Support ConjugateTranspose for TF (#21586)
* conj transpose
* Update
* Fix Mistakes
* Update src/frontends/tensorflow_common/src/op/conj_transpose.cpp
Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>
* Update src/frontends/tensorflow/src/op_table.cpp
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
* """Test the conjugate transpose layer."""
* u
* Resolve Issues
* Revert "conj transpose"
This reverts commit 27833f348a.
* Update ConjTranspose
* Update src/frontends/tensorflow_common/src/op/conj_transpose.cpp
* update clang-format | fix test errors
* Update src/frontends/tensorflow_common/src/op/conj_transpose.cpp
* fix test error
* Update src/frontends/tensorflow_common/src/op/conj_transpose.cpp
---------
Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
This commit is contained in:
Abdulrahman Adel Ibrahim
GitHub
parent
98c309f96b
commit
ded49387b0
|
|
@ -157,6 +157,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
|
|||
{"ClipByValue", CreatorFunction(translate_clip_by_value_op)},
|
||||
{"Complex", CreatorFunction(translate_complex_op)},
|
||||
{"ComplexAbs", CreatorFunction(translate_complex_abs_op)},
|
||||
{"ConjugateTranspose", CreatorFunction(translate_conj_transpose_op)},
|
||||
{"Concat", CreatorFunction(translate_concat_op)},
|
||||
{"ConcatV2", CreatorFunction(translate_concat_op)},
|
||||
{"Const", CreatorFunction(translate_const_op)},
|
||||
|
|
|
|||
|
|
@ -50,6 +50,7 @@ OP_CONVERTER(translate_clip_by_value_op);
|
|||
OP_CONVERTER(translate_complex_op);
|
||||
OP_CONVERTER(translate_complex_abs_op);
|
||||
OP_CONVERTER(translate_concat_op);
|
||||
OP_CONVERTER(translate_conj_transpose_op);
|
||||
OP_CONVERTER(translate_const_op);
|
||||
OP_CONVERTER(translate_conv_2d_op);
|
||||
OP_CONVERTER(translate_conv_2d_backprop_input_op);
|
||||
|
|
|
|||
|
|
@ -0,0 +1,65 @@
|
|||
// Copyright (C) 2018-2023 Intel Corporation
|
||||
// SPDX-License-Identifier: Apache-2.0
|
||||
//
|
||||
|
||||
#include "common_op_table.hpp"
|
||||
#include "helper_ops/complex_type_mark.hpp"
|
||||
#include "openvino/op/concat.hpp"
|
||||
#include "openvino/op/constant.hpp"
|
||||
#include "openvino/op/gather.hpp"
|
||||
#include "openvino/op/negative.hpp"
|
||||
#include "openvino/op/shape_of.hpp"
|
||||
#include "openvino/op/transpose.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace ov::op;
|
||||
|
||||
namespace ov {
|
||||
namespace frontend {
|
||||
namespace tensorflow {
|
||||
namespace op {
|
||||
|
||||
OutputVector translate_conj_transpose_op(const NodeContext& node) {
|
||||
default_op_checks(node, 2, {"ConjugateTranspose"}, true);
|
||||
|
||||
auto x = node.get_input(0);
|
||||
auto perm = node.get_input(1);
|
||||
|
||||
auto complex_type_mark = as_type_ptr<ComplexTypeMark>(x.get_node_shared_ptr());
|
||||
if (complex_type_mark) {
|
||||
element::Type complex_part_type = complex_type_mark->get_complex_part_type();
|
||||
auto x = complex_type_mark->input_value(0);
|
||||
|
||||
auto real_index = make_shared<v0::Constant>(element::i32, Shape{1}, 0);
|
||||
auto imag_index = make_shared<v0::Constant>(element::i32, Shape{1}, 1);
|
||||
|
||||
auto gather_axis = make_shared<v0::Constant>(element::i32, Shape{1}, -1);
|
||||
|
||||
auto real = make_shared<v8::Gather>(x, real_index, gather_axis)->output(0);
|
||||
auto imag = make_shared<v8::Gather>(x, imag_index, gather_axis)->output(0);
|
||||
|
||||
imag = make_shared<v0::Negative>(imag);
|
||||
|
||||
auto conj_tensor = make_shared<v0::Concat>(OutputVector{real, imag}, -1)->output(0);
|
||||
|
||||
OutputVector concat_inputs;
|
||||
concat_inputs.push_back(perm);
|
||||
concat_inputs.push_back(make_shared<v3::ShapeOf>(perm, perm.get_element_type()));
|
||||
|
||||
auto concat = make_shared<v0::Concat>(concat_inputs, 0);
|
||||
auto conj_transpose = make_shared<v1::Transpose>(conj_tensor, concat);
|
||||
|
||||
set_node_name(node.get_name(), conj_transpose);
|
||||
auto complex_transpose = make_shared<ComplexTypeMark>(conj_transpose, complex_part_type);
|
||||
return {complex_transpose->output(0)};
|
||||
}
|
||||
|
||||
auto conj_transpose = make_shared<v1::Transpose>(x, perm);
|
||||
set_node_name(node.get_name(), conj_transpose);
|
||||
return {conj_transpose};
|
||||
}
|
||||
|
||||
} // namespace op
|
||||
} // namespace tensorflow
|
||||
} // namespace frontend
|
||||
} // namespace ov
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
