def add_layer(incoming, num_channels, dropout):
layer = ScaleLayer(incoming)
layer = BiasLayer(layer)
# Bottleneck layer to reduce number of input channels to 4 times the number of output channels
layer = NonlinearityLayer(layer, nonlinearity=rectify)
layer = Conv2DLayer(layer,
num_filters=4 * num_channels,
filter_size=(1, 1),
stride=(1, 1),
W=HeNormal(gain='relu'), b=None,
flip_filters=False,
nonlinearity=None)
layer = BatchNormLayer(layer, beta=None, gamma=None)
if dropout > 0:
layer = DropoutLayer(layer, p=dropout)
# Convolutional layer (using padding to keep same dimensions)
layer = NonlinearityLayer(layer, nonlinearity=rectify)
layer = Conv2DLayer(layer,
num_filters=num_channels,
filter_size=(3, 3),
stride=(1, 1),
W=HeNormal(gain='relu'), b=None,
pad='same',
flip_filters=False,
nonlinearity=None)
layer = BatchNormLayer(layer, beta=None, gamma=None)
if dropout > 0:
layer = DropoutLayer(layer, p=dropout)
# Concatenate the input filters with the new filters
layer = ConcatLayer([incoming, layer], axis=1)
return layer
def residual_block(l, transition=False, first=False, filters=16):
if transition:
first_stride = (2, 2)
else:
first_stride = (1, 1)
if first:
bn_pre_relu = l
else:
bn_pre_conv = BatchNormLayer(l)
bn_pre_relu = NonlinearityLayer(bn_pre_conv, rectify)
conv_1 = NonlinearityLayer(BatchNormLayer(
ConvLayer(bn_pre_relu,
num_filters=filters,
filter_size=(3, 3),
stride=first_stride,
nonlinearity=None,
pad='same',
W=he_norm)),
nonlinearity=rectify)
#dropout = DropoutLayer(conv_1, p=0.3)
conv_2 = ConvLayer(conv_1,
num_filters=filters,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=None,
pad='same',
W=he_norm)
# add shortcut connections
if transition:
# projection shortcut, as option B in paper
projection = ConvLayer(bn_pre_relu,
num_filters=filters,
filter_size=(1, 1),
stride=(2, 2),
nonlinearity=None,
pad='same',
b=None)
elif conv_2.output_shape == l.output_shape:
projection = l
else:
projection = ConvLayer(bn_pre_relu,
num_filters=filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
b=None)
return ElemwiseSumLayer([conv_2, projection])
def conv_bn_rectify(net, num_filters):
net = ConvLayer(net,
int(num_filters),
3,
W=init.Normal(),
pad=1,
nonlinearity=None)
net = BatchNormLayer(net, epsilon=1e-3)
net = ll.NonlinearityLayer(net)
return net
def bn_relu_conv(network, channels, filter_size, stride, dropout, name_prefix):
network = BatchNormLayer(network, name=name_prefix + '_bn')
network = NonlinearityLayer(network, nonlinearity=rectify,
name=name_prefix + '_relu')
network = Conv2DLayer(network, channels, filter_size, stride=stride, pad='same',
W=lasagne.init.HeNormal(gain='relu'),
b=None, nonlinearity=None,
name=name_prefix + '_conv')
if dropout:
network = DropoutLayer(network, dropout)
return network
def conv_bn_rectify(net, num_filters):
net = layers.Conv2DVarDropOutARD(net,
int(num_filters),
3,
W=init.Normal(),
pad=1,
nonlinearity=nl.linear)
net = BatchNormLayer(net, epsilon=1e-3)
net = ll.NonlinearityLayer(net)
return net
def dense_block(network, transition=False, first=False, filters=16):
if transition:
network = NonlinearityLayer(BatchNormLayer(network),
nonlinearity=rectify)
network = ConvLayer(network,
network.output_shape[1],
1,
pad='same',
W=he_norm,
b=None,
nonlinearity=None)
network = Pool2DLayer(network, 2, mode='average_inc_pad')
network = NonlinearityLayer(BatchNormLayer(network), nonlinearity=rectify)
conv = ConvLayer(network,
filters,
3,
pad='same',
W=he_norm,
b=None,
nonlinearity=None)
return ConcatLayer([network, conv], axis=1)
def model(num_classes=101):
l_in = InputLayer(shape=(None, 3, 224, 224))
l = NonlinearityLayer(BatchNormLayer(
ConvLayer(l_in,
num_filters=64,
filter_size=(7, 7),
stride=(2, 2),
nonlinearity=None,
