Implement avg pool test

include/layers/avg_pooling.cuh

@@ -19,6 +19,15 @@ class AvgPooling2D : public SequentialLayer {
 
     float* forward(const float* d_input);
 
+    /**
+     * @brief Get the output width (/ height) of the layer
+     *
+     * @return int
+     */
+    int getOutputSize() {
+        return outputSize;
+    }
+
   private:
     int inputSize;
     int nChannels;
@@ -26,7 +35,6 @@ class AvgPooling2D : public SequentialLayer {
     int stride;
 
     int outputSize;
-    int gridSize;
 
     float* d_output;
 

include/layers/max_pooling.cuh

@@ -37,7 +37,6 @@ class MaxPooling2D : public SequentialLayer {
     int stride;
 
     int outputSize;
-    int gridSize;
 
     float* d_output;
 

src/kernels/pooling.cu

@@ -49,7 +49,7 @@ __global__ void Kernels::avg_pooling(
     int i = blockDim.y * blockIdx.y + threadIdx.y;
     int c = blockDim.z * blockIdx.z + threadIdx.z;
 
-    if (i >= inputSize || j >= inputSize || c >= nChannels) {
+    if (i >= outputSize || j >= outputSize || c >= outputSize) {
         return;
     }
 

src/layers/avg_pooling.cu

@@ -24,9 +24,6 @@ AvgPooling2D::AvgPooling2D(
     CUDA_CHECK(cudaMalloc(
         (void**)&d_output, sizeof(float) * outputSize * outputSize * nChannels
     ));
-
-    gridSize =
-        (outputSize * outputSize * nChannels + BLOCK_SIZE - 1) / BLOCK_SIZE;
 }
 
 AvgPooling2D::~AvgPooling2D() {

src/layers/max_pooling.cu

@@ -25,9 +25,6 @@ MaxPooling2D::MaxPooling2D(
     CUDA_CHECK(cudaMalloc(
         (void**)&d_output, sizeof(float) * outputSize * outputSize * nChannels
     ));
-
-    gridSize = (outputSize * outputSize * nChannels + BLOCK_SIZE - 1) / BLOCK_SIZE;
-
 }
 
 

test/layers/test_avg_pooling.cu

@@ -0,0 +1,70 @@
+#include <cuda_runtime.h>
+#include <gtest/gtest.h>
+
+#include <vector>
+
+#include "avg_pooling.cuh"
+
+TEST(AvgPoolingLayerTest, AvgPoolForwardTest) {
+    int inputSize   = 4;
+    int nChannels   = 2;
+    int poolingSize = 2;
+    int stride      = 2;
+
+    cudaError_t cudaStatus;
+
+    std::vector<float> input = {
+        // clang-format off
+        // Channel 0
+        0.573f, 0.619f, 0.732f, 0.055f,
+        0.243f, 0.316f, 0.573f, 0.619f,
+        0.712f, 0.055f, 0.243f, 0.316f,
+        0.573f, 0.619f, 0.742f, 0.055f,
+        // Channel 1
+        0.473f, 0.919f, 0.107f, 0.073f,
+        0.073f, 0.362f, 0.973f, 0.059f,
+        0.473f, 0.455f, 0.283f, 0.416f,
+        0.532f, 0.819f, 0.732f, 0.850f
+        // clang-format on
+    };
+
+    CUDANet::Layers::AvgPooling2D avgPoolingLayer(
+        inputSize, nChannels, poolingSize, stride,
+        CUDANet::Layers::ActivationType::NONE
+    );
+
+    float *d_input;
+
+    cudaStatus = cudaMalloc(
+        (void **)&d_input, sizeof(float) * inputSize * inputSize * nChannels
+    );
+    EXPECT_EQ(cudaStatus, cudaSuccess);
+
+    cudaStatus = cudaMemcpy(
+        d_input, input.data(),
+        sizeof(float) * inputSize * inputSize * nChannels,
+        cudaMemcpyHostToDevice
+    );
+    EXPECT_EQ(cudaStatus, cudaSuccess);
+
+    float *d_output = avgPoolingLayer.forward(d_input);
+
+    int outputSize = avgPoolingLayer.getOutputSize();
+
+    std::vector<float> output(outputSize * outputSize * nChannels);
+    cudaStatus = cudaMemcpy(
+        output.data(), d_output,
+        sizeof(float) * outputSize * outputSize * nChannels,
+        cudaMemcpyDeviceToHost
+    );
+    EXPECT_EQ(cudaStatus, cudaSuccess);
+
+    std::vector<float> expected = {0.43775f, 0.49475f, 0.48975f, 0.339f, 0.45675f, 0.303f, 0.56975f, 0.57025f};
+
+    for (int i = 0; i < output.size(); ++i) {
+        EXPECT_NEAR(expected[i], output[i], 1e-4);
+    }
+
+    cudaFree(d_input);
+    cudaFree(d_output);
+}

tools/generate_test_results.py

@@ -1,8 +1,20 @@
 import torch
 
-def _conv2d(in_channels, out_channels, kernel_size, stride, padding, inputs, weights):
 