# Copyright (C) 2018-2023 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import pytest
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from common.tf_layer_test_class import CommonTFLayerTest
|
||||
|
||||
# Testing operation ConjugateTranspose
|
||||
# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/ConjugateTranspose
|
||||
|
||||
|
||||
class TestComplexConjugateTranspose(CommonTFLayerTest):
|
||||
|
||||
def _prepare_input(self, inputs_info):
|
||||
|
||||
rng = np.random.default_rng()
|
||||
assert 'real_part' in inputs_info
|
||||
real_part_shape = inputs_info['real_part']
|
||||
assert 'imag_part' in inputs_info
|
||||
imag_part_shape = inputs_info['imag_part']
|
||||
|
||||
inputs_data = {}
|
||||
inputs_data['real_part'] = 4 * rng.random(real_part_shape).astype(np.float32) - 2
|
||||
inputs_data['imag_part'] = 4 * rng.random(imag_part_shape).astype(np.float32) - 2
|
||||
|
||||
return inputs_data
|
||||
|
||||
def create_complex_conjugate_transpose_net(self, input_shape, perm):
|
||||
"""
|
||||
TensorFlow net IR net
|
||||
|
||||
Placeholder->ConjugateTranspose => Placeholder->Transpose->Conjugate->Transpose
|
||||
"""
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
# Create the graph and model
|
||||
with tf.compat.v1.Session() as sess:
|
||||
real_part = tf.compat.v1.placeholder(np.float32, input_shape, 'real_part')
|
||||
imag_part = tf.compat.v1.placeholder(np.float32, input_shape, 'imag_part')
|
||||
|
||||
complex_input = tf.raw_ops.Complex(real=real_part, imag=imag_part)
|
||||
|
||||
conj_tranpose = tf.raw_ops.ConjugateTranspose(x=complex_input, perm=perm, name = "Operation")
|
||||
real = tf.raw_ops.Real(input=conj_tranpose)
|
||||
img = tf.raw_ops.Imag(input=conj_tranpose)
|
||||
|
||||
tf.compat.v1.global_variables_initializer()
|
||||
tf_net = sess.graph_def
|
||||
|
||||
ref_net = None
|
||||
|
||||
return tf_net, ref_net
|
||||
|
||||
|
||||
test_data = [
|
||||
(dict(input_shape=[1, 2], perm=[1, 0])),
|
||||
(dict(input_shape=[1, 2, 3], perm=[2, 1, 0])),
|
||||
(dict(input_shape=[1, 2, 3, 4], perm=[0, 3, 2, 1])),
|
||||
(dict(input_shape=[1, 2, 3, 4, 5, 6], perm=[0, 2, 1, 3, 4, 5])),
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.precommit_tf_fe
|
||||
@pytest.mark.nightly
|
||||
def test_conjugate_transpose(self, params, ie_device, precision, ir_version, temp_dir,
|
||||
use_new_frontend, use_old_api):
|
||||
self._test(*self.create_complex_conjugate_transpose_net(**params),
|
||||
ie_device, precision, ir_version, temp_dir=temp_dir,
|
||||
use_new_frontend=use_new_frontend, use_old_api=use_old_api)
|
||||
|
||||
|
||||
class TestConjugateTranspose(CommonTFLayerTest):
|
||||
|
||||
def _prepare_input(self, inputs_info):
|
||||
|
||||
assert 'input' in inputs_info
|
||||
input_shape = inputs_info['input']
|
||||
|
||||
inputs_data = {}
|
||||
inputs_data['input'] = np.random.default_rng().random(input_shape).astype(np.float32)
|
||||
|
||||
return inputs_data
|
||||
|
||||
def create_conjugate_transpose_net(self, input_shape, perm):
|
||||
"""
|
||||
TensorFlow net IR net
|
||||
|
||||
Placeholder->ConjugateTranspose => Placeholder->Transpose->Conjugate->Transpose
|
||||
"""
|
||||
|
||||
tf.compat.v1.reset_default_graph()
|
||||
|
||||
# Create the graph and model
|
||||
with tf.compat.v1.Session() as sess:
|
||||
input = tf.compat.v1.placeholder(np.float32, input_shape, 'input')
|
||||
|
||||
tf.raw_ops.ConjugateTranspose(x=input, perm=perm, name = "Operation")
|
||||
|
||||
tf.compat.v1.global_variables_initializer()
|
||||
tf_net = sess.graph_def
|
||||
|
||||
ref_net = None
|
||||
|
||||
return tf_net, ref_net
|
||||
|
||||
test_data = [
|
||||
(dict(input_shape=[1, 2], perm=[1, 0])),
|
||||
(dict(input_shape=[1, 2, 3], perm=[2, 1, 0])),
|
||||
(dict(input_shape=[1, 2, 3, 4], perm=[0, 3, 2, 1])),
|
||||
(dict(input_shape=[1, 2, 3, 4, 5, 6], perm=[0, 2, 1, 3, 4, 5])),
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data)
|
||||
@pytest.mark.precommit_tf_fe
|
||||
@pytest.mark.nightly
|
||||
def test_conjugate_transpose(self, params, ie_device, precision, ir_version, temp_dir,
|
||||
use_new_frontend, use_old_api):
|
||||
self._test(*self.create_conjugate_transpose_net(**params),
|
||||
ie_device, precision, ir_version, temp_dir=temp_dir,
|
||||
use_new_frontend=use_new_frontend, use_old_api=use_old_api)
|
||||
Loading…
Reference in New Issue