pad='same',
W=he_norm)),
nonlinearity=rectify)
l = MaxPool2DLayer(l, 3, stride=2, pad='same')
l = residual_block(l, filters=256, first=True)
for _ in range(1, 3):
l = residual_block(l, filters=256)
l = residual_block(l, filters=512, transition=True)
for _ in range(1, 4):
l = residual_block(l, filters=512)
l = residual_block(l, filters=1024, transition=True)
for _ in range(1, 23):
l = residual_block(l, filters=1024)
l = residual_block(l, filters=2048, transition=True)
for _ in range(1, 3):
l = residual_block(l, filters=2048)
bn_post_conv = BatchNormLayer(l)
bn_post_relu = NonlinearityLayer(bn_post_conv, rectify)
avg_pool = GlobalPoolLayer(bn_post_relu)
return DenseLayer(avg_pool,
num_units=num_classes,
W=HeNormal(),
nonlinearity=softmax) #lasagne.init.HeNormal(gain=1)
def transition(network, dropout, name_prefix):
# a transition 1x1 convolution followed by avg-pooling
network = affine_relu_conv(network,
channels=network.output_shape[1],
filter_size=3,
stride=2,
dropout=dropout,
name_prefix=name_prefix)
# network = Pool2DLayer(network, 2, mode='average_inc_pad',
# name=name_prefix + '_pool')
network = BatchNormLayer(network,
name=name_prefix + '_bn',
beta=None,
gamma=None)
return network
def dense_block(network, num_layers, growth_rate, dropout, name_prefix):
# concatenated 3x3 convolutions
for n in range(num_layers):
conv = affine_relu_conv(network,
channels=growth_rate,
filter_size=3,
dropout=dropout,
name_prefix=name_prefix + '_l%02d' % (n + 1))
conv = BatchNormLayer(conv,
name=name_prefix + '_l%02dbn' % (n + 1),
beta=None,
gamma=None)
network = ConcatLayer([network, conv],
axis=1,
name=name_prefix + '_l%02d_join' % (n + 1))
return network
def net_vgglike(k, input_shape, nclass):
input_x, target_y, Winit = T.tensor4("input"), T.vector(
"target", dtype='int32'), init.Normal()
net = ll.InputLayer(input_shape, input_x)
net = conv_bn_rectify(net, 64 * k)
net = ll.DropoutLayer(net, 0.3)
net = conv_bn_rectify(net, 64 * k)
net = MaxPool2DLayer(net, 2, 2)
net = conv_bn_rectify(net, 128 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 128 * k)
net = MaxPool2DLayer(net, 2, 2)
net = conv_bn_rectify(net, 256 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 256 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 256 * k)
net = MaxPool2DLayer(net, 2, 2)
net = conv_bn_rectify(net, 512 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 512 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 512 * k)
net = MaxPool2DLayer(net, 2, 2)
net = conv_bn_rectify(net, 512 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 512 * k)
net = ll.DropoutLayer(net, 0.4)
net = conv_bn_rectify(net, 512 * k)
net = MaxPool2DLayer(net, 2, 2)
net = ll.DenseLayer(net,
int(512 * k),
W=init.Normal(),
nonlinearity=nl.rectify)
net = BatchNormLayer(net, epsilon=1e-3)
net = ll.NonlinearityLayer(net)
net = ll.DropoutLayer(net, 0.5)
net = ll.DenseLayer(net, nclass, W=init.Normal(), nonlinearity=nl.softmax)
return net, input_x, target_y, k
def add_transition(incoming, num_filters, dropout):
layer = ScaleLayer(incoming)
layer = BiasLayer(layer)
layer = NonlinearityLayer(layer, nonlinearity=rectify)
# Reduce the number of filters
layer = Conv2DLayer(layer,
num_filters=num_filters,
filter_size=(1, 1),
stride=(1, 1),
W=HeNormal(gain='relu'), b=None,
flip_filters=False,
nonlinearity=None)
if dropout > 0:
layer = DropoutLayer(layer, p=dropout)
# Pooling layer to reduce the last two dimensions by half
layer = Pool2DLayer(layer,
pool_size=(2, 2),
stride=(2, 2),
mode='average_exc_pad')
layer = BatchNormLayer(layer, beta=None, gamma=None)
return layer
def build_densenet(l_in,
input_var=None,
first_output=64,
growth_rate=32,
num_blocks=4,
dropout=0):
"""
Creates a DenseNet model in Lasagne.
