='upscale2d14')
deconv2d15 = Deconv2DLayer(upscale2d14,
conv2d1.input_shape[1],
conv2d1.filter_size,
stride=conv2d1.stride,
crop=(1, 0),
W=conv2d1.W,
flip_filters=not conv2d1.flip_filters,
name='deconv2d14',
nonlinearity=scaled_tanh)
reshape16 = ReshapeLayer(deconv2d15, ([0], -1), name='reshape16')
return reshape16, bottleneck
def build_densenet(
input_var,
input_shape=(None, 3, 224, 224),
num_filters_init=64,
growth_rate=32,
dropout=0.2,
num_classes=1000,
stages=[6, 12, 24, 16]):
if input_shape[2] % (2 ** len(stages)) != 0:
raise ValueError("input_shape[2] must be a multiple of {}.".format(2 ** len(stages)))
if input_shape[3] % (2 ** len(stages)) != 0:
raise ValueError("input_shape[3] must be a multiple of {}.".format(2 ** len(stages)))
# Input should be (BATCH_SIZE, NUM_CHANNELS, WIDTH, HEIGHT)
# NUM_CHANNELS is usually 3 (R,G,B) and for the ImageNet example the width and height are 224
network = InputLayer(input_shape, input_var)
# Apply 2D convolutions with a 7x7 filter (pad by 3 on each side)
# Because of the 2x2 stride the shape of the last two dimensions will be half the size of the input (112x112)
network = Conv2DLayer(network,
num_filters=num_filters_init,
filter_size=(7, 7),
stride=(2, 2),
pad=(3, 3),
W=HeNormal(gain='relu'), b=None,
flip_filters=False,
nonlinearity=None)
# Batch normalize
network = BatchNormLayer(network, beta=None, gamma=None)
# If dropout is enabled, apply after every convolutional and dense layer
if dropout > 0:
network = DropoutLayer(network, p=dropout)
# Apply ReLU
network = NonlinearityLayer(network, nonlinearity=rectify)
# Keep the maximum value of a 3x3 pool with a 2x2 stride
# This operation again divides the size of the last two dimensions by two (56x56)
network = MaxPool2DLayer(network,
pool_size=(3, 3),
stride=(2, 2),
pad=(1, 1))
# Add dense blocks
for i, num_layers in enumerate(stages):
# Except for the first block, we add a transition layer before the dense block that halves the number of filters, width and height
if i > 0:
network = add_transition(network, math.floor(network.output_shape[1] / 2), dropout)
network = build_block(network, num_layers, growth_rate, dropout)
# Apply global pooling and add a fully connected layer with softmax function
network = ScaleLayer(network)
network = BiasLayer(network)
network = NonlinearityLayer(network, nonlinearity=rectify)
network = GlobalPoolLayer(network)
network = DenseLayer(network,
num_units=num_classes,
W=HeNormal(gain=1),
nonlinearity=softmax)
return network
def build_stereo_cnn(input_var=None):
conv_num_filters1 = 16
conv_num_filters2 = 32
conv_num_filters3 = 64
conv_num_filters4 = 128
filter_size1 = 7
filter_size2 = 5
filter_size3 = 3
filter_size4 = 3
pool_size = 2
scale_factor = 2
pad_in = 'valid'
pad_out = 'full'
# Input layer, as usual:
network = InputLayer(shape=(None,2, X_train.shape[2], X_train.shape[3]),input_var=input_var,name="input_layer")
network = batch_norm(Conv2DLayer(
network, num_filters=conv_num_filters1, filter_size=(filter_size1, filter_size1),pad=pad_in,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="conv1"))
network = MaxPool2DLayer(network, pool_size=(pool_size, pool_size),name="pool1")
network = batch_norm(Conv2DLayer(
network, num_filters=conv_num_filters2, filter_size=(filter_size2, filter_size2),pad=pad_in,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="conv2"))
network = MaxPool2DLayer(network, pool_size=(pool_size, pool_size),name="pool2")
network = batch_norm(Conv2DLayer(
network, num_filters=conv_num_filters3, filter_size=(filter_size3, filter_size3),pad=pad_in,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="conv3"))
network = MaxPool2DLayer(network, pool_size=(pool_size, pool_size),name="pool3")
network = batch_norm(Conv2DLayer(
network, num_filters=conv_num_filters4, filter_size=(filter_size4, filter_size4),pad=pad_in,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="conv4"))
network = batch_norm(Conv2DLayer(
network, num_filters=32, filter_size=(filter_size4, filter_size4),pad=pad_out,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="deconv1"))
network = Upscale2DLayer(network, scale_factor=(pool_size, pool_size),name="upscale1")
network = batch_norm(Conv2DLayer(
network, num_filters=16, filter_size=(filter_size3, filter_size3),pad=pad_out,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="deconv2"))
network = Upscale2DLayer(network, scale_factor=(pool_size, pool_size),name="upscale2")
network = batch_norm(Conv2DLayer(
network, num_filters=8, filter_size=(filter_size2, filter_size2),pad=pad_out,
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform(),name="deconv3"))
network = Upscale2DLayer(network, scale_factor=(pool_size, pool_size),name="upscale3")
network = batch_norm(Conv2DLayer(
network, num_filters=1, filter_size=(filter_size1, filter_size1),pad=pad_out,
