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/external/executorch/backends/arm/quantizer/quantization_annotation/
Dadaptive_ang_pool2d_annotator.py25 @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")
/external/pytorch/aten/src/ATen/native/
DAdaptiveAveragePooling.cpp29 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()
DPooling.cpp13 #include <ATen/ops/adaptive_avg_pool2d.h>
47 auto output = at::adaptive_avg_pool2d( in adaptive_avg_pool1d()
/external/pytorch/test/quantization/pt2e/
Dtest_metadata_porting.py25 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)
Dtest_duplicate_dq.py37 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)
/external/executorch/backends/arm/test/ops/
Dtest_mean_dim.py45 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"])
Dtest_conv_combos.py98 self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
105 return self.adaptive_avg_pool2d(x)
/external/pytorch/aten/src/ATen/native/metal/ops/
DMetalPooling.mm72 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));
/external/pytorch/aten/src/ATen/native/vulkan/ops/
DPool.cpp13 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()
/external/executorch/backends/example/example_operators/
DTARGETS8 "adaptive_avg_pool2d.py",
Dops.py9 from executorch.backends.example.example_operators.adaptive_avg_pool2d import (
Dadaptive_avg_pool2d.py19 This is what the graph of a simple adaptive_avg_pool2d op looks like:
/external/executorch/backends/arm/quantizer/
Darm_quantizer.py74 "adaptive_avg_pool2d": [
76 [F.adaptive_avg_pool2d],
265 "adaptive_avg_pool2d",
/external/pytorch/torch/ao/quantization/quantizer/
Dxnnpack_quantizer.py79 "adaptive_avg_pool2d": [
81 [F.adaptive_avg_pool2d],
255 "adaptive_avg_pool2d",
Dxnnpack_quantizer_utils.py636 @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,
/external/pytorch/aten/src/ATen/native/mps/operations/
DAdaptivePooling.mm12 #include <ATen/ops/adaptive_avg_pool2d.h>
66 … "adaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, "
/external/pytorch/test/jit/
Dtest_dtype_analysis.py40 "nn.functional.adaptive_avg_pool2d",
270 return torch._C._nn.adaptive_avg_pool2d(input, output_size)
/external/pytorch/torch/csrc/api/include/torch/nn/functional/
Dpooling.h568 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()
/external/pytorch/torch/ao/quantization/pt2e/
Dgraph_utils.py24 {torch.nn.AdaptiveAvgPool2d, torch.nn.functional.adaptive_avg_pool2d},
/external/pytorch/docs/source/
Dnn.functional.rst48 adaptive_avg_pool2d
/external/pytorch/torch/ao/nn/quantized/
Dfunctional.py20 "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)
/external/executorch/exir/passes/
Dquant_fusion_pass.py108 # batchnorm2d, relu, adaptive_avg_pool2d, reshape, squeeze, permute
/external/pytorch/torch/ao/ns/fx/
Dmappings.py76 F.adaptive_avg_pool2d,
544 F.adaptive_avg_pool2d,
/external/pytorch/aten/src/ATen/native/cuda/
DAdaptiveAveragePooling.cu449 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()
/external/pytorch/functorch/op_analysis/
Dpublic_api442 nn.functional.adaptive_avg_pool2d

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