From 6f791c6b4913a5cc459c19c0e240180b98087ef2 Mon Sep 17 00:00:00 2001 From: changzherui Date: Thu, 2 Mar 2023 00:00:58 +0800 Subject: [PATCH] modify func dropout --- .../ops/mindspore.ops.func_dropout.rst | 13 ++++------ mindspore/python/mindspore/nn/layer/basic.py | 15 +++-------- .../python/mindspore/ops/function/nn_func.py | 25 ++++++++----------- tests/st/ops/cpu/test_dropout_op.py | 6 ++--- 4 files changed, 21 insertions(+), 38 deletions(-) diff --git a/docs/api/api_python/ops/mindspore.ops.func_dropout.rst b/docs/api/api_python/ops/mindspore.ops.func_dropout.rst index c5a9b933524..f9c8440c547 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_dropout.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_dropout.rst @@ -1,22 +1,19 @@ mindspore.ops.dropout ====================== -.. py:function:: mindspore.ops.dropout(x, p=0.5, seed0=0, seed1=0) +.. py:function:: mindspore.ops.dropout(input, p=0.5) 在训练期间，以服从伯努利分布的概率 `p` 随机将输入Tensor的某些值归零，起到减少神经元相关性的作用，避免过拟合。此概率与 `ops.Dropout` 和 `nn.Dropout` 中的含义相反。 参数： - - **x** (Tensor) - dropout的输入，任意维度的Tensor，其数据类型为float16或float32。 + - **input** (Tensor) - dropout的输入，任意维度的Tensor，其数据类型为float16或float32。 - **p** (float，可选) - 输入神经元丢弃概率，数值范围在0到1之间。例如，p=0.1，删除10%的神经元。默认值：0.5。 - - **seed0** (int，可选) - 算子层的随机种子，用于生成随机数。默认值：0。 - - **seed1** (int，可选) - 全局的随机种子，和算子层的随机种子共同决定最终生成的随机数。默认值：0。 返回： - - **output** (Tensor) - 归零后的Tensor，shape和数据类型与 `x` 相同。 + - **output** (Tensor) - 归零后的Tensor，shape和数据类型与 `input` 相同。 - **mask** (Tensor) - 用于归零的掩码，内部会按位压缩与对齐。 异常： - **TypeError** - `p` 不是float。 - - **TypeError** - `seed0` 或 `seed1` 不是int。 - - **TypeError** - `x` 的数据类型既不是float16也不是float32。 - - **TypeError** - `x` 不是Tensor。 \ No newline at end of file + - **TypeError** - `input` 的数据类型既不是float16也不是float32。 + - **TypeError** - `input` 不是Tensor。 \ No newline at end of file diff --git a/mindspore/python/mindspore/nn/layer/basic.py b/mindspore/python/mindspore/nn/layer/basic.py index d78e112f4ba..93359e4da00 100644 --- a/mindspore/python/mindspore/nn/layer/basic.py +++ b/mindspore/python/mindspore/nn/layer/basic.py @@ -252,10 +252,7 @@ class Dropout1d(Cell): self.prob = p def construct(self, x): - if not self.training: - return x - - if self.prob == 0: + if not self.training or self.prob == 0: return x out = F.dropout1d(x, self.prob) @@ -299,10 +296,7 @@ class Dropout2d(Cell): self.dropout2d = P.Dropout2D(self.keep_prob) def construct(self, x): - if not self.training: - return x - - if self.keep_prob == 1: + if not self.training or self.keep_prob == 1: return x out, _ = self.dropout2d(x) @@ -350,10 +344,7 @@ class Dropout3d(Cell): self.dropout3d = P.Dropout3D(self.keep_prob) def construct(self, x): - if not self.training: - return x - - if self.keep_prob == 1: + if not self.training or self.keep_prob == 1: return x out, _ = self.dropout3d(x) diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index 7fef4a6e49f..5fc0de0d82f 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -1158,7 +1158,7 @@ def binary_cross_entropy_with_logits(logits, label, weight, pos_weight, reductio return bce_with_logits_loss_op(logits, label, weight, pos_weight) -def dropout(x, p=0.5, seed0=0, seed1=0): +def dropout(input, p=0.5): """ During training, randomly zeroes some of the elements of the input tensor with probability `p` from a Bernoulli distribution. It plays the role of @@ -1166,34 +1166,31 @@ def dropout(x, p=0.5, seed0=0, seed1=0): here is opposite to that in `ops.Dropout` and `nn.Dropout`. Args: - x (Tensor): The input of Dropout, a Tensor of any shape with data type of float16 or float32. + input (Tensor): The input of Dropout, a Tensor of any shape with data type of float16 or float32. p (float, optional): The dropping rate, between 0 and 1, e.g. p = 0.1, means dropping out 10% of input units. Default: 0.5. - seed0 (int, optional): seed0 value for random generating. Default: 0. - seed1 (int, optional): seed1 value for random generating. Default: 0. Returns: - - **output** (Tensor) - Zeroed tensor, with the same shape and data type as `x`. + - **output** (Tensor) - Zeroed tensor, with the same shape and data type as `input`. - **mask** (Tensor) - Mask for zeroing, bitwise compression and alignment are performed internally. Raises: TypeError: If `p` is not a float. - TypeError: If `seed0` or `seed1` is not an int. - TypeError: If dtype of `x` is neither float16 nor float32. - TypeError: If `x` is not a Tensor. + TypeError: If dtype of `input` is neither float16 nor float32. + TypeError: If `input` is not a Tensor. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> x = Tensor(((20, 16), (50, 50)), mindspore.float32) - >>> output, mask = ops.dropout(x, p=0.5) - >>> print(output.shape, mask.shape, mask.dtype) - (2, 2) (16,) UInt8 + >>> input = Tensor(((20, 16), (50, 50)), mindspore.float32) + >>> output = ops.dropout(input, p=0.5) + >>> print(output.shape) + (2, 2) """ keep_prob = 1 - p - dropout_ = P.Dropout(keep_prob=keep_prob, Seed0=seed0, Seed1=seed1) - return dropout_(x) + out, _ = P.Dropout(keep_prob=keep_prob)(input) + return out def celu(x, alpha=1.0): diff --git a/tests/st/ops/cpu/test_dropout_op.py b/tests/st/ops/cpu/test_dropout_op.py index c2e637b693d..5ce25c3f3ab 100644 --- a/tests/st/ops/cpu/test_dropout_op.py +++ b/tests/st/ops/cpu/test_dropout_op.py @@ -125,12 +125,10 @@ def test_op1(): Expectation: No exception. """ x = Tensor(np.arange(0, 12).reshape(3, 4).astype(np.float16)) - output1, mask1 = ops.dropout(x, p=0.5, seed0=1, seed1=100) - output2, mask2 = ops.dropout(x, p=0.5, seed0=1, seed1=100) + output1 = ops.dropout(x, p=0.5) + output2 = ops.dropout(x, p=0.5) - assert mask1.shape == mask2.shape assert np.allclose(output1.asnumpy(), output2.asnumpy()) - assert np.allclose(mask1.asnumpy(), mask2.asnumpy()) @pytest.mark.level0