Parameters
----------
input_shape : tuple
The shape of the input layer, as ``(batchsize, channels, rows, cols)``.
Any entry except ``channels`` can be ``None`` to indicate free size.
input_var : Theano expression or None
Symbolic input variable. Will be created automatically if not given.
classes : int
The number of classes of the softmax output.
first_output : int
Number of channels of initial convolution before entering the first
dense block, should be of comparable size to `growth_rate`.
growth_rate : int
Number of feature maps added per layer.
num_blocks : int
Number of dense blocks (defaults to 3, as in the original paper).
dropout : float
The dropout rate. Set to zero (the default) to disable dropout.
batchsize : int or None
The batch size to build the model for, or ``None`` (the default) to
allow any batch size.
inputsize : int, tuple of int or None
Returns
-------
network : Layer instance
Lasagne Layer instance for the output layer.
References
----------
.. [1] Gao Huang et al. (2016):
Densely Connected Convolutional Networks.
https://arxiv.org/abs/1608.06993
"""
nb_layers = [6, 12, 32, 32] # For DenseNet-169
nb_layers = [6, 12, 24, 16] # For DenseNet-121
# initial convolution
network = Conv2DLayer(l_in,
first_output,
filter_size=7,
stride=2,
pad='same',
W=lasagne.init.HeNormal(gain='relu'),
b=None,
nonlinearity=None,
name='pre_conv')
network = BatchNormLayer(network, name='pre_bn', beta=None, gamma=None)
network = ScaleLayer(network, name='pre_scale')
network = BiasLayer(network, name='pre_shift')
network = dnn.MaxPool2DDNNLayer(network, pool_size=3, stride=2)
# note: The authors' implementation does *not* have a dropout after the
# initial convolution. This was missing in the paper, but important.
# if dropout:
# network = DropoutLayer(network, dropout)
# dense blocks with transitions in between
for b in range(num_blocks):
network = dense_block(network,
nb_layers[b],
growth_rate,
dropout,
name_prefix='block%d' % (b + 1))
if b < num_blocks - 1:
network = transition(network,
dropout,
name_prefix='block%d_trs' % (b + 1))
# post processing until prediction
network = ScaleLayer(network, name='post_scale')
network = BiasLayer(network, name='post_shift')
network = NonlinearityLayer(network,
nonlinearity=rectify,
name='post_relu')
return network
def residual_bottleneck_block(l, transition=False, first=False, filters=16):
if transition:
first_stride = (2, 2)
else:
first_stride = (1, 1)
if first:
bn_pre_relu = l
else:
bn_pre_conv = BatchNormLayer(l)
bn_pre_relu = NonlinearityLayer(bn_pre_conv, rectify)
bottleneck_filters = filters / 4
conv_1 = NonlinearityLayer(BatchNormLayer(
ConvLayer(bn_pre_relu,
num_filters=bottleneck_filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
W=he_norm)),
nonlinearity=rectify)
conv_2 = NonlinearityLayer(BatchNormLayer(
ConvLayer(conv_1,
num_filters=bottleneck_filters,
filter_size=(3, 3),
stride=first_stride,
nonlinearity=None,
pad='same',
W=he_norm)),
nonlinearity=rectify)
conv_3 = ConvLayer(conv_2,
num_filters=filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
W=he_norm)
if transition:
projection = ConvLayer(bn_pre_relu,
num_filters=filters,
filter_size=(1, 1),
stride=(2, 2),
nonlinearity=None,
pad='same',
b=None)
elif first:
projection = ConvLayer(bn_pre_relu,
num_filters=filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
b=None)
else:
projection = l
return ElemwiseSumLayer([conv_3, projection])
def ResNet_FullPre_Wide(input_var=None, nout=10, n=3, k=2, dropoutrate=0):
def gelu(x):
return 0.5 * x * (
1 + T.tanh(T.sqrt(2 / np.pi) * (x + 0.044715 * T.pow(x, 3))))
f = gelu
'''
Adapted from https://gist.github.com/FlorianMuellerklein/3d9ba175038a3f2e7de3794fa303f1ee
which was tweaked to be consistent with 'Identity Mappings in Deep Residual Networks', Kaiming He et al. 2016 (https://arxiv.org/abs/1603.05027)
