def conv(x,
num_filters,
filter_size=4,
stride=2,
padding=1,
name=None,
act=None):
if name is None:
name = get_parent_function_name()
# return fluid.nets.simple_img_conv_pool(
# input=x,
# filter_size=4,
# num_filters=num_filters,
# pool_size=2,
# pool_stride=2,
# param_attr=name + 'w',
# bias_attr=name + 'b',
# use_cudnn=use_cudnn,
# act=act)
return fluid.layers.conv2d(input=x,
filter_size=4,
num_filters=num_filters,
stride=stride,
padding=padding,
param_attr=name + 'w',
bias_attr=name + 'b',
act=act)
def fc(x, num_filters, name=None, act=None):
if name is None:
name = get_parent_function_name()
return fluid.layers.fc(input=x,
size=num_filters,
act=act,
param_attr=name + 'w',
bias_attr=name + 'b')
def bn(x, name=None, act='relu'):
if name is None:
name = get_parent_function_name()
#return fluid.layers.leaky_relu(x)
return fluid.layers.batch_norm(x,
param_attr=name + '1',
bias_attr=name + '2',
moving_mean_name=name + '3',
moving_variance_name=name + '4',
name=name,
act=act)
def conv(x, num_filters, name=None, act=None):
if name is None:
name = get_parent_function_name()
return fluid.nets.simple_img_conv_pool(input=x,
filter_size=5,
num_filters=num_filters,
pool_size=2,
pool_stride=2,
param_attr=name + 'w',
bias_attr=name + 'b',
use_cudnn=use_cudnn,
act=act)
def deconv(x,
num_filters,
name=None,
filter_size=5,
stride=2,
dilation=1,
padding=2,
output_size=None,
act=None):
if name is None:
name = get_parent_function_name()
return fluid.layers.conv2d_transpose(input=x,
param_attr=name + 'w',
bias_attr=name + 'b',
num_filters=num_filters,
output_size=output_size,
filter_size=filter_size,
stride=stride,
dilation=dilation,
padding=padding,
use_cudnn=use_cudnn,
act=act)