/aosp_15_r20/external/pytorch/aten/src/ATen/native/ |
H A D | Itertools.cpp | 14 #include <ATen/ops/meshgrid.h> 28 std::vector<Tensor> index_grids = at::meshgrid(std::vector<Tensor>(dims, range), "ij"); in _triu_mask() 53 std::vector<Tensor> grids = at::meshgrid(tensors, "ij"); in cartesian_prod() 67 std::vector<Tensor> grids = at::meshgrid(std::vector<Tensor>(r, self), "ij"); in combinations()
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H A D | TensorShape.cpp | 3594 std::vector<Tensor> meshgrid(TensorList tensors) { in meshgrid() function 3595 TORCH_WARN_ONCE("torch.meshgrid: in an upcoming release, it will be required to pass the " in meshgrid() 3597 return native::meshgrid(tensors, /*indexing=*/"ij"); in meshgrid() 3600 std::vector<Tensor> meshgrid(TensorList tensors, in meshgrid() function 3603 TORCH_CHECK(size > 0, "meshgrid expects a non-empty TensorList"); in meshgrid() 3606 …TORCH_CHECK(tensors[i].dtype() == tensors[i+1].dtype(), "meshgrid expects all tensors to have the … in meshgrid() 3607 …TORCH_CHECK(tensors[i].device() == tensors[i+1].device(), "meshgrid expects all tensors to have th… in meshgrid() 3652 "torch.meshgrid: indexing must be one of \"xy\" or \"ij\", " in meshgrid() 3659 … "torch.meshgrid: Expected 0D or 1D tensor in the tensor list but got: ", tensor_refs[i]); in meshgrid()
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/aosp_15_r20/external/pytorch/test/torch_np/numpy_tests/lib/ |
H A D | test_function_base.py | 61 meshgrid, 95 meshgrid, 2288 [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7]) 2293 [X] = meshgrid([1, 2, 3, 4]) 2298 assert_array_equal([], meshgrid(*args)) 2299 assert_array_equal([], meshgrid(*args, copy=False)) 2304 [X, Y] = meshgrid(x, y, indexing="ij") 2310 assert_(meshgrid(x, y)[0].shape == (4, 3)) 2311 assert_(meshgrid(x, y, indexing="ij")[0].shape == (3, 4)) 2312 assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2)) [all …]
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/aosp_15_r20/external/pytorch/torch/ |
H A D | functional.py | 35 "meshgrid", 418 def meshgrid( function 425 def meshgrid(*tensors, indexing: Optional[str] = None) -> Tuple[Tensor, ...]: function 443 `torch.meshgrid(*tensors)` currently has the same behavior 444 as calling `numpy.meshgrid(*arrays, indexing='ij')`. 446 In the future `torch.meshgrid` will transition to 486 >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 502 `torch.meshgrid` is commonly used to produce a grid for 509 >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 515 .. image:: ../_static/img/meshgrid.png [all …]
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/aosp_15_r20/external/pytorch/test/export/ |
H A D | testing.py | 135 aten.meshgrid.default, 136 aten.meshgrid.indexing,
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/aosp_15_r20/external/pytorch/test/ |
H A D | test_tensor_creation_ops.py | 1288 torch.meshgrid() 1293 torch.meshgrid(torch.tensor([1, 2]), indexing='') 1298 torch.meshgrid(torch.tensor([[1, 2], [3, 4]])) 1303 torch.meshgrid(torch.tensor([1], dtype=torch.int), 1309 torch.meshgrid(torch.tensor([1], device='cpu'), 1315 torch.meshgrid(torch.tensor([1, 2])) 1321 grid_a, grid_b, grid_c = torch.meshgrid([a, b, c]) 1325 grid_a2, grid_b2, grid_c2 = torch.meshgrid(a, b, c) 1347 grid_a, grid_b, grid_c = torch.meshgrid([a, b, c], indexing='xy') 1351 grid_a2, grid_b2, grid_c2 = torch.meshgrid(a, b, c, indexing='xy') [all …]
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/aosp_15_r20/external/tensorflow/tensorflow/python/ops/numpy_ops/ |
H A D | np_math_ops.py | 1322 @np_utils.np_doc('meshgrid') 1323 def meshgrid(*xi, **kwargs): function 1328 'Function `meshgrid` does not support returning sparse arrays yet. ' 1333 raise ValueError('Function `meshgrid` only supports copy=True. ' 1341 outputs = array_ops.meshgrid(*xi, **kwargs)
