feat(opr): add dct opr

GitOrigin-RevId: 3e2cc58cfe

src/core/impl/utils/debug.cpp

@@ -10,9 +10,9 @@
  * implied.
  */
 
-#include "megbrain/utils/debug.h"
 #include <cerrno>
 #include <cmath>
+#include "megbrain/utils/debug.h"
 #include "megdnn/tensor_iter.h"
 
 using namespace mgb;
@@ -127,7 +127,7 @@ void get_mem_map(
 }
 
 #ifndef WIN32
-//FIXME: imp SigHandlerInit backtrace for windows
+// FIXME: imp SigHandlerInit backtrace for windows
 class SigHandlerInit {
     static void death_handler(int signum) {
         char msg0[] =
@@ -146,7 +146,7 @@ class SigHandlerInit {
             mgb_log_error("%s: caught deadly signal %d(%s)", msg0, signum,
                           strsignal(signum));
         }
-//FIXME: imp backtrace for macos
+// FIXME: imp backtrace for macos
 #ifndef __APPLE__
         std::string bp;
         debug::backtrace(2).fmt_to_str(bp);
@@ -279,7 +279,7 @@ BacktraceResult mgb::debug::backtrace(int nr_exclude) {
     recursive_call = false;
     return result;
 #else
-    //FIXME: imp Backtrace for windows
+    // FIXME: imp Backtrace for windows
     BacktraceResult result;
     return result;
 #endif
@@ -352,6 +352,16 @@ std::string num2str(float val) {
     return ret;
 }
 #endif
+template <typename dnn_ctype>
+struct RealCtype {
+    using ctype = dnn_ctype;
+    static dnn_ctype trans(dnn_ctype val) { return val; }
+};
+template <>
+struct RealCtype<dt_qint8> {
+    using ctype = int;
+    static int trans(dt_qint8 val) { return val.as_int8(); }
+};
 
 template <typename ctype>
 Maybe<std::string> do_compare_tensor_value(const char* expr0, const char* expr1,
@@ -361,7 +371,8 @@ Maybe<std::string> do_compare_tensor_value(const char* expr0, const char* expr1,
     auto it0 = megdnn::tensor_iter<ctype>(v0.as_megdnn()).begin(),
          it1 = megdnn::tensor_iter<ctype>(v1.as_megdnn()).begin();
     for (size_t i = 0, it = v0.shape().total_nr_elems(); i < it; ++i) {
-        ctype iv0 = *it0, iv1 = *it1;
+        typename RealCtype<ctype>::ctype iv0 = RealCtype<ctype>::trans(*it0),
+                                         iv1 = RealCtype<ctype>::trans(*it1);
         double err = std::abs(iv0 - iv1) /
                      std::max<double>(
                              1, std::min(std::abs(static_cast<double>(iv0)),
@@ -424,7 +435,8 @@ Maybe<std::string> debug::compare_tensor_value(const HostTensorND& v0,
         return do_compare_tensor_value<DTypeTrait<_dt>::ctype>( \
                 expr0, expr1, v0, v1, maxerr);
         MEGDNN_FOREACH_COMPUTING_DTYPE(cb)
-        cb(::megdnn::dtype::Bool)
+        cb(::megdnn::dtype::QuantizedS8);
+        cb(::megdnn::dtype::Bool);
 #undef cb
         default:
             mgb_throw(MegBrainError, "unhandled dtype: %s", dtype.name());

src/opr/impl/imgproc.cpp

@@ -10,9 +10,9 @@
  * implied.
  */
 
-#include "megbrain/opr/imgproc.h"
 #include "./internal/megdnn_opr_wrapper.inl"
 #include "megbrain/graph/grad_impl.h"
+#include "megbrain/opr/imgproc.h"
 #include "megbrain/opr/utility.h"
 
 using namespace mgb;
@@ -267,9 +267,10 @@ void WarpPerspectiveBackwardMat::scn_do_execute() {
     }
 }
 
