From 98adc8f05408bbbc4eae06ae63daabb86394b9b6 Mon Sep 17 00:00:00 2001 From: Leo Chen Date: Mon, 23 Nov 2020 22:50:14 +0800 Subject: [PATCH] Dev/fix doc of some api (#28785) * refine doc of bernoulli * fix some problems * fix unsqueeze * fix squeeze * fix doc --- python/paddle/amp/grad_scaler.py | 26 +++++++++----- python/paddle/nn/layer/loss.py | 20 +++++------ python/paddle/tensor/manipulation.py | 54 ++++++++++------------------ python/paddle/tensor/random.py | 17 ++++----- 4 files changed, 54 insertions(+), 63 deletions(-) diff --git a/python/paddle/amp/grad_scaler.py b/python/paddle/amp/grad_scaler.py index 5ae04042c8..64b34ce834 100644 --- a/python/paddle/amp/grad_scaler.py +++ b/python/paddle/amp/grad_scaler.py @@ -54,12 +54,14 @@ class GradScaler(AmpScaler): optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) + with paddle.amp.auto_cast(): conv = model(data) loss = paddle.mean(conv) - scaled = scaler.scale(loss) # scale the loss - scaled.backward() # do backward - scaler.minimize(optimizer, scaled) # update parameters + + scaled = scaler.scale(loss) # scale the loss + scaled.backward() # do backward + scaler.minimize(optimizer, scaled) # update parameters """ def __init__(self, @@ -86,6 +88,7 @@ class GradScaler(AmpScaler): The scaled tensor or original tensor. Examples: + .. code-block:: python import paddle @@ -94,12 +97,14 @@ class GradScaler(AmpScaler): optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) + with paddle.amp.auto_cast(): conv = model(data) loss = paddle.mean(conv) - scaled = scaler.scale(loss) # scale the loss - scaled.backward() # do backward - scaler.minimize(optimizer, scaled) # update parameters + + scaled = scaler.scale(loss) # scale the loss + scaled.backward() # do backward + scaler.minimize(optimizer, scaled) # update parameters """ return super(GradScaler, self).scale(var) @@ -118,6 +123,7 @@ class GradScaler(AmpScaler): kwargs: Keyword arguments, which will be forward to `optimizer.minimize()`. Examples: + .. code-block:: python import paddle @@ -126,11 +132,13 @@ class GradScaler(AmpScaler): optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) scaler = paddle.amp.GradScaler(init_loss_scaling=1024) data = paddle.rand([10, 3, 32, 32]) + with paddle.amp.auto_cast(): conv = model(data) loss = paddle.mean(conv) - scaled = scaler.scale(loss) # scale the loss - scaled.backward() # do backward - scaler.minimize(optimizer, scaled) # update parameters + + scaled = scaler.scale(loss) # scale the loss + scaled.backward() # do backward + scaler.minimize(optimizer, scaled) # update parameters """ return super(GradScaler, self).minimize(optimizer, *args, **kwargs) diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py index a0186cc0e8..96db0dde54 100644 --- a/python/paddle/nn/layer/loss.py +++ b/python/paddle/nn/layer/loss.py @@ -491,31 +491,29 @@ class L1Loss(fluid.dygraph.Layer): If `reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is [1]. Examples: + .. code-block:: python + import paddle - import numpy as np - paddle.disable_static() - input_data = np.array([[1.5, 0.8], [0.2, 1.3]]).astype("float32") - label_data = np.array([[1.7, 1], [0.4, 0.5]]).astype("float32") - input = paddle.to_tensor(input_data) - label = paddle.to_tensor(label_data) + input = paddle.to_tensor([[1.5, 0.8], [0.2, 1.3]]) + label = paddle.to_tensor([[1.7, 1.0], [0.4, 0.5]]) l1_loss = paddle.nn.loss.L1Loss() output = l1_loss(input, label) - print(output.numpy()) + print(output) # [0.35] l1_loss = paddle.nn.loss.L1Loss(reduction='sum') output = l1_loss(input, label) - print(output.numpy()) + print(output) # [1.4] l1_loss = paddle.nn.loss.L1Loss(reduction='none') output = l1_loss(input, label) - print(output.numpy()) + print(output) # [[0.20000005 0.19999999] - # [0.2 0.79999995]] + # [0.2 0.79999995]] """ def __init__(self, reduction='mean', name=None): @@ -1001,7 +999,7 @@ class SmoothL1Loss(fluid.dygraph.Layer): is the same as the shape of input. Returns: - The tensor variable storing the smooth_l1_loss of input and label. + The tensor storing the smooth_l1_loss of input and label. Return type: Tensor. diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index d8b8dab525..0bda55a1fa 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -354,9 +354,6 @@ def roll(x, shifts, axis=None, name=None): def stack(x, axis=0, name=None): """ - :alias_main: paddle.stack - :alias: paddle.stack, paddle.tensor.stack, paddle.tensor.manipulation.stack - This OP stacks all the input tensors ``x`` along ``axis`` dimemsion. All tensors must be of the same shape and same dtype. @@ -423,13 +420,12 @@ def stack(x, axis=0, name=None): import paddle - paddle.disable_static() x1 = paddle.to_tensor([[1.0, 2.0]]) x2 = paddle.to_tensor([[3.0, 4.0]]) x3 = paddle.to_tensor([[5.0, 6.0]]) out = paddle.stack([x1, x2, x3], axis=0) print(out.shape) # [3, 1, 2] - print(out.numpy()) + print(out) # [[[1., 2.]], # [[3., 4.]], # [[5., 6.]]] @@ -459,34 +455,31 @@ def split(x, num_or_sections, axis=0, name=None): Example: .. code-block:: python - import numpy as np import paddle - # x is a Tensor which shape is [3, 9, 5] - x_np = np.random.random([3, 9, 5]).astype("int32") - x = paddle.to_tensor(x_np) + # x is a Tensor of shape [3, 9, 5] + x = paddle.rand([3, 9, 5]) - out0, out1, out22 = paddle.split(x, num_or_sections=3, axis=1) - # out0.shape [3, 3, 5] - # out1.shape [3, 3, 5] - # out2.shape [3, 3, 5] + out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1) + print(out0.shape) # [3, 3, 5] + print(out1.shape) # [3, 3, 5] + print(out2.shape) # [3, 3, 5] out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, 4], axis=1) - # out0.shape [3, 2, 5] - # out1.shape [3, 3, 5] - # out2.shape [3, 4, 5] + print(out0.shape) # [3, 2, 5] + print(out1.shape) # [3, 3, 5] + print(out2.shape) # [3, 4, 5] out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, -1], axis=1) - # out0.shape [3, 2, 5] - # out1.shape [3, 3, 5] - # out2.shape [3, 4, 5] + print(out0.shape) # [3, 2, 5] + print(out1.shape) # [3, 3, 5] + print(out2.shape) # [3, 4, 5] - # axis is negative, the real axis is (rank(x) + axis) which real - # value is 1. + # axis is negative, the real axis is (rank(x) + axis)=1 out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=-2) - # out0.shape [3, 3, 5] - # out1.shape [3, 3, 5] - # out2.shape [3, 3, 5] + print(out0.shape) # [3, 3, 5] + print(out1.shape) # [3, 3, 5] + print(out2.shape) # [3, 3, 5] """ return paddle.fluid.layers.split( input=x, num_or_sections=num_or_sections, dim=axis, name=name) @@ -494,9 +487,6 @@ def split(x, num_or_sections, axis=0, name=None): def squeeze(x, axis=None, name=None): """ - :alias_main: paddle.squeeze - :alias: paddle.squeeze, paddle.tensor.squeeze, paddle.tensor.manipulation.squeeze - This OP will squeeze the dimension(s) of size 1 of input tensor x's shape. If axis is provided, it will remove the dimension(s) by given axis that of size 1. @@ -552,12 +542,10 @@ def squeeze(x, axis=None, name=None): .. code-block:: python import paddle - - paddle.disable_static() x = paddle.rand([5, 1, 10]) output = paddle.squeeze(x, axis=1) - # output.shape [5, 10] + print(output.shape) # [5, 10] """ if axis is None: @@ -695,9 +683,6 @@ def unique(x, def unsqueeze(x, axis, name=None): """ - :alias_main: paddle.unsqueeze - :alias: paddle.unsqueeze, paddle.tensor.unsqueeze, paddle.tensor.manipulation.unsqueeze - Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one required argument axis, a dimension or list of dimensions that will be inserted. Dimension indices in axis are as seen in the output tensor. @@ -718,7 +703,6 @@ def unsqueeze(x, axis, name=None): import paddle - paddle.disable_static() x = paddle.rand([5, 10]) print(x.shape) # [5, 10] @@ -728,7 +712,7 @@ def unsqueeze(x, axis, name=None): out2 = paddle.unsqueeze(x, axis=[0, 2]) print(out2.shape) # [1, 5, 1, 10] - axis = paddle.fluid.dygraph.to_variable([0, 1, 2]) + axis = paddle.to_tensor([0, 1, 2]) out3 = paddle.unsqueeze(x, axis=axis) print(out3.shape) # [1, 1, 1, 5, 10] diff --git a/python/paddle/tensor/random.py b/python/paddle/tensor/random.py index 934008dc96..2971c3087b 100644 --- a/python/paddle/tensor/random.py +++ b/python/paddle/tensor/random.py @@ -59,17 +59,18 @@ def bernoulli(x, name=None): import paddle - paddle.seed(100) # on CPU device + paddle.set_device('cpu') # on CPU device + paddle.seed(100) + x = paddle.rand([2,3]) - print(x.numpy()) - # [[0.5535528 0.20714243 0.01162981] - # [0.51577556 0.36369765 0.2609165 ]] + print(x) + # [[0.55355281, 0.20714243, 0.01162981], + # [0.51577556, 0.36369765, 0.26091650]] - paddle.seed(200) # on CPU device out = paddle.bernoulli(x) - print(out.numpy()) - # [[0. 0. 0.] - # [1. 1. 0.]] + print(out) + # [[1., 0., 1.], + # [0., 1., 0.]] """ -- GitLab