Fix Assign, Matmul ops for correct work with keep_shape_ops feature for Kaldi models...

* fix assign and matmul ops

* fix unit test

* static shapes by default for kaldi

diff --git a/model-optimizer/mo/main.py b/model-optimizer/mo/main.py
index e4e7f8b..79d9153 100644 (file)
--- a/model-optimizer/mo/main.py
+++ b/model-optimizer/mo/main.py
@@ -225,7 +225,7 @@ def prepare_ir(argv: argparse.Namespace):
         from mo.front.mxnet.register_custom_ops import get_front_classes
         import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
     elif is_kaldi:
-        argv.static_shape = True  # ticket #36794
+        argv.static_shape = True
         from mo.front.kaldi.register_custom_ops import get_front_classes
         import_extensions.load_dirs(argv.framework, extensions, get_front_classes)
     elif is_onnx:

diff --git a/ngraph/core/src/op/assign.cpp b/ngraph/core/src/op/assign.cpp
index e3d0741..887a87a 100644 (file)
--- a/ngraph/core/src/op/assign.cpp
+++ b/ngraph/core/src/op/assign.cpp
@@ -61,11 +61,19 @@ void op::v3::Assign::validate_and_infer_types()
                           "Variables identifiers are inconsistent.");
     NODE_VALIDATION_CHECK(
         this, arg_t == variable_info.data_type, "Variables types are inconsistent.");
-    NODE_VALIDATION_CHECK(this,
-                          output_shape == variable_info.data_shape,
-                          "Variables output shapes are inconsistent.");
 
-    set_output_type(0, arg_t, output_shape);
+    if (output_shape.is_static() && variable_info.data_shape.is_static())
+    {
+        NODE_VALIDATION_CHECK(this,
+                              output_shape == variable_info.data_shape,
+                              "Variables output shapes are inconsistent.");
+
+        set_output_type(0, arg_t, output_shape);
+    }
+    else
+    {
+        set_output_type(0, arg_t, PartialShape::dynamic());
+    }
 }
 
 shared_ptr<Node> op::v3::Assign::clone_with_new_inputs(const OutputVector& new_args) const

diff --git a/ngraph/core/src/op/matmul.cpp b/ngraph/core/src/op/matmul.cpp
index 220ffe3..eca3b14 100644 (file)
--- a/ngraph/core/src/op/matmul.cpp
+++ b/ngraph/core/src/op/matmul.cpp
@@ -69,6 +69,10 @@ void op::MatMul::pre_validate_and_infer_types()
         Rank max_rank = A_rank.get_length() > B_rank.get_length() ? A_rank : B_rank;
         set_output_type(0, result_et, PartialShape::dynamic(max_rank));
     }
+    else
+    {
+        set_output_type(0, result_et, PartialShape::dynamic());
+    }
 }
 
 OutputVector op::MatMul::decompose_op() const

diff --git a/ngraph/test/op_eval/matmul.cpp b/ngraph/test/op_eval/matmul.cpp
index 49cabb9..b74c02a 100644 (file)
--- a/ngraph/test/op_eval/matmul.cpp
+++ b/ngraph/test/op_eval/matmul.cpp
@@ -68,18 +68,18 @@ TEST(op_eval, matmul_dynamic_0_elem_arg)
     std::vector<std::vector<Shape>> shapes{{Shape{2, 0}, Shape{0, 2}, Shape{2, 2}},
                                            {Shape{0, 2}, Shape{2, 0}, Shape{0, 0}}};
 
-    std::vector<std::vector<int32_t>> arg_inputs{{}, {}};
-    std::vector<std::vector<int32_t>> expected_result{{0, 0, 0, 0}, {}};
+    std::vector<std::vector<float>> arg_inputs{{}, {}};
+    std::vector<std::vector<float>> expected_result{{0, 0, 0, 0}, {}};
 
     for (size_t i = 0; i < arg_inputs.size(); i++)
     {
         auto result = make_shared<HostTensor>();
         ASSERT_TRUE(
             fun->evaluate({result},
-                          {make_host_tensor<element::Type_t::i32>(shapes[i][0], arg_inputs[i]),
-                           make_host_tensor<element::Type_t::i32>(shapes[i][1], arg_inputs[i])}));
+                          {make_host_tensor<element::Type_t::f32>(shapes[i][0], arg_inputs[i]),
+                           make_host_tensor<element::Type_t::f32>(shapes[i][1], arg_inputs[i])}));
         EXPECT_EQ(result->get_shape(), (shapes[i][2]));
-        ASSERT_EQ(read_vector<int32_t>(result), expected_result[i]);
+        ASSERT_EQ(read_vector<float>(result), expected_result[i]);
     }
 }