-    conv2d = torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
+def _conv2d(in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            inputs,
+            weights):
+
+    conv2d = torch.nn.Conv2d(in_channels=in_channels,
+                             out_channels=out_channels,
+                             kernel_size=kernel_size,
+                             stride=stride,
+                             padding=padding,
+                             bias=False)
     conv2d.weight = torch.nn.Parameter(weights)
 
     output = conv2d(inputs)
@@ -11,6 +23,7 @@ def _conv2d(in_channels, out_channels, kernel_size, stride, padding, inputs, wei
     output = torch.flatten(output)
     return output
 
+
 def _print_cpp_vector(vector):
     print("std::vector<float> expected = {", end="")
     for i in range(len(vector)):
@@ -20,6 +33,19 @@ def _print_cpp_vector(vector):
     print("};")
 
 
+def _get_pool_input():
+    return torch.tensor([
+        0.573, 0.619, 0.732, 0.055,
+        0.243, 0.316, 0.573, 0.619,
+        0.712, 0.055, 0.243, 0.316,
+        0.573, 0.619, 0.742, 0.055,
+        0.473, 0.919, 0.107, 0.073,
+        0.073, 0.362, 0.973, 0.059,
+        0.473, 0.455, 0.283, 0.416,
+        0.532, 0.819, 0.732, 0.850
+    ]).reshape(1, 2, 4, 4)
+
+
 def gen_convd_padded_test_result():
 
     in_channels = 3
@@ -68,9 +94,16 @@ def gen_convd_padded_test_result():
         0.011, 0.345, 0.678
     ], dtype=torch.float).reshape(2, 3, 3, 3)
 
-    output = _conv2d(in_channels, out_channels, kernel_size, stride, padding, inputs, weights)
+    output = _conv2d(in_channels,
+                     out_channels,
+                     kernel_size,
+                     stride,
+                     padding,
+                     inputs,
+                     weights)
     _print_cpp_vector(output)
 
+
 def gen_convd_strided_test_result():
 
     in_channels = 2
@@ -78,7 +111,7 @@ def gen_convd_strided_test_result():
     kernel_size = 3
     stride = 2
     padding = 3
-    
+
     input = torch.tensor([
         0.946, 0.879, 0.382, 0.542, 0.453,
         0.128, 0.860, 0.778, 0.049, 0.974,
@@ -106,9 +139,16 @@ def gen_convd_strided_test_result():
         0.939, 0.891, 0.006
     ], dtype=torch.float).reshape(2, 2, 3, 3)
 
-    output = _conv2d(in_channels, out_channels, kernel_size, stride, padding, input, weights)
+    output = _conv2d(in_channels,
+                     out_channels,
+                     kernel_size,
+                     stride,
+                     padding,
+                     input,
+                     weights)
     _print_cpp_vector(output)
 
+
 def gen_softmax_test_result():
     input = torch.tensor([
         0.573, 0.619, 0.732, 0.055, 0.243
@@ -117,17 +157,9 @@ def gen_softmax_test_result():
     output = torch.nn.Softmax(dim=0)(input)
     _print_cpp_vector(output)
 
+
 def gen_max_pool_test_result():
-    input = torch.tensor([
+    input = _get_pool_input()
-        0.573, 0.619, 0.732, 0.055,
-        0.243, 0.316, 0.573, 0.619,
-        0.712, 0.055, 0.243, 0.316,
-        0.573, 0.619, 0.742, 0.055,
-        0.473, 0.919, 0.107, 0.073,
-        0.073, 0.362, 0.973, 0.059,
-        0.473, 0.455, 0.283, 0.416,
-        0.532, 0.819, 0.732, 0.850
-    ]).reshape(1, 2, 4, 4)
 
     output = torch.nn.MaxPool2d(kernel_size=2, stride=2)(input)
     output = torch.flatten(output)
@@ -135,13 +167,25 @@ def gen_max_pool_test_result():
     _print_cpp_vector(output)
 
 
+def gen_avg_pool_test_result():
+
+    input = _get_pool_input()
+
+    output = torch.nn.AvgPool2d(kernel_size=2, stride=2)(input)
+    output = torch.flatten(output)
+
+    _print_cpp_vector(output)
+
+
 if __name__ == "__main__":
-    # print("Generating test results...")
+    print("Generating test results...")
-    # print("Padded convolution test:")
+    print("Padded convolution test:")
-    # gen_convd_padded_test_result()
+    gen_convd_padded_test_result()
-    # print("Strided convolution test:")
+    print("Strided convolution test:")
-    # gen_convd_strided_test_result()
+    gen_convd_strided_test_result()
-    # print("Softmax test:")
+    print("Softmax test:")
-    # gen_softmax_test_result()
+    gen_softmax_test_result()
     print("Max pool test:")
-    gen_max_pool_test_result()
+    gen_max_pool_test_result()
+    print("Avg pool test:")
+    gen_avg_pool_test_result()