*/
#include "Frontend.h"
-#include "ConvolutionSpec.h"
#include "PoolingSpec.h"
#include "ConcatSpec.h"
#include "IRBuilder.h"
graph_builder->build(layer, &opbuilder_context);
}
// TODO move type handlers to separate builder
- else if (layer.type() == "Convolution")
- {
- assert(layer.bottom().size() == 1);
- assert(layer.top().size() == 1);
-
- assert(layer.has_convolution_param());
- const auto ¶m = layer.convolution_param();
-
- ConvolutionSpec spec{param};
- {
- const auto ifm_name = layer.bottom(0);
- const auto ifm_shape = shape_ctx.at(ifm_name);
- spec.ifm_shape(ifm_shape);
- }
-
- // NOTE The current implementation focuses on 2D convolution
- // TODO Support general ND convolution
- assert(spec.num_batch_axes() == 1);
- assert(spec.num_spatial_axes() == 2);
-
- // Create an object for an input feature map
- const auto ifm_name = layer.bottom(0);
- const auto ifm_shape = shape_ctx.at(ifm_name);
- auto ifm_bag = bag_ctx.at(ifm_name);
- auto ifm_obj = module->entity()->object()->create<coco::FeatureObject>();
-
- ifm_obj->bag(ifm_bag);
- ifm_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ifm_shape)));
-
- // Create an object for an output feature map
- const auto ofm_name = layer.top(0);
- const auto ofm_shape = spec.ofm_shape();
- auto ofm_bag = module->entity()->bag()->create(num_elements(ofm_shape));
- auto ofm_obj = module->entity()->object()->create<coco::FeatureObject>();
-
- ofm_obj->bag(ofm_bag);
- ofm_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ofm_shape)));
-
- // Create an object for kernel
- using namespace coco::KernelLayouts;
-
- const auto ker_shape = spec.ker_shape();
- auto ker_bag = module->entity()->bag()->create(num_elements(ker_shape));
- auto ker_obj = module->entity()->object()->create<coco::KernelObject>();
-
- ker_obj->bag(ker_bag);
- ker_obj->layout(NCHW::create(as_kernel_shape(ker_shape)));
-
- // Create a kernel overlay for the kernel object
- data->f32()->allocate(ker_obj);
-
- // Initialize the kernel overlay
- assert(weight_ctx.blob_count(layer.name()) >= 1);
- auto ker_blob = weight_ctx.blob_get(layer.name(), 0);
-
- assert(ker_shape == caffeimport::as_tensor_shape(ker_blob));
-
- auto ker_dst = data->f32()->access(ker_obj);
- auto ker_src = kernel::OverlayFactory<float, kernel::NCHWLayout>::make(
- ker_obj->shape(), ker_blob->mutable_data()->begin());
-
- for (uint32_t n = 0; n < ker_obj->shape().count(); ++n)
- {
- for (uint32_t ch = 0; ch < ker_obj->shape().depth(); ++ch)
- {
- for (uint32_t row = 0; row < ker_obj->shape().height(); ++row)
- {
- for (uint32_t col = 0; col < ker_obj->shape().width(); ++col)
- {
- ker_dst->at(n, ch, row, col) = ker_src.at(n, ch, row, col);
- }
- }
- }
- }
-
- // Create a Load op
- auto load = op_builder(module).load(ifm_obj).pop();
-
- // Create a Conv2D op
- auto op = module->entity()->op()->create<coco::Conv2D>();
-
- op->group(spec.group());
-
- op->ker(ker_obj);
- op->stride()->vertical(spec.stride(0));
- op->stride()->horizontal(spec.stride(1));
-
- op->pad()->top(spec.pad(0));
- op->pad()->bottom(spec.pad(0));
- op->pad()->left(spec.pad(1));
- op->pad()->right(spec.pad(1));
-
- op->arg(load);
-
- // Create an Eval instruction
- auto ins = instr_builder(module).eval(ofm_obj, op);
-
- // Append the instruction to the block
- blk->instr()->append(ins);
-
- //
- // coco IR allows Conv2D fused with Add, but the current implementation of enco backend
- // is unable to process such a tree.
