From 02538dfcbef16c8846d43fed3fa4a05c0c7c34d3 Mon Sep 17 00:00:00 2001 From: LoneRanger <836253168@qq.com> Date: Fri, 18 Aug 2023 10:39:15 +0800 Subject: [PATCH] [xdoctest] reformat example code with google style in No.16-No.20 (#56296) * fix sample codes * fix bug * fix bug * fix bug --- python/paddle/audio/features/layers.py | 84 ++++++++++---------- python/paddle/audio/functional/functional.py | 68 ++++++++-------- python/paddle/audio/functional/window.py | 10 +-- python/paddle/autograd/autograd.py | 66 ++++++++------- python/paddle/autograd/backward_mode.py | 47 ++++++----- 5 files changed, 143 insertions(+), 132 deletions(-) diff --git a/python/paddle/audio/features/layers.py b/python/paddle/audio/features/layers.py index d7534b63fe3..59b8967eb7d 100644 --- a/python/paddle/audio/features/layers.py +++ b/python/paddle/audio/features/layers.py @@ -43,18 +43,18 @@ class Spectrogram(nn.Layer): Examples: .. code-block:: python - import paddle - from paddle.audio.features import Spectrogram - - sample_rate = 16000 - wav_duration = 0.5 - num_channels = 1 - num_frames = sample_rate * wav_duration - wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 - waveform = wav_data.tile([num_channels, 1]) - - feature_extractor = Spectrogram(n_fft=512, window = 'hann', power = 1.0) - feats = feature_extractor(waveform) + >>> import paddle + >>> from paddle.audio.features import Spectrogram + + >>> sample_rate = 16000 + >>> wav_duration = 0.5 + >>> num_channels = 1 + >>> num_frames = sample_rate * wav_duration + >>> wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 + >>> waveform = wav_data.tile([num_channels, 1]) + + >>> feature_extractor = Spectrogram(n_fft=512, window = 'hann', power = 1.0) + >>> feats = feature_extractor(waveform) """ def __init__( @@ -128,18 +128,18 @@ class MelSpectrogram(nn.Layer): Examples: .. code-block:: python - import paddle - from paddle.audio.features import MelSpectrogram + >>> import paddle + >>> from paddle.audio.features import MelSpectrogram - sample_rate = 16000 - wav_duration = 0.5 - num_channels = 1 - num_frames = sample_rate * wav_duration - wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 - waveform = wav_data.tile([num_channels, 1]) + >>> sample_rate = 16000 + >>> wav_duration = 0.5 + >>> num_channels = 1 + >>> num_frames = sample_rate * wav_duration + >>> wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 + >>> waveform = wav_data.tile([num_channels, 1]) - feature_extractor = MelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) - feats = feature_extractor(waveform) + >>> feature_extractor = MelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) + >>> feats = feature_extractor(waveform) """ def __init__( @@ -231,18 +231,18 @@ class LogMelSpectrogram(nn.Layer): Examples: .. code-block:: python - import paddle - from paddle.audio.features import LogMelSpectrogram + >>> import paddle + >>> from paddle.audio.features import LogMelSpectrogram - sample_rate = 16000 - wav_duration = 0.5 - num_channels = 1 - num_frames = sample_rate * wav_duration - wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 - waveform = wav_data.tile([num_channels, 1]) + >>> sample_rate = 16000 + >>> wav_duration = 0.5 + >>> num_channels = 1 + >>> num_frames = sample_rate * wav_duration + >>> wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 + >>> waveform = wav_data.tile([num_channels, 1]) - feature_extractor = LogMelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) - feats = feature_extractor(waveform) + >>> feature_extractor = LogMelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) + >>> feats = feature_extractor(waveform) """ def __init__( @@ -335,18 +335,18 @@ class MFCC(nn.Layer): Examples: .. code-block:: python - import paddle - from paddle.audio.features import MFCC + >>> import paddle + >>> from paddle.audio.features import MFCC - sample_rate = 16000 - wav_duration = 0.5 - num_channels = 1 - num_frames = sample_rate * wav_duration - wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 - waveform = wav_data.tile([num_channels, 1]) + >>> sample_rate = 16000 + >>> wav_duration = 0.5 + >>> num_channels = 1 + >>> num_frames = sample_rate * wav_duration + >>> wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1 + >>> waveform = wav_data.tile([num_channels, 1]) - feature_extractor = MFCC(sr=sample_rate, n_fft=512, window = 'hann') - feats = feature_extractor(waveform) + >>> feature_extractor = MFCC(sr=sample_rate, n_fft=512, window = 'hann') + >>> feats = feature_extractor(waveform) """ def __init__( diff --git a/python/paddle/audio/functional/functional.py b/python/paddle/audio/functional/functional.py index 60dc359ee92..930f4123845 100644 --- a/python/paddle/audio/functional/functional.py +++ b/python/paddle/audio/functional/functional.py @@ -34,12 +34,12 @@ def hz_to_mel( Examples: .. code-block:: python - import paddle + >>> import paddle - val = 3.0 - htk_flag = True - mel_paddle_tensor = paddle.audio.functional.hz_to_mel( - paddle.to_tensor(val), htk_flag) + >>> val = 3.0 + >>> htk_flag = True + >>> mel_paddle_tensor = paddle.audio.functional.hz_to_mel( + ... paddle.to_tensor(val), htk_flag) """ if htk: @@ -90,13 +90,13 @@ def mel_to_hz( Examples: .. code-block:: python - import paddle - - val = 3.0 - htk_flag = True - mel_paddle_tensor = paddle.audio.functional.mel_to_hz( - paddle.to_tensor(val), htk_flag) + >>> import paddle + >>> val = 3.0 + >>> htk_flag = True + >>> mel_paddle_tensor = paddle.audio.functional.mel_to_hz( + ... paddle.to_tensor(val), htk_flag) + ... """ if htk: return 700.0 * (10.0 ** (mel / 2595.0) - 1.0) @@ -142,15 +142,15 @@ def mel_frequencies( Examples: .. code-block:: python - import paddle + >>> import paddle - n_mels = 64 - f_min = 0.5 - f_max = 10000 - htk_flag = True + >>> n_mels = 64 + >>> f_min = 0.5 + >>> f_max = 10000 + >>> htk_flag = True - paddle_mel_freq = paddle.audio.functional.mel_frequencies( - n_mels, f_min, f_max, htk_flag, 'float64') + >>> paddle_mel_freq = paddle.audio.functional.mel_frequencies( + ... n_mels, f_min, f_max, htk_flag, 'float64') """ # 'Center freqs' of mel bands - uniformly spaced between limits min_mel = hz_to_mel(f_min, htk=htk) @@ -174,11 +174,11 @@ def fft_frequencies(sr: int, n_fft: int, dtype: str = 'float32') -> Tensor: Examples: .. code-block:: python - import paddle + >>> import paddle - sr = 16000 - n_fft = 128 - fft_freq = paddle.audio.functional.fft_frequencies(sr, n_fft) + >>> sr = 16000 + >>> n_fft = 128 + >>> fft_freq = paddle.audio.functional.fft_frequencies(sr, n_fft) """ return paddle.linspace(0, float(sr) / 2, int(1 + n_fft // 2), dtype=dtype) @@ -211,11 +211,11 @@ def compute_fbank_matrix( Examples: .. code-block:: python - import paddle + >>> import paddle - n_mfcc = 23 - n_mels = 51 - paddle_dct = paddle.audio.functional.create_dct(n_mfcc, n_mels) + >>> sr = 23 + >>> n_fft = 51 + >>> fbank = paddle.audio.functional.compute_fbank_matrix(sr, n_fft) """ if f_max is None: @@ -276,11 +276,11 @@ def power_to_db( Examples: .. code-block:: python - import paddle + >>> import paddle - val = 3.0 - decibel_paddle = paddle.audio.functional.power_to_db( - paddle.to_tensor(val)) + >>> val = 3.0 + >>> decibel_paddle = paddle.audio.functional.power_to_db( + ... paddle.to_tensor(val)) """ if amin <= 0: raise Exception("amin must be strictly positive") @@ -320,10 +320,10 @@ def create_dct( Examples: .. code-block:: python - import paddle - n_mfcc = 23 - n_mels = 257 - dct = paddle.audio.functional.create_dct(n_mfcc, n_mels) + >>> import paddle + >>> n_mfcc = 23 + >>> n_mels = 257 + >>> dct = paddle.audio.functional.create_dct(n_mfcc, n_mels) """ n = paddle.arange(n_mels, dtype=dtype) k = paddle.arange(n_mfcc, dtype=dtype).unsqueeze(1) diff --git a/python/paddle/audio/functional/window.py b/python/paddle/audio/functional/window.py index eb84d6f1889..60eb9626ac6 100644 --- a/python/paddle/audio/functional/window.py +++ b/python/paddle/audio/functional/window.py @@ -352,13 +352,13 @@ def get_window( Examples: .. code-block:: python - import paddle + >>> import paddle - n_fft = 512 - cosine_window = paddle.audio.functional.get_window('cosine', n_fft) + >>> n_fft = 512 + >>> cosine_window = paddle.audio.functional.get_window('cosine', n_fft) - std = 7 - gaussian_window = paddle.audio.functional.get_window(('gaussian',std), n_fft) + >>> std = 7 + >>> gaussian_window = paddle.audio.functional.get_window(('gaussian',std), n_fft) """ sym = not fftbins diff --git a/python/paddle/autograd/autograd.py