# Copyright (C) 2020-2022, François-Guillaume Fernandez.
# This program is licensed under the Apache License version 2.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
from holocron.nn import GlobalAvgPool2d
from ..presets import IMAGENETTE
from ..utils import conv_sequence, load_pretrained_params
from .resnet import ResNet, _ResBlock
__all__ = ['SoftAttentionLayer', 'SKConv2d', 'SKBottleneck', 'sknet50', 'sknet101', 'sknet152']
default_cfgs: Dict[str, Dict[str, Any]] = {
'sknet50': {
**IMAGENETTE,
'input_shape': (3, 224, 224),
'url': 'https://github.com/frgfm/Holocron/releases/download/v0.1.3/sknet50_224-5d2160f2.pth',
},
'sknet101': {
**IMAGENETTE,
'input_shape': (3, 224, 224),
'url': None,
},
'sknet152': {
**IMAGENETTE,
'input_shape': (3, 224, 224),
'url': None,
},
}
class SoftAttentionLayer(nn.Sequential):
def __init__(
self,
channels: int,
sa_ratio: int = 16,
out_multiplier: int = 1,
act_layer: Optional[nn.Module] = None,
norm_layer: Optional[Callable[[int], nn.Module]] = None,
drop_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super().__init__(
GlobalAvgPool2d(flatten=False),
*conv_sequence(channels, max(channels // sa_ratio, 32), act_layer, norm_layer, drop_layer,
kernel_size=1, stride=1, bias=(norm_layer is None)),
*conv_sequence(max(channels // sa_ratio, 32), channels * out_multiplier,
nn.Sigmoid(), None, drop_layer, kernel_size=1, stride=1)
)
class SKConv2d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
m: int = 2,
sa_ratio: int = 16,
act_layer: Optional[nn.Module] = None,
norm_layer: Optional[Callable[[int], nn.Module]] = None,
drop_layer: Optional[Callable[..., nn.Module]] = None,
**kwargs: Any
) -> None:
super().__init__()
self.path_convs = nn.ModuleList([
nn.Sequential(*conv_sequence(in_channels, out_channels, act_layer, norm_layer, drop_layer, kernel_size=3,
bias=(norm_layer is None), dilation=idx + 1, padding=idx + 1, **kwargs))
for idx in range(m)])
self.sa = SoftAttentionLayer(out_channels, sa_ratio, m, act_layer, norm_layer, drop_layer)
def forward(self, x: torch.Tensor) -> torch.Tensor:
paths = torch.stack([path_conv(x) for path_conv in self.path_convs], dim=1)
b, m, c = paths.shape[:3]
z = self.sa(paths.sum(dim=1)).view(b, m, c, 1, 1)
attention_factors = torch.softmax(z, dim=1)
out = (attention_factors * paths).sum(dim=1)
return out
class SKBottleneck(_ResBlock):
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 32,
base_width: int = 64,
dilation: int = 1,
act_layer: Optional[nn.Module] = None,
norm_layer: Optional[Callable[[int], nn.Module]] = None,
drop_layer: Optional[Callable[..., nn.Module]] = None,
conv_layer: Optional[Callable[..., nn.Module]] = None,
**kwargs: Any
) -> None:
width = int(planes * (base_width / 64.)) * groups
super().__init__(
[*conv_sequence(inplanes, width, act_layer, norm_layer, drop_layer, conv_layer, kernel_size=1,
stride=1, bias=(norm_layer is None), **kwargs),
SKConv2d(width, width, 2, 16, act_layer, norm_layer, drop_layer, groups=groups, stride=stride),
*conv_sequence(width, planes * self.expansion, None, norm_layer, drop_layer, conv_layer, kernel_size=1,
stride=1, bias=(norm_layer is None), **kwargs)],
downsample, act_layer)
def _sknet(
arch: str,
pretrained: bool,
progress: bool,
num_blocks: List[int],
out_chans: List[int],
**kwargs: Any,
) -> ResNet:
# Build the model
model = ResNet(SKBottleneck, num_blocks, out_chans, **kwargs) # type: ignore[arg-type]
model.default_cfg = default_cfgs[arch] # type: ignore[assignment]
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]['url'], progress)
return model
[docs]
def sknet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""SKNet-50 from
`"Selective Kernel Networks" <https://arxiv.org/pdf/1903.06586.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
torch.nn.Module: classification model
"""
return _sknet('sknet50', pretrained, progress, [3, 4, 6, 3], [64, 128, 256, 512], **kwargs)
[docs]
def sknet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""SKNet-101 from
`"Selective Kernel Networks" <https://arxiv.org/pdf/1903.06586.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
torch.nn.Module: classification model
"""
return _sknet('sknet101', pretrained, progress, [3, 4, 23, 3], [64, 128, 256, 512], **kwargs)
[docs]
def sknet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""SKNet-152 from
`"Selective Kernel Networks" <https://arxiv.org/pdf/1903.06586.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
torch.nn.Module: classification model
"""
return _sknet('sknet152', pretrained, progress, [3, 8, 86, 3], [64, 128, 256, 512], **kwargs)