【Paddle Hackathon 5th No.93】add paddle pool3d op

src/frontends/paddle/src/op/pool3d.cpp

@@ -0,0 +1,328 @@
+//*****************************************************************************
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+//*****************************************************************************
+
+#include <string>
+
+#include "default_opset.hpp"
+#include "openvino/frontend/paddle/node_context.hpp"
+
+namespace ov {
+namespace frontend {
+namespace paddle {
+namespace op {
+// helper func - get pad_begin and pad_end
+static void get_paddings(const NodeContext& node,
+                         ov::Shape& pad_begin,
+                         ov::Shape& pad_end,
+                         ov::op::PadType& auto_pad,
+                         std::string& data_format) {
+    if (node.has_attribute("padding_algorithm")) {
+        auto pad_algo = node.get_attribute<std::string>("padding_algorithm");
+        if (pad_algo == "SAME") {
+            auto_pad = ov::op::PadType::SAME_UPPER;
+        } else if (pad_algo == "VALID") {
+            auto_pad = ov::op::PadType::VALID;
+        } else if (pad_algo == "EXPLICIT") {
+            auto_pad = ov::op::PadType::EXPLICIT;
+        } else {
+            throw std::runtime_error("Unsupported pooling padding_algorithm " + pad_algo);
+        }
+    } else {
+        // adaptive_maxpool with no such attr.
+        auto_pad = ov::op::PadType::EXPLICIT;
+    }
+
+    /*If pool padding size is a tuple or list, it could be in three forms:
+    [pad_depth, pad_height, pad_width] or [pad_depth_front, pad_depth_back,
+    pad_height_top, pad_height_bottom, pad_width_left, pad_width_right],
+    and when data_format is “NCDHW”, pool_padding can
+    be in the form [[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top,
+    pad_height_bottom], [pad_width_left, pad_width_right]]. when
+    data_format is “NDHWC”, pool_padding can be in the form
+    [[0,0], [pad_depth_front, pad_depth_back], [pad_height_top,
+    pad_height_bottom], [pad_width_left, pad_width_right], [0,0]].
+    Otherwise, the pool padding size will be a square of an int.*/
+    auto paddings = node.get_attribute<std::vector<int32_t>>("paddings");
+
+    switch (paddings.size()) {
+    case 3:
+        pad_begin =
+            Shape{static_cast<size_t>(paddings[0]), static_cast<size_t>(paddings[1]), static_cast<size_t>(paddings[2])};
+        pad_end = pad_begin;
+        break;
+    case 6:
+        pad_begin =
+            Shape{static_cast<size_t>(paddings[0]), static_cast<size_t>(paddings[2]), static_cast<size_t>(paddings[4])};
+        pad_end = Shape{
+            static_cast<size_t>(paddings[1]),
+            static_cast<size_t>(paddings[3]),
+            static_cast<size_t>(paddings[5]),
+        };
+        break;
+    default:
+        throw std::runtime_error("Unsupported pooling paddings " + std::to_string(paddings.size()));
+    }
+}
+
+NamedOutputs pool3d(const NodeContext& node) {
+    auto data = node.get_input("X");
+
+    auto pooling_type = node.get_attribute<std::string>("pooling_type", {});
+    auto global_pooling = node.get_attribute<bool>("global_pooling");
+    auto adaptive = node.get_attribute<bool>("adaptive");
+    auto kernel_shape = node.get_attribute<std::vector<int32_t>>("ksize");
+
+    auto rounding_type =
+        node.get_attribute<bool>("ceil_mode", false) ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR;
+
+    if (pooling_type.empty()) {
+        pooling_type = "max";
+    }
+
+    PADDLE_OP_CHECK(node, (pooling_type == "max") || (pooling_type == "avg"), "pool3d: not supported pooling type !");
+    PADDLE_OP_CHECK(node, kernel_shape.size() == 1 || kernel_shape.size() == 3, "pool3d: ksize must be 1 or 3!");
+
+    PartialShape input_shape = data.get_partial_shape();
+
+    int32_t input_rank = static_cast<int32_t>(input_shape.rank().get_length());
+    PADDLE_OP_CHECK(node, input_rank >= 2, "input tensor rank must be greater than 2");
+
+    auto auto_pad = ov::op::PadType::EXPLICIT;
+    ov::Shape pad_begin, pad_end;
+    std::string data_format = node.get_attribute<std::string>("data_format", "NCDHW");
+
+    get_paddings(node, pad_begin, pad_end, auto_pad, data_format);
+
+    if (data_format == "NDHWC") {
+        data = std::make_shared<default_opset::Transpose>(
+            data,
+            std::make_shared<default_opset::Constant>(ov::element::i64, Shape{5}, std::vector<int64_t>{0, 4, 1, 2, 3}));
