From: Павел Ильютченко/AI Tools Lab /SRR/Engineer/삼성전자 Date: Mon, 5 Aug 2019 13:49:31 +0000 (+0300) Subject: [mir2loco] Support Conv2D operation (#5899) X-Git-Tag: submit/tizen/20190809.050447~156 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=ca288c68a34535f8ced0399c90330985657b850a;p=platform%2Fcore%2Fml%2Fnnfw.git [mir2loco] Support Conv2D operation (#5899) * Implemented `mir::Conv2DOp` transformation to `loco::Conv2D` * Added test for this Signed-off-by: Pavel Iliutchenko --- diff --git a/compiler/mir2loco/src/mir2loco.cpp b/compiler/mir2loco/src/mir2loco.cpp index c005b30..6f70dd7 100644 --- a/compiler/mir2loco/src/mir2loco.cpp +++ b/compiler/mir2loco/src/mir2loco.cpp @@ -19,6 +19,7 @@ #include "mir/ops/BiasAddOp.h" #include "mir/ops/ConcatOp.h" #include "mir/ops/ConstantOp.h" +#include "mir/ops/Conv2DOp.h" #include "mir/ops/PoolOp.h" #include "mir/ops/ReluOp.h" #include "mir/ops/ReshapeOp.h" @@ -200,7 +201,52 @@ void Transformer::visit(mir::ops::ConstantOp &op) _mir2loco_map.emplace(&op, const_node); } -void Transformer::visit(mir::ops::Conv2DOp &op) { throw std::runtime_error("NYI"); } +void Transformer::visit(mir::ops::Conv2DOp &op) +{ + auto input = op.getInput(0)->getProducer()->getNode(); + auto kernel = op.getInput(1)->getProducer()->getNode(); + // Get ConstantOp + auto const_node = _mir2loco_map.at(kernel); + + auto filter_enc = _loco_graph->nodes()->create(); + { + auto enc = stdex::make_unique>(); + + // mir using filter convention as TF + // In TensorFlow, conv2d filter is a 4-D tensor of following shape: + // [filter_height, filter_width, in_channels, out_channels] -> HWIO (HWCN) + enc->perm()->axis(loco::FilterAxis::Height) = 0; + enc->perm()->axis(loco::FilterAxis::Width) = 1; + enc->perm()->axis(loco::FilterAxis::Depth) = 2; + enc->perm()->axis(loco::FilterAxis::Count) = 3; + + filter_enc->encoder(std::move(enc)); + } + // Set filter input + filter_enc->input(const_node); + // Setting up conv2d + + // FeatureEncode + auto encode_node = createNHWCFeatureEncode(_loco_graph.get()); + // Set Input + auto loco_it = _mir2loco_map.find(input); + assert(loco_it != _mir2loco_map.end()); // can't find the input + encode_node->input(loco_it->second); + // Conv2D + auto conv2d_node = _loco_graph->nodes()->create(); + setupStride(op.getStrides(), conv2d_node->stride()); + setupPad(op.getPaddingBefore(), op.getPaddingAfter(), conv2d_node->pad()); + // Set Input + conv2d_node->ifm(encode_node); + conv2d_node->ker(filter_enc); + // FeatureDecode + auto decode_node = createNHWCFeatureDecode(_loco_graph.get()); + // Set Input + decode_node->input(conv2d_node); + // Not set Shape + // Add to map + _mir2loco_map.emplace(&op, decode_node); +} void Transformer::visit(mir::ops::DeConv2DOp &op) { throw std::runtime_error("NYI"); } diff --git a/compiler/mir2loco/src/mir2loco.test.cpp b/compiler/mir2loco/src/mir2loco.test.cpp index b8d6770..077ee80 100644 --- a/compiler/mir2loco/src/mir2loco.test.cpp +++ b/compiler/mir2loco/src/mir2loco.test.cpp @@ -19,6 +19,7 @@ #include "mir/ops/BiasAddOp.h" #include "mir/ops/ConcatOp.h" #include "mir/ops/ConstantOp.h" +#include "mir/ops/Conv2DOp.h" #include "mir/ops/PoolOp.h" #include "mir/ops/ReluOp.h" #include "mir/ops/ReshapeOp.h" @@ -323,3 +324,53 @@ TEST_F(TestTransformer_mir2loco, Bias_Add_Test) ASSERT_EQ(bias_add_node->axis(), 3); } + +TEST_F(TestTransformer_mir2loco, Conv2D_Test) +{ + mir::Graph mir_graph; + + mir::Shape input_shape = mir::Shape({7, 7, 9, 1}); + auto *input = mir_graph.create("input", input_shape); + mir::Shape shape = mir::Shape({2, 3, 1, 1}); + const float data[] = {5.9, 6.7, 5.32, 54.11231, 43.2444, 3.409}; + auto mir_tensor = mir::TensorVariant(mir::DTYPE::FLOAT32, shape, (const void *)data); + auto *constant = mir_graph.create("constant", mir_tensor); + auto *conv = mir_graph.create( + "conv", input->getOutput(0), constant->getOutput(0), mir::Shape{2, 3}, + std::vector{5, 9}, std::vector{7, 4}); + auto *output = mir_graph.create("output", conv->getOutput(0)); + + mir2loco::Transformer transformer; + auto loco_graph = transformer.transform(&mir_graph); + + loco::Pull *pull_node = dynamic_cast(loco_graph->nodes()->at(0)); + loco::ConstGen *const_node = dynamic_cast(loco_graph->nodes()->at(1)); + loco::FilterEncode *filter_node = dynamic_cast(loco_graph->nodes()->at(2)); + loco::FeatureEncode *encode_node = + dynamic_cast(loco_graph->nodes()->at(3)); + loco::Conv2D *conv_node = dynamic_cast(loco_graph->nodes()->at(4)); + loco::FeatureDecode *decode_node = + dynamic_cast(loco_graph->nodes()->at(5)); + loco::Push *push_node = dynamic_cast(loco_graph->nodes()->at(6)); + + ASSERT_NE(pull_node, nullptr); + ASSERT_NE(const_node, nullptr); + ASSERT_NE(filter_node, nullptr); + ASSERT_NE(encode_node, nullptr); + ASSERT_NE(conv_node, nullptr); + ASSERT_NE(decode_node, nullptr); + ASSERT_NE(push_node, nullptr); + ASSERT_EQ(encode_node->input(), pull_node); + ASSERT_EQ(filter_node->input(), const_node); + ASSERT_EQ(conv_node->ifm(), encode_node); + ASSERT_EQ(conv_node->ker(), filter_node); + ASSERT_EQ(decode_node->input(), conv_node); + ASSERT_EQ(push_node->from(), decode_node); + // Check params + ASSERT_EQ(conv_node->pad()->left(), 5); + ASSERT_EQ(conv_node->pad()->top(), 9); + ASSERT_EQ(conv_node->pad()->right(), 7); + ASSERT_EQ(conv_node->pad()->bottom(), 4); + ASSERT_EQ(conv_node->stride()->horizontal(), 2); + ASSERT_EQ(conv_node->stride()->vertical(), 3); +}