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/dports/misc/glow/glow-f24d960e3cc80db95ac0bc17b1900dbf60ca044a/torch_glow/tests/nodes/
H A Dbatchnorm_test.py14 def test_f(inputs, running_mean, running_var): argument
15 return F.batch_norm(inputs, running_mean, running_var)
19 running_var = torch.rand(4)
25 running_var,
32 def test_f(inputs, weight, bias, running_mean, running_var): argument
34 inputs, running_mean, running_var, weight=weight, bias=bias
41 running_var = torch.rand(4)
49 running_var,
/dports/math/py-theano/Theano-1.0.5/theano/gpuarray/c_code/
H A Ddnn_batchnorm.c46 PyGpuArrayObject *running_var = NULL; in dnn_batchnorm_op() local
61 running_var = in_running_var; in dnn_batchnorm_op()
62 Py_INCREF(running_var); in dnn_batchnorm_op()
64 running_var = *out_running_var; in dnn_batchnorm_op()
65 running_var = theano_try_copy(running_var, in_running_var); in dnn_batchnorm_op()
66 if (running_var == NULL) { in dnn_batchnorm_op()
100 running_averages ? PyGpuArray_DEV_DATA(running_var): NULL, in dnn_batchnorm_op()
112 *out_running_var = running_var; in dnn_batchnorm_op()
/dports/math/py-theano/Theano-1.0.5/theano/tensor/nnet/
H A Dbn.py211 if running_var is not None and running_var.ndim != params_ndim:
237 running_var = as_tensor_variable(running_var)
240 running_var = running_var.dimshuffle(params_dimshuffle_pattern)
243 running_var = T.addbroadcast(running_var, *axes)
247 running_mean=running_mean, running_var=running_var)
423 if running_var is not None:
424 running_var = as_tensor_variable(running_var)
429 assert (running_var is None or running_var.ndim == x.ndim)
497 running_var = inputs[6]
498 running_var = running_var * (1.0 - running_average_factor) + \
[all …]
/dports/misc/ncnn/ncnn-20211208/tools/pnnx/src/pass_ncnn/
H A DF_batch_norm.cpp49 Attribute running_var; in write() local
55 running_var = x.second; in write()
65 op->attrs["2"] = running_var; in write()
102 Attribute running_var; in write() local
110 running_var = x.second; in write()
122 op->attrs["2"] = running_var; in write()
/dports/math/py-pytorchvideo/pytorchvideo-0.1.3/pytorchvideo/layers/
H A Dbatch_norm.py39 self.running_var += self.momentum * (var.detach() - self.running_var)
83 self.running_var += self.momentum * (var.detach() - self.running_var)
118 self.running_var += self.momentum * (var.detach() - self.running_var)
/dports/science/py-chainer/chainer-7.8.0/chainer/functions/normalization/
H A Dbatch_normalization.py22 beta, eps, decay, running_mean, running_var): argument
55 running_mean, running_var)
59 (running_mean, running_var))
64 running_var *= decay
81 running_var = backend.to_chx(running_var)
175 running_var.inplace_axpby(
178 running_var *= decay
219 var = running_var
303 self.running_var = var
369 and self.running_var.is_contiguous):
[all …]
H A Dbatch_renormalization.py47 def running_var(self): member in BatchRenormalizationFunction
165 running_mean=None, running_var=None, decay=0.9, argument
208 if running_var is None:
211 eps, running_mean, running_var, decay, rmax, dmax, update_statistics
/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/cuda/cuda_device/
H A Dbatch_norm.cc93 const Array& running_var, in Call() argument
106 CHAINERX_ASSERT(running_var.GetTotalSize() == reduced_total_size); in Call()
111 CHAINERX_ASSERT(&x.device() == &running_var.device()); in Call()
117 CHAINERX_ASSERT(GetKind(running_var.dtype()) == DtypeKind::kFloat); in Call()
125 if (!running_var.IsContiguous()) { in Call()
147 CHAINERX_ASSERT(running_var.IsContiguous()); in Call()
153 …running_var.dtype() != gamma_beta_mean_var_dtype ? running_var.AsType(gamma_beta_mean_var_dtype) :… in Call()
188 UpdateRunning(running_var, running_var_casted); in Call()
/dports/misc/mmdnn/MMdnn-0.3.1/mmdnn/conversion/darknet/
H A Dcfg.py198 bn_model.running_var.copy_(torch.from_numpy(buf[start:start+num_b])); start = start + num_b
207 convert2cpu(bn_model.running_var).numpy().tofile(fp)
213 bn_model.running_var.numpy().tofile(fp)
218 …l.bias.data - bn_model.running_mean * bn_model.weight.data / torch.sqrt(bn_model.running_var + eps)
221 …weight = conv_model.weight.data * (bn_model.weight.data / torch.sqrt(bn_model.running_var + eps)).…
224 …l.bias.data - bn_model.running_mean * bn_model.weight.data / torch.sqrt(bn_model.running_var + eps)
227 …weight = conv_model.weight.data * (bn_model.weight.data / torch.sqrt(bn_model.running_var + eps)).…
/dports/misc/ncnn/ncnn-20211208/tools/pnnx/src/pass_level1/
H A Dnn_BatchNorm1d.cpp39 const auto& running_var = mod.attr("running_var").toTensor(); in write() local
46 op->attrs["running_var"] = running_var; in write()
