| /aosp_15_r20/external/pytorch/test/quantization/pt2e/ |
| H A D | test_metadata_porting.py | 25 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
30 x = self.adaptive_avg_pool2d(x)
161 annotated_partitions = OP_TO_ANNOTATOR["adaptive_avg_pool2d"](
165 backend_string, "adaptive_avg_pool2d", annotated_partitions
201 torch.ops.aten.adaptive_avg_pool2d.default: "BackendA_adaptive_avg_pool2d_0",
269 annotated_partitions = OP_TO_ANNOTATOR["adaptive_avg_pool2d"](
273 backend_string, "adaptive_avg_pool2d", annotated_partitions
438 OP_TO_ANNOTATOR["adaptive_avg_pool2d"](gm, quantization_config)
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| H A D | test_duplicate_dq.py | 37 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
42 x = self.adaptive_avg_pool2d(x)
71 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
75 w = self.adaptive_avg_pool2d(x)
136 OP_TO_ANNOTATOR["adaptive_avg_pool2d"](gm, quantization_config)
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| /aosp_15_r20/external/executorch/backends/arm/quantizer/quantization_annotation/ |
| H A D | adaptive_ang_pool2d_annotator.py | 25 @register_annotator("adaptive_avg_pool2d")
31 """Always annotate adaptive_avg_pool2d op"""
33 gm.graph, [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d], filter_fn
41 or pool_node.target != torch.ops.aten.adaptive_avg_pool2d.default
43 raise ValueError(f"{pool_node} is not an aten adaptive_avg_pool2d operator")
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/ |
| H A D | AdaptiveAveragePooling.cpp | 29 TORCH_CHECK(output_size.size() == 2, "adaptive_avg_pool2d: output_size must be 2"); in adaptive_avg_pool2d_out_cpu_template()
32 "adaptive_avg_pool2d(): Expected 3D or 4D tensor, but got ", input.sizes()); in adaptive_avg_pool2d_out_cpu_template()
35 "adaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, " in adaptive_avg_pool2d_out_cpu_template()
110 TORCH_CHECK(output_size.size() == 2, "adaptive_avg_pool2d: output_size must be 2"); in adaptive_avg_pool2d_symint()
113 "adaptive_avg_pool2d: elements of output_size must be greater than or equal to 0 ", in adaptive_avg_pool2d_symint()
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| H A D | Pooling.cpp | 13 #include <ATen/ops/adaptive_avg_pool2d.h>
47 auto output = at::adaptive_avg_pool2d( in adaptive_avg_pool1d()
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| /aosp_15_r20/external/executorch/backends/arm/test/ops/ |
| H A D | test_mean_dim.py | 45 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(output_size=(1, 1))
48 return self.adaptive_avg_pool2d(x)
87 .check(["torch.ops.aten.adaptive_avg_pool2d.default"])
108 .check_count({"torch.ops.aten.adaptive_avg_pool2d.default": 1})
132 .check(["torch.ops.aten.adaptive_avg_pool2d.default"])
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| H A D | test_conv_combos.py | 98 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
105 return self.adaptive_avg_pool2d(x)
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/vulkan/ops/ |
| H A D | Pool.cpp | 13 Tensor adaptive_avg_pool2d( in adaptive_avg_pool2d() function
18 "Vulkan adaptive_avg_pool2d expects 4-dimensional input!"); in adaptive_avg_pool2d()
66 VK_KERNEL(adaptive_avg_pool2d), in adaptive_avg_pool2d()
288 TORCH_FN(adaptive_avg_pool2d)); in TORCH_LIBRARY_IMPL()
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| /aosp_15_r20/external/pytorch/torch/ao/quantization/quantizer/ |
| H A D | xnnpack_quantizer_utils.py | 636 @register_annotator("adaptive_avg_pool2d")
642 """Always annotate adaptive_avg_pool2d op"""
644 gm.graph, [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d], filter_fn
652 or pool_node.target != torch.ops.aten.adaptive_avg_pool2d.default
654 raise ValueError(f"{pool_node} is not an aten adaptive_avg_pool2d operator")
1012 torch.ops.aten.adaptive_avg_pool2d.default,
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| H A D | xnnpack_quantizer.py | 79 "adaptive_avg_pool2d": [
81 [F.adaptive_avg_pool2d],
255 "adaptive_avg_pool2d",
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| H A D | x86_inductor_quantizer.py | 88 torch.ops.aten.adaptive_avg_pool2d.default,
211 [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d],
212 torch.ops.aten.adaptive_avg_pool2d.default,
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/metal/ops/ |
| H A D | MetalPooling.mm | 72 static Tensor adaptive_avg_pool2d(const Tensor& input, IntArrayRef output_size) {
106 m.impl(TORCH_SELECTIVE_NAME("aten::adaptive_avg_pool2d"), TORCH_FN(adaptive_avg_pool2d));
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| /aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/functional/ |
| H A D | pooling.h | 568 inline Tensor adaptive_avg_pool2d( in adaptive_avg_pool2d() function
573 return torch::adaptive_avg_pool2d(input, output_size_); in adaptive_avg_pool2d()
579 /// https://pytorch.org/docs/main/nn.functional.html#torch.nn.functional.adaptive_avg_pool2d
589 /// F::adaptive_avg_pool2d(x, F::AdaptiveAvgPool2dFuncOptions(3));
591 inline Tensor adaptive_avg_pool2d( in adaptive_avg_pool2d() function
594 return detail::adaptive_avg_pool2d(input, options.output_size()); in adaptive_avg_pool2d()
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| /aosp_15_r20/external/executorch/backends/arm/quantizer/ |
| H A D | arm_quantizer.py | 74 "adaptive_avg_pool2d": [
76 [F.adaptive_avg_pool2d],
265 "adaptive_avg_pool2d",
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| /aosp_15_r20/external/pytorch/torch/ao/nn/quantized/ |
| H A D | functional.py | 20 "adaptive_avg_pool2d",
133 def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor: function
148 "Input to 'quantized.functional.adaptive_avg_pool2d' must be quantized!"
