Merge branch 'pooling'

src/include/rtg/operators.hpp

@@ -179,6 +179,10 @@ struct pooling
 
         const shape& input = inputs.at(0);
         auto t             = input.type();
+
+        assert(lengths[0] < (input.lens()[3] + 2 * padding[0]));
+        assert(lengths[1] < (input.lens()[4] + 2 * padding[1]));
+
         return {t,
                 {
                     input.lens()[0],

src/targets/cpu/cpu_target.cpp

@@ -54,6 +54,79 @@ struct cpu_convolution
     }
 };
 
+struct max_pool
+{
+    static std::string name() { return "max"; }
+    static double start() { return std::numeric_limits<double>::lowest(); }
+
+    static double apply(double x, double y) { return x + y; }
+
+    static double final(double x, double) { return (x); }
+};
+
+struct avg_pool
+{
+    static std::string name() { return "average"; }
+    static double start() { return 0.0; }
+
+    static double apply(double x, double y)
+    {
+        double m = std::max(x, y);
+        return (m);
+    }
+
+    static double final(double x, double y) { return x / y; }
+};
+
+template <class Op>
+struct cpu_pooling
+{
+    pooling op;
+
+    std::string name() const { return "cpu::pooling_" + Op::name(); }
+    shape compute_shape(std::vector<shape> inputs) const { return op.compute_shape(inputs); }
+    argument compute(shape output_shape, std::vector<argument> args) const
+    {
+        argument result{output_shape};
+        visit_all(result, args[0])([&](auto output, auto input) {
+            using type = typename decltype(output)::value_type;
+            auto in_h  = input.get_shape().lens()[2];
+            auto in_w  = input.get_shape().lens()[3];
+
+            dfor(output_shape.lens()[0],
+                 output_shape.lens()[1],
+                 output_shape.lens()[2],
+                 output_shape.lens()[3])(
+                [&](std::size_t o, std::size_t w, std::size_t i, std::size_t j) {
+                    const int start_x0 = i * op.stride[0] - op.padding[0];
+                    const int start_y0 = j * op.stride[1] - op.padding[1];
+
+                    const int hend = std::min(start_x0 + op.lengths[0], in_h);
+                    const int wend = std::min(start_y0 + op.lengths[1], in_w);
+
+                    const int start_x = std::max(start_x0, 0);
+                    const int start_y = std::max(start_y0, 0);
+
+                    const int w_h       = (hend - start_x);
+                    const int w_w       = (wend - start_y);
+                    const int pool_size = std::max(w_h * w_w, 1);
+
+                    double acc = Op::start();
+                    dfor(w_h, w_w)([&](int x, int y) {
+                        const int in_x = start_x + x;
+                        const int in_y = start_y + y;
+                        if(in_x >= 0 && in_x < in_h && in_y >= 0 && in_y < in_w)
+                        {
+                            acc = Op::apply(acc, input(o, w, in_x, in_y));
+                        }
+                    });
+                    output(o, w, i, j) = type(Op::final(acc, pool_size));
+                });
+        });
+        return result;
+    }
+};
+
 struct cpu_transpose
 {
     transpose op;
@@ -485,6 +558,15 @@ struct cpu_apply
         if(op.mode == "relu")
             prog->replace_instruction(ins, cpu_unary<relu_op>{}, ins->arguments);
     }
+
+    void apply_pooling(instruction_ref ins)
+    {
+        auto&& op = any_cast<pooling>(ins->op);
+        if(op.mode == "max")
+            prog->replace_instruction(ins, cpu_pooling<max_pool>{op}, ins->arguments);
+        else if(op.mode == "average")
+            prog->replace_instruction(ins, cpu_pooling<avg_pool>{op}, ins->arguments);
+    }
 };
 
 std::string cpu_target::name() const { return "cpu"; }