nonlinearity=lasagne.nonlinearities.sigmoid,
W=lasagne.init.GlorotUniform(),name="deconv4"))
return network
def __init__(self,
n_in,
n_filters,
filter_sizes,
n_out,
pool_sizes=None,
n_hidden=(512),
ccf=False,
trans_func=rectify,
out_func=softmax,
dense_dropout=0.0,
stats=2,
input_noise=0.0,
batch_norm=False,
conv_dropout=0.0):
super(CNN, self).__init__(n_in, n_hidden, n_out, trans_func)
self.outf = out_func
self.log = ""
# Define model using lasagne framework
dropout = True if not dense_dropout == 0.0 else False
# Overwrite input layer
sequence_length, n_features = n_in
self.l_in = InputLayer(shape=(None, sequence_length, n_features))
l_prev = self.l_in
# Separate into raw values and statistics
sequence_length -= stats
stats_layer = SliceLayer(l_prev,
indices=slice(sequence_length, None),
axis=1)
stats_layer = ReshapeLayer(stats_layer, (-1, stats * n_features))
print('Stats layer shape', stats_layer.output_shape)
l_prev = SliceLayer(l_prev, indices=slice(0, sequence_length), axis=1)
print('Conv input layer shape', l_prev.output_shape)
# Apply input noise
l_prev = GaussianNoiseLayer(l_prev, sigma=input_noise)
if ccf:
self.log += "\nAdding cross-channel feature layer"
l_prev = ReshapeLayer(l_prev, (-1, 1, sequence_length, n_features))
l_prev = Conv2DLayer(l_prev,
num_filters=4 * n_features,
filter_size=(1, n_features),
nonlinearity=None)
n_features *= 4
if batch_norm:
l_prev = batch_norm_layer(l_prev)
l_prev = ReshapeLayer(l_prev, (-1, n_features, sequence_length))
l_prev = DimshuffleLayer(l_prev, (0, 2, 1))
# 2D Convolutional layers
l_prev = ReshapeLayer(l_prev, (-1, 1, sequence_length, n_features))
l_prev = DimshuffleLayer(l_prev, (0, 3, 2, 1))
# Add the convolutional filters
for n_filter, filter_size, pool_size in zip(n_filters, filter_sizes,
pool_sizes):
self.log += "\nAdding 2D conv layer: %d x %d" % (n_filter,
filter_size)
l_prev = Conv2DLayer(l_prev,
num_filters=n_filter,
filter_size=(filter_size, 1),
nonlinearity=self.transf,
pad=filter_size // 2)
if batch_norm:
l_prev = batch_norm_layer(l_prev)
if pool_size > 1:
self.log += "\nAdding max pooling layer: %d" % pool_size
l_prev = Pool2DLayer(l_prev, pool_size=(pool_size, 1))
self.log += "\nAdding dropout layer: %.2f" % conv_dropout
l_prev = TiedDropoutLayer(l_prev, p=conv_dropout)
print("Conv out shape", get_output_shape(l_prev))
# Global pooling layer
l_prev = GlobalPoolLayer(l_prev,
pool_function=T.mean,
name='Global Mean Pool')
print("GlobalPoolLayer out shape", get_output_shape(l_prev))
# Concatenate stats
l_prev = ConcatLayer((l_prev, stats_layer), axis=1)
for n_hid in n_hidden:
self.log += "\nAdding dense layer with %d units" % n_hid
print("Dense input shape", get_output_shape(l_prev))
l_prev = DenseLayer(l_prev, n_hid, init.GlorotNormal(),
init.Normal(1e-3), self.transf)
if batch_norm:
l_prev = batch_norm_layer(l_prev)
if dropout:
self.log += "\nAdding dense dropout with probability: %.2f" % dense_dropout
l_prev = DropoutLayer(l_prev, p=dense_dropout)
if batch_norm:
self.log += "\nUsing batch normalization"
self.model = DenseLayer(l_prev, num_units=n_out, nonlinearity=out_func)
self.model_params = get_all_params(self.model)
self.sym_x = T.tensor3('x')
self.sym_t = T.matrix('t')
def conv_pool_up(net, no_f_base,f_size,conv_depth,pad,nonlinearity,halt=False):
for i in xrange(conv_depth):
net = Conv2DLayer(net,no_f_base,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
if not halt:
net = Deconv2DLayer(net,no_f_base/2,2,2)
return net
def construct_vgg16net(channels=1, no_f_base=64, f_size=3, dropout=False, bs=None, num_classes=3, pad=1,c_nonlinearity=lasagne.nonlinearities.rectify,f_nonlinearity=lasagne.nonlinearities.tanh, input_dim=[512,512], flip=False, fcn=4096):
net = {}
net["input"] = InputLayer(shape=(bs, channels, input_dim[0], input_dim[1]))
net["conv_11"] = Conv2DLayer(net["input"], no_f_base, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_12"] = Conv2DLayer(net["conv_11"], no_f_base, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool1"] = Pool2DLayer(net["conv_12"], 2)
net["conv_21"] = Conv2DLayer(net["pool1"], no_f_base*2, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_22"] = Conv2DLayer(net["conv_21"], no_f_base*2, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool2"] = Pool2DLayer(net["conv_22"], 2)
net["conv_31"] = Conv2DLayer(net["pool2"], no_f_base*4, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_32"] = Conv2DLayer(net["conv_31"], no_f_base*4, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_33"] = Conv2DLayer(net["conv_32"], no_f_base*4, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool3"] = Pool2DLayer(net["conv_33"], 2)
