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04e0cbf5a9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45667 First part of #3867 (Pooling operators still to do) This adds a `padding='same'` mode to the interface of `conv{n}d`and `nn.Conv{n}d`. This should match the behaviour of `tensorflow`. I couldn't find it explicitly documented but through experimentation I found `tensorflow` returns the shape `ceil(len/stride)` and always adds any extra asymmetric padding onto the right side of the input. Since the `native_functions.yaml` schema doesn't seem to support strings or enums, I've moved the function interface into python and it now dispatches between the numerically padded `conv{n}d` and the `_conv{n}d_same` variant. Underscores because I couldn't see any way to avoid exporting a function into the `torch` namespace. A note on asymmetric padding. The total padding required can be odd if both the kernel-length is even and the dilation is odd. mkldnn has native support for asymmetric padding, so there is no overhead there, but for other backends I resort to padding the input tensor by 1 on the right hand side to make the remaining padding symmetrical. In these cases, I use `TORCH_WARN_ONCE` to notify the user of the performance implications. Test Plan: Imported from OSS Reviewed By: ejguan Differential Revision: D27170744 Pulled By: jbschlosser fbshipit-source-id: b3d8a0380e0787ae781f2e5d8ee365a7bfd49f22 |
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| _C | ||
| autograd | ||
| backends | ||
| contrib | ||
| csrc | ||
| cuda | ||
| distributed | ||
| distributions | ||
| fft | ||
| for_onnx | ||
| futures | ||
| fx | ||
| jit | ||
| legacy | ||
| lib | ||
| linalg | ||
| multiprocessing | ||
| nn | ||
| onnx | ||
| optim | ||
| package | ||
| profiler | ||
| quantization | ||
| sparse | ||
| special | ||
| testing | ||
| utils | ||
| __config__.py | ||
| __future__.py | ||
| __init__.py | ||
| _appdirs.py | ||
| _autograd_functions.py | ||
| _classes.py | ||
| _deploy.py | ||
| _jit_internal.py | ||
| _linalg_utils.py | ||
| _lobpcg.py | ||
| _lowrank.py | ||
| _namedtensor_internals.py | ||
| _ops.py | ||
| _python_dispatcher.py | ||
| _six.py | ||
| _storage_docs.py | ||
| _tensor_docs.py | ||
| _tensor_str.py | ||
| _tensor.py | ||
| _torch_docs.py | ||
| _utils_internal.py | ||
| _utils.py | ||
| _VF.py | ||
| _vmap_internals.py | ||
| abi-check.cpp | ||
| CMakeLists.txt | ||
| custom_class_detail.h | ||
| custom_class.h | ||
| deploy.h | ||
| extension.h | ||
| functional.py | ||
| hub.py | ||
| library.h | ||
| overrides.py | ||
| py.typed | ||
| quasirandom.py | ||
| random.py | ||
| README.txt | ||
| script.h | ||
| serialization.py | ||
| storage.py | ||
| types.py | ||
Note [TH abstraction violation] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TH/THC provide some hpp headers, which are proper C++ headers rather than C headers. These headers serve double duty as *internal implementation detail* headers, whose contents should largely not be used by external clients. Ideally, we would not install these headers at all; instead, you should use public functions (in headers like `THTensor.h`, NOT `THTensor.hpp`) to manipulate these structs. However, there are a few places in torch/csrc where we violate this abstraction. They are marked with a pointer to this note. Each of those sites will have to be refactored when we refactor the guts of THTensor and related structures.