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Глеб Брыкин
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‎Implementation/src/torch/nn/AvgPool2d.cs‎

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//***************************************************************************************************
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//* (C) ColorfulSoft corp., 2019 - 2021. All rights reserved.
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//* The code is available under the Apache-2.0 license. Read the License for details.
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//***************************************************************************************************
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namespace System
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{
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namespace AI
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{
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public static partial class torch
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{
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public static partial class nn
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{
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public sealed class AvgPool2d : Module
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{
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public Union<int, Tuple<int, int>> kernel_size;
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public Union<int, Tuple<int, int>> stride;
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public Union<int, Tuple<int, int>> padding;
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public Union<int, Tuple<int, int>> dilation;
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public AvgPool2d(Union<int, Tuple<int, int>> kernel_size, Union<int, Tuple<int, int>> stride = null, Union<int, Tuple<int, int>> padding = null, Union<int, Tuple<int, int>> dilation = null)
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{
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this.kernel_size = kernel_size;
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if(stride == null)
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{
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this.stride = 1;
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}
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else
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{
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this.stride = stride;
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}
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if(padding == null)
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{
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this.padding = 0;
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}
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else
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{
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this.padding = padding;
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}
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if(dilation == null)
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{
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this.dilation = 1;
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}
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else
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{
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this.dilation = dilation;
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}
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}
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public override Tensor forward(Tensor x)
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{
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return functional.avg_pool2d(x, this.kernel_size, this.stride, this.padding, this.dilation);
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}
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}
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}
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}
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}
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}

‎Implementation/src/torch/nn/Conv2d.cs‎

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@@ -124,7 +124,7 @@ public Conv2d(int in_channels, int out_channels, Union<int, Tuple<int, int>> ker
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}
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}
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public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.conv2d(x, this.weight, this.bias, this.stride, this.padding, this.dilation, this.groups);
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}

‎Implementation/src/torch/nn/Dropout.cs‎

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@@ -25,7 +25,7 @@ public Dropout(double p = 0.5)
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this.p = p;
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}
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public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.dropout(x, this.p);
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}

‎Implementation/src/torch/nn/MaxPool2d.cs‎

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@@ -55,7 +55,7 @@ public MaxPool2d(Union<int, Tuple<int, int>> kernel_size, Union<int, Tuple<int,
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}
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}
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public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.max_pool2d(x, this.kernel_size, this.stride, this.padding, this.dilation);
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}

‎Implementation/src/torch/nn/Module.cs‎

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@@ -217,7 +217,7 @@ public void load_state_dict(Dictionary<string, Tensor> state_dict)
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}
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}
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public Tensor forward(Tensor x)
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public virtual Tensor forward(Tensor x)
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{
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throw new NotImplementedException("Forward pass through the abstract module is not implemented.");
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}

‎Implementation/src/torch/nn/ReLU.cs‎

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@@ -28,7 +28,7 @@ public ReLU()
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{
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}
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public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.relu(x);
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}

‎Implementation/src/torch/nn/Sequential.cs‎

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}
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}
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public Module this[int i]
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{
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get
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{
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return this.__modules[i];
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}
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set
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{
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this.__modules[i] = value;
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}
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}
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private string __format_print(string s, int tab)
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{
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s += "Sequential(";

‎Implementation/src/torch/nn/Sigmoid.cs‎

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@@ -28,7 +28,7 @@ public Sigmoid()
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{
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}
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31-
public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.sigmoid(x);
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}

‎Implementation/src/torch/nn/Tanh.cs‎

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@@ -28,7 +28,7 @@ public Tanh()
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{
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}
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public Tensor forward(Tensor x)
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public override Tensor forward(Tensor x)
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{
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return functional.tanh(x);
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}