-SymbolVar WarpPerspectiveBackwardMat::make(
-        SymbolVar i0, SymbolVar i1, SymbolVar i2, SymbolVar i3,
-        const Param& param, const OperatorNodeConfig& config) {
+SymbolVar WarpPerspectiveBackwardMat::make(SymbolVar i0, SymbolVar i1,
+                                           SymbolVar i2, SymbolVar i3,
+                                           const Param& param,
+                                           const OperatorNodeConfig& config) {
     intl::MegDNNOprInitInputsModifier<WarpPerspectiveBackwardMat>::apply(
             param, {&i0, &i1, &i2, &i3});
     return i0.insert_single_output_opr<WarpPerspectiveBackwardMat>(
@@ -447,14 +448,12 @@ void RemapForward::init_output_dtype() {
 MGB_IMPL_OPR_GRAD(RemapForward) {
     mgb_assert(opr.input().size() == 2);
     if (wrt_idx == 0) {
-        SymbolVar grad =
-                RemapBackwardData::make(opr.input(1), out_grad[0],
-                                        opr.input(0), opr.param());
+        SymbolVar grad = RemapBackwardData::make(opr.input(1), out_grad[0],
+                                                 opr.input(0), opr.param());
         return grad.node();
     } else if (wrt_idx == 1) {
-        SymbolVar grad = 
-                RemapBackwardMat::make(opr.input(0), opr.input(1),
-                                       out_grad[0], opr.param());
+        SymbolVar grad = RemapBackwardMat::make(opr.input(0), opr.input(1),
+                                                out_grad[0], opr.param());
         return grad.node();
     } else
         return InvalidGrad::make(opr, wrt_idx);
@@ -468,4 +467,73 @@ MEGDNN_OPR_INIT3(RemapBackwardData, "remap_bwd_data", 2, false);
 MGB_DYN_TYPE_OBJ_FINAL_IMPL(RemapBackwardMat);
 MEGDNN_OPR_INIT3(RemapBackwardMat, "remap_bwd_mat", 1, true);
 
+/* ======================= DctChannelSelectForward ======================= */
+
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(DctChannelSelectForward);
+namespace mgb {
+namespace opr {
+namespace intl {
+template <>
+struct MegDNNOprInitPostCtor<DctChannelSelectForward> {
+    static void apply(cg::OperatorNodeBase& opr) {
+        if (opr.config().output_dtype().valid()) {
+            opr.output(0)->dtype(opr.config().output_dtype());
+        } else {
+            opr.output(0)->dtype(dtype::Float32());
+        }
+    }
+};
+}  // namespace intl
+}  // namespace opr
+}  // namespace mgb
+void DctChannelSelectForward::get_output_var_shape(
+        const TensorShapeArray& inp_shape, TensorShapeArray& out_shape) const {
+    auto mo = megdnn_opr();
+    TensorLayout dst;
+    dst.dtype = output(0)->dtype();
+    if (inp_shape.size() == 1) {
+        mo->deduce_layout({inp_shape[0], input(0)->dtype(), input(0)->format()},
+                          {}, {}, dst);
+    } else {
+        mgb_assert(inp_shape.size() == 3, "no support input tensor num %zu",
+                   inp_shape.size());
+        mo->deduce_layout({inp_shape[0], input(0)->dtype(), input(0)->format()},
+                          {inp_shape[1], input(1)->dtype(), input(1)->format()},
+                          {inp_shape[2], input(2)->dtype(), input(2)->format()},
+                          dst);
+    }
+    out_shape[0] = dst;
+}
+size_t DctChannelSelectForward::get_workspace_size_bytes(
+        const TensorShapeArray& input_shapes,
+        const TensorShapeArray& output_shapes) const {
+    auto mo = megdnn_opr();
+
+    return mo->get_workspace_in_bytes(
+            {input_shapes[0], input(0)->dtype(), input(0)->format()}, {}, {},
+            {output_shapes[0], output(0)->dtype(), output(0)->format()});
+}
+void DctChannelSelectForward::scn_do_execute() {
+    auto&& inp = input();
+    auto mo = megdnn_opr();
+    if (inp.size() == 1) {
+        mo->exec(inp[0]->dev_tensor().as_megdnn(), {}, {},
+                 output(0)->dev_tensor().as_megdnn(),
+                 intl::get_megdnn_workspace_from_var(output().back()));
+
+    } else {
+        mgb_assert(inp.size() == 3, "no support input tensor num %zu",
+                   inp.size());
+
+        mo->exec(inp[0]->dev_tensor().as_megdnn(),
+                 inp[1]->dev_tensor().as_megdnn(),
+                 inp[2]->dev_tensor().as_megdnn(),
+                 output(0)->dev_tensor().as_megdnn(),
+                 intl::get_megdnn_workspace_from_var(output().back()));
+    }
+}
+
+MEGDNN_OPR_INIT3(DctChannelSelectForward, "dct_channel_select")
+MEGDNN_OPR_INIT1(DctChannelSelectForward, "dct_channel_select")
+
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/opr/impl/imgproc.oprdecl

