cleanup #2

src/plugins/intel_cpu/src/nodes/executors/pooling.hpp

@@ -24,7 +24,6 @@ struct PoolingAttrs {
     std::vector<ptrdiff_t> stride;
     std::vector<ptrdiff_t> kernel;
     std::vector<ptrdiff_t> dilation;
-    //ov::Strides dilation;
 
     std::vector<ptrdiff_t> data_pad_begin;
     std::vector<ptrdiff_t> data_pad_end;

src/plugins/intel_cpu/src/nodes/executors/pooling_list.cpp

@@ -10,7 +10,6 @@ namespace intel_cpu {
 const std::vector<PoolingExecutorDesc>& getPoolingExecutorsList() {
     static std::vector<PoolingExecutorDesc> descs = {
         OV_CPU_INSTANCE_ACL(ExecutorType::Acl, std::make_shared<AclPoolingExecutorBuilder>())
-        //OV_CPU_INSTANCE_DNNL(ExecutorType::Dnnl, std::make_shared<DnnlPoolingExecutorBuilder>())
     };
 
     return descs;

src/plugins/intel_cpu/src/nodes/executors/pooling_list.hpp

@@ -11,8 +11,6 @@
 #include "acl/acl_pooling.hpp"
 #endif
 
-//#include "dnnl/dnnl_matmul.hpp"
-
 namespace ov {
 namespace intel_cpu {
 

src/plugins/intel_cpu/src/nodes/pooling.cpp

@@ -4,19 +4,17 @@
 
 #include "pooling.h"
 
-#include <dnnl_extension_utils.h>
-#include <memory_desc/cpu_memory_desc_utils.h>
-#include <onednn/dnnl.h>
-#include <utils/general_utils.h>
-
-#include <common/primitive_hashing_utils.hpp>
+#include "fake_quantize.h"
+#include "conv.h"
+#include "concat.h"
 #include <string>
 #include <vector>
-
-#include "concat.h"
-#include "conv.h"
-#include "fake_quantize.h"
+#include <onednn/dnnl.h>
+#include <dnnl_extension_utils.h>
+#include <utils/general_utils.h>
+#include <memory_desc/cpu_memory_desc_utils.h>
 #include "memory_desc/dnnl_blocked_memory_desc.h"
+#include <common/primitive_hashing_utils.hpp>
 
 #if defined(OV_CPU_WITH_ACL)
 #    include "executors/acl/acl_utils.hpp"
@@ -553,8 +551,8 @@ dnnl::algorithm Pooling::getPoolingAlgorithm() const {
 
 std::shared_ptr<pooling_v2_forward::desc> Pooling::createDescriptorInternal(
     const dnnl::memory::desc& in_candidate,
-                                                                            const dnnl::memory::desc& out_candidate,
-                                                                            const dnnl::algorithm alg) const {
+    const dnnl::memory::desc& out_candidate,
+    const dnnl::algorithm alg) const {
     return createDescriptorHelper(in_candidate,
                                   out_candidate,
                                   alg,
@@ -669,7 +667,7 @@ void Pooling::initSupportedPrimitiveDescriptors() {
             }
         }
     }
-    }
+}
 
 void Pooling::initDescriptor(const NodeConfig& config) {
     if (useACL)

src/plugins/intel_cpu/src/nodes/pooling.h

@@ -54,33 +54,8 @@ class Pooling : public Node {
                                                                                const dnnl::algorithm alg) const;
 
     AttrPtr pAttr;
-
     Shape inShape;
-
     bool useACL = false;
-    //bool isMaxPool8 = false;
-    //bool auto_pad = false;
-    //bool exclude_pad = false;
-    //std::vector<ptrdiff_t> dilation;
-    //std::vector<ptrdiff_t> stride;
-    //std::vector<ptrdiff_t> kernel;
-
-    /// Effective padding. Used to define correct output shape by oneDNN
-    /// reshape formula: (iw - kernel + pad_l + pad_r) / strides[i - 2] + 1
-    /// should be passed into pooling desc constructor.
-    //std::vector<ptrdiff_t> effective_pad_begin;
-    //std::vector<ptrdiff_t> effective_pad_end;
-
-    /// Effective dilation. Used to define correct dilation for OneDNN.
-    /// For OneDNN default dilation is vector of zero
-    //std::vector<ptrdiff_t> effective_dilation;
-
-    /// Effective pad value. Describe how much zero element added to input
-    /// data tensor. May be less than "Effective padding" values.
-    /// If pooling window is out of this padding, the region of averaging
-    /// is decreased.
-    //std::vector<ptrdiff_t> data_pad_begin;
-    //std::vector<ptrdiff_t> data_pad_end;
 };
 
 }   // namespace node

src/plugins/intel_cpu/tests/functional/single_layer_tests/pooling.cpp

@@ -116,15 +116,6 @@ class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestP
             poolInput = ngraph::builder::makeFakeQuantize(poolInput, inPrc, 256, newShape);
         }
 
-        std::cout << "creating node in test: " << std::endl;
-        std::cout << "kernel" << CommonTestUtils::vec2str(kernel) << "_";
-        std::cout << "stride" << CommonTestUtils::vec2str(stride) << "_";
-        std::cout << "excludePad" << excludePad << "_";
-        std::cout << "padBegin" << CommonTestUtils::vec2str(padBegin) << "_";
-        std::cout << "padEnd" << CommonTestUtils::vec2str(padEnd) << "_";
-        std::cout << "roundingType=" << roundingType << "_";
-        std::cout << "padType=" << padType << "_";
-
         std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makePooling(poolInput,
                                                                              stride,
                                                                              padBegin,
@@ -203,17 +194,6 @@ class MaxPoolingV8LayerCPUTest : public testing::WithParamInterface<maxPoolV8Lay
 
         init_input_shapes({inputShapes});
 
-        std::cout << "creating MaxPoolingV8 node in test: " << std::endl;
-        std::cout << "kernel" << CommonTestUtils::vec2str(kernel) << "_";
-        std::cout << "stride" << CommonTestUtils::vec2str(stride) << "_";
-        std::cout << "dilation" << CommonTestUtils::vec2str(dilation) << "_";
-        std::cout << "padBegin" << CommonTestUtils::vec2str(padBegin) << "_";
-        std::cout << "padEnd" << CommonTestUtils::vec2str(padEnd) << "_";
-        std::cout << "roundingType=" << roundingType << "_";
-        std::cout << "padType=" << padType << "_";
-        std::cout << "indexElementType=" << indexElementType << "_";
-        std::cout << "axis=" << axis << "_";
-
         auto params = ngraph::builder::makeDynamicParams(inPrc, inputDynamicShapes);
         std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makeMaxPoolingV8(params[0], stride, dilation, padBegin, padEnd,
                                                                                   kernel, roundingType, padType,