def test_scale_layer1(self):
dict1 = Scale(name='scale',
src_layers=[Pooling(name='pool')]).to_model_params()
self.assertTrue(self.sample_syntax['scale1'] == dict1)
def test_scale_layer2(self):
if not __dev__:
with self.assertRaises(DLPyError):
Scale(not_a_parameter=1)
def _MBConvBlock(inputs, in_channels, out_channels, ksize, stride, expansion, se_ratio, stage_id, block_id,
noskip=False, activation_fn='relu'):
'''
Inverted Residual Block
Parameters
----------
inputs: input tensor
Speecify input tensor for block.
in_channels: integer
Specifies the number of input tensor's channel.
out_channels: integer
Specifies the number of output tensor's channel
ksize:
Specifies the kernel size of the convolution
stride: integer
Specifies the stride of the convolution
expansion: double
Specifies the expansion factor for the input layer.
se_ratio: double
Specifies the ratio to squeeze the input filters for squeeze-and-excitation block.
stage_id: integer
Specifies stage id for naming layers
block_id:
Specifies block id for naming layers
noskip: bool
Specifies whether the skip connection is used. By default, the skip connection is used.
activation_fn:
Specifies activation function
'''
# mobilenetv2 block is also known as inverted residual block, which consists of three convolutions:
# the first is 1*1 convolution for expansion
# the second is depthwise convolution
# the third is 1*1 convolution without any non-linearity for projection
x = inputs
prefix = 'stage_{}_block_{}'.format(stage_id, block_id)
n_groups = in_channels # for expansion=1, n_groups might be different from pointwise_filters
if expansion > 1:
# For MobileNet V2, expansion>1 when stage>0
n_groups = int(expansion * in_channels) ## update n_groups
x = Conv2d(n_groups, 1, include_bias=False, act='identity',
name=prefix + 'expand')(x)
x = BN(name=prefix + 'expand_BN', act='identity')(x)
# Depthwise convolution
x = GroupConv2d(n_groups, n_groups, ksize, stride=stride, act='identity',
include_bias=False, name=prefix + 'depthwise')(x)
x = BN(name=prefix + 'depthwise_BN', act=activation_fn)(x)
# Squeeze-Excitation
if 0 < se_ratio <= 1:
se_input = x # features to be squeezed
x = GlobalAveragePooling2D(name=prefix + "global_avg_pool")(x)
# Squeeze
channels_se = max(1, int(in_channels * se_ratio))
x = Conv2d(channels_se, 1, include_bias=True, act=activation_fn, name=prefix + 'squeeze')(x)
x = Conv2d(n_groups, 1, include_bias=True, act='sigmoid', name=prefix + 'excitation')(x)
x = Reshape(name=prefix + 'reshape', width=n_groups, height=1, depth=1)(x)
x = Scale(name=prefix + 'scale')([se_input, x]) # x = out*w
# Project
x = Conv2d(out_channels, 1, include_bias=False, act='identity', name=prefix + 'project')(x)
x = BN(name=prefix + 'project_BN', act='identity')(x) # identity activation on narrow tensor
# Prepare output for MBConv block
if in_channels == out_channels and stride == 1 and (not noskip):
# dropout can be added.
return Res(name=prefix + 'add_se_residual')([x, inputs])
else:
return x