def dwconvLayer(self,
kernel,
multi,
stride=1,
pad='SAME',
activation=-1,
batch_norm=False,
weight=None):
with tf.variable_scope('dwconv_' + str(self.layernum)):
if isinstance(kernel, list):
kernel = kernel
else:
kernel = [kernel, kernel]
self.result = L.conv2Ddw(self.result,
self.inpsize[3],
kernel,
multi,
'dwconv_' + str(self.layernum),
stride=stride,
pad=pad,
weight=weight)
if batch_norm:
self.result = L.batch_norm(self.result,
'batch_norm_' + str(self.layernum))
self.layernum += 1
self.activate(activation)
self.inpsize = self.result.get_shape().as_list()
return [self.result, list(self.inpsize)]
def dwconvLayer(self,
kernel,
multi,
stride=1,
pad='SAME',
activation=-1,
batch_norm=False,
weight=None,
usebias=True):
with tf.variable_scope('dwconv_' + str(self.layernum)):
if isinstance(kernel, list):
kernel = kernel
else:
kernel = [kernel, kernel]
self.result = L.conv2Ddw(self.result,
self.inpsize[3],
kernel,
multi,
'dwconv_' + str(self.layernum),
stride=stride,
pad=pad,
weight_data=weight,
usebias=usebias)
if batch_norm:
self.result = L.batch_norm(self.result,
'batch_norm_' + str(self.layernum),
training=self.bntraining,
epsilon=self.epsilon)
self.layernum += 1
self.inpsize = self.result.get_shape().as_list()
self.activate(activation)
return self.result
def dwconvLayer(self,
kernel,
multi,
stride=1,
pad='SAME',
activation=-1,
batch_norm=False):
with tf.variable_scope('dwconv_' + str(self.layernum)):
if isinstance(kernel, list):
kernel = kernel
else:
kernel = [kernel, kernel]
self.result = L.conv2Ddw(self.result,
self.inpsize[3],
kernel,
multi,
'dwconv_' + str(self.layernum),
stride=stride,
pad=pad)
if batch_norm:
self.result = L.batch_norm(self.result,
'batch_norm_' + str(self.layernum))
self.layernum += 1
if pad == 'VALID':
self.inpsize[1] -= kernel[0] - stride
self.inpsize[2] -= kernel[1] - stride
self.inpsize[1] = self.inpsize[1] // stride
self.inpsize[2] = self.inpsize[2] // stride
self.inpsize[3] = self.inpsize[3] * multi
self.activate(activation)
return [self.result, list(self.inpsize)]