1 files changed, 160 insertions, 0 deletions
diff --git a/compiler/enco/frontend/caffe/src/Layer/Scale.cpp b/compiler/enco/frontend/caffe/src/Layer/Scale.cpp
new file mode 100644
index 000000000..b9925978c
--- /dev/null
+++ b/compiler/enco/frontend/caffe/src/Layer/Scale.cpp
@@ -0,0 +1,160 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Scale.h"
+#include "IRBuilder.h"
+
+#include <coco/IR/FeatureLayouts.h>
+
+#include <morph/caffe.h>
+
+#include <cassert>
+
+using namespace nncc::core::ADT;
+using namespace morph::caffe;
+
+namespace caffeimport
+{
+
+void ScaleBuilder::build(const ::caffe::LayerParameter &layer, GraphBuilderContext *context) const
+{
+  coco::Module *module = context->module();
+  coco::Data *data = context->data();
+  coco::Block *blk = context->block();
+  std::map<std::string, tensor::Shape> &shape_ctx = context->shape_ctx();
+  std::map<std::string, coco::Bag *> &bag_ctx = context->bag_ctx();
+  WeightContext &weight_ctx = context->weight_ctx();
+
+  // TODO Support Scale layer with 2 bottoms
+  assert(layer.bottom().size() == 1);
+  assert(layer.top().size() == 1);
+
+  assert(layer.has_scale_param());
+  const auto &param = layer.scale_param();
+
+  assert(param.axis() == 1);
+  assert(!param.has_num_axes());
+
+  assert(weight_ctx.blob_count(layer.name()) >= 1);
+
+  // NOTE The shape of "Scale" output is same as that of its input
+  // NOTE The current implementation assumes that input/output is of feature type
+  // TODO Support generic tensor arguments
+  auto shape = shape_ctx.at(layer.bottom(0));
+
+  coco::Bag *last_bag = bag_ctx.at(layer.bottom(0));
+
+  // Create channel-wise multiplication
+  {
+    auto in_bag = last_bag;
+    auto in_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    in_obj->bag(in_bag);
+    in_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
+
+    auto factor_bag = module->entity()->bag()->create(num_elements(shape));
+    auto factor_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    factor_obj->bag(factor_bag);
+    factor_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(shape)));
+
+    auto out_bag = module->entity()->bag()->create(num_elements(shape));
+    auto out_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    out_obj->bag(out_bag);
+    out_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
+
+    auto mul_op = op_builder(module).load(factor_obj).load(in_obj).mul().pop();
+    auto mul_ins = instr_builder(module).eval(out_obj, mul_op);
+
+    blk->instr()->append(mul_ins);
+
+    // Fill "factor" data
+    {
+      data->f32()->allocate(factor_bag);
+
+      auto span = data->f32()->weight(factor_bag);
+      auto blob = weight_ctx.blob_get(layer.name(), 0);
+
+      for (uint32_t ch = 0; ch < factor_obj->shape().depth(); ++ch)
+      {
+        span[ch] = blob->data(ch);
+      }
+    }
+
+    // Update "last_bag"
+    last_bag = out_bag;
+  }
+
+  assert(last_bag != nullptr);
+
+  // Create bias addition (as channel-wise addition)
+  if (param.bias_term())
+  {
+    assert(weight_ctx.blob_count(layer.name()) >= 2);
+
+    auto in_bag = last_bag; /* Use the output of the last computation as an input */
+    auto in_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    in_obj->bag(in_bag);
+    in_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
+
+    auto bias_bag = module->entity()->bag()->create(num_elements(shape));
+    auto bias_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    bias_obj->bag(bias_bag);
+    bias_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(shape)));
+
+    auto out_bag = module->entity()->bag()->create(num_elements(shape));
+    auto out_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+    out_obj->bag(out_bag);
+    out_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(shape)));
+
+    auto add_op = op_builder(module).load(bias_obj).load(in_obj).add().pop();
+    auto add_ins = instr_builder(module).eval(out_obj, add_op);
+
+    blk->instr()->append(add_ins);
+
+    // Fill bias data
+    {
+      data->f32()->allocate(bias_bag);
+
+      auto bias_span = data->f32()->weight(bias_bag);
+      auto bias_blob = weight_ctx.blob_get(layer.name(), 1);
+
+      for (uint32_t ch = 0; ch < bias_obj->shape().depth(); ++ch)
+      {
+        bias_span[ch] = bias_blob->data(ch);
+      }
+    }
+
+    // Update "last_bag"
+    last_bag = out_bag;
+  }
+
+  // Update bag and shape context
+  {
+    const auto &out_name = layer.top(0);
+    const auto &out_bag = last_bag;
+    const auto &out_shape = shape;
+
+    bag_ctx[out_name] = out_bag;
+    shape_ctx[out_name] = out_shape;
+  }
+}
+
+} // namespace caffeimport