[WIP] ResNet

.clang-format

@@ -27,7 +27,7 @@ IncludeCategories:
     Priority:        1
   # standard C++ headers
   # TODO: add as needed
-  - Regex:           ^<(algorithm|array|condition_variable|functional|iostream|map|memory|mutex|numeric|string|thread|vector)>$
+  - Regex:           ^<(algorithm|array|condition_variable|functional|iomanip|iostream|map|memory|mutex|numeric|string|thread|type_traits|vector)>$
     Priority:        2
   # third party headers
   - Regex:           ^<stdtensor>

.gitignore

@@ -6,6 +6,7 @@
 *.tar
 /3rdparty
 /bin
+/build
 /cmake-build
 /examples/Makefile
 /lib

CMakeLists.txt

@@ -6,8 +6,9 @@ SET(CMAKE_MODULE_PATH ${CMAKE_SOURCE_DIR}/cmake)
 
 FIND_PACKAGE(stdtensor REQUIRED)
 
-ADD_DEFINITIONS(-Wfatal-errors)
 ADD_DEFINITIONS(-Wall)
+ADD_DEFINITIONS(-Werror)
+ADD_DEFINITIONS(-Wfatal-errors)
 
 INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/3rdparty/include)
 INCLUDE_DIRECTORIES(${CMAKE_SOURCE_DIR}/include)

examples/example_1.cpp

@@ -1,6 +1,7 @@
-#include <nn/ops>
 #include <stdtensor>
 
+#include <nn/ops>
+
 struct examples {
     const uint32_t n = 10;
     const uint32_t c = 3;

examples/example_mlp.cpp

@@ -1,8 +1,9 @@
 #include <string>
 
-#include <nn/ops>
 #include <stdtensor>
 
+#include <nn/ops>
+
 #include "utils.hpp"
 
 void example_mlp()

examples/example_mnist.cpp

@@ -1,14 +1,17 @@
 #include <cstdlib>
+
 #include <string>
 
 #include <nn/ops>
 
 #ifdef USE_OPENCV
-#include <opencv2/opencv.hpp>
+#    include <opencv2/opencv.hpp>
 #endif
 
 #include <stdtensor>
 
+#include "utils.hpp"
+
 void example_mnist() {}
 
 int main()
@@ -25,6 +28,7 @@ int main()
     int i = 0;
     system("mkdir -p images");
     for (auto im : t) {
+        UNUSED(im);
         char name[20];
         sprintf(name, "images/%d.png", ++i);
 #ifdef USE_OPENCV

examples/example_model_plain34.cpp

@@ -0,0 +1,125 @@
+// #define STDNN_OPS_HAVE_CBLAS
+
+#include <algorithm>
+#include <string>
+
+#include <nn/models>
+
+#include "utils.hpp"
+
+template <typename image_order = nn::ops::nhwc,
+          typename filter_order = nn::ops::rscd>
+class plain34_model
+{
+    const size_t logits = 1000;
+
+    using relu = nn::ops::pointwise<nn::ops::relu>;
+    using bn_layer = nn::layers::batch_norm<image_order, relu>;
+
+    using flatten = nn::layers::flatten<1, 3>;
+    using dense = nn::layers::dense<>;
+    using softmax = nn::layers::activation<nn::ops::softmax>;
+
+    auto conv1(int d) const
+    {
+        using layer = nn::layers::conv<image_order, filter_order, true, relu>;
+        return layer(d, layer::ksize(7, 7), layer::padding_same(),
+                     layer::stride(2, 2));
+    }
+
+    auto pool1() const
+    {
+        using layer = nn::layers::pool<nn::ops::pool_max, image_order>;
+        return layer(layer::ksize(3, 3), layer::padding_same(),
+                     layer::stride(2, 2));
+    }
+
+    auto pool2() const
+    {
+        using layer = nn::layers::pool<nn::ops::pool_mean, image_order>;
+        return layer(layer::ksize(7, 7));
+    }
+
+    auto conv(int d, int s) const
+    {
+        using layer = nn::layers::conv<image_order, filter_order, false>;
+        return layer(d, layer::ksize(3, 3), layer::padding_same(),
+                     layer::stride(s, s));
+    }
+
+    const std::string prefix_;
+
+    const auto p(const std::string &name) const
+    {
+        return nn::ops::readtar(prefix_, name);
+    }
+
+  public:
+    const size_t h = 224;
+    const size_t w = 224;
+
+    plain34_model(const std::string &prefix) : prefix_(prefix) {}
+
+    template <typename R>
+    auto operator()(const ttl::tensor_ref<R, 4> &x, int m = 5) const
+    {
+        auto layers = nn::models::make_sequential()  //
+                      << conv1(64)                   //
+                      << pool1()                     //
+
+                      << conv(64, 1) << bn_layer()  //
+                      << conv(64, 1) << bn_layer() << conv(64, 1) << bn_layer()
+                      << conv(64, 1) << bn_layer() << conv(64, 1) << bn_layer()
+                      << conv(64, 1) << bn_layer()
+
+                      << conv(128, 2) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+                      << conv(128, 1) << bn_layer()  //
+
+                      << conv(256, 2) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+                      << conv(256, 1) << bn_layer()  //
+
+                      << conv(512, 2) << bn_layer()  //
+                      << conv(512, 1) << bn_layer()  //
+                      << conv(512, 1) << bn_layer()  //
+                      << conv(512, 1) << bn_layer()  //
+                      << conv(512, 1) << bn_layer()  //
+                      << conv(512, 1) << bn_layer()  //
+
+                      << pool2()        //
+                      << flatten()      //
+                      << dense(logits)  //
+                      << softmax()      //
+            ;
+
+        auto y = layers(x);
+        return y;
+    }
+};
+
+int main(int argc, char *argv[])
+{
+    const std::string home(std::getenv("HOME"));
+    const std::string prefix = home + "/var/models/resnet";
+    plain34_model model(prefix);
+    const auto x = ttl::tensor<float, 4>(1, model.h, model.w, 3);
+    const auto y = model(ref(x));
+    PPRINT(x);
+    PPRINT(*y);
+    return 0;
+}