net["conv_41"] = Conv2DLayer(net["pool3"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_42"] = Conv2DLayer(net["conv_41"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_43"] = Conv2DLayer(net["conv_42"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool4"] = Pool2DLayer(net["conv_43"], 2)
net["conv_51"] = Conv2DLayer(net["pool4"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_52"] = Conv2DLayer(net["conv_51"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_53"] = Conv2DLayer(net["conv_52"], no_f_base*8, f_size, pad=pad, flip_filters=flip, nonlinearity=c_nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool5"] = Pool2DLayer(net["conv_53"], 2)
net["full_con1"] = DenseLayer(net["pool5"], num_units=fcn, nonlinearity=f_nonlinearity)
net["drop_full_con1"] = DropoutLayer(net["full_con1"], p=0.5)
net["full_con2"] = DenseLayer(net["drop_full_con1"], num_units=fcn, nonlinearity=f_nonlinearity)
net["drop_full_con2"] = DropoutLayer(net["full_con2"], p=0.5)
net["full_con3"] = DenseLayer(net["drop_full_con2"], num_units=num_classes, nonlinearity=None)
net["out"] = NonlinearityLayer(net["full_con3"], nonlinearity=lasagne.nonlinearities.softmax)
return net
del net
def dense_fused_convnets(self,
fusion_level,
fusion_type,
input_var1=None,
input_var2=None,
bottleneck_W=None,
weights_dir=None):
net = OrderedDict()
net['input_rgb'] = InputLayer((None, 4, 128, 128),
input_var=input_var1)
layer = 0
for i in range(self._net_specs_dict['num_conv_layers']):
# Add convolution layers
net['conv_rgb{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
# Fc-layers
net['fc1_rgb'] = DenseLayer(net.values()[layer],
self._net_specs_dict['num_fc_units'][0])
layer += 1
if fusion_level == 2:
# Add dropout layer
net['dropout1_rgb'] = dropout(net['fc1_rgb'],
p=self._model_hp_dict['p'])
layer += 1
net['fc2_rgb'] = DenseLayer(
net['dropout1_rgb'], self._net_specs_dict['num_fc_units'][1])
layer += 1
net['input_depth'] = InputLayer((None, 1, 128, 128),
input_var=input_var2)
layer += 1
for i in range(self._net_specs_dict['num_conv_layers']):
# Add convolution layers
net['conv_depth{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2:
# Add pooling layers
net['pool_depth{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0:
# Add pooling layers
net['pool_depth{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7:
# Add pooling layers
net['pool_depth{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
# Fc-layers
net['fc1_depth'] = DenseLayer(net.values()[layer],
self._net_specs_dict['num_fc_units'][0])
layer += 1
if fusion_level == 2:
# Add dropout layer
net['dropout1_depth'] = dropout(net['fc1_depth'],
p=self._model_hp_dict['p'])
layer += 1
net['fc2_depth'] = DenseLayer(
net['dropout1_depth'], self._net_specs_dict['num_fc_units'][1])
layer += 1
# Fuse ConvNets by fusion_level and fusion_type
if fusion_type == self.MAX:
net['merge'] =\
ElemwiseMergeLayer([net['fc%i_rgb' % fusion_level],
net['fc%i_depth' % fusion_level]],
T.maximum)
layer += 1
elif fusion_type == self.SUM:
net['merge'] =\
ElemwiseMergeLayer([net['fc%i_rgb' % fusion_level],
net['fc%i_depth' % fusion_level]],
T.add)
layer += 1
elif fusion_type == self.CONCAT:
net['merge'] = concat([
net['fc%i_rgb' % fusion_level],
net['fc%i_depth' % fusion_level]
])
layer += 1
elif fusion_type == self.CONCATCONV:
net['fc%i_rgb_res' % fusion_level] =\
reshape(net['fc%i_rgb' % fusion_level], ([0], 1, [1]))
layer += 1
net['fc%i_depth_res' % fusion_level] =\
reshape(net['fc%i_depth' % fusion_level], ([0], 1, [1]))
layer += 1
net['concat'] = concat([
net['fc%i_rgb_res' % fusion_level],
net['fc%i_depth_res' % fusion_level]
])
layer += 1
net['merge_con'] = Conv1DLayer(net['concat'],
num_filters=1,
filter_size=(1, ),
nonlinearity=None)
layer += 1
net['merge'] = reshape(net['merge_con'], ([0], [2]))
layer += 1
if fusion_level == 1:
# Add dropout layer
net['dropout1'] = dropout(net['merge'], p=self._model_hp_dict['p'])
layer += 1
net['fc2'] = DenseLayer(net['dropout1'],
self._net_specs_dict['num_fc_units'][1])
layer += 1
# Add dropout layer
net['dropout2'] = dropout(net['fc2'], p=self._model_hp_dict['p'])
layer += 1
else:
# Add dropout layer
net['dropout2'] = dropout(net['merge'], p=self._model_hp_dict['p'])
layer += 1
# Add output layer(linear activation because it's regression)
if bottleneck_W is not None:
# Add bottleneck layer
net['bottleneck'] = DenseLayer(net['dropout2'], 30)
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['bottleneck'],
3 * self._num_joints,
W=bottleneck_W[0:30],
nonlinearity=lasagne.nonlinearities.tanh)
else:
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['dropout2'],
3 * self._num_joints,
nonlinearity=lasagne.nonlinearities.tanh)
if weights_dir is not None:
lw = LoadWeights(weights_dir, net)
lw.load_weights_numpy()
return net
def G_paper(
num_channels=1, # Overridden based on dataset.
resolution=32, # Overridden based on dataset.
label_size=0, # Overridden based on dataset.