+        input_shape = data.get_partial_shape();
+    }
+
+    std::vector<Output<Node>> pool_outputs;
+    if (global_pooling || (adaptive && std::any_of(kernel_shape.begin(), kernel_shape.end(), [](int32_t i) {
+                               return i == 1;
+                           }))) {
+        if (pooling_type == "max") {
+            auto axes = default_opset::Constant::create(ov::element::i64,
+                                                        {3},
+                                                        {input_rank - 3, input_rank - 2, input_rank - 1});
+            pool_outputs = std::make_shared<default_opset::ReduceMax>(data, axes, true)->outputs();
+        } else {
+            auto axes = default_opset::Constant::create(ov::element::i64,
+                                                        {3},
+                                                        {input_rank - 3, input_rank - 2, input_rank - 1});
+            pool_outputs = std::make_shared<default_opset::ReduceMean>(data, axes, true)->outputs();
+        }
+    } else if (adaptive) {
+        auto pool_size = std::vector<int64_t>(3, 0);
+
+        if (kernel_shape.size() == 1) {
+            // Not tested: implemented according to spec, but can't generate real
+            // model to test
+            pool_size[0] = pool_size[1] = pool_size[2] = kernel_shape[0];
+        } else {
+            pool_size[0] = kernel_shape[0];
+            pool_size[1] = kernel_shape[1];
+            pool_size[2] = kernel_shape[2];
+        }
+
+        const Output<ov::Node> output_shape =
+            default_opset::Constant::create(ov::element::i64, {pool_size.size()}, pool_size);
+
+        if (pooling_type == "max") {
+            pool_outputs =
+                std::make_shared<default_opset::AdaptiveMaxPool>(data, output_shape, ov::element::i32)->outputs();
+        } else {
+            pool_outputs = std::make_shared<default_opset::AdaptiveAvgPool>(data, output_shape)->outputs();
+        }
+    } else {
+        auto strides = node.get_attribute<std::vector<int32_t>>("strides");
+
+        size_t kernel_d, kernel_h, kernel_w;
+        if (kernel_shape.size() == 1) {
+            // Not tested: implemented according to spec, but can't generate real
+            // model to test
+            kernel_d = kernel_h = kernel_w = kernel_shape[0];
+        } else {
+            kernel_d = kernel_shape[0];
+            kernel_h = kernel_shape[1];
+            kernel_w = kernel_shape[2];
+        }
+
+        PADDLE_OP_CHECK(node,
+                        kernel_d > 0 && kernel_h > 0 && kernel_w > 0,
+                        "pool3d kernel shape must be greater than 0");
+
+        // Note: this shape check is only valid when the spatial dim of input_shape
+        // is static.
+        if (input_shape[2].is_static() && input_shape[3].is_static() && input_shape[4].is_static()) {
+            uint64_t input_d = input_shape[input_rank - 3].get_length();
+            uint64_t input_h = input_shape[input_rank - 2].get_length();
+            uint64_t input_w = input_shape[input_rank - 1].get_length();
+            if ((input_d > 0) && (input_d + pad_begin[0] + pad_end[0] < kernel_d)) {
+                kernel_d = input_d + pad_begin[0] + pad_end[0];
+            }
+            if ((input_h > 0) && (input_h + pad_begin[1] + pad_end[1] < kernel_h)) {
+                kernel_h = input_h + pad_begin[1] + pad_end[1];
+            }
+            if ((input_w > 0) && (input_w + pad_begin[2] + pad_end[2] < kernel_w)) {
+                kernel_w = input_w + pad_begin[2] + pad_end[2];
+            }
+        }
+
+        if (pooling_type == "max") {
+            pool_outputs = std::make_shared<default_opset::MaxPool>(data,
+                                                                    ov::Strides(strides.begin(), strides.end()),
+                                                                    ov::Strides{1, 1, 1},
+                                                                    pad_begin,
+                                                                    pad_end,
+                                                                    ov::Shape{kernel_d, kernel_h, kernel_w},
+                                                                    rounding_type,
+                                                                    auto_pad,
+                                                                    ov::element::i32,
