| /external/pytorch/test/quantization/core/experimental/ |
| D | test_adaround_eager.py | 107 ada_loss = F.mse_loss(ada_out, float_out)
108 fq_loss = F.mse_loss(fq_out, float_out)
134 ada_loss = F.mse_loss(ada_out, float_out)
135 fq_loss = F.mse_loss(fq_out, float_out)
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| /external/pytorch/functorch/examples/maml_regression/ |
| D | evjang_transforms_module.py | 38 # TODO: Use F.mse_loss
41 def mse_loss(x, y): function
83 loss = mse_loss(f, y1)
90 return mse_loss(v_f, y2)
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| D | evjang_transforms.py | 43 # TODO: use F.mse_loss
46 def mse_loss(x, y): function
87 loss = mse_loss(f, y1)
94 return mse_loss(v_f, y2)
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| D | evjang.py | 78 loss = F.mse_loss(f, y1)
87 return F.mse_loss(v_f, y2)
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| /external/pytorch/functorch/examples/lennard_jones/ |
| D | lennard_jones.py | 8 from torch.nn.functional import mse_loss
61 mse_loss(energies, predicted_energies)
62 + 0.01 * mse_loss(forces, predicted_forces) / 3
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| /external/pytorch/torch/ao/quantization/pt2e/ |
| D | _numeric_debugger.py | 126 def mse_loss(self) -> torch.Tensor: member in QuantizationComparisonResult
127 return F.mse_loss(
141 f"QuantizationComparisonResult(mse_loss={self.mse_loss}, sqnr={self.sqnr})"
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| /external/pytorch/aten/src/ATen/functorch/ |
| D | BatchRulesLoss.cpp | 57 return at::mse_loss(self, target, reduction); in mse_loss_batch_rule()
173 VMAP_SUPPORT(mse_loss, mse_loss_batch_rule); in TORCH_LIBRARY_IMPL()
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| /external/pytorch/torch/ao/quantization/experimental/ |
| D | adaround_optimization.py | 161 soft_quant_loss = F.mse_loss(out_soft_quant, fp_out)
162 hard_quant_loss = F.mse_loss(out_hard_quant, fp_out)
|
| D | adaround_loss.py | 80 return F.mse_loss(
|
| /external/pytorch/torch/csrc/api/include/torch/nn/functional/ |
| D | loss.h | 104 inline Tensor mse_loss( in mse_loss() function
122 return torch::mse_loss( in mse_loss()
129 /// https://pytorch.org/docs/main/nn.functional.html#torch.nn.functional.mse_loss
138 /// F::mse_loss(input, target, F::MSELossFuncOptions(torch::kNone));
140 inline Tensor mse_loss(
144 return detail::mse_loss(input, target, options.reduction());
|
| /external/pytorch/docs/source/ |
| D | nn.functional.rst | 181 mse_loss
|
| D | func.migrating.rst | 96 return torch.nn.functional.mse_loss(prediction, targets)
112 return torch.nn.functional.mse_loss(prediction, targets)
|
| D | amp.rst | 205 ``mse_loss``,
434 ``mse_loss``,
|
| /external/pytorch/test/cpp/api/ |
| D | parallel.cpp | 270 auto loss = torch::mse_loss(output, torch::zeros_like(output)); in TEST_F()
278 auto loss_dp = torch::mse_loss(output_dp, torch::zeros_like(output_dp)); in TEST_F()
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| /external/pytorch/functorch/op_analysis/ |
| D | public_api | 490 nn.functional.mse_loss
|
| D | annotated_ops | 448 mse_loss, reduction
|
| /external/pytorch/aten/src/ATen/ |
| D | autocast_mode.cpp | 276 KERNEL_MPS(mse_loss, fp32) in TORCH_LIBRARY_IMPL()
389 KERNEL_CPU(mse_loss, fp32) in TORCH_LIBRARY_IMPL()
|
| /external/pytorch/torch/_functorch/ |
| D | make_functional.py | 389 return nn.functional.mse_loss(y, t)
458 return nn.functional.mse_loss(y, t)
|
| D | top_operators_github_usage.py | 275 ("nn.functional.mse_loss", 1920),
413 ("nn.MSELoss", 82954, "nn.functional.mse_loss"),
|
| D | functional_call.py | 88 return nn.functional.mse_loss(y, t)
|
| /external/pytorch/test/profiler/ |
| D | test_memory_profiler.py | 940 torch.nn.functional.mse_loss(y, torch.rand((2, 1))).backward()
974 torch.nn.functional.mse_loss(y, torch.rand((2, 1))).backward()
1046 torch.nn.functional.mse_loss(y, targets).backward()
1090 torch.nn.functional.mse_loss(y, torch.rand((2, 1))).backward()
1101 torch.nn.functional.mse_loss(y, torch.rand((2, 1))).backward()
1437 loss = torch.nn.functional.mse_loss(y, targets)
|
| /external/pytorch/aten/src/ATen/test/ |
| D | basic.cpp | 303 auto result = tensor.m(relu).m(mse_loss, other, at::Reduction::Mean); in TestDispatch()
304 ASSERT_TRUE(result.allclose(mse_loss(relu(tensor), other))); in TestDispatch()
|
| /external/pytorch/test/distributed/_composable/ |
| D | test_replicate.py | 120 loss = F.mse_loss(output, target.to(output.device))
|
| /external/pytorch/test/distributed/ |
| D | test_c10d_spawn_gloo.py | 205 loss = nn.functional.mse_loss(output, y)
|
| D | test_data_parallel.py | 83 loss = F.mse_loss(output[0], torch.zeros_like(output[0]))
776 F.mse_loss(m(input).float(), target).backward()
777 F.mse_loss(m_dp(input).float(), target).backward()
|