@@ -90,4 +90,30 @@ decl_opr(
     desc='Remap transformation to batched 2D images; '
     'see https://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html?highlight=remap'
     'for details on remap transformations.')
+
+decl_raw_opr(
+    'dct_channel_select',
+    inputs=[
+        Doc('src', 'input image, uint8 data type with NCHW format'),
+        Doc('mask_offset', 'out channel offset array'),
+        Doc('mask_val', 'out index per channel'),
+        ], 
+    params='DctChannelSelect',   
+    body=[
+        'if mask_offset is None:',
+        '    all_inputs = _helper.canonize_input_vars([src], comp_graph=comp_graph, config=config)',
+        'else:',
+        '    all_inputs = _helper.canonize_input_vars([src, mask_offset, mask_val], comp_graph=comp_graph, config=config)',
+        'cvt_result_kwargs = {}',
+        'param = _helper.cvt_to_opr_param_def(param, _opr_param_defs.DctChannelSelect, kwargs)',
+        'assert not kwargs, "extra kwargs: {}".format(kwargs)',
+        'all_params = []',
+        'all_params.append(param.serialize())',
+        'output = _mgb._create_opr("DctChannelSelect", all_inputs, all_params, config)',
+    ],
+    has_out_dtype=True,
+    desc='DctChannelSelect do DCT with channel select'
+    'see https://docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html?highlight=dct#dct'
+    'for details on DCT transformations. It will output float32 or qint8')
+
 # vim: ft=python

src/opr/impl/imgproc.sereg.h

@@ -38,6 +38,26 @@ namespace serialization {
         }
     };
 
+    template <>
+    struct OprMaker<opr::DctChannelSelectForward, 0> {
+        using Opr = opr::DctChannelSelectForward;
+        using Param = Opr::Param;
+        static cg::OperatorNodeBase* make(const Param& param,
+                                          const cg::VarNodeArray& inputs,
+                                          ComputingGraph& graph,
+                                          const OperatorNodeConfig& config) {
+            MGB_MARK_USED_VAR(graph);
+            if (inputs.size() == 3) {
+                return Opr::make(inputs[0], inputs[1], inputs[2], param, config)
+                        .node()
+                        ->owner_opr();
+            } else {
+                mgb_assert(inputs.size() == 1);
+                return Opr::make(inputs[0], param, config).node()->owner_opr();
+            }
+        }
+    };
+
     template<>
     struct OprMaker<opr::WarpPerspectiveBackwardData, 0> {
         using Opr = opr::WarpPerspectiveBackwardData;
@@ -107,6 +127,8 @@ namespace opr {
     //! current resize version
     using ResizeV1 = opr::Resize;
     MGB_SEREG_OPR(ResizeV1, 2);
+
+    MGB_SEREG_OPR(DctChannelSelect, 0);
 } // namespace opr
 