fmap_base=4096,
fmap_decay=1.0,
fmap_max=256,
latent_size=None,
normalize_latents=True,
use_wscale=True,
use_pixelnorm=True,
use_leakyrelu=True,
use_batchnorm=False,
tanh_at_end=None,
**kwargs):
R = int(np.log2(resolution))
assert resolution == 2**R and resolution >= 4
cur_lod = theano.shared(np.float32(0.0))
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_max)
def PN(layer):
return PixelNormLayer(layer, name=layer.name +
'pn') if use_pixelnorm else layer
def BN(layer):
return lasagne.layers.batch_norm(layer) if use_batchnorm else layer
def WS(layer):
return WScaleLayer(layer, name=layer.name +
'S') if use_wscale else layer
if latent_size is None: latent_size = nf(0)
(act, iact) = (lrelu, ilrelu) if use_leakyrelu else (relu, irelu)
input_layers = [InputLayer(name='Glatents', shape=[None, latent_size])]
net = input_layers[-1]
if normalize_latents:
net = PixelNormLayer(net, name='Glnorm')
if label_size:
input_layers += [InputLayer(name='Glabels', shape=[None, label_size])]
net = ConcatLayer(name='Gina', incomings=[net, input_layers[-1]])
net = ReshapeLayer(name='Ginb', incoming=net, shape=[[0], [1], 1, 1])
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G1a',
num_filters=nf(1),
filter_size=4,
pad='full',
nonlinearity=act,
W=iact))))
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G1b',
num_filters=nf(1),
filter_size=3,
pad=1,
nonlinearity=act,
W=iact))))
lods = [net]
for I in xrange(2, R): # I = 2, 3, ..., R-1
net = Upscale2DLayer(net, name='G%dup' % I, scale_factor=2)
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G%da' % I,
num_filters=nf(I),
filter_size=3,
pad=1,
nonlinearity=act,
W=iact))))
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G%db' % I,
num_filters=nf(I),
filter_size=3,
pad=1,
nonlinearity=act,
W=iact))))
lods += [net]
lods = [
WS(
NINLayer(l,
name='Glod%d' % i,
num_units=num_channels,
nonlinearity=linear,
W=ilinear)) for i, l in enumerate(reversed(lods))
]
output_layer = LODSelectLayer(name='Glod',
incomings=lods,
cur_lod=cur_lod,
first_incoming_lod=0)
if tanh_at_end is not None:
output_layer = NonlinearityLayer(output_layer,
name='Gtanh',
nonlinearity=tanh)
if tanh_at_end != 1.0:
output_layer = non_trainable(
ScaleLayer(output_layer,
name='Gtanhs',
scales=lasagne.init.Constant(tanh_at_end)))
return dict(input_layers=input_layers,
output_layers=[output_layer],
cur_lod=cur_lod)
def simple_convnet(self,
input_channels,
input_var=None,
bottleneck_W=None):
"""
This is a classical convnet. It contains convolution and
fully-connected(fc) layers.
Keyword arguments:
input_var -- theano variable that specifies the type and dimension of
the input(default None)
Return:
net -- dictionary that contains all the network layers
"""
net = OrderedDict()
net['input'] = InputLayer((None, input_channels, 128, 128),
input_var=input_var)
layer = 0
for i in range(self._net_specs_dict['num_conv_layers']):
# Add convolution layers
net['conv{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7:
# Add pooling layers
net['pool{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
# Add fc-layers
net['fc1'] = DenseLayer(net.values()[layer],
self._net_specs_dict['num_fc_units'][0])
# Add dropout layer
net['dropout1'] = dropout(net['fc1'], p=self._model_hp_dict['p'])
net['fc2'] = DenseLayer(net['dropout1'],
self._net_specs_dict['num_fc_units'][1])
# Add dropout layer
net['dropout2'] = dropout(net['fc2'], p=self._model_hp_dict['p'])
if bottleneck_W is not None:
# Add bottleneck layer
net['bottleneck'] = DenseLayer(net['dropout2'], 30)
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['bottleneck'],
3 * self._num_joints,
W=bottleneck_W[0:30],
nonlinearity=lasagne.nonlinearities.tanh)
else:
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['dropout2'],
3 * self._num_joints,
nonlinearity=lasagne.nonlinearities.tanh)
return net
def G_mnist_mode_recovery(num_channels=1,
resolution=32,
fmap_base=64,
fmap_decay=1.0,
fmap_max=256,
latent_size=None,
label_size=10,
normalize_latents=True,
use_wscale=False,
use_pixelnorm=False,
use_batchnorm=True,
tanh_at_end=True,
progressive=False,
**kwargs):
R = int(np.log2(resolution))
assert resolution == 2**R and resolution >= 4
cur_lod = theano.shared(np.float32(0.0))
def nf(stage):
return min(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_max)
def PN(layer):
return PixelNormLayer(layer, name=layer.name +
'pn') if use_pixelnorm else layer
def BN(layer):
return lasagne.layers.batch_norm(layer) if use_batchnorm else layer
def WS(layer):
return WScaleLayer(layer, name=layer.name +
'S') if use_wscale else layer
if latent_size is None: latent_size = nf(0)
input_layers = [InputLayer(name='Glatents', shape=[None, latent_size])]
net = input_layers[-1]
if normalize_latents:
net = PixelNormLayer(net, name='Glnorm')
if label_size:
input_layers += [InputLayer(name='Glabels', shape=[None, label_size])]
net = ConcatLayer(name='Gina', incomings=[net, input_layers[-1]])
net = ReshapeLayer(name='Ginb', incoming=net, shape=[[0], [1], 1, 1])
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G1a',
num_filters=64,
filter_size=4,
pad='full',
nonlinearity=vlrelu,
W=irelu))))
lods = [net]
for I in xrange(2, R): # I = 2, 3, ..., R-1
net = Upscale2DLayer(net, name='G%dup' % I, scale_factor=2)
net = PN(
BN(
WS(
Conv2DLayer(net,
name='G%da' % I,
num_filters=nf(I - 1),
filter_size=3,
pad=1,
nonlinearity=vlrelu,
W=irelu))))
lods += [net]
if progressive:
lods = [
WS(
Conv2DLayer(l,
name='Glod%d' % i,
num_filters=num_channels,
filter_size=3,
pad=1,
nonlinearity=linear,
W=ilinear)) for i, l in enumerate(reversed(lods))
] # Should be this
#lods = [WS(NINLayer(l, name='Glod%d' % i, num_units=num_channels, nonlinearity=linear, W=ilinear)) for i, l in enumerate(reversed(lods))] # .. but this is better
output_layer = LODSelectLayer(name='Glod',
incomings=lods,
cur_lod=cur_lod,
first_incoming_lod=0)
else:
net = WS(
Conv2DLayer(net,
name='toRGB',
num_filters=num_channels,
filter_size=3,
pad=1,
nonlinearity=linear,
W=ilinear)) # Should be this
#net = WS(NINLayer(net, name='toRGB', num_units=num_channels, nonlinearity=linear, W=ilinear)) # .. but this is better
output_layer = net
if tanh_at_end:
