use bgemm_naive instead of bconv_naive when non-optimize

JDAI-CV · Aug 22, 2019 · cec3499 · cec3499
1 parent 418c8d7
commit cec3499
Showing 1 changed file with 27 additions and 33 deletions.
diff --git a/dabnn/layers/BinConv.cpp b/dabnn/layers/BinConv.cpp
@@ -29,9 +29,9 @@ BinConv::BinConv(NetCP net, const std::string &name, css input, css weight,
         const auto binaized_name = "binaized_for_" + output + "_cal";
         if (mat_map.find(binaized_name) == mat_map.end()) {
             auto &input_mat = *mat_map[input];
-            mat_map[binaized_name] =
-                std::make_shared<Mat>(input_mat.h, input_mat.w, input_mat.elem_c,
-                                      DataType::Bit, binaized_name);
+            mat_map[binaized_name] = std::make_shared<Mat>(
+                input_mat.h, input_mat.w, input_mat.elem_c, DataType::Bit,
+                binaized_name);
         }
         binarized_mat = mat(binaized_name);
     }
@@ -46,12 +46,12 @@ BinConv::BinConv(NetCP net, const std::string &name, css input, css weight,
     padded_mat = mat(pad_name);
 
     if (net.lock()->optimize && !direct_conv_compatible()) {
-
         const auto col_mat_name = "col_for_" + output + "_cal";
         if (mat_map.find(col_mat_name) == mat_map.end()) {
             const auto len =
                 output_mat->h * output_mat->w *
-                align_to(weight_mat->h * weight_mat->w * input_mat->elem_c, 128);
+                align_to(weight_mat->h * weight_mat->w * input_mat->elem_c,
+                         128);
             mat_map[col_mat_name] =
                 std::make_shared<Mat>(1, 1, len, bnn::DataType::Bit);
         }
@@ -123,36 +123,30 @@ bool BinConv::gemm_compatible() const {
 }
 
 void BinConv::forward_impl() const {
-    if (net_.lock()->optimize) {
-        if (direct_conv_compatible()) {
-            pack_mat(*input_mat, *binarized_mat);
-            pad(*binarized_mat, pad_h, pad_w, *padded_mat);
-            bconv_3x3(*padded_mat, *weight_mat, *output_mat, stride_h);
+    if (net_.lock()->optimize && direct_conv_compatible()) {
+        pack_mat(*input_mat, *binarized_mat);
+        pad(*binarized_mat, pad_h, pad_w, *padded_mat);
+        bconv_3x3(*padded_mat, *weight_mat, *output_mat, stride_h);
+    } else {
+        output_mat->fill<float>(0.f);
+
+        bnn::fused_binarize_im2col(*input_mat, weight_mat->h, weight_mat->w,
+                                   pad_h, pad_w, stride_h, stride_w, 1, 1,
+                                   *col_mat);
+
+        const int m = weight_mat->n;
+        const int n = output_mat->h * output_mat->w;
+        const int k = weight_mat->total() / weight_mat->n;
+        if (net_.lock()->optimize && gemm_compatible()) {
+            bgemm(m, n, k, static_cast<uint64_t *>(transposed_weight_mat->data),
+                  m, static_cast<uint64_t *>(col_mat->data), k,
+                  static_cast<float *>(output_mat->data), m);
         } else {
-            output_mat->fill<float>(0.f);
-
-            bnn::fused_binarize_im2col(*input_mat, weight_mat->h, weight_mat->w,
-                                       pad_h, pad_w, stride_h, stride_w, 1, 1,
-                                       *col_mat);
-
-            const int m = weight_mat->n;
-            const int n = output_mat->h * output_mat->w;
-            const int k = weight_mat->total() / weight_mat->n;
-            if (gemm_compatible()) {
-                bgemm(m, n, k, static_cast<uint64_t *>(transposed_weight_mat->data),
-                      m, static_cast<uint64_t *>(col_mat->data), k,
-                      static_cast<float *>(output_mat->data), m);
-            } else {
-                bgemm_naive(m, n, k, static_cast<uint64_t *>(transposed_weight_mat->data),
-                      m, static_cast<uint64_t *>(col_mat->data), k,
-                      static_cast<float *>(output_mat->data), m);
-            }
+            bgemm_naive(m, n, k,
+                        static_cast<uint64_t *>(transposed_weight_mat->data), m,
+                        static_cast<uint64_t *>(col_mat->data), k,
+                        static_cast<float *>(output_mat->data), m);
         }
-    } else {
-        pack_mat(*input_mat, *binarized_mat);
-        baseline_bconv(*binarized_mat, *weight_mat, weight_mat->h,
-                       weight_mat->w, pad_h, pad_w, stride_h, stride_w, 1, 1,
-                       output_mat->c, *output_mat);
     }
 }