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Merged
merged 5 commits into from
Jul 20, 2023
Merged

Adds IRFFT Op to Signal Library #2137

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03e16b2
irfft op
suleshahid Jul 19, 2023
e66e325
formatting
suleshahid Jul 19, 2023
4e1ee06
review changes
suleshahid Jul 19, 2023
cfa5161
formatting
suleshahid Jul 19, 2023
75a61b7
Merge branch 'main' into signal-irfft-op
mergify[bot] Jul 20, 2023
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1 change: 1 addition & 0 deletions python/tflite_micro/python_ops_resolver.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,7 @@ PythonOpsResolver::PythonOpsResolver() {
AddGreaterEqual();
AddHardSwish();
AddIf();
AddIrfft();
AddL2Normalization();
AddL2Pool2D();
AddLeakyRelu();
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2 changes: 2 additions & 0 deletions python/tflite_micro/signal/ops/fft_ops.py
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Expand Up @@ -80,7 +80,9 @@ def _fft_auto_scale(input_tensor, name=default_name):


rfft = _fft_wrapper(gen_fft_ops.signal_rfft, "signal_rfft")
irfft = _fft_wrapper(gen_fft_ops.signal_irfft, "signal_irfft")
fft_auto_scale = _fft_auto_scale_wrapper(gen_fft_ops.signal_fft_auto_scale,
"signal_fft_auto_scale")
tf.no_gradient("signal_rfft")
tf.no_gradient("signal_irfft")
tf.no_gradient("signal_fft_auto_scale")
55 changes: 55 additions & 0 deletions python/tflite_micro/signal/ops/fft_ops_test.py
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Expand Up @@ -251,6 +251,61 @@ def testFftLengthNoEven(self):
with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
self.evaluate(fft_ops.rfft(fft_input, 127))

def testIrfftTest(self):
for dtype in [np.int16, np.int32, np.float32]:
fft_length = fft_ops._MIN_FFT_LENGTH
while fft_length <= fft_ops._MAX_FFT_LENGTH:
if dtype == np.float32:
# Random input in the range [-1, 1)
fft_input = np.random.random(fft_length).astype(dtype) * 2 - 1
else:
fft_input = np.random.randint(
np.iinfo(np.int16).min,
np.iinfo(np.int16).max + 1, fft_length).astype(dtype)
fft_output = self.evaluate(fft_ops.rfft(fft_input, fft_length))
self.assertEqual(fft_output.shape[0], (fft_length / 2 + 1) * 2)
ifft_output = self.evaluate(fft_ops.irfft(fft_output, fft_length))
self.assertEqual(ifft_output.shape[0], fft_length)
# Output of integer RFFT and IRFFT is scaled by 1/fft_length
if dtype == np.int16:
self.assertArrayNear(fft_input,
ifft_output.astype(np.int32) * fft_length, 6500)
elif dtype == np.int32:
self.assertArrayNear(fft_input,
ifft_output.astype(np.int32) * fft_length, 7875)
else:
self.assertArrayNear(fft_input, ifft_output, 5e-7)
fft_length = 2 * fft_length

def testIrfftLargeOuterDimension(self):
for dtype in [np.int16, np.int32, np.float32]:
fft_length = fft_ops._MIN_FFT_LENGTH
while fft_length <= fft_ops._MAX_FFT_LENGTH:
if dtype == np.float32:
# Random input in the range [-1, 1)
fft_input = np.random.random([2, 5, fft_length
]).astype(dtype) * 2 - 1
else:
fft_input = np.random.randint(
np.iinfo(np.int16).min,
np.iinfo(np.int16).max + 1, [2, 5, fft_length]).astype(dtype)
fft_output = self.evaluate(fft_ops.rfft(fft_input, fft_length))
self.assertEqual(fft_output.shape[-1], (fft_length / 2 + 1) * 2)
ifft_output = self.evaluate(fft_ops.irfft(fft_output, fft_length))
self.assertEqual(ifft_output.shape[-1], fft_length)
# Output of integer RFFT and IRFFT is scaled by 1/fft_length
if dtype == np.int16:
self.assertAllClose(fft_input,
ifft_output.astype(np.int32) * fft_length,
atol=7875)
elif dtype == np.int32:
self.assertAllClose(fft_input,
ifft_output.astype(np.int32) * fft_length,
atol=7875)
else:
self.assertAllClose(fft_input, ifft_output, rtol=5e-7, atol=5e-7)
fft_length = 2 * fft_length

def testAutoScale(self):
self.SingleFftAutoScaleTest('testdata/fft_auto_scale_test1.txt')

