[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

@@ -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__(

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)

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
 

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:

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.]]
 
     """