public ResNetV1(string block, int[] layers, int[] channels, int classes = 1000, bool thumbnail = false,
string prefix = "", ParameterDict @params = null) : base(prefix, @params)
{
if (layers.Length != channels.Length - 1)
{
throw new Exception("layers.length should be equal to channels.length - 1");
}
Features = new HybridSequential();
if (thumbnail)
{
Features.Add(ResNet.Conv3x3(channels[0], 1, 0));
}
else
{
Features.Add(new Conv2D(channels[0], (7, 7), (2, 2), (3, 3), use_bias: false));
Features.Add(new BatchNorm());
Features.Add(new Activation(ActivationType.Relu));
Features.Add(new MaxPool2D((3, 3), (2, 2), (1, 1)));
}
for (var i = 0; i < layers.Length; i++)
{
var stride = i == 0 ? 1 : 2;
var num_layer = layers[i];
Features.Add(MakeLayer(block, num_layer, channels[i + 1], stride, i + 1, channels[i]));
}
Features.Add(new GlobalAvgPool2D());
Output = new Dense(classes, in_units: channels.Last());
RegisterChild(Features, "features");
RegisterChild(Output, "output");
}
public BottleneckV2(int channels, int stride, bool downsample = false, int in_channels = 0,
string prefix = "", ParameterDict @params = null) : base(prefix, @params)
{
var channel_one_fourth = Convert.ToInt32(channels / 4);
bn1 = new BatchNorm();
conv1 = new Conv2D(channel_one_fourth, (1, 1), (1, 1), use_bias: false);
bn2 = new BatchNorm();
conv2 = ResNet.Conv3x3(channel_one_fourth, stride, channel_one_fourth);
bn3 = new BatchNorm();
conv3 = new Conv2D(channels, (1, 1), (1, 1), use_bias: false);
RegisterChild(bn1, "bn1");
RegisterChild(conv1, "conv1");
RegisterChild(bn2, "bn2");
RegisterChild(conv2, "conv2");
RegisterChild(bn3, "bn3");
RegisterChild(conv3, "conv3");
if (downsample)
{
ds = new Conv2D(channels, (1, 1), (stride, stride), use_bias: false, in_channels: in_channels);
RegisterChild(ds, "downsample");
}
else
{
ds = null;
}
}
public BottleneckV1(int channels, int stride, bool downsample = false, int in_channels = 0,
string prefix = "", ParameterDict @params = null) : base(prefix, @params)
{
var channel_one_fourth = Convert.ToInt32(channels / 4);
body = new HybridSequential("");
body.Add(new Conv2D(channel_one_fourth, (1, 1), (stride, stride)));
body.Add(new BatchNorm());
body.Add(new Activation(ActivationType.Relu));
body.Add(ResNet.Conv3x3(channel_one_fourth, 1, channel_one_fourth));
body.Add(new BatchNorm());
body.Add(new Activation(ActivationType.Relu));
body.Add(new Conv2D(channels, (1, 1), (1, 1)));
body.Add(new BatchNorm());
if (downsample)
{
ds = new HybridSequential();
ds.Add(new Conv2D(channels, (1, 1), (stride, stride), use_bias: false, in_channels: in_channels));
ds.Add(new BatchNorm());
RegisterChild(ds, "downsample");
}
else
{
ds = null;
}
RegisterChild(body, "body");
}
public BasicBlockV2(int channels, int stride, bool downsample = false, int in_channels = 0,
string prefix = null, ParameterDict @params = null) : base(prefix, @params)
{
bn1 = new BatchNorm();
conv1 = ResNet.Conv3x3(channels, stride, in_channels);
bn2 = new BatchNorm();
conv2 = ResNet.Conv3x3(channels, 1, in_channels);
if (downsample)
{
ds = new Conv2D(channels, (1, 1), (stride, stride), use_bias: false, in_channels: in_channels);
}
else
{
ds = null;
}
}
public BasicBlockV1(int channels, int stride, bool downsample = false, int in_channels = 0,
string prefix = null, ParameterDict @params = null) : base(prefix, @params)
{
body = new HybridSequential("");
body.Add(ResNet.Conv3x3(channels, stride, in_channels));
body.Add(new BatchNorm());
body.Add(new Activation(ActivationType.Relu));
body.Add(ResNet.Conv3x3(channels, 1, in_channels));
body.Add(new BatchNorm());
if (downsample)
{
ds = new HybridSequential();
ds.Add(new Conv2D(channels, (1, 1), (stride, stride), use_bias: false, in_channels: in_channels));
ds.Add(new BatchNorm());
}
else
{
ds = null;
}
}
