update

2021-06-09 13:12:21 +08:00
parent 51db3ce668
commit 3ee304b0e1
39 changed files with 636 additions and 150 deletions
@@ -0,0 +1,59 @@
+--- /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py
+++ /usr/local/lib/python3.5/dist-packages/torch/nn/modules/batchnorm.py
+@@ -1,8 +1,7 @@
+ class BatchNorm2d(_BatchNorm):
+     r"""Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
+     with additional channel dimension) as described in the paper
+-    `Batch Normalization: Accelerating Deep Network Training by Reducing
+-    Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`__ .
+    `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`_ .
+ 
+     .. math::
+ 
+@@ -10,9 +9,8 @@
+ 
+     The mean and standard-deviation are calculated per-dimension over
+     the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors
+-    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set
+-    to 1 and the elements of :math:`\beta` are set to 0. The standard-deviation is calculated
+-    via the biased estimator, equivalent to `torch.var(input, unbiased=False)`.
+    of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are sampled
+    from :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0.
+ 
+     Also by default, during training this layer keeps running estimates of its
+     computed mean and variance, which are then used for normalization during
+@@ -27,7 +25,7 @@
+         This :attr:`momentum` argument is different from one used in optimizer
+         classes and the conventional notion of momentum. Mathematically, the
+         update rule for running statistics here is
+-        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`,
+        :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`,
+         where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the
+         new observed value.
+ 
+@@ -46,10 +44,8 @@
+             learnable affine parameters. Default: ``True``
+         track_running_stats: a boolean value that when set to ``True``, this
+             module tracks the running mean and variance, and when set to ``False``,
+-            this module does not track such statistics, and initializes statistics
+-            buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
+-            When these buffers are ``None``, this module always uses batch statistics.
+-            in both training and eval modes. Default: ``True``
+            this module does not track such statistics and always uses batch
+            statistics in both training and eval modes. Default: ``True``
+ 
+     Shape:
+         - Input: :math:`(N, C, H, W)`
+@@ -63,8 +59,12 @@
+         >>> m = nn.BatchNorm2d(100, affine=False)
+         >>> input = torch.randn(20, 100, 35, 45)
+         >>> output = m(input)
+
+    .. _`Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift`:
+        https://arxiv.org/abs/1502.03167
+     """
+ 
+    @weak_script_method
+     def _check_input_dim(self, input):
+         if input.dim() != 4:
+             raise ValueError('expected 4D input (got {}D input)'