pylint

docs/source/index.rst

@@ -10,7 +10,7 @@ Created by `Hang Zhang <http://hangzh.com/>`_
 An optimized PyTorch package with CUDA backend. 
 
 .. note::
-    PyTorch compatible Synchronized Cross-GPU :class:`encoding.nn.BatchNorm2d` has been released.
+    Please checkout the PyTorch compatible Synchronized Cross-GPU :class:`encoding.nn.BatchNorm2d` and the `mnist example <https://github.com/zhanghang1989/PyTorch-SyncBatchNorm>`_.
 
 .. toctree::
    :glob:

docs/source/notes/syncbn.rst

@@ -53,7 +53,7 @@ Suppose we have :math:`K` number of GPUs, :math:`sum(x)_k` and :math:`sum(x^2)_k
     * :math:`\frac{d_\ell}{d_{x_i}}=\frac{d_\ell}{d_{y_i}}\frac{\gamma}{\sigma}` can be calculated locally in each GPU.
     * Calculate the gradient of :math:`sum(x)` and :math:`sum(x^2)` individually in each GPU :math:`\frac{d_\ell}{d_{sum(x)_k}}` and :math:`\frac{d_\ell}{d_{sum(x^2)_k}}`. 
 
-    * Then Sync the gradient (automatically handled by :class:`encoding.parallel.AllReduce`) and continue the backward.
+    * Then Sync the gradient (automatically handled by :class:`encoding.parallel.allreduce`) and continue the backward.
 
 .. image:: http://hangzh.com/blog/images/bn3.png
     :align: center

encoding/nn/syncbn.py

@@ -14,14 +14,16 @@ import torch
 from torch.nn import Module, Sequential, Conv1d, Conv2d, ConvTranspose2d, \
     ReLU, Sigmoid, MaxPool2d, AvgPool2d, AdaptiveAvgPool2d, Dropout2d, Linear
 from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parameter import Parameter
 
 from ..functions import batchnormtrain, batchnormeval, sum_square
 from ..parallel import allreduce
 
-#__all__ = ['BatchNorm1d', 'BatchNorm2d', 'BatchNorm3d']
+__all__ = ['BatchNorm1d', 'BatchNorm2d', 'BatchNorm3d', 'Module', 'Sequential', 'Conv1d',
+           'Conv2d', 'ConvTranspose2d', 'ReLU', 'Sigmoid', 'MaxPool2d', 'AvgPool2d',
+           'AdaptiveAvgPool2d', 'Dropout2d', 'Linear']
 
 class _SyncBatchNorm(_BatchNorm):
+    # pylint: disable=access-member-before-definition
     def __init__(self, num_features, eps=1e-5, momentum=0.1, **kwargs):
         super(_SyncBatchNorm, self).__init__(num_features, eps=1e-5, momentum=0.1, **kwargs)
         # syncBN

encoding/parallel.py

@@ -13,15 +13,12 @@ import threading
 import torch
 from torch.autograd import Function
 import torch.cuda.comm as comm
-from torch.autograd import Variable
-from torch.nn.modules import Module
 from torch.nn.parallel.data_parallel import DataParallel
-from torch.nn.parallel.scatter_gather import scatter_kwargs
 from torch.nn.parallel.replicate import replicate
 from torch.nn.parallel.parallel_apply import get_a_var
 from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
 
-__all__ = ['allreduce', 'ModelDataParallel', 'CriterionDataParallel']
+__all__ = ['allreduce', 'DataParallelModel', 'DataParallelCriterion']
 
 
 def allreduce(num_inputs, *inputs):
@@ -162,4 +159,3 @@ def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices
             raise output
         outputs.append(output)
     return outputs
-