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CodeCamp #33 [Fix]:add type hints for res_layer, se_layer,normed_predictor,positional_encoding #9346

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merged 7 commits into from
Jan 3, 2023
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CodeCamp #33 [Fix]:add type hints for res_layer, se_layer,normed_predictor,positional_encoding #9346

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4219abe
[Fix]:add type hints for res_layer
ZhikangNiu Nov 18, 2022
cff1bd6
Update mmdet/models/layers/res_layer.py
ZhikangNiu Nov 18, 2022
34c3066
[Fix]:add type hint of norm1 and norm2
ZhikangNiu Nov 18, 2022
54c6a6f
[WIP]:add res_layer type hints
ZhikangNiu Dec 12, 2022
4dac59f
[WIP]:add layer type hints about issue 9234
ZhikangNiu Dec 12, 2022
beee50d
[FIX]:add all type hints and change some function note
ZhikangNiu Dec 28, 2022
bfc849c
[FIX]:add docstrings and change default -> defaults
ZhikangNiu Jan 1, 2023
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51 changes: 28 additions & 23 deletions mmdet/models/layers/res_layer.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,13 @@
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

from mmcv.cnn import build_conv_layer, build_norm_layer
from mmengine.model import BaseModule, Sequential
from torch import Tensor
from torch import nn as nn

from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig


class ResLayer(Sequential):
"""ResLayer to build ResNet style backbone.
Expand All @@ -24,16 +29,16 @@ class ResLayer(Sequential):
"""

def __init__(self,
block,
inplanes,
planes,
num_blocks,
stride=1,
avg_down=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
downsample_first=True,
**kwargs):
block: BaseModule,
inplanes: int,
planes: int,
num_blocks: int,
stride: int = 1,
avg_down: bool = False,
conv_cfg: OptConfigType = None,
norm_cfg: ConfigType = dict(type='BN'),
downsample_first: bool = True,
**kwargs) -> None:
self.block = block

downsample = None
Expand Down Expand Up @@ -114,18 +119,18 @@ class SimplifiedBasicBlock(BaseModule):
expansion = 1
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Line 109: super().init(*layers)


def __init__(self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None,
init_fg=None):
inplanes: int,
planes: int,
stride: int = 1,
dilation: int = 1,
downsample: Optional[Sequential] = None,
style: ConfigType = 'pytorch',
with_cp: bool = False,
conv_cfg: OptConfigType = None,
norm_cfg: ConfigType = dict(type='BN'),
dcn: OptConfigType = None,
plugins: OptConfigType = None,
init_fg: OptMultiConfig = None) -> None:
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super(SimplifiedBasicBlock, self).__init__(init_fg)
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assert dcn is None, 'Not implemented yet.'
assert plugins is None, 'Not implemented yet.'
Expand Down Expand Up @@ -168,7 +173,7 @@ def norm2(self):
"""nn.Module: normalization layer after the second convolution layer"""
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return getattr(self, self.norm2_name) if self.with_norm else None

def forward(self, x):
def forward(self, x: Tensor) -> Tensor:
"""Forward function."""

identity = x
Expand Down