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| import unittest |
|
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| import torch |
| import torch.nn as nn |
| from torch.autograd import Variable |
|
|
| from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback |
| from sync_batchnorm.unittest import TorchTestCase |
|
|
|
|
| def handy_var(a, unbias=True): |
| n = a.size(0) |
| asum = a.sum(dim=0) |
| as_sum = (a ** 2).sum(dim=0) |
| sumvar = as_sum - asum * asum / n |
| if unbias: |
| return sumvar / (n - 1) |
| else: |
| return sumvar / n |
|
|
|
|
| def _find_bn(module): |
| for m in module.modules(): |
| if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)): |
| return m |
|
|
|
|
| class SyncTestCase(TorchTestCase): |
| def _syncParameters(self, bn1, bn2): |
| bn1.reset_parameters() |
| bn2.reset_parameters() |
| if bn1.affine and bn2.affine: |
| bn2.weight.data.copy_(bn1.weight.data) |
| bn2.bias.data.copy_(bn1.bias.data) |
|
|
| def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False): |
| """Check the forward and backward for the customized batch normalization.""" |
| bn1.train(mode=is_train) |
| bn2.train(mode=is_train) |
|
|
| if cuda: |
| input = input.cuda() |
|
|
| self._syncParameters(_find_bn(bn1), _find_bn(bn2)) |
|
|
| input1 = Variable(input, requires_grad=True) |
| output1 = bn1(input1) |
| output1.sum().backward() |
| input2 = Variable(input, requires_grad=True) |
| output2 = bn2(input2) |
| output2.sum().backward() |
|
|
| self.assertTensorClose(input1.data, input2.data) |
| self.assertTensorClose(output1.data, output2.data) |
| self.assertTensorClose(input1.grad, input2.grad) |
| self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean) |
| self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var) |
|
|
| def testSyncBatchNormNormalTrain(self): |
| bn = nn.BatchNorm1d(10) |
| sync_bn = SynchronizedBatchNorm1d(10) |
|
|
| self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True) |
|
|
| def testSyncBatchNormNormalEval(self): |
| bn = nn.BatchNorm1d(10) |
| sync_bn = SynchronizedBatchNorm1d(10) |
|
|
| self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False) |
|
|
| def testSyncBatchNormSyncTrain(self): |
| bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) |
| sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) |
| sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) |
|
|
| bn.cuda() |
| sync_bn.cuda() |
|
|
| self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True) |
|
|
| def testSyncBatchNormSyncEval(self): |
| bn = nn.BatchNorm1d(10, eps=1e-5, affine=False) |
| sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) |
| sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) |
|
|
| bn.cuda() |
| sync_bn.cuda() |
|
|
| self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True) |
|
|
| def testSyncBatchNorm2DSyncTrain(self): |
| bn = nn.BatchNorm2d(10) |
| sync_bn = SynchronizedBatchNorm2d(10) |
| sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) |
|
|
| bn.cuda() |
| sync_bn.cuda() |
|
|
| self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True) |
|
|
|
|
| if __name__ == '__main__': |
| unittest.main() |
|
|
