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Add type annotations to conv-relu (#47680)

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Summary:
Fixes https://github.com/pytorch/pytorch/issues/47679

Pull Request resolved: https://github.com/pytorch/pytorch/pull/47680

Reviewed By: zhangguanheng66

Differential Revision: D25416628

Pulled By: malfet

fbshipit-source-id: 103bea1e8c300990f74689787a71b1cfe916cfef
This commit is contained in:
Guilherme Leobas
2020-12-09 17:10:23 -08:00
committed by Facebook GitHub Bot
parent e9ef1fe309
commit 5375a479aa
3 changed files with 10 additions and 19 deletions
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15
mypy.ini
View File

@ -101,15 +101,15 @@ ignore_errors = True
[mypy-torch.nn.quantized.modules.conv]
ignore_errors = True
[mypy-torch._lobpcg]
ignore_errors = True
[mypy-torch._appdirs]
ignore_errors = True
[mypy-torch._utils]
ignore_errors = True
[mypy-torch._overrides]
ignore_errors = True
[mypy-torch.utils.tensorboard._caffe2_graph]
ignore_errors = True
@ -131,15 +131,6 @@ ignore_errors = True
[mypy-torch.nn.quantized.modules.batchnorm]
ignore_errors = True
[mypy-torch.nn.intrinsic.quantized.modules.conv_relu]
ignore_errors = True
[mypy-torch.nn.intrinsic.quantized.modules.bn_relu]
ignore_errors = True
[mypy-torch.nn.intrinsic.quantized.modules.linear_relu]
ignore_errors = True
[mypy-torch.nn.intrinsic.qat.modules.conv_fused]
ignore_errors = True

8
torch/_lobpcg.py
View File

@ -262,7 +262,7 @@ def _symeig_backward(D_grad, U_grad, A, D, U, largest):
class LOBPCGAutogradFunction(torch.autograd.Function):
@staticmethod
def forward(ctx,
def forward(ctx, # type: ignore[override]
A: Tensor,
k: Optional[int] = None,
B: Optional[Tensor] = None,
@ -606,7 +606,7 @@ def _lobpcg(A: Tensor,
bparams['ortho_use_drop'] = bparams.get('ortho_use_drop', False)
if not torch.jit.is_scripting():
LOBPCG.call_tracker = LOBPCG_call_tracker
LOBPCG.call_tracker = LOBPCG_call_tracker # type: ignore
if len(A.shape) > 2:
N = int(torch.prod(torch.tensor(A.shape[:-2])))
@ -628,7 +628,7 @@ def _lobpcg(A: Tensor,
bXret[i] = worker.X[:, :k]
if not torch.jit.is_scripting():
LOBPCG.call_tracker = LOBPCG_call_tracker_orig
LOBPCG.call_tracker = LOBPCG_call_tracker_orig # type: ignore
return bE.reshape(A.shape[:-2] + (k,)), bXret.reshape(A.shape[:-2] + (m, k))
@ -640,7 +640,7 @@ def _lobpcg(A: Tensor,
worker.run()
if not torch.jit.is_scripting():
LOBPCG.call_tracker = LOBPCG_call_tracker_orig
LOBPCG.call_tracker = LOBPCG_call_tracker_orig # type: ignore
return worker.E[:k], worker.X[:, :k]

6
torch/nn/intrinsic/quantized/modules/conv_relu.py
View File

@ -16,7 +16,7 @@ class ConvReLU1d(nnq.Conv1d):
Same as torch.nn.quantized.Conv1d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU1d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
@ -55,7 +55,7 @@ class ConvReLU2d(nnq.Conv2d):
Same as torch.nn.quantized.Conv2d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU2d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,
@ -94,7 +94,7 @@ class ConvReLU3d(nnq.Conv3d):
Attributes: Same as torch.nn.quantized.Conv3d
"""
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d
_FLOAT_MODULE = torch.nn.intrinsic.ConvReLU3d # type: ignore[assignment]
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True,