Revise less equal (#6720)

* update spec, add visitors, backend test

* remove visitors test as it is implemented in another PR

* remove visitors test from CMakeLists

* remove old backend tests, refactor minor parts of the code

* add namespace

* refaactor template test for less_equal op

docs/ops/comparison/LessEqual_1.md

@@ -4,48 +4,50 @@
 
 **Category**: Comparison binary operation
 
-**Short description**: *LessEqual* performs element-wise comparison operation with two given tensors applying multi-directional broadcast rules.
+**Short description**: *LessEqual* performs element-wise comparison operation with two given tensors applying broadcast rules specified in the *auto_broadcast* attribute.
+
+**Detailed description**
+Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
+
+After broadcasting *LessEqual* does the following with the input tensors *a* and *b*:
+
+\f[
+o_{i} = a_{i} <= b_{i}
+\f]
 
 **Attributes**:
 
 * *auto_broadcast*
 
   * **Description**: specifies rules used for auto-broadcasting of input tensors.
   * **Range of values**:
-    * *none* - no auto-broadcasting is allowed, all input shapes should match
-    * *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
+    * *none* - no auto-broadcasting is allowed, all input shapes should match,
+    * *numpy* -  numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md),
+    * *pdpd* - PaddlePaddle-style implicit broadcasting, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md).
   * **Type**: string
   * **Default value**: "numpy"
   * **Required**: *no*
 
 **Inputs**
 
-* **1**: A tensor of type *T*. **Required.**
-* **2**: A tensor of type *T*. **Required.**
+* **1**: A tensor of type *T* and arbitrary shape. **Required.**
+* **2**: A tensor of type *T* and arbitrary shape. **Required.**
 
 **Outputs**
 
-* **1**: The result of element-wise comparison operation. A tensor of type boolean.
+* **1**: The result of element-wise comparison operation applied to the input tensors. A tensor of type **boolean** and  shape equal to broadcasted shape of two inputs.
 
 **Types**
 
 * *T*: arbitrary supported type.
 
-**Detailed description**
-Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
-
-After broadcasting *LessEqual* does the following with the input tensors *a* and *b*:
-
-\f[
-o_{i} = a_{i} <= b_{i}
-\f]
-
 **Examples**
 
-*Example 1*
+*Example 1: no broadcast*
 
 ```xml
 <layer ... type="LessEqual">
+    <data auto_broadcast="none"/>
     <input>
         <port id="0">
             <dim>256</dim>
@@ -65,9 +67,10 @@ o_{i} = a_{i} <= b_{i}
 </layer>
 ```
 
