[torchlib] Fix _fft_c2r #1844
[torchlib] Fix _fft_c2r #1844
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #1844 +/- ##
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- Coverage 75.07% 74.92% -0.15%
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Files 249 249
Lines 27046 27060 +14
Branches 4943 4945 +2
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- Hits 20304 20276 -28
- Misses 5797 5839 +42
Partials 945 945 ☔ View full report in Codecov by Sentry. |
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Just specify dft_length |
| @@ -1,7 +1,5 @@ | |||
| # -------------------------------------------------------------------------- | |||
| # Copyright (c) Microsoft Corporation. All rights reserved. | |||
| # Copyright (c) Microsoft Corporation. | |||
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RUFF-FORMAT/format Warning
| @@ -1,7 +1,5 @@ | |||
| # -------------------------------------------------------------------------- | |||
| # Copyright (c) Microsoft Corporation. All rights reserved. | |||
| # Copyright (c) Microsoft Corporation. | |||
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RUFF/format Warning
| @@ -12,7 +10,7 @@ | |||
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| from __future__ import annotations | |||
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| from typing import Optional, Sequence | |||
| from typing import Literal, Optional, Sequence | |||
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PYLINT/W0611 Warning
| @@ -12,7 +10,7 @@ | |||
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| from __future__ import annotations | |||
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| from typing import Optional, Sequence | |||
| from typing import Literal, Optional, Sequence | |||
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RUFF/F401 Warning
| dims: Sequence[int], | ||
| forward: bool, |
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PYLINT/W0613 Warning
| result = transformed | ||
| transformed = self | ||
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| signal_size = _compute_signal_size(self, dims, last_dim_size) |
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PYLINT/W0612 Warning
| # If normalization is 1/n and we are in backward mode, we use the inverse | ||
| # mode in ONNX to get the 1/n normalization. | ||
| inverse = normalization == 2 and not forward | ||
| ortho = normalization == 1 |
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RUFF/F841 Warning
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| # Remove the batch dimension | ||
| transformed = op.Squeeze(transformed, axes=[0]) | ||
| normalized = _fftn_onnx_normalization( |
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PYLINT/E0602 Error
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| # Remove the batch dimension | ||
| transformed = op.Squeeze(transformed, axes=[0]) | ||
| normalized = _fftn_onnx_normalization( |
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RUFF/F821 Error
| self_rank = len(self.shape) | ||
| # ONNX DFT input assumes the last dimension is the complex dimension. | ||
| # Thus dim=-1 in PyTorch is dim=-2 in ONNX. | ||
| dim = [(d - 1) + self_rank if d < 0 else d for d in dim] | ||
| transformed = _fftn_onnx(self, dim, normalization, inverse=True, onesided=False) | ||
| transformed = _fftn_onnx( |
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PYLINT/E1123 Error
| self_rank = len(self.shape) | ||
| # ONNX DFT input assumes the last dimension is the complex dimension. | ||
| # Thus dim=-1 in PyTorch is dim=-2 in ONNX. | ||
| dim = [(d - 1) + self_rank if d < 0 else d for d in dim] | ||
| transformed = _fftn_onnx(self, dim, normalization, inverse=True, onesided=False) | ||
| transformed = _fftn_onnx( |
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PYLINT/E1120 Error
Fix #1271