【PaddlePaddle Hackathon 4】add paddle grid_sampler op

src/frontends/paddle/src/op/grid_sampler.cpp

@@ -0,0 +1,29 @@
+// 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 grid_sampler(const NodeContext& node) {
+    auto data = node.get_input("X");
+    auto grid = node.get_input("Grid");
+    default_opset::GridSample::Attributes attributes{};
+
+    attributes.align_corners = node.get_attribute<bool>("align_corners", 1);
+    attributes.mode = ov::EnumNames<default_opset::GridSample::InterpolationMode>::as_enum(
+        node.get_attribute<std::string>("mode", "bilinear"));
+    attributes.padding_mode = ov::EnumNames<default_opset::GridSample::PaddingMode>::as_enum(
+        node.get_attribute<std::string>("padding_mode", "zeros"));
+
+    return node.default_single_output_mapping({std::make_shared<default_opset::GridSample>(data, grid, attributes)},
+                                              {"Output"});
+}
+}  // namespace op
+}  // namespace paddle
+}  // namespace frontend
+}  // namespace ov

src/frontends/paddle/src/op_table.cpp

@@ -50,6 +50,7 @@ OP_CONVERTER(gather);
 OP_CONVERTER(gather_nd);
 OP_CONVERTER(gelu);
 OP_CONVERTER(greater_than);
+OP_CONVERTER(grid_sampler);
 OP_CONVERTER(group_norm);
 OP_CONVERTER(hard_sigmoid);
 OP_CONVERTER(hard_swish);
@@ -163,6 +164,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"generate_proposals_v2", op::generate_proposals_v2},
             {"greater_equal", op::elementwise_greater_equal},
             {"greater_than", op::greater_than},
+            {"grid_sampler", op::grid_sampler},
             {"group_norm", op::group_norm},
             {"hard_sigmoid", op::hard_sigmoid},
             {"hard_swish", op::hard_swish},

src/frontends/paddle/tests/op_fuzzy.cpp

@@ -235,6 +235,9 @@ static const std::vector<std::string> models{
     std::string("greater_than_float32"),
     std::string("greater_than_int32"),
     std::string("greater_than_int64"),
+    std::string("grid_sampler_1"),
+    std::string("grid_sampler_2"),
+    std::string("grid_sampler_dyn"),
     std::string("group_norm_1/group_norm_1.pdmodel"),
     std::string("group_norm_2/group_norm_2.pdmodel"),
     std::string("group_norm_3/group_norm_3.pdmodel"),

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

@@ -0,0 +1,59 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+#
+# grid_sampler paddle model generator
+#
+import paddle
+import numpy as np
+from save_model import saveModel
+import sys
+
+
+def grid_sampler(name: str, x, grid, mode="bilinear", padding_mode="zeros", align_corners=True, not_empty=True,
+                 is_dynamic=False):
+    paddle.enable_static()
+
+    with paddle.static.program_guard(paddle.static.Program(), paddle.static.Program()):
+        if is_dynamic:
+            x_node = paddle.static.data(name="x", shape=(-1, -1, -1, -1), dtype=x.dtype)
+            grid_node = paddle.static.data(name="grid", shape=(-1, -1, -1, 2), dtype=grid.dtype)
+        else:
+            x_node = paddle.static.data(name="x", shape=x.shape, dtype=x.dtype)
+            grid_node = paddle.static.data(name="grid", shape=grid.shape, dtype=grid.dtype)
+        out = paddle.nn.functional.grid_sample(x_node, grid_node, mode=mode, padding_mode=padding_mode,
+                                               align_corners=align_corners) if not_empty else paddle.nn.functional.grid_sample(
+            x_node, grid_node)
+        place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        exe.run(paddle.static.default_startup_program())
+        outs = exe.run(feed={"x": x, "grid": grid}, fetch_list=[out])
+        saveModel(name, exe, feedkeys=['x', 'grid'], fetchlist=[out], inputs=[x, grid], outputs=[outs[0]],
+                  target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def main():
+    dtype = np.float32
+    x = np.random.randn(2, 2, 3, 3).astype(dtype)
+    grid = np.random.uniform(-1, 1, [2, 3, 3, 2]).astype(dtype)
+    grid_sampler(name='grid_sampler_1', x=x, grid=grid, not_empty=False)
+
+    x = np.random.randn(2, 3, 5, 6).astype(dtype)
+    grid = np.random.uniform(-1, 1, [2, 8, 9, 2]).astype(dtype)
+    mode = "nearest"
+    padding_mode = "reflection"
+    align_corners = False
+    grid_sampler(name='grid_sampler_2', x=x, grid=grid, mode=mode, padding_mode=padding_mode,
+                 align_corners=align_corners)
+
+    x = np.random.randn(2, 3, 128, 128).astype(dtype)
+    grid = np.random.uniform(-1, 1, [2, 130, 130, 2]).astype(dtype)
+    padding_mode = "border"
+    grid_sampler(name='grid_sampler_dyn', x=x, grid=grid, mode=mode, padding_mode=padding_mode,
+                 align_corners=align_corners, is_dynamic=True)
+
+
+if __name__ == "__main__":
+    main()