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3 changes: 3 additions & 0 deletions signal/micro/kernels/BUILD
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Expand Up @@ -16,12 +16,14 @@ cc_library(
"filter_bank_spectral_subtraction.cc",
"filter_bank_square_root.cc",
"framer.cc",
"irfft.cc",
"overlap_add.cc",
"rfft.cc",
"stacker.cc",
"window.cc",
],
hdrs = [
"irfft.h",
"rfft.h",
],
copts = micro_copts(),
Expand All @@ -36,6 +38,7 @@ cc_library(
"//signal/src:filter_bank_log",
"//signal/src:filter_bank_spectral_subtraction",
"//signal/src:filter_bank_square_root",
"//signal/src:irfft",
"//signal/src:overlap_add",
"//signal/src:rfft",
"//signal/src:window",
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158 changes: 158 additions & 0 deletions signal/micro/kernels/fft_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -303,6 +303,164 @@ TF_LITE_MICRO_TEST(RfftTestSize512Int32) {
g_gen_data_size_fft_length_512_int32, output, 0));
}

TF_LITE_MICRO_TEST(IrfftTestLength64Float) {
constexpr int kOutputLen = 64;
int input_shape[] = {1, 66};
const float input[] = {256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0};
int output_shape[] = {1, kOutputLen};
const float golden[] = {256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
float output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_FLOAT();
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<float>(
input_shape, input, output_shape, golden, *registration,
g_gen_data_fft_length_64_float,
g_gen_data_size_fft_length_64_int16, output, 1e-7));
}

TF_LITE_MICRO_TEST(IrfftTestLength64Int16) {
constexpr int kOutputLen = 64;
int input_shape[] = {1, 66};
const int16_t input[] = {
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0};
int output_shape[] = {1, kOutputLen};
const int16_t golden[] = {256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int16_t output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_INT16();
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<int16_t>(
input_shape, input, output_shape, golden, *registration,
g_gen_data_fft_length_64_int16,
g_gen_data_size_fft_length_64_int16, output, 0));
}

TF_LITE_MICRO_TEST(IrfftTestLength64Int32) {
constexpr int kOutputLen = 64;
int input_shape[] = {1, 66};
const int32_t input[] = {
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0};
int output_shape[] = {1, kOutputLen};
const int32_t golden[] = {256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int32_t output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_INT32();
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<int32_t>(
input_shape, input, output_shape, golden, *registration,
g_gen_data_fft_length_64_int32,
g_gen_data_size_fft_length_64_int32, output, 0));
}

TF_LITE_MICRO_TEST(IrfftTestLength64Int32OuterDims4) {
constexpr int kOutputLen = 64;
constexpr int kOuterDim = 2;
int input_shape[] = {3, kOuterDim, kOuterDim, 66};
const int32_t input[] = {
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0,
256, 0, 256, 0, 256, 0, 256, 0, 256, 0, 256, 0};
int output_shape[] = {3, kOuterDim, kOuterDim, kOutputLen};
const int32_t golden[] = {
256, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 256, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int32_t output[kOuterDim * kOuterDim * kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_INT32();
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<int32_t>(
input_shape, input, output_shape, golden, *registration,
g_gen_data_fft_length_64_int32,
g_gen_data_size_fft_length_64_int32, output, 0));
}

TF_LITE_MICRO_TEST(IrfftTestLength512Float) {
constexpr int kOutputLen = 512;
int input_shape[] = {1, 514};
int output_shape[] = {1, kOutputLen};
float output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_FLOAT();
TF_LITE_MICRO_EXPECT_EQ(
kTfLiteOk, tflite::testing::TestFFT<float>(
input_shape, tflite::kIrfftFloatLength512Input,
output_shape, tflite::kIrfftFloatLength512Golden,
*registration, g_gen_data_fft_length_512_float,
g_gen_data_size_fft_length_512_float, output, 1e-7));
}

TF_LITE_MICRO_TEST(IrfftTestLength512Int16) {
constexpr int kOutputLen = 512;
int input_shape[] = {1, 514};
int output_shape[] = {1, kOutputLen};
int16_t output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_INT16();
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk,
tflite::testing::TestFFT<int16_t>(
input_shape, tflite::kIrfftInt16Length512Input,
output_shape, tflite::kIrfftInt16Length512Golden,
*registration, g_gen_data_fft_length_512_int16,
g_gen_data_size_fft_length_512_int16, output, 0));
}

TF_LITE_MICRO_TEST(IrfftTestLength512Int32) {
constexpr int kOutputLen = 512;
int input_shape[] = {1, 514};
int output_shape[] = {1, kOutputLen};
int32_t output[kOutputLen];
const TFLMRegistration* registration =
tflite::tflm_signal::Register_IRFFT_INT32();
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk,
tflite::testing::TestFFT<int32_t>(
input_shape, tflite::kIrfftInt32Length512Input,
output_shape, tflite::kIrfftInt32Length512Golden,
*registration, g_gen_data_fft_length_512_int32,
g_gen_data_size_fft_length_512_int32, output, 0));
}

TF_LITE_MICRO_TEST(FftAutoScaleTestSmall) {
constexpr int kTensorsSize = 8;
int shape[] = {1, 8};
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