 

src/opr/include/megbrain/opr/imgproc.h

@@ -259,6 +259,37 @@ void record_execute_deps(ExecDependencyArray& deps) override;
 };
 using WarpAffine = WarpAffineForward;
 
+/*!
+ * \brief apply DCT transformation to batched 2D images
+ */
+MGB_DEFINE_OPR_CLASS(
+        DctChannelSelectForward,
+        intl::MegDNNOprWrapperFwd<megdnn::DctChannelSelectForward>)  // {
+
+public:
+DctChannelSelectForward(VarNode* src, VarNode* mask_offset, VarNode* mask_val,
+                        const Param& param, const OperatorNodeConfig& config);
+
+static SymbolVar make(SymbolVar src, SymbolVar mask_offset, SymbolVar mask_val,
+                      const Param& param,
+                      const OperatorNodeConfig& config = {});
+
+DctChannelSelectForward(VarNode* src, const Param& param,
+                        const OperatorNodeConfig& config);
+
+static SymbolVar make(SymbolVar src, const Param& param,
+                      const OperatorNodeConfig& config = {});
+void get_output_var_shape(const TensorShapeArray& inp_shape,
+                          TensorShapeArray& out_shape) const override;
+
+size_t get_workspace_size_bytes(
+        const TensorShapeArray& input_shapes,
+        const TensorShapeArray& output_shapes) const override;
+void scn_do_execute() override;
+};
+
+using DctChannelSelect = DctChannelSelectForward;
+
 }  // opr
 }  // mgb
 

src/opr/include/megbrain/opr/internal/megdnn_opr_wrapper.h

@@ -294,7 +294,7 @@ namespace intl {
             void scn_do_execute() override;
             void get_output_var_shape(
                     const TensorShapeArray &inp_shape,
-                    TensorShapeArray &out_shape) const override final;
+                    TensorShapeArray &out_shape) const override;
 
             void record_execute_deps(
                     cg::GraphExecutable::ExecDependencyArray& deps) override {

src/opr/test/imgproc.cpp

@@ -713,4 +713,94 @@ TEST(TestOprImgproc, Remap_NHWC) {
             .run({TensorShape{N, 20, 20, C}, TensorShape{N, 10, 10, 2}}, opt);
 }
 