- //
- // As a workaround, caffe frontend constructs a instruction for Conv2D and Add.
- //
- if (param.bias_term())
- {
- assert(weight_ctx.blob_count(layer.name()) >= 2);
-
- // Create Bag & Object
- auto bias_bag = module->entity()->bag()->create(ker_shape.dim(0));
- auto bias_obj = module->entity()->object()->create<coco::FeatureObject>();
-
- bias_obj->bag(bias_bag);
- bias_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(ofm_shape)));
-
- auto added_bag = module->entity()->bag()->create(num_elements(ofm_shape));
- auto added_obj = module->entity()->object()->create<coco::FeatureObject>();
-
- added_obj->bag(added_bag);
- added_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ofm_shape)));
-
- // Create Op
- auto bias_add = op_builder(module).load(bias_obj).load(ofm_obj).add().pop();
-
- // Create Instr
- auto bias_add_ins = instr_builder(module).eval(added_obj, bias_add);
-
- // Append the instruction
- blk->instr()->append(bias_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 < ker_obj->shape().count(); ++ch)
- {
- bias_span[ch] = bias_blob->data(ch);
- }
-
- // Update output
- ofm_bag = added_bag;
- }
-
- // Update bag and shape context
- bag_ctx[ofm_name] = ofm_bag;
- shape_ctx[ofm_name] = ofm_shape;
- }
else if (layer.type() == "Pooling")
{
assert(layer.bottom().size() == 1);
--- /dev/null
+/*
+ * 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 "Convolution.h"
+#include "ConvolutionSpec.h"
+#include "Convert.h"
+#include "IRBuilder.h"
+
+#include <nncc/core/ADT/kernel/Overlay.h>
+#include <nncc/core/ADT/kernel/NCHWLayout.h>
+
+#include <morph/caffe.h>
+
+#include <cassert>
+
+using namespace nncc::core::ADT;
+using namespace morph::caffe;
+
+using tensor::num_elements;
+
+namespace caffeimport
+{
+
+void ConvolutionBuilder::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();
+
+ assert(layer.bottom().size() == 1);
+ assert(layer.top().size() == 1);
+
+ assert(layer.has_convolution_param());
+ const auto ¶m = layer.convolution_param();
+
+ ConvolutionSpec spec{param};
+ {
+ const auto ifm_name = layer.bottom(0);
+ const auto ifm_shape = shape_ctx.at(ifm_name);
+ spec.ifm_shape(ifm_shape);
+ }
+
+ // NOTE The current implementation focuses on 2D convolution
+ // TODO Support general ND convolution
+ assert(spec.num_batch_axes() == 1);
+ assert(spec.num_spatial_axes() == 2);
+
+ // Create an object for an input feature map
+ const auto ifm_name = layer.bottom(0);
+ const auto ifm_shape = shape_ctx.at(ifm_name);
+ auto ifm_bag = bag_ctx.at(ifm_name);
+ auto ifm_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+ ifm_obj->bag(ifm_bag);
+ ifm_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ifm_shape)));
+
+ // Create an object for an output feature map
+ const auto ofm_name = layer.top(0);
+ const auto ofm_shape = spec.ofm_shape();
+ auto ofm_bag = module->entity()->bag()->create(num_elements(ofm_shape));
+ auto ofm_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+ ofm_obj->bag(ofm_bag);
+ ofm_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ofm_shape)));
+
+ // Create an object for kernel
+ using namespace coco::KernelLayouts;
+
+ const auto ker_shape = spec.ker_shape();
