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@@ -126,7 +126,7 @@ class BatchNorm2d(_BatchNorm): |
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By default, during training this layer keeps running estimates of its |
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computed mean and variance, which are then used for normalization during |
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evaluation. The running estimates are kept with a default :attr:`momentum` |
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of 0.1. |
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of 0.9. |
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If :attr:`track_running_stats` is set to ``False``, this layer will not |
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keep running estimates, and batch statistics are instead used during |
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@@ -154,7 +154,7 @@ class BatchNorm2d(_BatchNorm): |
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:type momentum: float |
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:param momentum: the value used for the `running_mean` and `running_var` |
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computation. |
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Default: 0.1 |
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Default: 0.9 |
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:type affine: bool |
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:param affine: a boolean value that when set to ``True``, this module has |
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learnable affine parameters. Default: ``True`` |
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@@ -174,12 +174,18 @@ class BatchNorm2d(_BatchNorm): |
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# With Learnable Parameters |
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m = M.BatchNorm2d(4) |
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inp = mge.tensor(np.random.rand(64, 4, 32, 32)) |
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inp = mge.tensor(np.random.rand(1, 4, 3, 3).astype("float32")) |
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oup = m(inp) |
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print(m.weight, m.bias) |
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# Without Learnable Parameters |
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m = M.BatchNorm2d(4, affine=False) |
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oup = m(inp) |
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print(m.weight, m.bias) |
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.. testoutput:: |
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Tensor([1. 1. 1. 1.]) Tensor([0. 0. 0. 0.]) |
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None None |
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""" |
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def _check_input_ndim(self, inp): |
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Megvii Engine Team
5 years ago