[TF FE] Refactor Pad, PadV2, MirrorPad and add layer tests (#13597)
* [TF FE] Refactor Pad, PadV2, MirrorPad and add layer tests Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> * Recover translators for DynamicStitch * Apply code-review feedback and workaround for i32 paddings * Return required type for convert_pad_mode function * Work around IR reader limitation with i32 type for Pad Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
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Roman Kazantsev
GitHub
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@ -3,71 +3,82 @@
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//
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#include "op_table.hpp"
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#include "openvino/opsets/opset8.hpp"
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#include "openvino/opsets/opset9.hpp"
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using namespace std;
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using namespace ov::opset8;
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using namespace ov;
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using namespace ov::opset9;
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// 3 different Pad Ops: Pad, PadV2, MirrorPad
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// See https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/pad
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// See https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/pad-v2
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// See https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/mirror-pad
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namespace ov {
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namespace frontend {
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namespace tensorflow {
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namespace op {
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static void slice_pads_begin_end(const Output<Node>& paddings,
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shared_ptr<Node>& pads_begin,
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shared_ptr<Node>& pads_end) {
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// TODO: fix IR reader to accept padding of i32 type
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auto paddings_i64 = make_shared<Convert>(paddings, element::i64);
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auto axis = make_shared<Constant>(element::i32, Shape{}, 1);
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auto index_zero = make_shared<Constant>(element::i32, Shape{1}, 0);
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auto index_one = make_shared<Constant>(element::i32, Shape{1}, 1);
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auto unsqueeze_pad_begin = make_shared<Gather>(paddings_i64, index_zero, axis);
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auto unsqueeze_pad_end = make_shared<Gather>(paddings_i64, index_one, axis);
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pads_begin = make_shared<Squeeze>(unsqueeze_pad_begin, axis);
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pads_end = make_shared<Squeeze>(unsqueeze_pad_end, axis);
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}
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static OutputVector translate_pad_base_op(const NodeContext& node,
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const Output<Node>& input,
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const Output<Node>& paddings,
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const Output<Node>& constant_value) {
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auto pad_mode = ov::op::PadMode::CONSTANT;
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// prepare pads_begin and pads_end for OpenVINO Pad
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shared_ptr<Node> pads_begin, pads_end;
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slice_pads_begin_end(paddings, pads_begin, pads_end);
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auto pad = make_shared<Pad>(input, pads_begin, pads_end, constant_value, pad_mode);
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set_node_name(node.get_name(), pad);
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return {pad};
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}
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OutputVector translate_pad_op(const NodeContext& node) {
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auto ng_input = node.get_input(0), ng_paddings_op = node.get_input(1);
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Output<Node> pad_val_op;
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default_op_checks(node, 2, {"Pad"});
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auto input = node.get_input(0);
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auto paddings = node.get_input(1);
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auto constant_value = make_shared<Constant>(input.get_element_type(), Shape{}, 0);
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// Set inputs and pad_val_op
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auto op_type = node.get_op_type();
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if (op_type == "Pad" || op_type == "MirrorPad") {
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pad_val_op = make_shared<Constant>(ng_input.get_element_type(), Shape(), std::vector<int>({0}));
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} else if (op_type == "PadV2") {
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pad_val_op = node.get_input(2);
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} else {
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TENSORFLOW_OP_VALIDATION(node, false, "Incorrect TF Pad OpType: " + node.get_op_type());
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}
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return translate_pad_base_op(node, input, paddings, constant_value);
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}
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// Set pad_mode
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auto pad_mode = ov::op::PadMode::CONSTANT;
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if (op_type == "MirrorPad") {
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auto pad_mode_str = node.get_attribute<std::string>("mode");
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if (pad_mode_str == "REFLECT") {
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pad_mode = ov::op::PadMode::REFLECT;
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} else if (pad_mode_str == "SYMMETRIC") {
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pad_mode = ov::op::PadMode::SYMMETRIC;
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} else {
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TENSORFLOW_OP_VALIDATION(node, false, pad_mode_str + " is not an allowed padding mode.");
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}
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}
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OutputVector translate_padv2_op(const NodeContext& node) {
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default_op_checks(node, 3, {"PadV2"});
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auto input = node.get_input(0);
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auto paddings = node.get_input(1);
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auto constant_value = node.get_input(2);
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// Set pads_begin & pads_end (from the pad_val_op)
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std::vector<int64_t> paddings;
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get_const_input(node, 1, &paddings);
