| /external/ComputeLibrary/tests/validation/fixtures/ |
| D | NormalizePlanarYUVLayerFixture.h | 57 void fill(U &&src_tensor, U &&mean_tensor, U &&std_tensor) in fill() argument
68 library->fill(mean_tensor, distribution, 1); in fill()
78 library->fill(mean_tensor, distribution, 1); in fill()
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| D | BatchNormalizationLayerFixture.h | 60 void fill(U &&src_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U &&gamma_tensor) in fill() argument
71 library->fill(mean_tensor, distribution, 1); in fill()
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| D | BatchNormalizationLayerFusionFixture.h | 66 …void fill(U &&src, U &&w_tensor, U &&b_tensor, U &&mean_tensor, U &&var_tensor, U &&beta_tensor, U… in fill() argument
76 library->fill(mean_tensor, distribution, 2); in fill()
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| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| D | export_output_test.py | 243 mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
289 mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
312 mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
332 mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
371 mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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| /external/executorch/backends/qualcomm/builders/ |
| D | op_batch_norm.py | 47 mean_tensor = get_parameter(mean_node, self.edge_program)
63 amount = (filter_tensor * mean_tensor) / torch.sqrt(var_tensor + eps)
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| /external/tensorflow/tensorflow/python/ops/ |
| D | random_ops.py | 88 mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
94 value = math_ops.add(mul, mean_tensor, name=name)
197 mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
203 value = math_ops.add(mul, mean_tensor, name=name)
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| D | stateful_random_ops.py | 700 mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
704 return math_ops.add(mul, mean_tensor, name=name)
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| D | metrics_impl.py | 1513 def mean_tensor(values, function
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| /external/pytorch/aten/src/ATen/native/ |
| D | DistributionTemplates.h | 232 auto mean_tensor = at::full({}, mean, output.options()); in normal_out_impl() local
233 auto shape = at::infer_size(mean_tensor.sizes(), std.sizes()); in normal_out_impl()
240 output.mul_(std).add_(mean_tensor); in normal_out_impl()
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.metrics.-mean-tensor.pbtxt | 141 …ame\', \'dtype\', \'shape\'], varargs=None, keywords=None, defaults=[\'mean_tensor\', \'None\', \'…
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| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.metrics.pbtxt | 60 name: "mean_tensor"
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| /external/tensorflow/tensorflow/python/distribute/ |
| D | metrics_v1_test.py | 217 distribution, _dataset_fn, metrics.mean_tensor, _expected_fn)
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | metrics_test.py | 356 metrics.mean_tensor(array_ops.ones([4, 3]))
363 mean, _ = metrics.mean_tensor(
370 _, update_op = metrics.mean_tensor(
385 mean, update_op = metrics.mean_tensor(values)
407 mean, update_op = metrics.mean_tensor(values)
426 mean, update_op = metrics.mean_tensor(values)
458 mean, update_op = metrics.mean_tensor(values, weights)
486 mean, update_op = metrics.mean_tensor(values, weights)
514 mean, update_op = metrics.mean_tensor(values, weights)
542 mean, update_op = metrics.mean_tensor(values, weights)
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| /external/pytorch/torch/csrc/autograd/ |
| D | FunctionsManual.cpp | 4954 const Tensor mean_tensor = mean.reshape_symint({N, G, 1, 1}); in infinitely_differentiable_native_group_norm_backward() local
4984 b = (c * mean_tensor - b) * rstd_cube * s; in infinitely_differentiable_native_group_norm_backward()
4985 c = -b * mean_tensor - c * rstd_tensor * std::move(s); in infinitely_differentiable_native_group_norm_backward()
5009 dgamma = ((ds - db * mean_tensor) * rstd_tensor) in infinitely_differentiable_native_group_norm_backward()
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| /external/pytorch/test/ |
| D | test_mps.py | 7867 mean_tensor = cpu_mean_tensor.detach().clone().to('mps')
7876 torch.normal(mean_tensor, std, out=mps_out)
7882 torch.normal(mean_tensor, std_tensor, out=mps_out)
7885 mps_out = torch.normal(mean_tensor, std)
7886 self.assertEqual(mps_out.size(), mean_tensor.size())
7891 inferred_shape = torch.broadcast_shapes(mean_tensor.size(), std_tensor.size())
7892 mps_out = torch.normal(mean_tensor, std_tensor)
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