output_layer = NonlinearityLayer(output_layer,
name='Gtanh',
nonlinearity=tanh)
return dict(input_layers=input_layers,
output_layers=[output_layer],
cur_lod=cur_lod)
def fused_convnets(self,
fusion_level,
fusion_type,
input_var1=None,
input_var2=None,
bottleneck_W=None,
weights_dir=None):
net = OrderedDict()
net['input_rgb'] = InputLayer((None, 4, 128, 128),
input_var=input_var1)
layer = 0
for i in range(fusion_level):
# Add convolution layers
net['conv_rgb{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2 and\
i != fusion_level - 1:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0 and i != fusion_level - 1:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7 and i != fusion_level - 1:
# Add pooling layers
net['pool_rgb{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
net['input_depth'] = InputLayer((None, 1, 128, 128),
input_var=input_var2)
layer += 1
for i in range(fusion_level):
# Add convolution layers
net['conv_depth{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], ) *
2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2 and\
i != fusion_level - 1:
# Add pooling layers
net['pool_depth{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0 and i != fusion_level - 1:
# Add pooling layers
net['pool_depth{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7 and i != fusion_level - 1:
# Add pooling layers
net['pool_depth{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
# Fuse ConvNets by fusion_level and fusion_type
if fusion_type == self.MAX:
net['merge'] =\
ElemwiseMergeLayer([net['conv_rgb{0:d}'.format(fusion_level)],
net['conv_depth{0:d}'.format(fusion_level)]
], T.maximum)
layer += 1
elif fusion_type == self.SUM:
net['merge'] =\
ElemwiseMergeLayer([net['conv_rgb{0:d}'.format(fusion_level)],
net['conv_depth{0:d}'.format(fusion_level)]
], T.add)
layer += 1
elif fusion_type == self.CONCAT:
net['merge'] = concat([
net['conv_rgb{0:d}'.format(fusion_level)],
net['conv_depth{0:d}'.format(fusion_level)]
])
layer += 1
elif fusion_type == self.CONCATCONV:
net['concat'] = concat([
net['conv_rgb{0:d}'.format(fusion_level)],
net['conv_depth{0:d}'.format(fusion_level)]
])
layer += 1
net['merge'] = Conv2DLayer(
net['concat'],
num_filters=self._net_specs_dict['num_conv_filters'][
fusion_level - 1],
filter_size=(1, 1),
nonlinearity=None)
layer += 1
# Max-pooling to the merged
if fusion_level in [2, 4, 7]:
net['pool_merged'] = MaxPool2DLayer(net['merge'], pool_size=(3, 3))
layer += 1
# Continue the rest of the convolutional part of the network,
# if the fusion took place before the last convolutional layer,
# else just connect the convolutional part with the fully connected
# part
if self._net_specs_dict['num_conv_layers'] > fusion_level:
for i in range(fusion_level,
self._net_specs_dict['num_conv_layers']):
# Add convolution layers
net['conv_merged{0:d}'.format(i + 1)] = Conv2DLayer(
net.values()[layer],
num_filters=self._net_specs_dict['num_conv_filters'][i],
filter_size=(self._net_specs_dict['conv_filter_size'][i], )
* 2,
pad='same')
layer += 1
if self._net_specs_dict['num_conv_layers'] <= 2:
# Add pooling layers
net['pool_merged{0:d}'.format(i + 1)] = MaxPool2DLayer(
net.values()[layer], pool_size=(3, 3))
layer += 1
else:
if i < 4:
if (i + 1) % 2 == 0:
# Add pooling layers
net['pool_merged{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
else:
if (i + 1) == 7:
# Add pooling layers
net['pool_merged{0:d}'.format(i+1)] =\
MaxPool2DLayer(net.values()[layer],
pool_size=(3, 3))
layer += 1
# Fc-layers
net['fc1'] = DenseLayer(net.values()[layer],
self._net_specs_dict['num_fc_units'][0])
# Add dropout layer
net['dropout1'] = dropout(net['fc1'], p=self._model_hp_dict['p'])
net['fc2'] = DenseLayer(net['dropout1'],
self._net_specs_dict['num_fc_units'][1])
# Add dropout layer
net['dropout2'] = dropout(net['fc2'], p=self._model_hp_dict['p'])
if bottleneck_W is not None:
# Add bottleneck layer
net['bottleneck'] = DenseLayer(net['dropout2'], 30)
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['bottleneck'],
3 * self._num_joints,
W=bottleneck_W[0:30],
nonlinearity=lasagne.nonlinearities.tanh)
else:
# Add output layer(linear activation because it's regression)
net['output'] = DenseLayer(
net['dropout2'],
3 * self._num_joints,
nonlinearity=lasagne.nonlinearities.tanh)
if weights_dir is not None:
lw = LoadWeights(weights_dir, net)
lw.load_weights_numpy()
return net
def Decoder(latent_var, use_batch_norm=False):
net = InputLayer(shape=(None, 20), input_var=latent_var)
net = DenseLayer(net,
num_units=64,
nonlinearity=lasagne.nonlinearities.elu)
net = DenseLayer(net,
num_units=64 * 16,
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = ReshapeLayer(net, (-1, 16, 8, 8))
net = Conv2DLayer(net,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = TransposedConv2DLayer(net,
8,
4,
stride=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
net = Conv2DLayer(net,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = TransposedConv2DLayer(net,
8,
4,
stride=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
net = Conv2DLayer(net,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
net = TransposedConv2DLayer(net,
8,
4,
stride=(2, 2),
nonlinearity=lasagne.nonlinearities.elu)
net = Conv2DLayer(net,
num_filters=32,
filter_size=(5, 5),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=32,
filter_size=(3, 3),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=8,
filter_size=(1, 1),
nonlinearity=lasagne.nonlinearities.elu)
if use_batch_norm:
net = BatchNormLayer(net)
net = Conv2DLayer(net,
num_filters=1,
filter_size=(1, 1),
nonlinearity=lasagne.nonlinearities.sigmoid)
return net
def bn_conv(input_layer, **kwargs):
l = Conv2DLayer(input_layer, **kwargs)
l = batch_norm(l, epsilon=0.001)
return l
def reference_model():
net = {}
net['data'] = InputLayer(shape=(None, 3, 227, 227))
# conv1
net['conv1'] = Conv2DLayer(
net['data'],
num_filters=96,
filter_size=(11, 11),
stride = 4,
nonlinearity=lasagne.nonlinearities.rectify)
# pool1
net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(3, 3), stride=2)
# norm1
net['norm1'] = LocalResponseNormalization2DLayer(net['pool1'],
n=5,
alpha=0.0001/5.0,
beta = 0.75,
k=1)
