【PaddlePaddle Hackathon 4】add paddle one_hot_v2 op

src/frontends/paddle/src/op/one_hot_v2.cpp

@@ -0,0 +1,31 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "default_opset.hpp"
+#include "openvino/frontend/paddle/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace paddle {
+namespace op {
+NamedOutputs one_hot_v2(const NodeContext& node) {
+    auto data = node.get_input("X");
+    Output<Node> depth;
+    if (node.has_input("depth_tensor")) {
+        auto depth_value = node.get_input("depth_tensor");
+        depth = std::make_shared<default_opset::Squeeze>(depth_value);
+    } else {
+        auto depth_value = node.get_attribute<int>("depth");
+        depth = default_opset::Constant::create(element::i32, Shape{}, {depth_value});
+    }
+    auto on_value = default_opset::Constant::create(element::f32, Shape{}, {1});
+    auto off_value = default_opset::Constant::create(element::f32, Shape{}, {0});
+    const auto indices_axis = 1;
+    auto result = std::make_shared<default_opset::OneHot>(data, depth, on_value, off_value, indices_axis);
+    return node.default_single_output_mapping({result}, {"Out"});
+}
+}  // namespace op
+}  // namespace paddle
+}  // namespace frontend
+}  // namespace ov

src/frontends/paddle/src/op_table.cpp

@@ -69,6 +69,7 @@ OP_CONVERTER(matrix_nms);
 OP_CONVERTER(meshgrid);
 OP_CONVERTER(multiclass_nms);
 OP_CONVERTER(nearest_interp_v2);
+OP_CONVERTER(one_hot_v2);
 OP_CONVERTER(p_norm);
 OP_CONVERTER(pad3d);
 OP_CONVERTER(pow);
@@ -185,6 +186,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"nearest_interp_v2", op::nearest_interp_v2},
             {"nearest_interp", op::nearest_interp_v2},
             {"not_equal", op::elementwise_not_equal},
+            {"one_hot_v2", op::one_hot_v2},
             {"p_norm", op::p_norm},
             {"pad3d", op::pad3d},
             {"pow", op::pow},

src/frontends/paddle/tests/op_fuzzy.cpp

@@ -362,6 +362,8 @@ static const std::vector<std::string> models{
     std::string("not_equal_float32"),
     std::string("not_equal_int32"),
     std::string("not_equal_int64"),
+    std::string("one_hot_v2_1"),
+    std::string("one_hot_v2_2"),
     std::string("p_norm1"),
     std::string("p_norm2"),
     std::string("p_norm3"),

src/frontends/paddle/tests/test_models/gen_scripts/generate_one_hot_v2.py

@@ -0,0 +1,52 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+#
+# one_hot_v2 paddle model generator
+#
+import paddle
+import numpy as np
+from save_model import saveModel
+import sys
+
+
+def one_hot_v2_1(name: str, x, num_classes):
+    paddle.enable_static()
+
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        x_node = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
+        out = paddle.nn.functional.one_hot(x_node, num_classes=num_classes)
+        place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        outs = exe.run(feed={"x": x}, fetch_list=[out])
+        saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+def one_hot_v2_2(name: str, x, num_classes):
+    paddle.enable_static()
+
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        x_node = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
+        depth_node = paddle.static.data(name="depth_tensor", shape=num_classes.shape, dtype=num_classes.dtype)
+        out = paddle.nn.functional.one_hot(x_node, num_classes=depth_node)
+        place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        outs = exe.run(feed={"x": x, "depth_tensor": num_classes}, fetch_list=[out])
+        saveModel(name, exe, feedkeys=["x", "depth_tensor"], fetchlist=[out], inputs=[x, num_classes], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+def main():
+    # int32
+    data = np.array([1, 1, 3, 0]).astype("int32")
+    num_classes = 4
+    one_hot_v2_1("one_hot_v2_1", data, num_classes)
+    # int64
+    data = np.array([4, 1, 3, 3]).astype("int64")
+    num_classes = np.array([5]).astype("int32")
+    one_hot_v2_2("one_hot_v2_2", data, num_classes)
+
+
+if __name__ == "__main__":
+    main()