def __init__(self, ch_in, ch_out, norm_type='bn'):
super(DeConv, self).__init__()
self.deconv = nn.Sequential()
conv1 = ConvNormLayer(ch_in=ch_in,
ch_out=ch_out,
stride=1,
filter_size=1,
norm_type=norm_type,
initializer=XavierUniform())
conv2 = nn.Conv2DTranspose(
in_channels=ch_out,
out_channels=ch_out,
kernel_size=4,
padding=1,
stride=2,
groups=ch_out,
weight_attr=ParamAttr(initializer=XavierUniform()),
bias_attr=False)
bn = batch_norm(ch_out, norm_type=norm_type, norm_decay=0.)
conv3 = ConvNormLayer(ch_in=ch_out,
ch_out=ch_out,
stride=1,
filter_size=1,
norm_type=norm_type,
initializer=XavierUniform())
self.deconv.add_sublayer('conv1', conv1)
self.deconv.add_sublayer('relu6_1', nn.ReLU6())
self.deconv.add_sublayer('conv2', conv2)
self.deconv.add_sublayer('bn', bn)
self.deconv.add_sublayer('relu6_2', nn.ReLU6())
self.deconv.add_sublayer('conv3', conv3)
self.deconv.add_sublayer('relu6_3', nn.ReLU6())
def __init__(self,
ch_in,
ch_out,
filter_size=3,
stride=1,
groups=1,
padding=0,
norm_type='bn',
norm_decay=0.,
act="leaky",
name=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name + '.conv.weights'),
bias_attr=False)
self.batch_norm = batch_norm(
ch_out, norm_type=norm_type, norm_decay=norm_decay, name=name)
self.act = act
def __init__(self,
ch_in,
ch_out,
filter_size=3,
stride=1,
groups=1,
padding=0,
norm_type='bn',
norm_decay=0.,
act="leaky",
freeze_norm=False,
data_format='NCHW',
name=''):
"""
conv + bn + activation layer
Args:
ch_in (int): input channel
ch_out (int): output channel
filter_size (int): filter size, default 3
stride (int): stride, default 1
groups (int): number of groups of conv layer, default 1
padding (int): padding size, default 0
norm_type (str): batch norm type, default bn
norm_decay (str): decay for weight and bias of batch norm layer, default 0.
act (str): activation function type, default 'leaky', which means leaky_relu
freeze_norm (bool): whether to freeze norm, default False
data_format (str): data format, NCHW or NHWC
"""
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
data_format=data_format,
bias_attr=False)
self.batch_norm = batch_norm(
ch_out,
norm_type=norm_type,
norm_decay=norm_decay,
freeze_norm=freeze_norm,
data_format=data_format)
self.act = act
def __init__(self, ch_in, ch_out, norm_type='bn'):
super(Upsample, self).__init__()
fan_in = ch_in * 3 * 3
stdv = 1. / math.sqrt(fan_in)
self.dcn = DeformableConvV2(
ch_in,
ch_out,
kernel_size=3,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Constant(0),
regularizer=L2Decay(0.),
learning_rate=2.),
lr_scale=2.,
regularizer=L2Decay(0.))
self.bn = batch_norm(ch_out,
norm_type=norm_type,
initializer=Constant(1.))