# conv2
# The caffe reference model uses a parameter called group.
# This parameter splits input to the convolutional layer.
# The first half of the filters operate on the first half
# of the input from the previous layer. Similarly, the
# second half operate on the second half of the input.
#
# Lasagne does not have this group parameter, but we can
# do it ourselves.
#
# see https://github.com/BVLC/caffe/issues/778
# also see https://code.google.com/p/cuda-convnet/wiki/LayerParams
# before conv2 split the data
net['conv2_data1'] = SliceLayer(net['norm1'], indices=slice(0, 48), axis=1)
net['conv2_data2'] = SliceLayer(net['norm1'], indices=slice(48,96), axis=1)
# now do the convolutions
net['conv2_part1'] = Conv2DLayer(net['conv2_data1'],
num_filters=128,
filter_size=(5, 5),
pad = 2)
net['conv2_part2'] = Conv2DLayer(net['conv2_data2'],
num_filters=128,
filter_size=(5,5),
pad = 2)
# now combine
net['conv2'] = concat((net['conv2_part1'],net['conv2_part2']),axis=1)
# pool2
net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(3, 3), stride = 2)
# norm2
net['norm2'] = LocalResponseNormalization2DLayer(net['pool2'],
n=5,
alpha=0.0001/5.0,
beta = 0.75,
k=1)
# conv3
# no group
net['conv3'] = Conv2DLayer(net['norm2'],
num_filters=384,
filter_size=(3, 3),
pad = 1)
# conv4
# group = 2
net['conv4_data1'] = SliceLayer(net['conv3'], indices=slice(0, 192), axis=1)
net['conv4_data2'] = SliceLayer(net['conv3'], indices=slice(192,384), axis=1)
net['conv4_part1'] = Conv2DLayer(net['conv4_data1'],
num_filters=192,
filter_size=(3, 3),
pad = 1)
net['conv4_part2'] = Conv2DLayer(net['conv4_data2'],
num_filters=192,
filter_size=(3,3),
pad = 1)
net['conv4'] = concat((net['conv4_part1'],net['conv4_part2']),axis=1)
# conv5
# group 2
net['conv5_data1'] = SliceLayer(net['conv4'], indices=slice(0, 192), axis=1)
net['conv5_data2'] = SliceLayer(net['conv4'], indices=slice(192,384), axis=1)
net['conv5_part1'] = Conv2DLayer(net['conv5_data1'],
num_filters=128,
filter_size=(3, 3),
pad = 1)
net['conv5_part2'] = Conv2DLayer(net['conv5_data2'],
num_filters=128,
filter_size=(3,3),
pad = 1)
net['conv5'] = concat((net['conv5_part1'],net['conv5_part2']),axis=1)
# pool 5
net['pool5'] = MaxPool2DLayer(net['conv5'], pool_size=(3, 3), stride = 2)
# fc6
net['fc6'] = DenseLayer(
net['pool5'],num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
# fc7
net['fc7'] = DenseLayer(
net['fc6'],
num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
# fc8
net['fc8'] = DenseLayer(
net['fc7'],
num_units=1000,
nonlinearity=lasagne.nonlinearities.softmax)
return net
def build_fcn_segmenter(input_var, shape, version=2):
ret = {}
if version == 2:
ret['input'] = la = InputLayer(shape, input_var)
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=8, filter_size=7))
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=16, filter_size=3))
ret['pool%d' % len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=32, filter_size=3))
ret['pool%d' % len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=64, filter_size=3))
ret['pool%d' % len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=64, filter_size=3))
ret['dec%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=64, filter_size=3, pad='full'))
ret['ups%d' % len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=64, filter_size=3, pad='full'))
ret['ups%d' % len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=32, filter_size=7, pad='full'))
ret['ups%d' % len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
ret['dec%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=16, filter_size=3, pad='full'))
ret['conv%d' % len(ret)] = la = bn(
Conv2DLayer(la, num_filters=8, filter_size=7))
ret['output'] = la = Conv2DLayer(
la,
num_filters=1,
filter_size=7,
pad='full',
nonlinearity=nn.nonlinearities.sigmoid)
return ret, nn.layers.get_output(ret['output']), \
nn.layers.get_output(ret['output'], deterministic=True)
def build_model(x=None, layer='fc8', shape=(None, 3, 227, 227), up_scale=4):
net = {'data': InputLayer(shape=shape, input_var=x)}
net['data_s'] = Upscale2DLayer(net['data'], up_scale)
net['conv1'] = Conv2DLayer(net['data_s'],
num_filters=96,
filter_size=(11, 11),
stride=4,
nonlinearity=lasagne.nonlinearities.rectify)
if layer is 'conv1':
return net
# pool1
net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(3, 3), stride=2)
# norm1
net['norm1'] = LocalResponseNormalization2DLayer(net['pool1'],
n=5,
alpha=0.0001 / 5.0,
beta=0.75,
k=1)
# conv2
# before conv2 split the data
net['conv2_data1'] = SliceLayer(net['norm1'], indices=slice(0, 48), axis=1)
net['conv2_data2'] = SliceLayer(net['norm1'],
indices=slice(48, 96),
axis=1)
# now do the convolutions
net['conv2_part1'] = Conv2DLayer(net['conv2_data1'],
num_filters=128,
filter_size=(5, 5),
pad=2)
net['conv2_part2'] = Conv2DLayer(net['conv2_data2'],
num_filters=128,
filter_size=(5, 5),
pad=2)
# now combine
net['conv2'] = concat((net['conv2_part1'], net['conv2_part2']), axis=1)
if layer is 'conv2':
return net
# pool2
net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(3, 3), stride=2)
