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[TF FE] Fix ResizeBilinear for uint8 type and test Resize operations (#14801)

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* [TF FE] Fix ResizeBilinear for uint8 type and test Resize operations

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Convert to fp32 right before interpolation

* Add one more test for fp64

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
This commit is contained in:
Roman Kazantsev
2022-12-23 19:39:46 +04:00
committed by GitHub
parent 3414fc402a
commit 6b19acb3f3
2 changed files with 75 additions and 2 deletions
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11
src/frontends/tensorflow/src/op/interpolate.cpp
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@@ -6,13 +6,14 @@
#include "openvino/opsets/opset8.hpp"
using namespace std;
using namespace ov;
using namespace ov::opset8;
namespace ov {
namespace frontend {
namespace tensorflow {
namespace op {
ov::OutputVector translate_interpolate_op(const NodeContext& node) {
OutputVector translate_interpolate_op(const NodeContext& node) {
default_op_checks(node, 2, {"ResizeBilinear", "ResizeNearestNeighbor"});
auto images = node.get_input(0);
auto size = node.get_input(1);
@@ -56,7 +57,7 @@ ov::OutputVector translate_interpolate_op(const NodeContext& node) {
}
// prepare scales input
auto images_shape = make_shared<ShapeOf>(images, ov::element::i32);
auto images_shape = make_shared<ShapeOf>(images, element::i32);
auto spatial_shape = make_shared<Slice>(images_shape,
make_shared<Constant>(element::i64, Shape{1}, std::vector<int64_t>{1}),
make_shared<Constant>(element::i64, Shape{1}, std::vector<int64_t>{3}),
@@ -69,6 +70,12 @@ ov::OutputVector translate_interpolate_op(const NodeContext& node) {
// we can avoid Transpose operation by specifying axes = {1, 2} for original NHWC layout
auto axes = make_shared<Constant>(element::i32, Shape{2}, std::vector<int>({1, 2}));
// according to the specification of ResizeBilinear,
// it always returns FP32 output type so we immediately align input type for it
if (op_type == "ResizeBilinear") {
images = make_shared<Convert>(images, element::f32);
}
auto interpolate = make_shared<Interpolate>(images, size, scales, axes, interpolate_attrs);
set_node_name(node.get_name(), interpolate);
return {interpolate};

66
tests/layer_tests/tensorflow_tests/test_tf_Resize.py Normal file
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@@ -0,0 +1,66 @@
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import tensorflow as tf
from common.tf_layer_test_class import CommonTFLayerTest
class TestResize(CommonTFLayerTest):
def _prepare_input(self, inputs_info):
assert 'images' in inputs_info, "Test error: inputs_info must contain `x`"
images_shape = inputs_info['images']
inputs_data = {}
inputs_data['images'] = np.random.randint(0, 10, images_shape)
return inputs_data
def create_resize_net(self, images_shape, images_type, size_value, align_corners, half_pixel_centers,
resize_op):
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
images = tf.compat.v1.placeholder(images_type, images_shape, 'images')
size = tf.constant(size_value, dtype=tf.int32)
resize_op(images=images, size=size, align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
return tf_net, None
test_data_basic = [
# ResizeBilinear testing
dict(images_shape=[1, 30, 30, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
dict(images_shape=[1, 30, 30, 3], images_type=tf.float64, size_value=[40, 40], align_corners=False,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
dict(images_shape=[2, 100, 100, 3], images_type=tf.float32, size_value=[40, 40], align_corners=True,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
dict(images_shape=[2, 10, 10, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeBilinear),
dict(images_shape=[2, 40, 40, 3], images_type=tf.uint8, size_value=[10, 10], align_corners=False,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
dict(images_shape=[1, 40, 40, 3], images_type=tf.int32, size_value=[10, 10], align_corners=False,
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeBilinear),
# ResizeNearestNeighbor testing
dict(images_shape=[1, 30, 30, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
dict(images_shape=[2, 100, 100, 3], images_type=tf.float32, size_value=[40, 40], align_corners=True,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
dict(images_shape=[2, 10, 10, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeNearestNeighbor),
dict(images_shape=[2, 40, 40, 3], images_type=tf.uint8, size_value=[10, 10], align_corners=False,
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
dict(images_shape=[1, 40, 40, 3], images_type=tf.int32, size_value=[10, 10], align_corners=False,
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeNearestNeighbor),
]
@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit_tf_fe
def test_resize_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_new_frontend, use_old_api):
self._test(*self.create_resize_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_new_frontend=use_new_frontend, use_old_api=use_old_api)