[ONNX] Extended BatchNorm to support opsets 1-6-7-9-14-15 (openvinoto…

…olkit#23337) ### Details: - *Addition of Batchnorm opset 1 6 7 9 14 & 15* - *Creation of the tests CPP for the respective opset, and their prototxt models.* ### Tickets: - 20554 : [20554](openvinotoolkit#20554) - 18485 : [18485](openvinotoolkit#18485) --------- Co-authored-by: Ubuntu <[email protected]>
siddhant-0707 · Mar 29, 2024 · 34ebb77 · 34ebb77
1 parent 3e114be
commit 34ebb77
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diff --git a/src/frontends/onnx/frontend/src/op/batch_norm.cpp b/src/frontends/onnx/frontend/src/op/batch_norm.cpp
@@ -52,6 +52,12 @@ ov::OutputVector batch_norm(const ov::frontend::onnx::Node& node) {
     OPENVINO_THROW("Cannot create OpenVINO batch norm with unsupported number of inputs");
 }
 }  // namespace set_1
+/*
+     Opset 6 is skipped because there are no significant difference between opset1 and opset6.
+     Found difference is:
+     1. In Training, the computation of ReduceMean and ReduceVar uses float
+        to avoid overflow for float16 inputs.
+ */
 
 namespace set_7 {
 // This version supports ONNX BatchNormalization-7 and BatchNormalization-9
@@ -71,8 +77,42 @@ ov::OutputVector batch_norm(const ov::frontend::onnx::Node& node) {
 
     return {std::make_shared<v5::BatchNormInference>(x, scale, bias, mean, var, epsilon)};
 }
-
 }  // namespace set_7
+/*
+    Opset 9 is skipped because there are no significant difference between opset7 and opset9.
+    Found difference is:
+    1. removed -> spatial : int (default is 1)
+    If true, compute the mean and variance across per activation. If false, compute the mean and variance across
+    per feature over each mini-batch.
+
+ */
+
+namespace set_14 {
+// This version supports ONNX BatchNormalization-14 BatchNormalization-15
+ov::OutputVector batch_norm(const ov::frontend::onnx::Node& node) {
+    ov::OutputVector inputs{node.get_ov_inputs()};
+    auto x = inputs.at(0);
+    auto scale = inputs.at(1);
+    auto bias = inputs.at(2);
+    auto mean = inputs.at(3);
+    auto var = inputs.at(4);
+
+    double epsilon{node.get_attribute_value<double>("epsilon", 1e-5)};
+    int64_t training_mode{node.get_attribute_value<int64_t>("training_mode", 0)};
+
+    CHECK_VALID_NODE(node,
+                     training_mode == false && node.get_outputs_size() == 1,
+                     "Training mode of BatchNormalization is not supported.");
+    return {std::make_shared<v5::BatchNormInference>(x, scale, bias, mean, var, epsilon)};
+}
+}  // namespace set_14
+/*
+     Opset 15 is skipped because there are no significant difference between opset14 and opset15.
+     Found difference is:
+     1. In Training, the computation of ReduceMean and ReduceVar uses float
+        to avoid overflow for float16 inputs.
+ */
+
 }  // namespace op
 }  // namespace onnx
 }  // namespace frontend

diff --git a/src/frontends/onnx/frontend/src/op/batch_norm.hpp b/src/frontends/onnx/frontend/src/op/batch_norm.hpp
@@ -19,6 +19,11 @@ namespace set_7 {
 ov::OutputVector batch_norm(const ov::frontend::onnx::Node& node);
 
 }  // namespace set_7
+
+namespace set_14 {
+ov::OutputVector batch_norm(const ov::frontend::onnx::Node& node);
+
+}  // namespace set_14
 }  // namespace op
 }  // namespace onnx
 }  // namespace frontend

diff --git a/src/frontends/onnx/frontend/src/ops_bridge.cpp b/src/frontends/onnx/frontend/src/ops_bridge.cpp
@@ -360,6 +360,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR("AveragePool", 1, average_pool);
     REGISTER_OPERATOR("BatchNormalization", 1, batch_norm);
     REGISTER_OPERATOR("BatchNormalization", 7, batch_norm);
+    REGISTER_OPERATOR("BatchNormalization", 14, batch_norm);
     REGISTER_OPERATOR("BitShift", 1, bitshift);
     REGISTER_OPERATOR("BitwiseAnd", 1, bitwise_and);
     REGISTER_OPERATOR("BitwiseNot", 1, bitwise_not);

diff --git a/src/frontends/onnx/tests/models/batchnorm_opset1.prototxt b/src/frontends/onnx/tests/models/batchnorm_opset1.prototxt
@@ -0,0 +1,113 @@
+ir_version: 3
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  node {
+    input: "x"
+    input: "s"
+    input: "bias"
+    input: "mean"
+    input: "var"
+    output: "y"
+    op_type: "BatchNormalization"
+  }
+  name: "test_batchnorm_example"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "s"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "bias"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "mean"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "var"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/src/frontends/onnx/tests/models/batchnorm_opset14.prototxt b/src/frontends/onnx/tests/models/batchnorm_opset14.prototxt
@@ -0,0 +1,113 @@
+ir_version: 3
+producer_name: "OpenVINO ONNX Frontend"
+graph {
+  node {
+    input: "x"
+    input: "s"
+    input: "bias"
+    input: "mean"
+    input: "var"
+    output: "y"
+    op_type: "BatchNormalization"
+  }
+  name: "test_batchnorm_example"
+  input {
+    name: "x"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "s"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "bias"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "mean"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "var"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "y"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 2
+          }
+          dim {
+            dim_value: 1
+          }
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 14
+}