def net(self, higher_res_feature, lower_res_feature):
h, w = higher_res_feature.shape[2:]
lower_res_feature = fluid.layers.resize_bilinear(lower_res_feature,
[h, w],
align_mode=0)
with scope('dwconv'):
lower_res_feature = relu(
bn(conv(lower_res_feature, self.out_channels,
1))) #(lower_res_feature)
with scope('conv_lower_res'):
lower_res_feature = bn(
conv(lower_res_feature, self.out_channels, 1, bias_attr=True))
with scope('conv_higher_res'):
higher_res_feature = bn(
conv(higher_res_feature, self.out_channels, 1, bias_attr=True))
out = higher_res_feature + lower_res_feature
return relu(out)
def xception_block(self,
input,
channels,
strides=1,
filters=3,
dilation=1,
skip_conv=True,
has_skip=True,
activation_fn_in_separable_conv=False):
repeat_number = 3
channels = check_data(channels, repeat_number)
filters = check_data(filters, repeat_number)
strides = check_data(strides, repeat_number)
data = input
results = []
for i in range(repeat_number):
with scope('separable_conv' + str(i + 1)):
if not activation_fn_in_separable_conv:
data = relu(data)
data = separate_conv(
data,
channels[i],
strides[i],
filters[i],
dilation=dilation)
else:
data = separate_conv(
data,
channels[i],
strides[i],
filters[i],
dilation=dilation,
act=relu)
results.append(data)
if not has_skip:
return data, results
if skip_conv:
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=None,
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=0.09))
with scope('shortcut'):
skip = bn(
conv(
input,
channels[-1],
1,
strides[-1],
groups=1,
padding=0,
param_attr=param_attr))
else:
skip = input
return data + skip, results
def net(self, x):
x, _ = inverted_blocks(x, self.in_channels, self.t,
self.block_channels[0], self.num_blocks[0], 2,
'inverted_block_1')
x, _ = inverted_blocks(x, self.block_channels[0], self.t,
self.block_channels[1], self.num_blocks[1], 2,
'inverted_block_2')
x, _ = inverted_blocks(x, self.block_channels[1], self.t,
self.block_channels[2], self.num_blocks[2], 1,
'inverted_block_3')
x = psp_module(x, self.block_channels[2] // 4)
with scope('out'):
x = relu(bn(conv(x, self.out_channels, 1)))
return x
def learning_to_downsample(x,
dw_channels1=32,
dw_channels2=48,
out_channels=64):
x = relu(bn(conv(x, dw_channels1, 3, 2)))
with scope('dsconv1'):
x = separate_conv(x,
dw_channels2,
stride=2,
filter=3,
act=fluid.layers.relu)
with scope('dsconv2'):
x = separate_conv(x,
out_channels,
stride=2,
filter=3,
act=fluid.layers.relu)
return x
def aux_layer(x, num_classes):
x = relu(bn(conv(x, 32, 3, padding=1)))
x = dropout2d(x, 0.1, is_train=(cfg.PHASE == 'train'))
with scope('logit'):
x = conv(x, num_classes, 1, bias_attr=True)
return x