-*Example 2: broadcast*
+*Example 2: numpy broadcast*
 ```xml
 <layer ... type="LessEqual">
+    <data auto_broadcast="numpy"/>
     <input>
         <port id="0">
             <dim>8</dim>

docs/template_plugin/tests/functional/op_reference/less_eq.cpp

@@ -0,0 +1,82 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <ie_core.hpp>
+#include <ie_ngraph_utils.hpp>
+#include <ngraph/ngraph.hpp>
+#include <shared_test_classes/base/layer_test_utils.hpp>
+
+#include "comparison.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using ComparisonTypes = ngraph::helpers::ComparisonTypes;
+
+namespace reference_tests {
+namespace ComparisonOpsRefTestDefinitions {
+namespace {
+TEST_P(ReferenceComparisonLayerTest, LessEqualCompareWithHardcodedRefs) {
+    Exec();
+}
+
+template <element::Type_t IN_ET>
+std::vector<RefComparisonParams> generateComparisonParams(const element::Type& type) {
+    using T = typename element_type_traits<IN_ET>::value_type;
+    std::vector<RefComparisonParams> compParams {
+        // 1D // 2D // 3D // 4D
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
+            .input2({{2, 2}, type, std::vector<T> {0, 12, 23, 0}})
+            .expected({{2, 2}, element::boolean, std::vector<char> {1, 1, 1, 1}}),
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{2, 3}, type, std::vector<T> {0, 6, 45, 1, 21, 21}})
+            .input2({{2, 3}, type, std::vector<T> {1, 18, 23, 1, 19, 21}})
+            .expected({{2, 3}, element::boolean, std::vector<char> {1, 1, 0, 1, 0, 1}}),
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{1}, type, std::vector<T> {53}})
+            .input2({{1}, type, std::vector<T> {53}})
+            .expected({{1}, element::boolean, std::vector<char> {1}}),
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 1, 5, 11, 8}})
+            .input2({{2, 4}, type, std::vector<T> {0, 12, 23, 0, 10, 5, 11, 8}})
+            .expected({{2, 4}, element::boolean, std::vector<char> {1, 1, 1, 1, 1, 1, 1, 1}}),
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{3, 1, 2}, type, std::vector<T> {2, 1, 4, 1, 3, 1}})
+            .input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
+            .expected({{3, 2, 2}, element::boolean, std::vector<char> {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}}),
+        Builder {}
+            .compType(ComparisonTypes::LESS_EQUAL)
+            .input1({{2, 1, 2, 1}, type, std::vector<T> {2, 1, 4, 1}})
+            .input2({{1, 2, 1}, type, std::vector<T> {1, 1}})
+            .expected({{2, 1, 2, 1}, element::boolean, std::vector<char> {0, 1, 0, 1}})};
+    return compParams;
+}
+
+std::vector<RefComparisonParams> generateComparisonCombinedParams() {
+    const std::vector<std::vector<RefComparisonParams>> compTypeParams {
+        generateComparisonParams<element::Type_t::f32>(element::f32),
+        generateComparisonParams<element::Type_t::f16>(element::f16),
+        generateComparisonParams<element::Type_t::i32>(element::i32),
+        generateComparisonParams<element::Type_t::u32>(element::u32),
+        generateComparisonParams<element::Type_t::u8>(element::boolean)};
+    std::vector<RefComparisonParams> combinedParams;
+
+    for (const auto& params : compTypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+} // namespace
+INSTANTIATE_TEST_SUITE_P(smoke_Comparison_With_Hardcoded_Refs, ReferenceComparisonLayerTest, ::testing::ValuesIn(generateComparisonCombinedParams()),
+                         ReferenceComparisonLayerTest::getTestCaseName);
+} // namespace ComparisonOpsRefTestDefinitions
+} // namespace reference_tests

inference-engine/tests/ie_test_utils/functional_test_utils/layer_tests_summary/utils/constants.py

@@ -54,6 +54,7 @@
     'HardSigmoid-1',
     'Interpolate-4',
     'Less-1',
+    'LessEqual-1'
     'LRN-1',
     'LSTMCell-4',
     'LSTMSequence-5',

ngraph/test/backend/comparison.in.cpp

@@ -109,70 +109,3 @@ NGRAPH_TEST(${BACKEND_NAME}, greatereq)
     handle->call_with_validate({result}, {a, b});
     EXPECT_EQ((vector<char>{1, 1, 1, 1, 0, 1, 1, 0}), read_vector<char>(result));
 }
-
-NGRAPH_TEST(${BACKEND_NAME}, lesseq)
-{
-    Shape shape{2, 2, 2};
-    auto A = make_shared<op::Parameter>(element::f32, shape);
-    auto B = make_shared<op::Parameter>(element::f32, shape);
-    auto f = make_shared<Function>(make_shared<op::v1::LessEqual>(A, B), ParameterVector{A, B});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::f32, shape);
-    copy_data(a, vector<float>{1, 8, -8, 17, -0.5, 0, 2, 1});
-    auto b = backend->create_tensor(element::f32, shape);
-    copy_data(b, vector<float>{1, 2, -8, 8, 0, 0, 0.5, 1.5});
-    auto result = backend->create_tensor(element::boolean, shape);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a, b});
-    EXPECT_EQ((vector<char>{1, 0, 1, 0, 1, 1, 0, 1}), read_vector<char>(result));
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, lesseq_int32)
-{
-    Shape shape{2, 2};
-    auto A = make_shared<op::Parameter>(element::i32, shape);
-    auto B = make_shared<op::Parameter>(element::i32, shape);
-    auto f = make_shared<Function>(make_shared<op::v1::LessEqual>(A, B), ParameterVector{A, B});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::i32, shape);
-    copy_data(a, vector<int32_t>{0x40000170, 0x40000005, 0x40000005, -5});
-    auto b = backend->create_tensor(element::i32, shape);
-    copy_data(b, vector<int32_t>{0x40000140, 0x40000001, 0x40000005, 0});
-    auto result = backend->create_tensor(element::boolean, shape);
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a, b});
-    EXPECT_EQ((vector<char>{0, 0, 1, 1}), read_vector<char>(result)); // NNP result {1, 1, 0, 1}
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, lesseq_bool)
-{
-    Shape shape{2, 2, 2};
-    auto A = make_shared<op::Parameter>(element::boolean, shape);
-    auto B = make_shared<op::Parameter>(element::boolean, shape);
-    auto f = make_shared<Function>(make_shared<op::v1::LessEqual>(A, B), ParameterVector{A, B});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    // Create some tensors for input/output
-    auto a = backend->create_tensor(element::boolean, shape);
-    copy_data(a, vector<char>{1, 1, 1, 1, 1, 1, 1, 1});
-    auto b = backend->create_tensor(element::boolean, shape);
-    copy_data(b, vector<char>{0, 0, 0, 0, 0, 0, 0, 0});
-    auto result = backend->create_tensor(element::boolean, shape);
-
-    // Overwrite the initial result vector to make sure we're not just coincidentally getting the
-    // right value.
-    copy_data(result, vector<char>{1, 1, 1, 1, 1, 1, 1, 1});
-
-    auto handle = backend->compile(f);
-    handle->call_with_validate({result}, {a, b});
-    EXPECT_EQ((vector<char>{0, 0, 0, 0, 0, 0, 0, 0}), read_vector<char>(result));
-}

ngraph/test/runtime/ie/unit_test.manifest

@@ -426,7 +426,6 @@ notequal
 greater
 greatereq
 less
-lesseq
 sum_3d_to_scalar_int32
 sum_2d_to_scalar_int8
 max_pool_uint8
@@ -489,7 +488,6 @@ logical_xor
 logical_or
 logical_and
 gather_axis_0_bool
-lesseq_bool
 auto_bcast_binary_elementwise
 auto_bcast_binary_elementwise_pdpd
 any_2x2_to_scalar_true
@@ -758,7 +756,6 @@ strided_slice_stride_optional
 divide_int32
 divide_cpp_rounding_int32
 divide_python_rounding_int32
-lesseq_int32
 
 # Constant and Low Precision
 constant_equality_u4_2x2x3