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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#define EIGEN_USE_THREADS
#include <functional>
#include <memory>
#include <vector>
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/audio_ops.h"
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/math_ops.h"
#include "tensorflow/core/framework/shape_inference_testutil.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
namespace tensorflow {
using namespace ops; // NOLINT(build/namespaces)
TEST(DecodeWavOpTest, DecodeWavTest) {
Scope root = Scope::NewRootScope();
std::vector<uint8> wav_data = {
'R', 'I', 'F', 'F', 44, 0, 0, 0, // size of whole file - 8
'W', 'A', 'V', 'E', 'f', 'm', 't', ' ', 16, 0, 0,
0, // size of fmt block - 8: 24 - 8
1, 0, // format: PCM (1)
1, 0, // channels: 1
0x13, 0x37, 0, 0, // sample rate: 14099
0x26, 0x6e, 0, 0, // byte rate: 2 * 14099
2, 0, // block align: NumChannels * BytesPerSample
16, 0, // bits per sample: 2 * 8
'd', 'a', 't', 'a', 8, 0, 0, 0, // size of payload: 8
0, 0, // first sample: 0
0xff, 0x3f, // second sample: 16383
0xff, 0x7f, // third sample: 32767 (saturated)
0x00, 0x80, // fourth sample: -32768 (saturated)
};
Tensor content_tensor =
test::AsScalar<string>(string(wav_data.begin(), wav_data.end()));
Output content_op =
Const(root.WithOpName("content_op"), Input::Initializer(content_tensor));
DecodeWav decode_wav_op =
DecodeWav(root.WithOpName("decode_wav_op"), content_op);
TF_ASSERT_OK(root.status());
ClientSession session(root);
std::vector<Tensor> outputs;
TF_EXPECT_OK(session.Run(ClientSession::FeedType(),
{decode_wav_op.audio, decode_wav_op.sample_rate},
&outputs));
const Tensor& audio = outputs[0];
const int sample_rate = outputs[1].flat<int32>()(0);
EXPECT_EQ(2, audio.dims());
EXPECT_EQ(1, audio.dim_size(1));
EXPECT_EQ(4, audio.dim_size(0));
EXPECT_NEAR(0.0f, audio.flat<float>()(0), 1e-4f);
EXPECT_NEAR(0.5f, audio.flat<float>()(1), 1e-4f);
EXPECT_NEAR(1.0f, audio.flat<float>()(2), 1e-4f);
EXPECT_NEAR(-1.0f, audio.flat<float>()(3), 1e-4f);
EXPECT_EQ(14099, sample_rate);
}
TEST(DecodeWavOpTest, DecodeWav_ShapeFn) {
ShapeInferenceTestOp op("DecodeWav");
INFER_ERROR("Shape must be rank 0 but is rank 1", op, "[1]");
// audio shape is unknown when desired_{samples,channels} are default.
TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
.Input({"a", 0, DT_STRING})
.Finalize(&op.node_def));
INFER_OK(op, "[]", "[?,?];[]");
TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
.Input({"a", 0, DT_STRING})
.Attr("desired_samples", 42)
.Finalize(&op.node_def));
INFER_OK(op, "[]", "[42,?];[]");
// Negative sample value is rejected.
TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
.Input({"a", 0, DT_STRING})
.Attr("desired_samples", -2)
.Finalize(&op.node_def));
INFER_ERROR("samples must be non-negative, got -2", op, "[]");
TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
.Input({"a", 0, DT_STRING})
.Attr("desired_channels", 2)
.Finalize(&op.node_def));
INFER_OK(op, "[]", "[?,2];[]");
// Negative channel value is rejected.
TF_ASSERT_OK(NodeDefBuilder("test", "DecodeWav")
.Input({"a", 0, DT_STRING})
.Attr("desired_channels", -2)
.Finalize(&op.node_def));
INFER_ERROR("channels must be non-negative, got -2", op, "[]");
}
} // namespace tensorflow
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