def __resBlock(self,
x,
channels=64,
kernel_size=(3, 3),
scale=1.0,
layer=0):
nn = ReluLayer(x, name='res%d/ru1' % (layer))
nn = tl.Conv2d(nn,
channels - self.prunedlist[layer],
kernel_size,
act=tf.nn.relu,
name='res%d/c1' % (layer))
self.op.append(nn.outputs)
#self.out_t = nn.outputs
#from c1_nn get a rand input [3,3,input_channels]
#nn.print_layers()
nn = tl.Conv2d(nn,
channels,
kernel_size,
act=None,
name='res%d/c2' % (layer))
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn],
tf.add,
name='res%d/res_add' % (layer))
return n
def _res_block(x, n_features=64, kernel_size=(3, 3), scale=1.0, layer=0):
"""
a resBlock is defined in the paper as (excuse the ugly ASCII graph)
x
|\
| \
| relu
| conv2d
| relu
| conv2d
| /
|/
+ (addition here)
|
result
"""
nn = ReluLayer(x, name='res%d/ru1' % layer)
nn = tl.Conv2d(nn,
n_features,
kernel_size,
act=tf.nn.relu,
name='res%d/c1' % layer)
nn = tl.Conv2d(nn,
n_features,
kernel_size,
act=None,
name='res%d/c2' % layer)
nn = ScaleLayer(nn, scale, name='res%d/scale' % layer)
n = tl.ElementwiseLayer([x, nn], tf.add, name='res%d/res_add' % layer)
return n
def __denseBlock(self,
x,
growth_rate=16,
num_layers=8,
kernel_size=[3, 3],
layer=0):
dense_block_output = x
for i in range(num_layers):
'''
In Paper <Densely Connected Convolutional Networks>
each composite function contains three consecutive operations:
batch normalization(BN), followed by a rectified linear unit (ReLU) and a 3*3 convolution (Conv).
'''
if self.is_bn:
x = tl.BatchNormLayer(x, name='denseblock%d/BN%d' % (layer, i))
x = ReluLayer(x, name='denseblock%d/relu%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='denseblock%d/conv%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='denseblock%d/concat%d' %
(layer, i))
x = dense_block_output
return dense_block_output
def __resBlock(self, x, channels = 64, kernel_size = (3, 3), scale = 1.0,layer = 0):
nn = ReluLayer(x, name='res%d/ru1'%(layer))
nn = tl.Conv2d(nn, channels-self.prunedlist[layer], kernel_size, act=tf.nn.relu, name='res%d/c1'%(layer))
nn = tl.Conv2d(nn, channels, kernel_size, act=None, name='res%d/c2'%(layer))
nn = ScaleLayer(nn,scale, name='res%d/scale'%(layer))
n = tl.ElementwiseLayer([x,nn],tf.add, name='res%d/res_add'%(layer))
#这里调用dictionary(X, W2, Y)函数，注意X就是x,即本层的输入，
#W2仅仅是此block里第一层卷积的权值，Y=n-Y'这里的Y'是上一层剪枝后的输出
#即在残差模块中不断调整，弥补shortcut中的无法影响的部分
#把本层的beta记录起来（append到model的beta数组里），减掉的通道数也要记录起来，影响新模型上一层的filter数量
#所以上面是算完旧的，这里还要再算一次新的
return n
def __resBlock(self,
x,
channels=64,
kernel_size=(3, 3),
scale=1.0,
layer=0):
nn = ReluLayer(x, name='res%d/ru1' % (layer))
nn = tl.Conv2d(nn,
channels - self.prunedlist[layer],
kernel_size,
act=tf.nn.relu,
name='res%d/c1' % (layer))
nn = tl.Conv2d(nn,
channels,
kernel_size,
act=None,
name='res%d/c2' % (layer))
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn],
tf.add,
name='res%d/res_add' % (layer))
return n
def dense_block(self,
x,
growth_rate=16,
n_conv=8,
kernel_size=(3, 3),
layer=0):
dense_block_output = x
for i in range(n_conv):
x = tl.BatchNormLayer(x, name='dense_%d/bn_%d' % (layer, i))
x = ReluLayer(x, name='dense_%d/relu_%d' % (layer, i))
x = tl.Conv2d(x,
growth_rate,
kernel_size,
name='dense_%d/conv_%d' % (layer, i))
# concat the output of layer
dense_block_output = tl.ConcatLayer([dense_block_output, x],
concat_dim=3,
name='dense_%d/concat_%d' %
(layer, i))
x = dense_block_output
return x
def __resBlock2(self, x, channels=64, kernel_size=[3, 3], scale=1, layer=0):
"""
Creates a convolutional residual block
as defined in the paper. More on
this inside model.py
a resBlock is defined in the paper as
(excuse the ugly ASCII graph)
x
|\
| \
| relu
| conv2d
| relu
| conv2d
| /
|/
+ (addition here)
|
result
x: input to pass through the residual block
channels: number of channels to compute
stride: convolution stride
:param x: input tensor
:param channnels: channnels in the block
:param kernel_size: filter kernel size
:param scale: scale for residual skip
:param layer: layer number
"""
nn = ReluLayer(x, name='relu%d' % (layer))
nn = tl.Conv2d(nn, channels, kernel_size, act=tf.nn.relu, name='res%d/c1' % (layer))
nn = tl.Conv2d(nn, channels, kernel_size, act=None, name='res%d/c2' % (layer))
nn = ScaleLayer(nn, scale, name='res%d/scale' % (layer))
n = tl.ElementwiseLayer([x, nn], tf.add, name='res%d/res_add' % (layer))
return n