+                                                                    2)
+                               ->outputs();
+        } else {
+            bool exclude_pad = node.get_attribute<bool>("exclusive", false);
+            pool_outputs = std::make_shared<default_opset::AvgPool>(data,
+                                                                    ov::Strides(strides.begin(), strides.end()),
+                                                                    pad_begin,
+                                                                    pad_end,
+                                                                    ov::Shape{kernel_d, kernel_h, kernel_w},
+                                                                    exclude_pad,
+                                                                    rounding_type,
+                                                                    auto_pad)
+                               ->outputs();
+        }
+    }
+
+    if (data_format == "NDHWC") {
+        pool_outputs[0] = std::make_shared<default_opset::Transpose>(
+            pool_outputs[0],
+            std::make_shared<default_opset::Constant>(ov::element::i64, Shape{5}, std::vector<int64_t>{0, 2, 3, 4, 1}));
+    }
+
+    return NamedOutputs{{"Out", {pool_outputs[0]}}};
+}
+
+NamedOutputs pool3d_with_index(const NodeContext& node) {
+    auto data = node.get_input("X");
+    auto pooling_type = node.get_attribute<std::string>("pooling_type", {});
+    auto adaptive = node.get_attribute<bool>("adaptive");
+    auto kernel_shape = node.get_attribute<std::vector<int32_t>>("ksize");
+
+    auto rounding_type =
+        node.get_attribute<bool>("ceil_mode", false) ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR;
+
+    if (pooling_type.empty()) {
+        pooling_type = "max";
+    }
+
+    PADDLE_OP_CHECK(node, (pooling_type == "max") || (pooling_type == "avg"), "pool3d: not supported pooling type !");
+    PADDLE_OP_CHECK(node, kernel_shape.size() == 1 || kernel_shape.size() == 3, "pool3d: ksize must be 1 or 3!");
+
+    PartialShape input_shape = data.get_partial_shape();
+
+    int32_t input_rank = static_cast<int32_t>(input_shape.rank().get_length());
+    PADDLE_OP_CHECK(node, input_rank >= 2, "input tensor rank must be greater than 2");
+
+    auto auto_pad = ov::op::PadType::EXPLICIT;
+    ov::Shape pad_begin, pad_end;
+    std::string data_format = node.get_attribute<std::string>("data_format", "NCDHW");
+
+    get_paddings(node, pad_begin, pad_end, auto_pad, data_format);
+
+    if (data_format == "NDHWC") {
+        data = std::make_shared<default_opset::Transpose>(
+            data,
+            std::make_shared<default_opset::Constant>(ov::element::i64, Shape{5}, std::vector<int64_t>{0, 4, 1, 2, 3}));
+        input_shape = data.get_partial_shape();
+    }
+
+    std::vector<Output<Node>> pool_outputs;
+    if (adaptive) {
+        auto pool_size = std::vector<int64_t>(3, 0);
+
+        if (kernel_shape.size() == 1) {
+            // Not tested: implemented according to spec, but can't generate real
+            // model to test
+            pool_size[0] = pool_size[1] = pool_size[2] = kernel_shape[0];
+        } else {
+            pool_size[0] = kernel_shape[0];
+            pool_size[1] = kernel_shape[1];
+            pool_size[2] = kernel_shape[2];
+        }
+
+        const Output<ov::Node> output_shape =
+            default_opset::Constant::create(ov::element::i64, {pool_size.size()}, pool_size);
+
+        pool_outputs =
+            std::make_shared<default_opset::AdaptiveMaxPool>(data, output_shape, ov::element::i32)->outputs();
+
+    } else {
+        auto strides = node.get_attribute<std::vector<int32_t>>("strides");
+
+        size_t kernel_d, kernel_h, kernel_w;
+        if (kernel_shape.size() == 1) {
+            // Not tested: implemented according to spec, but can't generate real
+            // model to test
+            kernel_d = kernel_h = kernel_w = kernel_shape[0];
+        } else {
+            kernel_d = kernel_shape[0];
+            kernel_h = kernel_shape[1];
+            kernel_w = kernel_shape[2];
+        }
+
+        PADDLE_OP_CHECK(node,
+                        kernel_d > 0 && kernel_h > 0 && kernel_w > 0,
+                        "pool3d kernel shape must be greater than 0");
+
+        // Note: this shape check is only valid when the spatial dim of input_shape
+        // is static.
