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 __resBlock(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
|\
| \
| 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 = tl.Conv2d(x, 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
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, layer=0):
nn = tl.Conv2d(x,
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
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