And 'Wide Residual Networks', Sergey Zagoruyko, Nikos Komodakis 2016 (http://arxiv.org/pdf/1605.07146v1.pdf)
'''
n_filters = {0: 16, 1: 16 * k, 2: 32 * k, 3: 64 * k}
# create a residual learning building block with two stacked 3x3 convlayers and dropout
def residual_block(l, first=False, increase_dim=False, filters=16):
if increase_dim:
first_stride = (2, 2)
else:
first_stride = (1, 1)
conv_1 = ConvLayer(l,
num_filters=filters,
filter_size=(3, 3),
stride=first_stride,
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
if dropoutrate > 0: # with dropout
dropout = DropoutLayer(conv_1, p=dropoutrate)
# contains the last weight portion, step 6
conv_2 = ConvLayer(dropout,
num_filters=filters,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
else: # without dropout
conv_2 = ConvLayer(conv_1,
num_filters=filters,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
stack_3 = BatchNormLayer(conv_2)
# add shortcut connections
if increase_dim:
# projection shortcut, as option B in paper
projection = ConvLayer(l,
num_filters=filters,
filter_size=(1, 1),
stride=(2, 2),
nonlinearity=None,
pad='same',
b=None)
block = ElemwiseSumLayer([stack_3, projection])
elif first:
# projection shortcut, as option B in paper
projection = ConvLayer(l,
num_filters=filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
b=None)
block = ElemwiseSumLayer([stack_3, projection])
else:
block = ElemwiseSumLayer([stack_3, l])
return block
# Building the network
l_in = InputLayer(shape=(None, 3, 32, 32), input_var=input_var)
# we're normalizing the input as the net sees fit, and we normalize the output
l = batch_norm(
ConvLayer(l_in,
num_filters=n_filters[0],
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu')))
l = BatchNormLayer(l)
# first stack of residual blocks
l = residual_block(l, first=True, filters=n_filters[1])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[1])
# second stack of residual blocks
l = residual_block(l, increase_dim=True, filters=n_filters[2])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[2])
# third stack of residual blocks
l = residual_block(l, increase_dim=True, filters=n_filters[3])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[3])
bn_post_conv = BatchNormLayer(l)
bn_post_relu = NonlinearityLayer(bn_post_conv, f)
# average pooling
avg_pool = GlobalPoolLayer(bn_post_relu)
# fully connected layer
network = DenseLayer(avg_pool,
num_units=nout,
W=HeNormal(),
nonlinearity=softmax)
return network
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_densenet(input_shape=(None, 3, 32, 32),
input_var=None,
classes=10,
depth=40,
first_output=16,
growth_rate=12,
num_blocks=3,
dropout=0):
"""
Creates a DenseNet model in Lasagne.
Parameters
----------
input_shape : tuple
The shape of the input layer, as ``(batchsize, channels, rows, cols)``.
Any entry except ``channels`` can be ``None`` to indicate free size.
input_var : Theano expression or None
Symbolic input variable. Will be created automatically if not given.
classes : int
The number of classes of the softmax output.
depth : int
Depth of the network. Must be ``num_blocks * n + 1`` for some ``n``.
(Parameterizing by depth rather than n makes it easier to follow the
paper.)
first_output : int
Number of channels of initial convolution before entering the first
dense block, should be of comparable size to `growth_rate`.
growth_rate : int
Number of feature maps added per layer.
num_blocks : int
Number of dense blocks (defaults to 3, as in the original paper).
dropout : float
The dropout rate. Set to zero (the default) to disable dropout.
batchsize : int or None
The batch size to build the model for, or ``None`` (the default) to
allow any batch size.
inputsize : int, tuple of int or None
Returns
-------
network : Layer instance
Lasagne Layer instance for the output layer.