# norm2
net['norm2'] = LocalResponseNormalization2DLayer(net['pool2'],
n=5,
alpha=0.0001 / 5.0,
beta=0.75,
k=1)
# conv3
# no group
net['conv3'] = Conv2DLayer(net['norm2'],
num_filters=384,
filter_size=(3, 3),
pad=1)
if layer is 'conv3':
return net
# conv4
net['conv4_data1'] = SliceLayer(net['conv3'],
indices=slice(0, 192),
axis=1)
net['conv4_data2'] = SliceLayer(net['conv3'],
indices=slice(192, 384),
axis=1)
net['conv4_part1'] = Conv2DLayer(net['conv4_data1'],
num_filters=192,
filter_size=(3, 3),
pad=1)
net['conv4_part2'] = Conv2DLayer(net['conv4_data2'],
num_filters=192,
filter_size=(3, 3),
pad=1)
net['conv4'] = concat((net['conv4_part1'], net['conv4_part2']), axis=1)
if layer is 'conv4':
return net
# conv5
# group 2
net['conv5_data1'] = SliceLayer(net['conv4'],
indices=slice(0, 192),
axis=1)
net['conv5_data2'] = SliceLayer(net['conv4'],
indices=slice(192, 384),
axis=1)
net['conv5_part1'] = Conv2DLayer(net['conv5_data1'],
num_filters=128,
filter_size=(3, 3),
pad=1)
net['conv5_part2'] = Conv2DLayer(net['conv5_data2'],
num_filters=128,
filter_size=(3, 3),
pad=1)
net['conv5'] = concat((net['conv5_part1'], net['conv5_part2']), axis=1)
if layer is 'conv5':
return net
# pool 5
net['pool5'] = MaxPool2DLayer(net['conv5'], pool_size=(3, 3), stride=2)
# fc6
net['fc6'] = DenseLayer(net['pool5'],
num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
if layer is 'fc6':
return net
# fc7
net['fc7'] = DenseLayer(net['fc6'],
num_units=4096,
nonlinearity=lasagne.nonlinearities.rectify)
if layer is 'fc7':
return net
# fc8
net['fc8'] = DenseLayer(net['fc7'],
num_units=1000,
nonlinearity=lasagne.nonlinearities.softmax)
if layer is 'fc8':
# st()
return net
def construct_unet(channels=1, no_f_base=8, f_size=3, dropout=False, bs=None, class_nums=2, pad="same",nonlinearity=lasagne.nonlinearities.rectify, input_dim=[512,512]):
net={}
net["input"]= InputLayer(shape=(bs, channels, input_dim[0], input_dim[1]))
# Moving downwards the U-shape. Simplified:
net["conv_down11"] = Conv2DLayer(net["input"],no_f_base,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_down12"] = Conv2DLayer(net["conv_down11"],no_f_base,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool1"] = Pool2DLayer(net["conv_down12"],pool_size=2)
net["conv_down21"] = Conv2DLayer(net["pool1"],no_f_base*2,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_down22"] = Conv2DLayer(net["conv_down21"],no_f_base*2,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool2"] = Pool2DLayer(net["conv_down22"],pool_size=2)
net["conv_down31"] = Conv2DLayer(net["pool2"],no_f_base*4,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_down32"] = Conv2DLayer(net["conv_down31"],no_f_base*4,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["pool3"] = Pool2DLayer(net["conv_down32"],pool_size=2)
net["conv_down41"] = Conv2DLayer(net["pool3"],no_f_base*8,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_down42"] = Conv2DLayer(net["conv_down41"],no_f_base*8,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
to_drop1 = net["pool4"] = Pool2DLayer(net["conv_down42"],pool_size=2)
if dropout:
to_drop1 = DropoutLayer(to_drop1, p=0.5)
#vvvv bottom vvvv
net["conv_bottom1"] = Conv2DLayer(to_drop1,no_f_base*16,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_bottom2"] = Conv2DLayer(net["conv_bottom1"],no_f_base*16,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["deconv_bottom1"] = Deconv2DLayer(net["conv_bottom2"], no_f_base*8, 2, 2)
#^^^^ bottom ^^^^
# Moving upwards the U-shape. Simplified:
net["concat1"] = concat([net["deconv_bottom1"], net["conv_down42"]], cropping=(None, None, "center", "center"))
net["conv_up11"]= Conv2DLayer(net["concat1"], no_f_base*8, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_up11"]= Conv2DLayer(net["conv_up11"], no_f_base*8, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["deconv_up1"] = Deconv2DLayer(net["conv_up11"], no_f_base*4, 2, 2)
net["concat2"] = concat([net["deconv_up1"], net["conv_down32"]], cropping=(None, None, "center", "center"))
net["conv_up21"]= Conv2DLayer(net["concat2"], no_f_base*4, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_up22"]= Conv2DLayer(net["conv_up21"], no_f_base*4, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["deconv_up2"] = Deconv2DLayer(net["conv_up22"], no_f_base*2, 2, 2)
net["concat3"] = concat([net["deconv_up2"], net["conv_down22"]], cropping=(None, None, "center", "center"))
net["conv_up31"]= Conv2DLayer(net["concat3"], no_f_base*2, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_up32"]= Conv2DLayer(net["conv_up31"], no_f_base*2, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["deconv_up3"] = Deconv2DLayer(net["conv_up32"], no_f_base, 2, 2)
net["concat4"] = concat([net["deconv_up3"], net["conv_down12"]], cropping=(None, None, "center", "center"))
net["conv_up41"]= Conv2DLayer(net["concat4"], no_f_base, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
net["conv_up42"]= Conv2DLayer(net["conv_up41"], no_f_base, f_size, pad=pad, nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
