Add flexible bilinear upsampling aspect ratio redux#1317
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soumith merged 10 commits intopytorch:masterfrom May 3, 2017
andrewgiessel:add-flexible-bilinear-upsampling-aspect-ratio-redux
Merged
Add flexible bilinear upsampling aspect ratio redux#1317soumith merged 10 commits intopytorch:masterfrom andrewgiessel:add-flexible-bilinear-upsampling-aspect-ratio-redux
soumith merged 10 commits intopytorch:masterfrom
andrewgiessel:add-flexible-bilinear-upsampling-aspect-ratio-redux
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apaszke
reviewed
Apr 21, 2017
| self.size = size | ||
| if scale_factor is not None and not isinstance(scale_factor, (Integral, tuple)): | ||
| raise ValueError('scale_factor must be of integer type or tuple of integer types') | ||
| self.size = _pair(size) |
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| # we have to be a tuple at this point | ||
| try: | ||
| assert len(self.scale_factor) == 2 | ||
| for i in self.scale_factor: |
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| self.output_size = ( | ||
| input.size(2) * self.scale_factor, | ||
| input.size(3) * self.scale_factor, | ||
| input.size(2) * self.scale_factor[0], |
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| """ | ||
|
|
||
| def __init__(self, size=None, scale_factor=None): | ||
| super(UpsamplingBilinear2d, self).__init__(size, scale_factor) |
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torch/nn/modules/upsampling.py
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| raise ValueError('scale_factor must be of integer type') | ||
| if scale_factor is not None and not isinstance(scale_factor, (Integral, tuple)): | ||
| raise ValueError('scale_factor must be of integer type or tuple of integer types') | ||
| self.size = _pair(size) |
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|
|
||
| if self.scale_factor is not None: | ||
| self.scale_factor = _pair(self.scale_factor) | ||
| # we have to be a tuple at this point |
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added 3 commits
April 21, 2017 20:33
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Thanks for the comments @apaszke! I think I made all the changes you asked for. Please, let me know if I didn't understand something (in particular I just c/p your |
This allows for the base class to be used for upsampling routines other than 2d. I also renamed _check_bilinear_2d_scale_factor().
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soumith
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May 3, 2017
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thanks Andrew! |
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…8ffb52 (pytorch#11346) Summary: Pull Request resolved: pytorch#11346 Previous import was 1b09eb14c2c781fae078fa6b1c0390ba6fc0898c Included changes: - **[bff0b88](onnx/onnx@bff0b88)**: Add DynamicSlice experimental op (pytorch#1377) <James Reed> - **[91a7b8e](onnx/onnx@91a7b8e)**: statCoverage(model) (pytorch#1246) <Akshay Chalana> - **[36643c6](onnx/onnx@36643c6)**: fix the doc for softmax (pytorch#1374) <Lu Fang> - **[8c64acd](onnx/onnx@8c64acd)**: Silence usused result warning in ONNXIFI wrapper cleanup. Fix pytorch#1344 (pytorch#1371) <Marat Dukhan> - **[53b20f6](onnx/onnx@53b20f6)**: Add the ability to deprecate an OpSchema (pytorch#1317) <Ryan Hill> - **[8aec4e2](onnx/onnx@8aec4e2)**: [Anderspapitto patch] fix the shape inference for broadcasting (pytorch#1368) <Lu Fang> Reviewed By: jamesr66a Differential Revision: D9691533 fbshipit-source-id: 1a8c22262ae4946897e4be030d3f1cf3a3ad58b6
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…8ffb52 (#11346) Summary: Pull Request resolved: #11346 Previous import was 1b09eb14c2c781fae078fa6b1c0390ba6fc0898c Included changes: - **[bff0b88](onnx/onnx@bff0b88)**: Add DynamicSlice experimental op (#1377) <James Reed> - **[91a7b8e](onnx/onnx@91a7b8e)**: statCoverage(model) (#1246) <Akshay Chalana> - **[36643c6](onnx/onnx@36643c6)**: fix the doc for softmax (#1374) <Lu Fang> - **[8c64acd](onnx/onnx@8c64acd)**: Silence usused result warning in ONNXIFI wrapper cleanup. Fix #1344 (#1371) <Marat Dukhan> - **[53b20f6](onnx/onnx@53b20f6)**: Add the ability to deprecate an OpSchema (#1317) <Ryan Hill> - **[8aec4e2](onnx/onnx@8aec4e2)**: [Anderspapitto patch] fix the shape inference for broadcasting (#1368) <Lu Fang> Reviewed By: jamesr66a Differential Revision: D9691533 fbshipit-source-id: 6aff6ce04ade37182e2ffe9bc83eb86846bc722d
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…8ffb52 (pytorch#11346) Summary: Pull Request resolved: pytorch#11346 Previous import was 1b09eb14c2c781fae078fa6b1c0390ba6fc0898c Included changes: - **[bff0b88](onnx/onnx@bff0b88)**: Add DynamicSlice experimental op (pytorch#1377) <James Reed> - **[91a7b8e](onnx/onnx@91a7b8e)**: statCoverage(model) (pytorch#1246) <Akshay Chalana> - **[36643c6](onnx/onnx@36643c6)**: fix the doc for softmax (pytorch#1374) <Lu Fang> - **[8c64acd](onnx/onnx@8c64acd)**: Silence usused result warning in ONNXIFI wrapper cleanup. Fix pytorch#1344 (pytorch#1371) <Marat Dukhan> - **[53b20f6](onnx/onnx@53b20f6)**: Add the ability to deprecate an OpSchema (pytorch#1317) <Ryan Hill> - **[8aec4e2](onnx/onnx@8aec4e2)**: [Anderspapitto patch] fix the shape inference for broadcasting (pytorch#1368) <Lu Fang> Reviewed By: jamesr66a Differential Revision: D9691533 fbshipit-source-id: 6aff6ce04ade37182e2ffe9bc83eb86846bc722d
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… and `fused_weight_gradient_mlp_cuda` is missing (pytorch#1317)
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This PR addresses issue #1257, which aims to add non-coupled scaling factors for bilinear 2d upsampling. I've added two new tests, and currently everything passes. This is a relatively simple change which only touches python code.
Furthermore, this is is a re-do of PR #1279, wherein I messed up a rebase. There are some in-line comments there that might be worth skimming through, as well, but I tried to address most of the concerns raised there. In particular, I maintained the base class of all the upsampling methods.
Tests pass, on GPU and CPU.
tagging @apaszke @fmassa @soumith. Thanks in advance everyone.
ps: I'll probably need a pointer on how to best rebase to master, eventually ;)