‎Implementation/src/torch/nn/functional.cs‎

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@@ -20,6 +20,166 @@ public static partial class nn
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public static class functional
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{
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public static Tensor avg_pool2d(Tensor x, Union<int, Tuple<int, int>> kernel_size, Union<int, Tuple<int, int>> stride = null, Union<int, Tuple<int, int>> padding = null, Union<int, Tuple<int, int>> dilation = null)
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{
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if(x.dtype == torch.@bool)
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{
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throw new TorchException("TorchException: nn.functional.avg_pool2d is not implemented for bool tensors.");
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}
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if(x.__shape.Length != 4)
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{
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throw new TorchException("TorchException: nn.functional.avg_pool2d requires 4D input, but " + x.__shape.Length.ToString() + "D given.");
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}
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Tuple<int, int> kernel_size__;
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if(kernel_size == null)
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{
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throw new TorchException("TorchException: kernel_size is not an optional parameter.");
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}
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else
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{
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if((Tuple<int, int>)kernel_size != null)
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{
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kernel_size__ = (Tuple<int, int>)kernel_size;
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}
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else
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{
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kernel_size__ = new Tuple<int, int>((int)kernel_size, (int)kernel_size);
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}
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}
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Tuple<int, int> stride__;
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if(stride == null)
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{
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stride__ = kernel_size__;
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}
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else
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{
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if((Tuple<int, int>)stride != null)
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{
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stride__ = (Tuple<int, int>)stride;
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}
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else
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{
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stride__ = new Tuple<int, int>((int)stride, (int)stride);
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}
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}
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Tuple<int, int> padding__;
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if(padding == null)
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{
68+
padding__ = new Tuple<int, int>(0, 0);
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}
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else
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{
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if((Tuple<int, int>)padding != null)
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{
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padding__ = (Tuple<int, int>)padding;
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}
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else
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{
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padding__ = new Tuple<int, int>((int)padding, (int)padding);
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}
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}
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Tuple<int, int> dilation__;
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if(dilation == null)
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{
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dilation__ = new Tuple<int, int>(1, 1);
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}
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else
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{
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if((Tuple<int, int>)dilation != null)
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{
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dilation__ = (Tuple<int, int>)dilation;
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}
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else
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{
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dilation__ = new Tuple<int, int>((int)dilation, (int)dilation);
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}
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}
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if((kernel_size__.Item1 < 1) || (kernel_size__.Item2 < 1))
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{
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throw new TorchException("TorchException: kernel size should be >= 1.");
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}
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if((kernel_size__.Item1 == 1) || (kernel_size__.Item2 == 1))
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{
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__Warnings.warn("AvgPool2d with kernel size = 1 is useless.");
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}
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if((stride__.Item1 < 1) || (stride__.Item2 < 1))
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{
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throw new TorchException("TorchException: stride should be >= 1.");
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}
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if((padding__.Item1 < 0) || (padding__.Item2 < 0))
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{
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throw new TorchException("TorchException: padding should be >= 0.");
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}
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if((dilation__.Item1 < 1) || (dilation__.Item2 < 1))
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{
115+
throw new TorchException("TorchException: dilation should be >= 1.");
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}
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var y = new Tensor(x.__shape[0], x.__shape[1],
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(x.__shape[2] + 2 * padding__.Item1 - dilation__.Item1 * (kernel_size__.Item1 - 1) - 1) / stride__.Item1 + 1,
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(x.__shape[3] + 2 * padding__.Item2 - dilation__.Item2 * (kernel_size__.Item2 - 1) - 1) / stride__.Item2 + 1,
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dtype: x.dtype, requires_grad: (!torch.autograd.grad_mode.no_grad.prev) && x.requires_grad);
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switch(x.dtype)
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{
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case torch.float16:
124+
{
125+
MKL.AvgPool2d(x.__half, x.__shape, kernel_size__, stride__, padding__, dilation__, y.__half, y.__shape);
126+
if(y.requires_grad)
127+
{
128+
y.__backward_fn = () =>
129+
{
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MKL.dAvgPool2d(x.grad.__half, x.grad.__shape, kernel_size__, stride__, padding__, dilation__, y.grad.__half, y.__shape);
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if(x.__backward_fn != null)
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{
133+
x.__backward_fn();
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}
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};
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}
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break;
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}
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case torch.float32:
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{
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MKL.AvgPool2d(x.__float, x.__shape, kernel_size__, stride__, padding__, dilation__, y.__float, y.__shape);
142+
if(y.requires_grad)
143+
{
144+
y.__backward_fn = () =>
145+
{
146+
MKL.dAvgPool2d(x.grad.__float, x.grad.__shape, kernel_size__, stride__, padding__, dilation__, y.grad.__float, y.__shape);
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if(x.__backward_fn != null)
148+
{
149+
x.__backward_fn();
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}
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};
152+
}
153+
break;
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}
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case torch.float64:
156+
{
157+
MKL.AvgPool2d(x.__double, x.__shape, kernel_size__, stride__, padding__, dilation__, y.__double, y.__shape);
158+
if(y.requires_grad)
159+
{
160+
y.__backward_fn = () =>
161+
{
162+
MKL.dAvgPool2d(x.grad.__double, x.grad.__shape, kernel_size__, stride__, padding__, dilation__, y.grad.__double, y.__shape);
163+
if(x.__backward_fn != null)
164+
{
165+
x.__backward_fn();
166+
}
167+
};
168+
}
169+
break;
170+
}
171+
case torch.int8:
172+
case torch.uint8:
173+
case torch.int16:
174+
case torch.int32:
175+
case torch.int64:
176+
{
177+
throw new TorchException("TorchException: nn.functional.avg_pool2d is not implemented for integer tensors.");
178+
}
179+
}
180+
return y;
181+
}
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public static Tuple<Tensor, Tensor> max_pool2d_with_indices(Tensor x, Union<int, Tuple<int, int>> kernel_size, Union<int, Tuple<int, int>> stride = null, Union<int, Tuple<int, int>> padding = null, Union<int, Tuple<int, int>> dilation = null)
24184
{
25185
if(x.dtype == torch.@bool)

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