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/aosp_15_r20/external/pytorch/aten/src/ATen/functorch/ |
H A D | BatchRulesDecompositions.cpp | 202 OP_DECOMPOSE(meshgrid); in TORCH_LIBRARY_IMPL() 203 OP_DECOMPOSE2(meshgrid, indexing); in TORCH_LIBRARY_IMPL()
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/aosp_15_r20/external/pytorch/test/mobile/model_test/ |
H A D | model_ops.yaml | 234 aten::meshgrid: 2 235 aten::meshgrid.indexing: 2
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H A D | coverage.yaml | 395 - aten::meshgrid 396 - aten::meshgrid.indexing 886 aten::meshgrid.indexing: 2
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H A D | math_ops.py | 354 torch.meshgrid(a), 355 torch.meshgrid(a, indexing="xy"),
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/aosp_15_r20/external/pytorch/torch/_refs/ |
H A D | __init__.py | 270 "meshgrid", 5213 def meshgrid(tensors: Sequence[TensorLikeType], indexing: str): function 5218 def meshgrid(*tensors: TensorLikeType, indexing: str): function 5222 @register_decomposition(aten.meshgrid) # type: ignore[misc] 5223 def meshgrid( function 5228 # The `indexing` argument is currently optional for torch.meshgrid, but we 5236 lambda: "meshgrid expects its inputs to be tensors", 5239 torch._check(len(tensors) > 0, lambda: "meshgrid expects a non-empty TensorList") 5244 lambda: "meshgrid expects all tensors to have the same dtype", 5248 lambda: "meshgrid expects all tensors to have the same device", [all …]
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/aosp_15_r20/external/libvpx/vp8/decoder/ |
H A D | error_concealment.c | 32 * x = meshgrid((1:4), (1:4)); 33 * y = meshgrid((1:4), (1:4))';
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/aosp_15_r20/external/pytorch/test/distributed/_tensor/ |
H A D | test_dtensor_ops.py | 479 skip("meshgrid", "list_of_tensors"), 480 skip("meshgrid", "variadic_tensors"),
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/aosp_15_r20/external/pytorch/torch/onnx/ |
H A D | symbolic_opset7.py | 27 "meshgrid",
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/aosp_15_r20/external/executorch/backends/vulkan/_passes/ |
H A D | test_custom_ops.py | 111 shift_xx, shift_yy = torch.meshgrid(shift_y, shift_x)
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H A D | custom_ops_defs.py | 125 shift_xx, shift_yy = torch.meshgrid([shift_y, shift_x], indexing="ij")
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/aosp_15_r20/external/tensorflow/tensorflow/python/ops/ |
H A D | array_ops_test.py | 74 self.assertEqual(array_ops.meshgrid(), [])
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/aosp_15_r20/external/pytorch/functorch/op_analysis/ |
H A D | public_api | 369 meshgrid
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H A D | annotated_ops | 340 meshgrid, view/reshape
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/aosp_15_r20/external/pytorch/docs/source/ |
H A D | masked.rst | 284 meshgrid
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H A D | torch.rst | 554 meshgrid
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/aosp_15_r20/external/executorch/backends/vulkan/ |
H A D | custom_ops_lib.py | 125 shift_xx, shift_yy = torch.meshgrid([shift_y, shift_x], indexing="ij")
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/aosp_15_r20/external/fonttools/Lib/fontTools/varLib/ |
H A D | plot.py | 160 X, Y = np.meshgrid(X, Y)
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/aosp_15_r20/external/tensorflow/tensorflow/tools/compatibility/testdata/ |
H A D | test_file_v0_11.py | 204 x, y = np.meshgrid(np.linspace(-10, 10, 256), np.linspace(-10, 10, 256))
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