H A Dnn_BatchNorm2d.cpp39 const auto& running_var = mod.attr("running_var").toTensor(); in write() local
46 op->attrs["running_var"] = running_var; in write()
H A Dnn_BatchNorm3d.cpp39 const auto& running_var = mod.attr("running_var").toTensor(); in write() local
46 op->attrs["running_var"] = running_var; in write()
/dports/math/py-theano/Theano-1.0.5/theano/tensor/nnet/tests/
H A Dtest_bn.py152 x, scale, bias, running_mean, running_var = (vartype(n)
170 running_average_factor, running_mean, running_var)
187 out_running_var2 = running_var * (1 - running_average_factor) + \
205 consider_constant=[x, dy, scale, bias, x_mean2, x_var2, running_mean, running_var],
208 f = theano.function([x, scale, bias, running_mean, running_var, dy, dx, dscale, dbias],
358 scale, bias, running_mean, running_var = (param_type(n)
367 running_var_bc = running_var.dimshuffle(params_dimshuffle)
373 running_average_factor, running_mean, running_var)
385 x, scale, bias, running_mean, running_var, axes, eps)
397 f = theano.function([x, scale, bias, running_mean, running_var],
[all …]
/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/routines/
H A Dnormalization.cc162 const Array& running_var, in Call() argument
175 CHAINERX_ASSERT(GetKind(running_var.dtype()) == DtypeKind::kFloat); in Call()
191 running_var *= decay; in Call()
192 …running_var += (inv_decay * (static_cast<double>(n) / std::max(n - 1, int64_t{1})) * x_var).AsType… in Call()
271 const Array& running_var, in BatchNorm() argument
276 …ocessBatchNormResult result = PreprocessBatchNorm(x, gamma, beta, running_mean, running_var, axis); in BatchNorm()
H A Dnormalization.h19 const Array& running_var,
/dports/math/py-fvcore/fvcore-0.1.5.post20210924/fvcore/nn/
H A Dprecise_bn.py160 _PopulationVarianceEstimator(bn.running_mean, bn.running_var)
179 estimators[i].update(bn.running_mean, bn.running_var, batch_size)
188 bn.running_var = estimators[i].pop_var
/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/torch/model_zoo/action_recognition/
H A Di3d_resnet.py440 conv1_bn.running_var.data.copy_(R2D.bn1.running_var.data)
468 … block.bn1.running_var.data.copy_(R2Dlayers[s]._modules[str(k)].bn1.running_var.data)
473 … block.bn2.running_var.data.copy_(R2Dlayers[s]._modules[str(k)].bn2.running_var.data)
478 … block.bn3.running_var.data.copy_(R2Dlayers[s]._modules[str(k)].bn3.running_var.data)
490 down_bn.running_var.data.copy_(
491 R2Dlayers[s]._modules[str(k)].downsample._modules['1'].running_var.data)
/dports/science/py-chainer/chainer-7.8.0/chainerx_cc/chainerx/kernels/
H A Dnormalization.h40 const Array& running_var,
85 const Array& running_var,
/dports/math/ideep/ideep-2.0.0-119-gb57539e/tests/
H A Dbench_ideep_batch_normalization.cc56 tensor dst, mean, variance, running_mean, running_var; in test_forward_training() local
60 running_mean, running_var, 0.9f, p.eps); in test_forward_training()
/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/nn/
H A Dblock.py21 def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): argument
22 return F.BatchNorm(x, gamma, beta, running_mean, running_var,
/dports/misc/mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/nn/
H A Dbasic_layers.py245 def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): argument
246 return F.contrib.SyncBatchNorm(x, gamma, beta, running_mean, running_var,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/python/mxnet/gluon/contrib/nn/
H A Dbasic_layers.py245 def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): argument
246 return F.contrib.SyncBatchNorm(x, gamma, beta, running_mean, running_var,
/dports/math/py-theano/Theano-1.0.5/theano/gpuarray/
H A Ddnn.py1850 running_mean=None, running_var=None): argument
1855 assert (running_var is None or running_var.ndim == x.ndim)
2773 if (running_mean is None) != (running_var is None):
2780 if running_var is not None and running_var.ndim != ndim:
2783 (running_var.ndim, ndim))
2795 running_var = theano.tensor.shape_padright(running_var, 4 - ndim)
2804 running_var = theano.tensor.flatten(running_var, 5)
2813 running_var=gpu_contiguous(running_var))
3914 running_var = inputs[6] if len(inputs) > 6 else None
3944 if running_mean is not None and running_var is not None:
[all …]
/dports/misc/mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/
H A DBasicLayers.pm458 $self->running_var(
470 GluonInput :$running_mean, GluonInput :$running_var
474 $x, $gamma, $beta, $running_mean, $running_var,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/NN/
H A DBasicLayers.pm458 $self->running_var(
470 GluonInput :$running_mean, GluonInput :$running_var
474 $x, $gamma, $beta, $running_mean, $running_var,

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