+TEST(TestOprImgproc, DCT) {
+    REQUIRE_GPU(1);
+    using Checker3 = AutoOprChecker<3, 1>;
+    using Checker1 = AutoOprChecker<1, 1>;
+    opr::DctChannelSelectForward::Param param;
+    opr::DctChannelSelectForward::Param param_nchw4;
+    param_nchw4.format = opr::DctChannelSelectForward::Param::Format::NCHW4;
+    auto make_graph3 =
+            [&](const Checker3::SymInpArray& inputs) -> Checker3::SymOutArray {
+        return {opr::DctChannelSelectForward::make(inputs[0], inputs[1],
+                                                   inputs[2], param)};
+    };
+    auto fwd3 = [&](Checker3::NumOutArray& dest, Checker3::NumInpArray inp) {
+        auto opr = megdnn_naive_handle()
+                           ->create_operator<megdnn::DctChannelSelectForward>();
+        auto& in_shape = inp[0]->shape();
+        TensorShape out_shp{in_shape[0], in_shape[1] * 64, in_shape[2] / 8,
+                            in_shape[3] / 8};
+        dest[0].comp_node(inp[0]->comp_node()).resize(out_shp);
+        opr->param() = param;
+        opr->exec(inp[0]->as_megdnn(), inp[1]->as_megdnn(), inp[2]->as_megdnn(),
+                  dest[0].as_megdnn(), {});
+    };
+    auto make_graph1 =
+            [&](const Checker1::SymInpArray& inputs) -> Checker1::SymOutArray {
+        return {opr::DctChannelSelectForward::make(inputs[0], param)};
+    };
+    auto make_graph1_s8 =
+            [&](const Checker1::SymInpArray& inputs) -> Checker1::SymOutArray {
+        return {opr::DctChannelSelectForward::make(
+                inputs[0], param_nchw4,
+                OperatorNodeConfig(dtype::QuantizedS8(10.f)))};
+    };
+    auto fwd1 = [&](Checker1::NumOutArray& dest, Checker1::NumInpArray inp) {
+        auto opr = megdnn_naive_handle()
+                           ->create_operator<megdnn::DctChannelSelectForward>();
+        auto& in_shape = inp[0]->shape();
+        TensorShape out_shp{in_shape[0], in_shape[1] * 64, in_shape[2] / 8,
+                            in_shape[3] / 8};
+        dest[0].comp_node(inp[0]->comp_node()).resize(out_shp);
+        opr->param() = param;
+        opr->exec(inp[0]->as_megdnn(), {}, {}, dest[0].as_megdnn(), {});
+    };
+    auto fwd1_s8 = [&](Checker1::NumOutArray& dest, Checker1::NumInpArray inp) {
+        auto opr = megdnn_naive_handle()
+                           ->create_operator<megdnn::DctChannelSelectForward>();
+        auto& in_shape = inp[0]->shape();
+        TensorShape out_shp{in_shape[0], in_shape[1] * 64 / 4, in_shape[2] / 8,
+                            in_shape[3] / 8, 4};
+        dest[0].comp_node(inp[0]->comp_node()).resize(out_shp);
+        opr->param() = param_nchw4;
+        opr->exec(inp[0]->as_megdnn(), {}, {}, dest[0].as_megdnn(), {});
+    };
+    Checker3::RunOptions opt3;
+    Checker1::RunOptions opt1;
+    Checker1::RunOptions opt1_qint8;
+    opt3.outputs_max_err = 1e-3;
+    opt1.outputs_max_err = 1e-3;
+    opt1_qint8.outputs_max_err = 1.001;
+
+    auto gen_input = [](HostTensorND& dest) {
+        HostTensorGenerator<dtype::Uint8, RandomDistribution::UNIFORM>
+                mask_generator{0, 255};
+        dest = *mask_generator(dest.shape(), dest.comp_node());
+    };
+    auto gen_mask = [](HostTensorND& dest) {
+        HostTensorGenerator<dtype::Int32, RandomDistribution::UNIFORM>
+                mask_generator{0, 8};
+        dest = *mask_generator(dest.shape(), dest.comp_node());
+    };
+    Checker1(make_graph1, fwd1, CompNode::load("gpu0"))
+            .disable_grad_check()
+            .set_input_generator(0, gen_input)
+            .set_input_dtype(0, dtype::Uint8())
+            .run({TensorShape{1, 1, 16, 16}}, opt1)
+            .run({TensorShape{1, 3, 256, 256}}, opt1)
+            .run({TensorShape{4, 3, 512, 512}}, opt1);
+
+    Checker1(make_graph1_s8, fwd1_s8, CompNode::load("gpu0"))
+            .disable_grad_check()
+            .set_input_generator(0, gen_input)
+            .set_input_dtype(0, dtype::Uint8())
+            .run({TensorShape{1, 1, 16, 16}}, opt1_qint8)
+            .run({TensorShape{1, 3, 256, 256}}, opt1_qint8)
+            .run({TensorShape{4, 3, 512, 512}}, opt1_qint8);
+
+    MGB_MARK_USED_VAR(make_graph3);
+    MGB_MARK_USED_VAR(fwd3);
+    MGB_MARK_USED_VAR(gen_mask);
+}
 // vim: syntax=cpp.doxygen foldmethod=marker foldmarker=f{{{,f}}}

src/serialization/impl/schema.fbs

@@ -101,6 +101,7 @@ union OperatorParam {
     param.NMSKeep = 69,
     param.AdaptivePooling = 70,
     param.NvOf = 71,
+    param.DctChannelSelect = 72,
 }
 
 table Operator {