def Conv(
input_blob,
num_filter=1,
kernel=None,
stride=None,
pad="valid",
data_format="NCHW",
num_group=1,
bn_is_training=True,
name=None,
suffix="",
):
conv = _conv2d_layer(
name="%s%s_conv2d" % (name, suffix),
input=input_blob,
filters=num_filter,
kernel_size=kernel,
strides=stride,
padding=pad,
data_format=data_format,
group_num=num_group,
dilation_rate=1,
activation=None,
use_bias=False,
)
bn = _batch_norm(
conv,
epsilon=0.001,
is_training=bn_is_training,
data_format=data_format,
name="%s%s_batchnorm" % (name, suffix),
)
prelu = _prelu(bn, data_format, name="%s%s_relu" % (name, suffix))
return prelu
def get_symbol(input_blob):
filter_list = [64, 64, 128, 256, 512]
num_stages = 4
units = [3, 13, 30, 3]
num_classes = config.emb_size
fc_type = config.fc_type
bn_is_training = config.bn_is_training
data_format = config.data_format
if data_format.upper() == "NCHW":
input_blob = flow.transpose(input_blob,
name="transpose",
perm=[0, 3, 1, 2])
input_blob = _conv2d_layer(
name="conv0",
input=input_blob,
filters=filter_list[0],
kernel_size=3,
strides=[1, 1],
padding="same",
data_format=data_format,
use_bias=False,
dilation_rate=1,
activation=None,
)
input_blob = _batch_norm(input_blob,
epsilon=2e-5,
is_training=bn_is_training,
data_format=data_format,
name="bn0")
input_blob = _prelu(input_blob, data_format=data_format, name="relu0")
for i in range(num_stages):
input_blob = residual_unit_v3(
input_blob,
filter_list[i + 1],
[2, 2],
False,
bn_is_training=bn_is_training,
data_format=data_format,
name="stage%d_unit%d" % (i + 1, 1),
)
for j in range(units[i] - 1):
input_blob = residual_unit_v3(
input_blob,
filter_list[i + 1],
[1, 1],
True,
bn_is_training=bn_is_training,
data_format=data_format,
name="stage%d_unit%d" % (i + 1, j + 2),
)
fc1 = get_fc1(input_blob, num_classes, fc_type)
return fc1
def residual_unit_v3(
in_data, num_filter, stride, dim_match, bn_is_training, data_format, name
):
suffix = ""
use_se = 0
bn1 = _batch_norm(
in_data,
epsilon=2e-5,
is_training=bn_is_training,
data_format=data_format,
name="%s%s_bn1" % (name, suffix),
)
conv1 = _conv2d_layer(
name="%s%s_conv1" % (name, suffix),
input=bn1,
filters=num_filter,
kernel_size=3,
strides=[1, 1],
padding="same",
data_format=data_format,
use_bias=False,
dilation_rate=1,
activation=None,
)
bn2 = _batch_norm(
conv1,
epsilon=2e-5,
is_training=bn_is_training,
data_format=data_format,
name="%s%s_bn2" % (name, suffix),
)
prelu = _prelu(bn2, data_format=data_format,
name="%s%s_relu1" % (name, suffix))
conv2 = _conv2d_layer(
name="%s%s_conv2" % (name, suffix),
input=prelu,
filters=num_filter,
kernel_size=3,
strides=stride,
padding="same",
data_format=data_format,
use_bias=False,
dilation_rate=1,
activation=None,
)
bn3 = _batch_norm(
conv2,
epsilon=2e-5,
is_training=bn_is_training,
data_format=data_format,
name="%s%s_bn3" % (name, suffix),
)
if use_se:
# se begin
input_blob = _avg_pool(
bn3, pool_size=[7, 7], strides=[1, 1], padding="VALID"
)
input_blob = _conv2d_layer(
name="%s%s_se_conv1" % (name, suffix),
input=input_blob,
filters=num_filter // 16,
kernel_size=1,
strides=[1, 1],
padding="valid",
data_format=data_format,
use_bias=True,
dilation_rate=1,
activation=None,
)
input_blob = _prelu(input_blob, name="%s%s_se_relu1" % (name, suffix))
input_blob = _conv2d_layer(
name="%s%s_se_conv2" % (name, suffix),
input=input_blob,
filters=num_filter,
kernel_size=1,
strides=[1, 1],
padding="valid",
data_format=data_format,
use_bias=True,
dilation_rate=1,
activation=None,
)
input_blob = flow.math.sigmoid(input=input_blob)
bn3 = flow.math.multiply(x=input_blob, y=bn3)
# se end
if dim_match:
input_blob = in_data
else:
input_blob = _conv2d_layer(
name="%s%s_conv1sc" % (name, suffix),
input=in_data,
filters=num_filter,
kernel_size=1,
strides=stride,
padding="valid",
data_format=data_format,
use_bias=False,
dilation_rate=1,
activation=None,
)
input_blob = _batch_norm(
input_blob,
epsilon=2e-5,
is_training=bn_is_training,
data_format=data_format,
name="%s%s_sc" % (name, suffix),
)
identity = flow.math.add(x=bn3, y=input_blob)
return identity