150 return torch.nn.functional.adaptive_avg_pool2d(input, output_size)
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/ |
| H A D | AdaptiveAveragePooling.cu | 449 TORCH_CHECK(output_size.size() == 2, "adaptive_avg_pool2d: output_size must be 2"); in adaptive_avg_pool2d_out_cuda_template()
452 "adaptive_avg_pool2d(): Expected 3D or 4D tensor, but got ", input.sizes()); in adaptive_avg_pool2d_out_cuda_template()
455 "adaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, " in adaptive_avg_pool2d_out_cuda_template()
465 "adaptive_avg_pool2d(): Expected 4D tensor, but got ", in adaptive_avg_pool2d_out_cuda_template()
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| /aosp_15_r20/external/pytorch/test/ |
| H A D | test_mkldnn.py | 929 adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(7)
932 y1 = adaptive_avg_pool2d(x1)
933 y2 = adaptive_avg_pool2d(x2).to_dense()
950 adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d(7)
953 y = adaptive_avg_pool2d(x.to_mkldnn()).to_dense()
954 y_bf16 = adaptive_avg_pool2d(x.to_mkldnn()).to_dense(torch.float32)
960 lambda: adaptive_avg_pool2d(x_bf16.to_mkldnn()))
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| /aosp_15_r20/external/pytorch/aten/src/ATen/native/mps/operations/ |
| H A D | AdaptivePooling.mm | 12 #include <ATen/ops/adaptive_avg_pool2d.h>
66 … "adaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, "
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| /aosp_15_r20/external/pytorch/test/jit/ |
| H A D | test_dtype_analysis.py | 40 "nn.functional.adaptive_avg_pool2d",
270 return torch._C._nn.adaptive_avg_pool2d(input, output_size)
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| /aosp_15_r20/external/pytorch/torch/ao/ns/fx/ |
| H A D | mappings.py | 76 F.adaptive_avg_pool2d,
544 F.adaptive_avg_pool2d,
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| /aosp_15_r20/external/pytorch/torch/csrc/lazy/core/ |
| H A D | shape_inference.cpp | 901 output_size.size() == 2, "adaptive_avg_pool2d: output_size must be 2"); in compute_shape__adaptive_avg_pool2d()
904 "adaptive_avg_pool2d: elements of output_size must be greater than or equal to 0 ", in compute_shape__adaptive_avg_pool2d()
914 "adaptive_avg_pool2d(): Expected self to have non-zero size for non-batch dimensions, " in compute_shape__adaptive_avg_pool2d()
924 "adaptive_avg_pool2d(): Expected 3D or 4D tensor, but got ", in compute_shape__adaptive_avg_pool2d()
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| /aosp_15_r20/external/executorch/backends/example/example_operators/ |
| H A D | TARGETS | 8 "adaptive_avg_pool2d.py",
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| H A D | ops.py | 9 from executorch.backends.example.example_operators.adaptive_avg_pool2d import (
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| /aosp_15_r20/external/pytorch/torch/_functorch/ |
| H A D | top_operators_github_usage.py | 288 ("nn.functional.adaptive_avg_pool2d", 633),
419 ("nn.AdaptiveAvgPool2d", 59071, "nn.functional.adaptive_avg_pool2d"),
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| /aosp_15_r20/external/pytorch/torch/csrc/api/include/torch/nn/options/ |
| H A D | pooling.h | 315 /// Options for `torch::nn::functional::adaptive_avg_pool2d`.
323 /// F::adaptive_avg_pool2d(x, F::AdaptiveAvgPool2dFuncOptions(3));
|