def conv_stage(data,
names,
filters,
kernel=3,
stride=1,
dilate=1,
pad=-1,
groups=1,
no_bias=True,
dropout_rate=0.):
i = 0
bn1 = bn(data, names[i])
i += 1
relu1 = relu(bn1, names[i])
i += 1
if dropout_rate > 0.:
dropout1 = dropout(relu1, names[i], p=dropout_rate)
i += 1
top = conv(dropout1 if dropout_rate > 0. else relu1,
names[i],
filters,
kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
return relu1, top
def conv_stage(data,
name,
filters,
kernel=3,
stride=1,
dilate=1,
pad=-1,
groups=1,
no_bias=False,
has_bn=False,
dropout_rate=0.,
has_relu=True):
top = conv(data,
name,
filters,
kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
if has_bn:
top = bn(top, name='{}_bn'.format(name))
if dropout_rate > 0.0:
top = dropout(top, '{}_do'.format(name), p=dropout_rate)
if has_relu:
top = relu(top, '{}_relu'.format(name))
return top
def res_conv_stage(data,
name,
filters,
kernel=3,
stride=1,
dilate=1,
pad=-1,
groups=1,
no_bias=True,
has_relu=True,
dropout_rate=0.):
top = conv(data,
'res{}'.format(name),
filters,
kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
top = bn(data=top, name='bn{}'.format(name))
if dropout_rate > 0.0:
top = dropout(top, 'res{}_do'.format(name), p=dropout_rate)
if has_relu:
top = relu(top, 'res{}_relu'.format(name))
return top
def res_conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1,
groups=1, no_bias=True, has_relu=True, dropout_rate=0.):
top = conv(data, 'res{}'.format(name),
filters,
kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
top = bn(data=top, name='bn{}'.format(name))
if dropout_rate > 0.0:
top = dropout(top, 'res{}_do'.format(name), p=dropout_rate)
if has_relu:
top = relu(top, 'res{}_relu'.format(name))
return top
def conv_stage(data, name, filters, kernel=3, stride=1, dilate=1, pad=-1,
groups=1, no_bias=False, has_bn=False, dropout_rate=0., has_relu=True):
top = conv(data, name,
filters,
kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
if has_bn:
top = bn(top, name='{}_bn'.format(name))
if dropout_rate > 0.0:
top = dropout(top, '{}_do'.format(name), p=dropout_rate)
if has_relu:
top = relu(top, '{}_relu'.format(name))
return top
def conv_stage(data, names, filters, kernel=3, stride=1, dilate=1, pad=-1,
groups=1, no_bias=True, dropout_rate=0.):
i = 0
bn1 = bn(data, names[i])
i += 1
relu1 = relu(bn1, names[i])
i += 1
if dropout_rate > 0.:
dropout1 = dropout(relu1, names[i], p=dropout_rate)
i += 1
top = conv(dropout1 if dropout_rate > 0. else relu1, names[i],
filters, kernel=kernel,
stride=stride,
dilate=dilate,
pad=pad,
groups=groups,
no_bias=no_bias)
return relu1, top