【PaddlePaddle Hackathon 4】add paddle one_hot_v2 op

docs/MO_DG/prepare_model/Supported_Frameworks_Layers.md

@@ -771,6 +771,7 @@ paddlepaddle >= 2.1
  multiclass_nms                              Only supports IE CPU plugin with "number of selected boxes" static shape (e.g.: ``min(min(num_boxes, nms_top_k) * num_classes_output, keep_top_k)``).
  nearest_interp                              ``NCW``, ``NWC``, ``NHWC``, ``NCDHW``, ``NDHWC`` data_layout are not supported. 
  not_equal 
+ one_hot_v2 
  p_norm 
  pad3d                                       ``Circular`` mode is not supported. 
  pool2d                                      ``NHWC`` data_layout is not supported. 

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 {
+        const 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

@@ -73,6 +73,7 @@ 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);
@@ -195,6 +196,7 @@ 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

@@ -377,6 +377,9 @@ 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("one_hot_v2_3"),
     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 +1,47 @@
+# 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(name: str, x, num_classes, is_tensor):
+    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) if is_tensor else num_classes
+        out = paddle.nn.functional.one_hot(x_node, num_classes=depth_node)
+        place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        feed_list = {"x": x, "depth_tensor": num_classes} if is_tensor else {"x": x}
+        outs = exe.run(feed=feed_list, fetch_list=[out])
+        feedkey_list = ["x", "depth_tensor"] if is_tensor else ['x']
+        input_list = [x, num_classes] if is_tensor else [x]
+        saveModel(name, exe, feedkeys=feedkey_list, fetchlist=[out], inputs=input_list, outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def main():
+    # int 32
+    data = np.array([1]).astype("int32")
+    num_classes = 4
+    one_hot_v2("one_hot_v2_1", data, num_classes, is_tensor=False)
+    # rank 1 int64
+    data = np.array([4, 1, 3, 3]).astype("int64")
+    num_classes = np.array([5]).astype("int32")
+    one_hot_v2("one_hot_v2_2", data, num_classes, is_tensor=True)
+    # rank 2 int64
+    data = np.array([[4, 1, 3, 3], [1, 1, 3, 0]]).astype("int64")
+    num_classes = np.array([5]).astype("int32")
+    one_hot_v2("one_hot_v2_3", data, num_classes, is_tensor=True)
+
+
+if __name__ == "__main__":
+    main()