118 lines
4.9 KiB
Python
118 lines
4.9 KiB
Python
import ngraph as ng
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import numpy as np
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from tests.runtime import get_runtime
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def build_fft_input_data():
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np.random.seed(202104)
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return np.random.uniform(0, 1, (2, 10, 10, 2)).astype(np.float32)
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def test_dft_1d():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([2], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fft(np.squeeze(input_data.view(dtype=np.complex64), axis=-1),
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axis=2).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.00001)
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def test_dft_2d():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([1, 2], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fft2(np.squeeze(input_data.view(dtype=np.complex64), axis=-1),
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axes=[1, 2]).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.000062)
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def test_dft_3d():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([0, 1, 2], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fftn(np.squeeze(input_data.view(dtype=np.complex64), axis=-1),
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axes=[0, 1, 2]).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.0002)
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def test_dft_1d_signal_size():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([-2], dtype=np.int64))
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input_signal_size = ng.constant(np.array([20], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes, input_signal_size)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fft(np.squeeze(input_data.view(dtype=np.complex64), axis=-1), n=20,
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axis=-2).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.00001)
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def test_dft_2d_signal_size_1():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([0, 2], dtype=np.int64))
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input_signal_size = ng.constant(np.array([4, 5], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes, input_signal_size)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fft2(np.squeeze(input_data.view(dtype=np.complex64), axis=-1), s=[4, 5],
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axes=[0, 2]).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.000062)
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def test_dft_2d_signal_size_2():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([1, 2], dtype=np.int64))
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input_signal_size = ng.constant(np.array([4, 5], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes, input_signal_size)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fft2(np.squeeze(input_data.view(dtype=np.complex64), axis=-1), s=[4, 5],
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axes=[1, 2]).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.000062)
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def test_dft_3d_signal_size():
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runtime = get_runtime()
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input_data = build_fft_input_data()
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input_tensor = ng.constant(input_data)
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input_axes = ng.constant(np.array([0, 1, 2], dtype=np.int64))
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input_signal_size = ng.constant(np.array([4, 5, 16], dtype=np.int64))
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dft_node = ng.dft(input_tensor, input_axes, input_signal_size)
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computation = runtime.computation(dft_node)
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dft_results = computation()
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np_results = np.fft.fftn(np.squeeze(input_data.view(dtype=np.complex64), axis=-1),
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s=[4, 5, 16], axes=[0, 1, 2]).astype(np.complex64)
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expected_results = np.stack((np_results.real, np_results.imag), axis=-1)
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assert np.allclose(dft_results, expected_results, atol=0.0002)
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