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if (paddings.size() % 2 != 0) {
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TENSORFLOW_OP_VALIDATION(node,
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false,
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"Constant node for paddings does not have an even number of "
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"elements");
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}
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std::vector<int64_t> pad_begin(paddings.size() / 2);
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std::vector<int64_t> pad_end(paddings.size() / 2);
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for (size_t i = 0; i < paddings.size() / 2; i++) {
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pad_begin[i] = paddings[2 * i];
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pad_end[i] = paddings[2 * i + 1];
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}
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auto pads_begin_node = make_shared<Constant>(element::i64, Shape{pad_begin.size()}, pad_begin);
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auto pads_end_node = make_shared<Constant>(element::i64, Shape{pad_end.size()}, pad_end);
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return translate_pad_base_op(node, input, paddings, constant_value);
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}
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// Create final Op
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auto res = make_shared<Pad>(ng_input, pads_begin_node, pads_end_node, pad_val_op, pad_mode);
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set_node_name(node.get_name(), res);
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return res->outputs();
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OutputVector translate_mirror_pad_op(const NodeContext& node) {
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default_op_checks(node, 2, {"MirrorPad"});
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auto input = node.get_input(0);
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auto paddings = node.get_input(1);
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// retrieve attributes
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auto mode = node.get_attribute<std::string>("mode");
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auto pad_mode = convert_padding_mode(node, mode);
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// prepare pads_begin and pads_end for OpenVINO Pad
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shared_ptr<Node> pads_begin, pads_end;
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slice_pads_begin_end(paddings, pads_begin, pads_end);
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auto pad = make_shared<Pad>(input, pads_begin, pads_end, pad_mode);
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set_node_name(node.get_name(), pad);
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return {pad};
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}
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} // namespace op
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} // namespace tensorflow
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} // namespace frontend
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} // namespace ov
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} // namespace ov
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@ -77,15 +77,17 @@ OP_CONVERTER(translate_lrn_op);
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OP_CONVERTER(translate_mat_mul_op);
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OP_CONVERTER(translate_matrix_diag_op);
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OP_CONVERTER(translate_max_pool_op);
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OP_CONVERTER(translate_mirror_pad_op);
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OP_CONVERTER(translate_non_max_suppression_op);
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OP_CONVERTER(translate_normalize_l2_op);
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OP_CONVERTER(translate_pad_op);
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OP_CONVERTER(translate_parallel_dynamic_stitch_op);
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OP_CONVERTER(translate_placeholder_op);
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OP_CONVERTER(translate_placeholder_with_default_op);
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OP_CONVERTER(translate_no_op);
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OP_CONVERTER(translate_one_hot_op);
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OP_CONVERTER(translate_pack_op);
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OP_CONVERTER(translate_pad_op);
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OP_CONVERTER(translate_padv2_op);
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OP_CONVERTER(translate_range_op);
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OP_CONVERTER(translate_rank_op);
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OP_CONVERTER(translate_random_uniform_op);
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@ -253,7 +255,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
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{"MaxPool", translate_max_pool_op},
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{"MaxPoolV2", translate_max_pool_op},
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{"MaxPool3D", translate_max_pool_op},
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{"MirrorPad", translate_pad_op},
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{"MirrorPad", translate_mirror_pad_op},
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{"NonMaxSuppression", translate_non_max_suppression_op},
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{"NonMaxSuppressionV2", translate_non_max_suppression_op},
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{"NonMaxSuppressionV3", translate_non_max_suppression_op},
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@ -264,7 +266,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
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{"OneHot", translate_one_hot_op},
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{"Pack", translate_pack_op},
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{"Pad", translate_pad_op},
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{"PadV2", translate_pad_op},
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{"PadV2", translate_padv2_op},
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{"DynamicStitch", translate_parallel_dynamic_stitch_op},
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{"ParallelDynamicStitch", translate_parallel_dynamic_stitch_op},
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{"Placeholder", translate_placeholder_op},
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@ -9,9 +9,11 @@
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#include "openvino_conversions.hpp"
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using namespace ov;
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using namespace ov::op;
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using namespace ov::opset10;
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using namespace ov::opset8;
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using namespace std;
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using namespace ov::frontend::tensorflow;
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void ov::frontend::tensorflow::set_node_name(const std::string& node_name, const std::shared_ptr<Node>& node) {
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const auto& outputs = node->outputs();
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@ -42,14 +44,15 @@ ov::op::PadType ov::frontend::tensorflow::convert_tf_padding(const ov::frontend:
