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