+ auto ker_bag = module->entity()->bag()->create(num_elements(ker_shape));
+ auto ker_obj = module->entity()->object()->create<coco::KernelObject>();
+
+ ker_obj->bag(ker_bag);
+ ker_obj->layout(NCHW::create(as_kernel_shape(ker_shape)));
+
+ // Create a kernel overlay for the kernel object
+ data->f32()->allocate(ker_obj);
+
+ // Initialize the kernel overlay
+ assert(weight_ctx.blob_count(layer.name()) >= 1);
+ auto ker_blob = weight_ctx.blob_get(layer.name(), 0);
+
+ assert(ker_shape == caffeimport::as_tensor_shape(ker_blob));
+
+ auto ker_dst = data->f32()->access(ker_obj);
+ auto ker_src = kernel::OverlayFactory<float, kernel::NCHWLayout>::make(
+ ker_obj->shape(), ker_blob->mutable_data()->begin());
+
+ for (uint32_t n = 0; n < ker_obj->shape().count(); ++n)
+ {
+ for (uint32_t ch = 0; ch < ker_obj->shape().depth(); ++ch)
+ {
+ for (uint32_t row = 0; row < ker_obj->shape().height(); ++row)
+ {
+ for (uint32_t col = 0; col < ker_obj->shape().width(); ++col)
+ {
+ ker_dst->at(n, ch, row, col) = ker_src.at(n, ch, row, col);
+ }
+ }
+ }
+ }
+
+ // Create a Load op
+ auto load = op_builder(module).load(ifm_obj).pop();
+
+ // Create a Conv2D op
+ auto op = module->entity()->op()->create<coco::Conv2D>();
+
+ op->group(spec.group());
+
+ op->ker(ker_obj);
+ op->stride()->vertical(spec.stride(0));
+ op->stride()->horizontal(spec.stride(1));
+
+ op->pad()->top(spec.pad(0));
+ op->pad()->bottom(spec.pad(0));
+ op->pad()->left(spec.pad(1));
+ op->pad()->right(spec.pad(1));
+
+ op->arg(load);
+
+ // Create an Eval instruction
+ auto ins = instr_builder(module).eval(ofm_obj, op);
+
+ // Append the instruction to the block
+ blk->instr()->append(ins);
+
+ //
+ // coco IR allows Conv2D fused with Add, but the current implementation of enco backend
+ // is unable to process such a tree.
+ //
+ // As a workaround, caffe frontend constructs a instruction for Conv2D and Add.
+ //
+ if (param.bias_term())
+ {
+ assert(weight_ctx.blob_count(layer.name()) >= 2);
+
+ // Create Bag & Object
+ auto bias_bag = module->entity()->bag()->create(ker_shape.dim(0));
+ auto bias_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+ bias_obj->bag(bias_bag);
+ bias_obj->layout(coco::FeatureLayouts::BC::create(as_feature_shape(ofm_shape)));
+
+ auto added_bag = module->entity()->bag()->create(num_elements(ofm_shape));
+ auto added_obj = module->entity()->object()->create<coco::FeatureObject>();
+
+ added_obj->bag(added_bag);
+ added_obj->layout(coco::FeatureLayouts::BCHW::create(as_feature_shape(ofm_shape)));
+
+ // Create Op
+ auto bias_add = op_builder(module).load(bias_obj).load(ofm_obj).add().pop();
+
+ // Create Instr
+ auto bias_add_ins = instr_builder(module).eval(added_obj, bias_add);
+
+ // Append the instruction
+ blk->instr()->append(bias_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 < ker_obj->shape().count(); ++ch)
+ {
+ bias_span[ch] = bias_blob->data(ch);
+ }
+
+ // Update output
+ ofm_bag = added_bag;
+ }
+
+ // Update bag and shape context
+ bag_ctx[ofm_name] = ofm_bag;
+ shape_ctx[ofm_name] = ofm_shape;
+}
+
+} // namespace caffeimport
projects / platform / core / ml / nnfw.git / commitdiff