# Class layer: Work around standard softmax bc. it doesn't work with tensor4/3.
# Hence, we reshape and feed it to an external Nonlinearity layer.
# net["class_ns"] is the output in image-related shape.
net["out"] = Conv2DLayer(net["conv_up42"], class_nums, 1, nonlinearity=None,W=lasagne.init.HeNormal(gain='relu'))
net["layer_shuffle_dim"] = DimshuffleLayer(net["out"], (1, 0, 2, 3))
net["reshape_layer"] = ReshapeLayer(net["layer_shuffle_dim"], (class_nums, -1))
net["layer_shuffle_dim2"] = DimshuffleLayer(net["reshape_layer"], (1, 0))
# Flattened output to be able to feed it to lasagne.objectives.categorical_crossentropy.
net["out_optim"] = NonlinearityLayer(net["layer_shuffle_dim2"], nonlinearity=lasagne.nonlinearities.softmax)
return net
net = None
def build_network_final_layer(cls, input_shape=None):
if input_shape is None:
# Default input shape: 3 channel images of size 224 x 224.
input_shape = (3, 224, 224)
# Input layer: shape is of the form (sample, channel, height, width).
# We leave the sample dimension with no size (`None`) so that the
# minibatch size is whatever we need it to be when we use it
net = InputLayer((None, ) + input_shape, name='input')
# First two convolutional layers: 64 filters, 3x3 convolution, 1 pixel padding
net = Conv2DLayer(net,
64,
3,
pad=1,
flip_filters=False,
name='conv1_1')
net = Conv2DLayer(net,
64,
3,
pad=1,
flip_filters=False,
name='conv1_2')
# 2x2 max-pooling; will reduce size from 224x224 to 112x112
net = Pool2DLayer(net, 2, name='pool1')
# Two convolutional layers, 128 filters
net = Conv2DLayer(net,
128,
3,
pad=1,
flip_filters=False,
name='conv2_1')
net = Conv2DLayer(net,
128,
3,
pad=1,
flip_filters=False,
name='conv2_2')
# 2x2 max-pooling; will reduce size from 112x112 to 56x56
net = Pool2DLayer(net, 2, name='pool2')
# Four convolutional layers, 256 filters
net = Conv2DLayer(net,
256,
3,
pad=1,
flip_filters=False,
name='conv3_1')
net = Conv2DLayer(net,
256,
3,
pad=1,
flip_filters=False,
name='conv3_2')
net = Conv2DLayer(net,
256,
3,
pad=1,
flip_filters=False,
name='conv3_3')
net = Conv2DLayer(net,
256,
3,
pad=1,
flip_filters=False,
name='conv3_4')
# 2x2 max-pooling; will reduce size from 56x56 to 28x28
net = Pool2DLayer(net, 2, name='pool3')
# Four convolutional layers, 512 filters
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv4_1')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv4_2')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv4_3')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv4_4')
# 2x2 max-pooling; will reduce size from 28x28 to 14x14
net = Pool2DLayer(net, 2, name='pool4')
# Four convolutional layers, 512 filters
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv5_1')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv5_2')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv5_3')
net = Conv2DLayer(net,
512,
3,
pad=1,
flip_filters=False,
name='conv5_4')
# 2x2 max-pooling; will reduce size from 14x14 to 7x7
net = Pool2DLayer(net, 2, name='pool5')
# Dense layer, 4096 units
net = DenseLayer(net, num_units=4096, name='fc6')
# 50% dropout (only applied during training, turned off during prediction)
net = DropoutLayer(net, p=0.5, name='fc6_dropout')
# Dense layer, 4096 units
net = DenseLayer(net, num_units=4096, name='fc7')
# 50% dropout (only applied during training, turned off during prediction)
net = DropoutLayer(net, p=0.5, name='fc7_dropout')
# Final dense layer, 1000 units: 1 for each class
net = DenseLayer(net, num_units=1000, nonlinearity=None, name='fc8')
# Softmax non-linearity that will generate probabilities
net = NonlinearityLayer(net, softmax, name='prob')
return net
def conv_pool_down(net, no_f_base,f_size,conv_depth,pad,nonlinearity,dropout):
for i in xrange(conv_depth):
net = Conv2DLayer(net,no_f_base,f_size,pad=pad,nonlinearity=nonlinearity,W=lasagne.init.HeNormal(gain='relu'))
if dropout:
net = DropoutLayer(net,p=dropout)
return net