References
----------
.. [1] Gao Huang et al. (2016):
Densely Connected Convolutional Networks.
https://arxiv.org/abs/1608.06993
"""
if (depth - 1) % num_blocks != 0:
raise ValueError("depth must be num_blocks * n + 1 for some n")
# input and initial convolution
network = InputLayer(input_shape, input_var, name='input')
network = Conv2DLayer(network,
first_output,
3,
pad='same',
W=lasagne.init.HeNormal(gain='relu'),
b=None,
nonlinearity=None,
name='pre_conv')
network = BatchNormLayer(network, name='pre_bn', beta=None, gamma=None)
# note: The authors' implementation does *not* have a dropout after the
# initial convolution. This was missing in the paper, but important.
# if dropout:
# network = DropoutLayer(network, dropout)
# dense blocks with transitions in between
n = (depth - 1) // num_blocks
for b in range(num_blocks):
network = dense_block(network,
n - 1,
growth_rate,
dropout,
name_prefix='block%d' % (b + 1))
if b < num_blocks - 1:
network = transition(network,
dropout,
name_prefix='block%d_trs' % (b + 1))
# post processing until prediction
network = ScaleLayer(network, name='post_scale')
network = BiasLayer(network, name='post_shift')
network = NonlinearityLayer(network,
nonlinearity=rectify,
name='post_relu')
network = GlobalPoolLayer(network, name='post_pool')
network = DenseLayer(network,
classes,
nonlinearity=softmax,
W=lasagne.init.HeNormal(gain=1),
name='output')
return network
def model(shape,
n=18,
num_filters=16,
num_classes=10,
width=1,
block='normal'):
if block == "normal":
from residual_block import residual_block
n_filters = {
0: num_filters,
1: num_filters * width,
2: num_filters * 2 * width,
3: num_filters * 4 * width
}
elif block == "dense":
from residual_block import dense_block as residual_block
growth_rate = 12
n_filters = {
0: num_filters,
1: growth_rate,
2: growth_rate,
3: growth_rate
}
elif block == "dense_fast":
from residual_block import dense_fast_block as residual_block
growth_rate = 12
n_filters = {
0: num_filters,
1: growth_rate,
2: growth_rate,
3: growth_rate
}
else:
from residual_block import residual_bottleneck_block as residual_block
n_filters = {
0: num_filters,
1: num_filters * 4,
2: num_filters * 8,
3: num_filters * 16
}
l_in = InputLayer(shape=(None, shape[1], shape[2], shape[3]))
l = NonlinearityLayer(BatchNormLayer(
ConvLayer(l_in,
num_filters=n_filters[0],
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=None,
pad='same',
W=he_norm)),
nonlinearity=rectify)
l = residual_block(l, first=True, filters=n_filters[1])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[1])
l = residual_block(l, transition=True, filters=n_filters[2])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[2])
l = residual_block(l, transition=True, filters=n_filters[3])
for _ in range(1, n):
l = residual_block(l, filters=n_filters[3])
bn_post_conv = BatchNormLayer(l)
bn_post_relu = NonlinearityLayer(bn_post_conv, rectify)
avg_pool = GlobalPoolLayer(bn_post_relu)
return DenseLayer(avg_pool,
num_units=num_classes,
W=HeNormal(),
nonlinearity=softmax) #lasagne.init.HeNormal(gain=1)
def residual_block(l, first=False, increase_dim=False, filters=16):
if increase_dim:
first_stride = (2, 2)
else:
first_stride = (1, 1)
conv_1 = ConvLayer(l,
num_filters=filters,
filter_size=(3, 3),
stride=first_stride,
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
if dropoutrate > 0: # with dropout
dropout = DropoutLayer(conv_1, p=dropoutrate)
# contains the last weight portion, step 6
conv_2 = ConvLayer(dropout,
num_filters=filters,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
else: # without dropout
conv_2 = ConvLayer(conv_1,
num_filters=filters,
filter_size=(3, 3),
stride=(1, 1),
nonlinearity=f,
pad='same',
W=HeNormal(gain='relu'))
stack_3 = BatchNormLayer(conv_2)
# add shortcut connections
if increase_dim:
# projection shortcut, as option B in paper
projection = ConvLayer(l,
num_filters=filters,
filter_size=(1, 1),
stride=(2, 2),
nonlinearity=None,
pad='same',
b=None)
block = ElemwiseSumLayer([stack_3, projection])
elif first:
# projection shortcut, as option B in paper
projection = ConvLayer(l,
num_filters=filters,
filter_size=(1, 1),
stride=(1, 1),
nonlinearity=None,
pad='same',
b=None)
block = ElemwiseSumLayer([stack_3, projection])
else:
block = ElemwiseSumLayer([stack_3, l])
return block
