def __init__(self,
num_classes,
in_channels,
align_corners,
data_format='NCHW'):
super(Decoder, self).__init__()
self.data_format = data_format
self.conv_bn_relu1 = layers.ConvBNReLU(in_channels=in_channels,
out_channels=48,
kernel_size=1,
data_format=data_format)
self.conv_bn_relu2 = layers.SeparableConvBNReLU(
in_channels=304,
out_channels=256,
kernel_size=3,
padding=1,
data_format=data_format)
self.conv_bn_relu3 = layers.SeparableConvBNReLU(
in_channels=256,
out_channels=256,
kernel_size=3,
padding=1,
data_format=data_format)
self.conv = nn.Conv2D(in_channels=256,
out_channels=num_classes,
kernel_size=1,
data_format=data_format)
self.align_corners = align_corners
def __init__(self, dw_channels1=32, dw_channels2=48, out_channels=64):
super(LearningToDownsample, self).__init__()
self.conv_bn_relu = layers.ConvBNReLU(
in_channels=3, out_channels=dw_channels1, kernel_size=3, stride=2)
self.dsconv_bn_relu1 = layers.SeparableConvBNReLU(
in_channels=dw_channels1,
out_channels=dw_channels2,
kernel_size=3,
stride=2,
padding=1)
self.dsconv_bn_relu2 = layers.SeparableConvBNReLU(
in_channels=dw_channels2,
out_channels=out_channels,
kernel_size=3,
stride=2,
padding=1)
def __init__(self, num_classes, in_channels):
super(Decoder, self).__init__()
self.conv_bn_relu1 = layers.ConvBNReLU(in_channels=in_channels,
out_channels=48,
kernel_size=1)
self.conv_bn_relu2 = layers.SeparableConvBNReLU(in_channels=304,
out_channels=256,
kernel_size=3,
padding=1)
self.conv_bn_relu3 = layers.SeparableConvBNReLU(in_channels=256,
out_channels=256,
kernel_size=3,
padding=1)
self.conv = nn.Conv2D(in_channels=256,
out_channels=num_classes,
kernel_size=1)
def __init__(self, input_channels, num_classes):
super().__init__()
self.dsconv1 = layers.SeparableConvBNReLU(in_channels=input_channels,
out_channels=input_channels,
kernel_size=3,
padding=1)
self.dsconv2 = layers.SeparableConvBNReLU(in_channels=input_channels,
out_channels=input_channels,
kernel_size=3,
padding=1)
self.conv = nn.Conv2D(in_channels=input_channels,
out_channels=num_classes,
kernel_size=1)
self.dropout = nn.Dropout(p=0.1) # dropout_prob