+        if (input_shape[2].is_static() && input_shape[3].is_static() && input_shape[4].is_static()) {
+            uint64_t input_d = input_shape[input_rank - 3].get_length();
+            uint64_t input_h = input_shape[input_rank - 2].get_length();
+            uint64_t input_w = input_shape[input_rank - 1].get_length();
+            if ((input_d > 0) && (input_d + pad_begin[0] + pad_end[0] < kernel_d)) {
+                kernel_d = input_d + pad_begin[0] + pad_end[0];
+            }
+            if ((input_h > 0) && (input_h + pad_begin[1] + pad_end[1] < kernel_h)) {
+                kernel_h = input_h + pad_begin[1] + pad_end[1];
+            }
+            if ((input_w > 0) && (input_w + pad_begin[2] + pad_end[2] < kernel_w)) {
+                kernel_w = input_w + pad_begin[2] + pad_end[2];
+            }
+        }
+
+        pool_outputs = std::make_shared<default_opset::MaxPool>(data,
+                                                                ov::Strides(strides.begin(), strides.end()),
+                                                                ov::Strides{1, 1, 1},
+                                                                pad_begin,
+                                                                pad_end,
+                                                                ov::Shape{kernel_d, kernel_h, kernel_w},
+                                                                rounding_type,
+                                                                auto_pad,
+                                                                ov::element::i32,
+                                                                2)
+                           ->outputs();
+    }
+
+    if (data_format == "NDHWC") {
+        pool_outputs[0] = std::make_shared<default_opset::Transpose>(
+            pool_outputs[0],
+            std::make_shared<default_opset::Constant>(ov::element::i64, Shape{5}, std::vector<int64_t>{0, 2, 3, 4, 1}));
+    }
+
+    auto output_name = node.get_output_names();
+    return NamedOutputs{{"Out", {pool_outputs[0]}}, {"Mask", {pool_outputs[1]}}};
+}
+
+}  // namespace op
+}  // namespace paddle
+}  // namespace frontend
+}  // namespace ov

src/frontends/paddle/src/op_table.cpp

@@ -79,6 +79,8 @@ OP_CONVERTER(p_norm);
 OP_CONVERTER(pad3d);
 OP_CONVERTER(pow);
 OP_CONVERTER(pool2d);
+OP_CONVERTER(pool3d);
+OP_CONVERTER(pool3d_with_index);
 OP_CONVERTER(prior_box);
 OP_CONVERTER(quantize_linear);
 OP_CONVERTER(range);
@@ -199,6 +201,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"matmul", op::matmul},
             {"matmul_v2", op::matmul_v2},
             {"max_pool2d_with_index", op::pool2d},
+            {"max_pool3d_with_index", op::pool3d_with_index},
             {"matrix_nms", op::matrix_nms},
             {"meshgrid", op::meshgrid},
             {"multiclass_nms3", op::multiclass_nms},
@@ -210,6 +213,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"pad3d", op::pad3d},
             {"pow", op::pow},
             {"pool2d", op::pool2d},
+            {"pool3d", op::pool3d},
             {"prior_box", op::prior_box},
             {"quantize_linear", op::quantize_linear},
             {"range", op::range},

src/frontends/paddle/tests/op_fuzzy.cpp

@@ -34,6 +34,20 @@ static const std::vector<std::string> models{
     std::string("avgPool_test7"),
     std::string("avgPool_test8"),
     std::string("avgPool_test9"),
+    std::string("avgAdaptivePool3D_test1"),
+    std::string("avgAdaptivePool3D_test2"),
+    std::string("avgAdaptivePool3D_test3"),
+    std::string("avgAdaptivePool3D_test4"),
+    std::string("avg3dPool_test1"),
+    std::string("avg3dPool_test2"),
+    std::string("avg3dPool_test3"),
+    std::string("avg3dPool_test4"),
+    std::string("avg3dPool_test5"),
+    std::string("avg3dPool_test6"),
+    std::string("avg3dPool_test7"),
+    std::string("avg3dPool_test8"),
+    std::string("avg3dPool_test9"),
+    std::string("avg3dPool_test10"),
     std::string("batch_norm_nchw/batch_norm_nchw.pdmodel"),
     std::string("batch_norm_nhwc/batch_norm_nhwc.pdmodel"),
     std::string("bicubic_downsample_false_0/bicubic_downsample_false_0.pdmodel"),
@@ -330,6 +344,21 @@ static const std::vector<std::string> models{
     std::string("maxPool_test7"),
     std::string("maxPool_test8"),
     std::string("maxPool_test9"),
+    std::string("maxAdaptivePool3D_test1"),
+    std::string("maxAdaptivePool3D_test2"),
+    std::string("maxAdaptivePool3D_test3"),
+    std::string("maxAdaptivePool3D_test4"),
+    std::string("max3dPool_test1"),
+    std::string("max3dPool_test2"),
+    std::string("max3dPool_test3"),
+    std::string("max3dPool_test4"),
+    std::string("max3dPool_test5"),
+    std::string("max3dPool_test6"),
+    std::string("max3dPool_test7"),
+    std::string("max3dPool_test8"),
+    std::string("max3dPool_test9"),
+    std::string("max3dPool_test10"),
+    std::string("max3dRetureMask"),
     std::string("meshgrid/meshgrid.pdmodel"),
     std::string("multiclass_nms_by_background"),
     std::string("multiclass_nms_by_class_id"),