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"AvgPool",
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"AvgPool3D"};
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auto op_type = node.get_op_type();
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TENSORFLOW_OP_VALIDATION(node,
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supported_ops.count(op_type),
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"Conversion of padding mode for " + op_type + " is not supported.");
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TENSORFLOW_OP_VALIDATION(
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node,
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tf_padding == "VALID" || tf_padding == "SAME" || tf_padding == "EXPLICIT",
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"The deconvolutional operation must have one of the padding type: VALID, SAME, and EXPLICIT.");
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supported_ops.count(op_type),
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"OpenVINO TensorFlow Frontend does not support conversion of padding type for " + op_type + " operation.");
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std::set<std::string> supported_modes = {"VALID", "SAME", "EXPLICIT"};
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TENSORFLOW_OP_VALIDATION(node,
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supported_modes.count(tf_padding),
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"OpenVINO TensorFlow Frontend does not support " + tf_padding + " padding mode.");
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if (tf_padding == "VALID") {
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return ov::op::PadType::VALID;
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@ -216,6 +219,28 @@ ov::Output<ov::Node> ov::frontend::tensorflow::get_elements_number_1d(const ov::
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return num_elements;
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}
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PadMode ov::frontend::tensorflow::convert_padding_mode(const NodeContext& node, const std::string& padding_mode) {
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std::set<std::string> supported_ops = {"MirrorPad"};
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auto op_type = node.get_op_type();
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TENSORFLOW_OP_VALIDATION(
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node,
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supported_ops.count(op_type),
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"OpenVINO TensorFlow Frontend does not support conversion of padding mode for " + op_type + " operation.");
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std::set<std::string> supported_modes = {"REFLECT", "SYMMETRIC"};
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TENSORFLOW_OP_VALIDATION(node,
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supported_modes.count(padding_mode),
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"OpenVINO TensorFlow Frontend does not support " + padding_mode + " padding mode.");
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if (padding_mode == "REFLECT") {
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return PadMode::REFLECT;
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} else if (padding_mode == "SYMMETRIC") {
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return PadMode::SYMMETRIC;
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}
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return PadMode::REFLECT;
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}
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Output<Node> ov::frontend::tensorflow::compute_subgraph_scalar_rank(const Output<Node>& output,
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element::Type output_type,
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bool as_scalar) {
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@ -52,6 +52,8 @@ ov::Output<Node> get_elements_number_1d(const Output<Node>& output,
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ov::element::Type output_type,
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ov::pass::NodeRegistry& rg);
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ov::op::PadMode convert_padding_mode(const NodeContext& node, const std::string& padding_mode);
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Output<Node> compute_subgraph_scalar_rank(const Output<Node>& output,
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element::Type output_type,
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bool as_scalar = false);
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45
tests/layer_tests/tensorflow_tests/test_tf_Pad.py
Normal file
45
tests/layer_tests/tensorflow_tests/test_tf_Pad.py
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@ -0,0 +1,45 @@
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# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from common.tf_layer_test_class import CommonTFLayerTest
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class TestPad(CommonTFLayerTest):
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def create_pad_net(self, input_shape, pads_values, const_value, pad_mode, pad_op):
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import tensorflow as tf
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tf.compat.v1.reset_default_graph()
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with tf.compat.v1.Session() as sess:
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paddings = tf.constant(pads_values, dtype=tf.int32)
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placeholder = tf.compat.v1.placeholder(tf.float32, input_shape, 'input')
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if pad_op == 'Pad':
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tf.raw_ops.Pad(input=placeholder, paddings=paddings, name='pad')
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elif pad_op == 'PadV2':
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constant_values = tf.constant(const_value, dtype=tf.float32)
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tf.raw_ops.PadV2(input=placeholder, paddings=paddings, constant_values=constant_values, name='pad')
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elif pad_op == 'MirrorPad':
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tf.raw_ops.MirrorPad(input=placeholder, paddings=paddings, mode=pad_mode, name='pad')
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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ref_net = None
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return tf_net, ref_net
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test_data_basic = [
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dict(input_shape=[2, 3], pads_values=[[0, 1], [2, 3]], const_value=None, pad_mode=None, pad_op='Pad'),
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dict(input_shape=[2, 4, 3], pads_values=[[1, 2], [3, 4], [1, 1]], const_value=3, pad_mode=None, pad_op='PadV2'),
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dict(input_shape=[5, 6], pads_values=[[0, 1], [2, 3]], const_value=None, pad_mode='REFLECT',
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pad_op='MirrorPad'),
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dict(input_shape=[4, 6], pads_values=[[2, 1], [3, 1]], const_value=None, pad_mode='SYMMETRIC',
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pad_op='MirrorPad'),
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_pad_basic(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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use_old_api):
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self._test(*self.create_pad_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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