| /aosp_15_r20/external/pytorch/aten/src/ATen/native/ |
| H A D | Pooling.cpp | 114 Tensor avg_pool1d( in avg_pool1d() function
124 checkDimRange("avg_pool1d", TensorArg(self, "self", 1), 2, 4 /* exclusive */); in avg_pool1d()
125 check1d("avg_pool1d", "kernel_size", kernel_size); in avg_pool1d()
126 check1d("avg_pool1d", "stride", stride); in avg_pool1d()
127 check1d("avg_pool1d", "padding", padding); in avg_pool1d()
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| /aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/functional/ |
| H A D | pooling.h | 14 inline Tensor avg_pool1d( in avg_pool1d() function
21 return torch::avg_pool1d( in avg_pool1d()
28 /// https://pytorch.org/docs/main/nn.functional.html#torch.nn.functional.avg_pool1d
37 /// F::avg_pool1d(x, F::AvgPool1dFuncOptions(3).stride(2));
39 inline Tensor avg_pool1d( in avg_pool1d() function
42 return avg_pool1d( in avg_pool1d()
1014 Tensor out = detail::avg_pool1d( in lp_pool1d()
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| /aosp_15_r20/external/executorch/backends/cadence/aot/ |
| H A D | replace_ops.py | 1955 exir_ops.edge.aten.avg_pool1d.default,
1960 # Determine if the op is avg_pool1d or avg_pool2d
1961 avg_pool1d: bool = op == exir_ops.edge.aten.avg_pool1d.default
1978 # If the op is avg_pool1d, then we need to reshape the 3d input to a 4d
1980 if avg_pool1d:
1982 assert len(in_shape) == 3, "Expected 3d input for avg_pool1d"
1998 in_view_op if avg_pool1d else args[0],
2015 # If the node was avg_pool1d, we again reshape the 4d output to 3d output
2023 if avg_pool1d
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| /aosp_15_r20/external/libopus/dnn/torch/osce/utils/layers/ |
| H A D | td_shaper.py | 87 x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k)
89 x = F.avg_pool1d(x, self.avg_pool_k, self.avg_pool_k)
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| /aosp_15_r20/external/pytorch/torch/ao/ns/fx/ |
| H A D | mappings.py | 59 torch.avg_pool1d,
556 torch.avg_pool1d,
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| /aosp_15_r20/external/pytorch/torch/_functorch/ |
| H A D | top_operators_github_usage.py | 332 ("nn.functional.avg_pool1d", 105),
470 ("nn.AvgPool1d", 3195, "nn.functional.avg_pool1d"),
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| /aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/options/ |
| H A D | pooling.h | 63 /// Options for `torch::nn::functional::avg_pool1d`.
71 /// F::avg_pool1d(x, F::AvgPool1dFuncOptions(3).stride(2));
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| /aosp_15_r20/external/pytorch/torch/csrc/jit/passes/quantization/ |
| H A D | helper.cpp | 126 "avg_pool1d",
145 "avg_pool1d",
|
| H A D | quantization_patterns.h | 794 auto avg_pool1d = getInputTensorQParamOpFusionInfo( in quant_fusion_pattern_and_replacements() local
795 "aten::avg_pool1d", in quant_fusion_pattern_and_replacements()
1048 std::move(avg_pool1d), in quant_fusion_pattern_and_replacements()
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| /aosp_15_r20/external/pytorch/docs/source/ |
| H A D | nn.functional.rst | 32 avg_pool1d
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| /aosp_15_r20/external/pytorch/test/inductor/ |
| H A D | test_torchinductor_opinfo.py | 220 "nn.functional.avg_pool1d": {i64},
487 ("nn.functional.avg_pool1d", "cuda"): {f16, f32, f64},
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| /aosp_15_r20/external/pytorch/functorch/op_analysis/ |
| H A D | public_api | 447 nn.functional.avg_pool1d
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| /aosp_15_r20/external/pytorch/test/mobile/model_test/ |
| H A D | coverage.yaml | 98 - aten::avg_pool1d
757 aten::avg_pool1d: 6
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| H A D | model_ops.yaml | 77 aten::avg_pool1d: 32
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| /aosp_15_r20/external/pytorch/torch/csrc/jit/passes/utils/ |
| H A D | op_registry.cpp | 34 …"aten::avg_pool1d(Tensor self, int[] kernel_size, int[] stride, int[] padding, bool ceil_mode, boo… in nn_ops_first_input_preserving()
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| /aosp_15_r20/external/pytorch/torch/onnx/ |
| H A D | symbolic_opset10.py | 387 "aten::avg_pool1d",
388 decorate=[symbolic_helper._apply_params("avg_pool1d", 1)],
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| /aosp_15_r20/external/pytorch/test/jit/ |
| H A D | test_dtype_analysis.py | 44 "avg_pool1d",
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| /aosp_15_r20/external/pytorch/test/cpp_api_parity/ |
| H A D | parity-tracker.md | 166 F::avg_pool1d|Yes|No
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| /aosp_15_r20/external/pytorch/aten/src/ATen/functorch/ |
| H A D | BatchRulesDecompositions.cpp | 55 OP_DECOMPOSE(avg_pool1d); in TORCH_LIBRARY_IMPL()
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| /aosp_15_r20/external/tensorflow/tensorflow/python/ops/ |
| H A D | nn_test.py | 1577 y2 = nn_ops.avg_pool1d(x, ksize, strides, "SAME")
1590 y2 = nn_ops.avg_pool1d(x, ksize, strides, "SAME")
1599 y = nn_ops.avg_pool1d(x, ksize, strides, "SAME")
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| /aosp_15_r20/external/pytorch/torch/csrc/api/src/nn/modules/ |
| H A D | pooling.cpp | 26 return F::detail::avg_pool1d( in forward()
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| /aosp_15_r20/external/tensorflow/tensorflow/tools/api/golden/v2/ |
| H A D | tensorflow.nn.pbtxt | 44 name: "avg_pool1d"
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| /aosp_15_r20/external/pytorch/torch/nn/ |
| H A D | functional.py | 345 avg_pool1d = _add_docstr( variable
346 torch.avg_pool1d,
348 avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> …
372 >>> F.avg_pool1d(input, kernel_size=3, stride=2)
1175 out = avg_pool1d(input.pow(norm_type), kernel_size, stride, 0, ceil_mode)
1177 out = avg_pool1d(
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| /aosp_15_r20/external/pytorch/torch/ao/quantization/backend_config/ |
| H A D | _common_operator_config_utils.py | 651 torch.avg_pool1d,
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| /aosp_15_r20/external/tensorflow/tensorflow/tools/api/golden/v1/ |
| H A D | tensorflow.nn.pbtxt | 36 name: "avg_pool1d"
|