[TF FE] Add translators for ScatterND, Conv3DBackpropInputV2 ops (#10550)
* Add translators for ScatterND, ConvBackpropInputV2 ops * add a new line
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Ivan Tikhonov
GitHub
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commit
472ebc0cd9
104
src/frontends/tensorflow/src/op/conv_3d_backprop.cpp
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104
src/frontends/tensorflow/src/op/conv_3d_backprop.cpp
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@ -0,0 +1,104 @@
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// Copyright (C) 2018-2022 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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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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using namespace std;
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using namespace ov::opset8;
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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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OutputVector translate_conv_3d_backprop_input_v2_op(const NodeContext& node) {
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auto ng_filter = node.get_input(1);
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auto ng_out_backprop = node.get_input(2);
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// TODO: refactor me to be less redundant with other convolution ops
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auto tf_strides = node.get_attribute<std::vector<int64_t>>("strides");
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auto tf_dilations = node.get_attribute<std::vector<int64_t>>("dilations");
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auto tf_padding_type = node.get_attribute<std::string>("padding");
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auto tf_data_format = node.get_attribute<std::string>("data_format");
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TENSORFLOW_OP_VALIDATION(node,
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tf_data_format == "NDHWC" || tf_data_format == "NCDHW",
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"Conv3DBackpropInputV2 data format is neither NDHWC nor NCDHW. "
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"Provided data format: ",
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tf_data_format);
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std::vector<int64_t> tf_input_sizes;
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get_const_input(node, 0, &tf_input_sizes);
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if (std::any_of(tf_input_sizes.begin(), tf_input_sizes.end(), [](int32_t size) {
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return size <= 0;
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})) {
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FRONT_END_THROW("Conv3DBackpropInputV2 input sizes must be positive integers");
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}
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bool is_ndhwc = (tf_data_format == "NDHWC");
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ov::Strides ng_strides(3);
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ov::Strides ng_dilations(3);
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ov::Shape ng_image_shape(3);
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ov::Shape ng_kernel_shape(3);
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ov::Shape ng_batch_shape(5);
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convert_nhwc_to_hw(is_ndhwc, tf_strides, ng_strides);
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convert_nhwc_to_hw(is_ndhwc, tf_dilations, ng_dilations);
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convert_nhwc_to_hw(is_ndhwc, tf_input_sizes, ng_image_shape);
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convert_nhwc_to_nchw(node.get_name(), is_ndhwc, ng_out_backprop);
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if (is_ndhwc) {
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ng_batch_shape = {static_cast<unsigned long>(tf_input_sizes[0]),
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static_cast<unsigned long>(tf_input_sizes[4]),
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static_cast<unsigned long>(tf_input_sizes[1]),
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static_cast<unsigned long>(tf_input_sizes[2]),
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static_cast<unsigned long>(tf_input_sizes[3])};
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} else {
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ng_batch_shape = {static_cast<unsigned long>(tf_input_sizes[0]),
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static_cast<unsigned long>(tf_input_sizes[1]),
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static_cast<unsigned long>(tf_input_sizes[2]),
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static_cast<unsigned long>(tf_input_sizes[3]),
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static_cast<unsigned long>(tf_input_sizes[4])};
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}
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auto& ng_filter_shape = ng_filter.get_shape();
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ng_kernel_shape[0] = ng_filter_shape[0];
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ng_kernel_shape[1] = ng_filter_shape[1];
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ng_kernel_shape[2] = ng_filter_shape[2];
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transpose_3d<4, 3, 0, 1, 2>(ng_filter);
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ov::CoordinateDiff ng_padding_below;
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ov::CoordinateDiff ng_padding_above;
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make_padding(tf_padding_type,
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ng_image_shape,
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ng_kernel_shape,
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ng_strides,
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ng_dilations,
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ng_padding_below,
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ng_padding_above);
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auto ng_output_shape = make_shared<Constant>(element::i64,
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Shape{ng_batch_shape.size() - 2},
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vector<size_t>(ng_batch_shape.begin() + 2, ng_batch_shape.end()));
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auto res_node = make_shared<ConvolutionBackpropData>(ng_out_backprop,
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ng_filter,
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ng_output_shape,
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ng_strides,
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ng_padding_below,
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ng_padding_above,
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ng_dilations);
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auto res = res_node->output(0);
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convert_nchw_to_nhwc(node.get_name(), is_ndhwc, res);
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set_node_name(node.get_name(), res.get_node_shared_ptr());
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return {res};
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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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29
src/frontends/tensorflow/src/op/scatter_nd.cpp
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29
src/frontends/tensorflow/src/op/scatter_nd.cpp
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// Copyright (C) 2018-2022 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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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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using namespace std;
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using namespace ov::opset8;
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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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OutputVector translate_scatter_nd_op(const NodeContext& node) {
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auto input_indices = node.get_input(0);
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auto updates = node.get_input(1);
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auto shape = node.get_input(2);
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auto input_data = make_shared<opset8::Constant>(updates.get_element_type(), Shape{1}, 0);
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auto broadcast = make_shared<opset8::Broadcast>(input_data, shape);
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auto res = make_shared<opset8::ScatterNDUpdate>(broadcast, input_indices, updates);
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set_node_name(node.get_name(), res);
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return res->outputs();
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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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@ -33,6 +33,7 @@ OP_CONVERTER(translate_const_op);
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OP_CONVERTER(translate_conv_2d_op);
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OP_CONVERTER(translate_conv_2d_backprop_input_op);
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OP_CONVERTER(translate_conv_3d_op);
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OP_CONVERTER(translate_conv_3d_backprop_input_v2_op);
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OP_CONVERTER(translate_cumsum_op);
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OP_CONVERTER(translate_crop_and_resize_op);
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OP_CONVERTER(translate_depth_to_space_op);
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@ -73,6 +74,7 @@ OP_CONVERTER(translate_reverse_op);
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OP_CONVERTER(translate_roll_op);
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OP_CONVERTER(translate_round_op);
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OP_CONVERTER(translate_rsqrt_op);
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OP_CONVERTER(translate_scatter_nd_op);
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OP_CONVERTER(translate_select_op);
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OP_CONVERTER(translate_shape_op);
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OP_CONVERTER(translate_size_op);
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@ -167,6 +169,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
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{"Conv2D", translate_conv_2d_op},
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{"Conv2DBackpropInput", translate_conv_2d_backprop_input_op},
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{"Conv3D", translate_conv_3d_op},
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{"Conv3DBackpropInputV2", translate_conv_3d_backprop_input_v2_op},
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{"CropAndResize", translate_crop_and_resize_op},
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{"Cumsum", translate_cumsum_op},
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{"DepthToSpace", translate_depth_to_space_op},
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@ -220,6 +223,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
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{"Roll", translate_roll_op},
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{"Round", translate_round_op},
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{"Rsqrt", translate_rsqrt_op},
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{"ScatterNd", translate_scatter_nd_op},
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{"Select", translate_select_op},
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{"SelectV2", translate_select_op},
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{"Shape", translate_shape_op},
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