b/python/paddle/autograd/autograd.py index 7ad3f182c15..1df2659666e 100644 --- a/python/paddle/autograd/autograd.py +++ b/python/paddle/autograd/autograd.py @@ -501,21 +501,23 @@ def jacobian( .. code-block:: python - import paddle + >>> import paddle - x1 = paddle.randn([3, ]) - x2 = paddle.randn([3, ]) - x1.stop_gradient = False - x2.stop_gradient = False + >>> x1 = paddle.randn([3, ]) + >>> x2 = paddle.randn([3, ]) + >>> x1.stop_gradient = False + >>> x2.stop_gradient = False - y = x1 + x2 + >>> y = x1 + x2 - J = paddle.autograd.jacobian(y, (x1, x2)) - J_y_x1 = J[0][:] # evaluate result of dy/dx1 - J_y_x2 = J[1][:] # evaluate result of dy/dx2 + >>> J = paddle.autograd.jacobian(y, (x1, x2)) + >>> J_y_x1 = J[0][:] # evaluate result of dy/dx1 + >>> J_y_x2 = J[1][:] # evaluate result of dy/dx2 - print(J_y_x1.shape) # [3, 3] - print(J_y_x2.shape) # [3, 3] + >>> print(J_y_x1.shape) + [3, 3] + >>> print(J_y_x2.shape) + [3, 3] """ if batch_axis is not None and batch_axis != 0: @@ -583,25 +585,29 @@ def hessian( .. code-block:: python - import paddle - - x1 = paddle.randn([3, ]) - x2 = paddle.randn([4, ]) - x1.stop_gradient = False - x2.stop_gradient = False - - y = x1.sum() + x2.sum() - - H = paddle.autograd.hessian(y, (x1, x2)) - H_y_x1_x1 = H[0][0][:] # evaluate result of ddy/dx1x1 - H_y_x1_x2 = H[0][1][:] # evaluate result of ddy/dx1x2 - H_y_x2_x1 = H[1][0][:] # evaluate result of ddy/dx2x1 - H_y_x2_x2 = H[1][1][:] # evaluate result of ddy/dx2x2 - - print(H_y_x1_x1.shape) # [3, 3] - print(H_y_x1_x2.shape) # [3, 4] - print(H_y_x2_x1.shape) # [4, 3] - print(H_y_x2_x2.shape) # [4, 4] + >>> import paddle + + >>> x1 = paddle.randn([3, ]) + >>> x2 = paddle.randn([4, ]) + >>> x1.stop_gradient = False + >>> x2.stop_gradient = False + + >>> y = x1.sum() + x2.sum() + + >>> H = paddle.autograd.hessian(y, (x1, x2)) + >>> H_y_x1_x1 = H[0][0][:] # evaluate result of ddy/dx1x1 + >>> H_y_x1_x2 = H[0][1][:] # evaluate result of ddy/dx1x2 + >>> H_y_x2_x1 = H[1][0][:] # evaluate result of ddy/dx2x1 + >>> H_y_x2_x2 = H[1][1][:] # evaluate result of ddy/dx2x2 + + >>> print(H_y_x1_x1.shape) + [3, 3] + >>> print(H_y_x1_x2.shape) + [3, 4] + >>> print(H_y_x2_x1.shape) + [4, 3] + >>> print(H_y_x2_x2.shape) + [4, 4] """ if batch_axis is None: diff --git a/python/paddle/autograd/backward_mode.py b/python/paddle/autograd/backward_mode.py index 5b61a91a150..4419a60598a 100644 --- a/python/paddle/autograd/backward_mode.py +++ b/python/paddle/autograd/backward_mode.py @@ -44,34 +44,39 @@ def backward(tensors, grad_tensors=None, retain_graph=False): Examples: .. code-block:: python - import paddle - x = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32', stop_gradient=False) - y = paddle.to_tensor([[3, 2], [3, 4]], dtype='float32') + >>> import paddle + >>> x = paddle.to_tensor([[1, 2], [3, 4]], dtype='float32', stop_gradient=False) + >>> y = paddle.to_tensor([[3, 2], [3, 4]], dtype='float32') - grad_tensor1 = paddle.to_tensor([[1,2], [2, 3]], dtype='float32') - grad_tensor2 = paddle.to_tensor([[1,1], [1, 1]], dtype='float32') + >>> grad_tensor1 = paddle.to_tensor([[1,2], [2, 3]], dtype='float32') + >>> grad_tensor2 = paddle.to_tensor([[1,1], [1, 1]], dtype='float32') - z1 = paddle.matmul(x, y) - z2 = paddle.matmul(x, y) + >>> z1 = paddle.matmul(x, y) + >>> z2 = paddle.matmul(x, y) - paddle.autograd.backward([z1, z2], [grad_tensor1, grad_tensor2], True) - print(x.grad) - #[[12. 18.] - # [17. 25.]] + >>> paddle.autograd.backward([z1, z2], [grad_tensor1, grad_tensor2], True) + >>> print(x.grad) + Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, + [[12., 18.], + [17., 25.]]) - x.clear_grad() - paddle.autograd.backward([z1, z2], [grad_tensor1, None], True) - print(x.grad) - #[[12. 18.] - # [17. 25.]] + >>> x.clear_grad() - x.clear_grad() + >>> paddle.autograd.backward([z1, z2], [grad_tensor1, None], True) + >>> print(x.grad) + Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, + [[12., 18.], + [17., 25.]]) + + >>> x.clear_grad() + + >>> paddle.autograd.backward([z1, z2]) + >>> print(x.grad) + Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=False, + [[10., 14.], + [10., 14.]]) - paddle.autograd.backward([z1, z2]) - print(x.grad) - #[[10. 14.] - # [10. 14.]] """ -- GitLab