Open rentainhe opened 3 years ago
Merging #283 (61df784) into develop (34300a7) will increase coverage by
0.03%
. The diff coverage is93.18%
.
@@ Coverage Diff @@
## develop #283 +/- ##
===========================================
+ Coverage 97.28% 97.31% +0.03%
===========================================
Files 86 87 +1
Lines 3056 3203 +147
===========================================
+ Hits 2973 3117 +144
- Misses 83 86 +3
Impacted Files | Coverage Δ | |
---|---|---|
glasses/nn/att/CBAM.py | 100.00% <ø> (ø) |
|
glasses/nn/att/ECA.py | 100.00% <ø> (ø) |
|
glasses/utils/Storage.py | 95.40% <ø> (+0.16%) |
:arrow_up: |
glasses/nn/att/utils.py | 93.75% <83.33%> (-6.25%) |
:arrow_down: |
glasses/nn/att/SK.py | 93.10% <93.10%> (ø) |
|
glasses/nn/att/__init__.py | 100.00% <100.00%> (ø) |
|
glasses/nn/att/se.py | 100.00% <100.00%> (ø) |
|
test/test_att.py | 100.00% <100.00%> (ø) |
|
test/test_auto.py | 100.00% <0.00%> (ø) |
|
... and 20 more |
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Δ = absolute <relative> (impact)
,ø = not affected
,? = missing data
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record the older code
import torch
import torch.nn as nn
from typing import Union, List
from glasses.nn.att.utils import make_divisible
from ..blocks import ConvBnAct
from einops.layers.torch import Rearrange, Reduce
def _kernel_valid(k):
if isinstance(k, (list, tuple)):
for ki in k:
return _kernel_valid(ki)
assert k >=3 and k % 2
class SelectiveKernelAtt(nn.Module):
def __init__(
self,
features: int,
num_paths: int = 2,
mid_features: int = 32,
act_layer: nn.Module = nn.ReLU,
norm_layer: nn.Module = nn.BatchNorm2d,
):
super().__init__()
self.num_paths = num_paths
self.att = nn.Sequential(
Reduce("b n c h w -> b c h w", reduction="sum"),
Reduce("b c h w -> b c 1 1", reduction="mean"),
nn.Conv2d(features, mid_features, kernel_size=1, bias=False),
norm_layer(mid_features),
act_layer(inplace=True),
nn.Conv2d(mid_features, features * num_paths, kernel_size=1, bias=False),
Rearrange('b (n c) h w -> b n c h w', n=num_paths, c=features),
nn.Softmax(dim=1),
)
def forward(self, x):
assert x.shape[1] == self.num_paths
x = self.att(x)
return x
class SelectiveKernel(nn.Module):
def __init__(
self,
in_features: int,
out_features: int = None,
kernel_size: Union[List, int] = None,
stride: int = 1,
dilation: int = 1,
groups: int = 1,
reduction: int = 16,
reduction_divisor: int = 8,
reduced_features: int = None,
keep_3x3: bool = True,
activation: nn.Module = nn.ReLU,
normalization: nn.Module = nn.BatchNorm2d,
):
super().__init__()
out_features = out_features or in_features
kernel_size = kernel_size or [3, 5]
_kernel_valid(kernel_size)
if not isinstance(kernel_size, list):
kernel_size = [kernel_size] * 2
if keep_3x3:
dilation = [dilation * (k - 1) // 2 for k in kernel_size]
kernel_size = [3] * len(kernel_size)
else:
dilation = [dilation] * len(kernel_size)
self.num_paths = len(kernel_size)
self.in_features = in_features
self.out_features = out_features,
groups = min(out_features, groups)
self.paths = nn.ModuleList([
ConvBnAct(in_features = in_features,
out_features = out_features,
activation = activation,
normalization=normalization,
mode = "same",
stride=stride,
kernel_size=k,
dilation=d)
for k, d in zip(kernel_size, dilation)
])
attn_features = reduced_features or make_divisible(out_features // reduction, divisor=reduction_divisor)
self.attn = SelectiveKernelAtt(out_features, self.num_paths, attn_features)
def forward(self, x):
x_paths = [op(x) for op in self.paths] # b, c, h, w
x = torch.stack(x_paths, dim=1) # b, n, c, h, w
x_attn = self.attn(x)
x = x * x_attn
return torch.sum(x, dim=1)
Thank you for the PR. Let's
- add typing
- remove bad practices such as:
if not isinstance(kernel_size, list): kernel_size = [kernel_size] * 2 if keep_3x3: dilation = [1 * (k - 1) // 2 for k in kernel_size] kernel_size = [3] * len(kernel_size) else: dilation = [1 * (k - 1) // 2 for k in kernel_size]
- decuple each part of the module
- let the user pass a black, to default
ConvBnAct
Sure, I will update my code tonight~, thanks for reviewing
Paper
Reference
TODO