def forward(self, inputs, drop_connect_rate=None):
"""
:param inputs: input tensor
:param drop_connect_rate: drop connect rate (float, between 0 and 1)
:return: output of block
"""
# Expansion and Depthwise Convolution
x = inputs
if self._block_args.expand_ratio != 1:
x = self._swish(self._bn0(self._expand_conv(inputs)))
x = self._swish(self._bn1(self._depthwise_conv(x)))
# Squeeze and Excitation
if self.has_se:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self._se_expand(
self._swish(self._se_reduce(x_squeezed)))
x = torch.sigmoid(x_squeezed) * x
x = self._bn2(self._project_conv(x))
# Skip connection and drop connect
input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters
if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:
if drop_connect_rate:
x = drop_connect(x,
p=drop_connect_rate,
training=self.training)
x = x + inputs # skip connection
return x
def forward(self, x):
residual = x
# Point-wise expansion
x = self.conv_pw(x)
x = self.bn1(x)
x = self.act_fn(x, inplace=True)
# Depth-wise convolution
x = self.conv_dw(x)
x = self.bn2(x)
x = self.act_fn(x, inplace=True)
# Squeeze-and-excitation
x = self.se(x)
# Point-wise linear projection
x = self.conv_pwl(x)
x = self.bn3(x)
if self.has_residual:
if self.drop_connect_rate > 0.:
x = drop_connect(x, self.training, self.drop_connect_rate)
x += residual
return x
def forward(self, x):
residual = x
x = self.conv_dw(x)
x = self.bn1(x)
x = self.act_fn(x, inplace=True)
x = self.se(x)
x = self.conv_pw(x)
x = self.bn2(x)
if self.has_residual:
if self.drop_connect_rate > 0.:
x = drop_connect(x, self.training, self.drop_connect_rate)
x += residual
return x
def call(self, inputs, training=True, drop_connect_rate=None):
"""Implementation of call().
Args:
inputs: the inputs tensor.
training: boolean, whether the model is constructed for training.
drop_connect_rate: float, between 0 to 1, drop connect rate.
Returns:
A output tensor.
"""
tf.logging.info('Block input: %s shape: %s' %
(inputs.name, inputs.shape))
if self._block_args.expand_ratio != 1:
x = self._relu_fn(
self._bn0(self._expand_conv(inputs), training=training))
else:
x = inputs
tf.logging.info('Expand: %s shape: %s' % (x.name, x.shape))
x = self._relu_fn(self._bn1(self._depthwise_conv(x),
training=training))
tf.logging.info('DWConv: %s shape: %s' % (x.name, x.shape))
if self._has_se:
with tf.variable_scope('se'):
x = self._call_se(x)
self.endpoints = {'expansion_output': x}
x = self._bn2(self._project_conv(x), training=training)
if self._block_args.id_skip:
if all(
s == 1 for s in self._block_args.strides
) and self._block_args.input_filters == self._block_args.output_filters:
# only apply drop_connect if skip presents.
if drop_connect_rate:
x = utils.drop_connect(x, training, drop_connect_rate)
x = tf.add(x, inputs)
tf.logging.info('Project: %s shape: %s' % (x.name, x.shape))
return x