from collections import OrderedDict
import torch
import torch.nn as nn
from ..nn.init import init_module
from .utils import conv_sequence, load_pretrained_params
from .resnet import _ResBlock
from holocron.nn import DropBlock2d, GlobalAvgPool2d
from typing import Dict, Any, Optional, Callable, List, Tuple
__all__ = ['DarknetV3', 'darknet53']
default_cfgs: Dict[str, Dict[str, Any]] = {
'darknet53': {'arch': 'DarknetV3',
'layout': [(64, 1), (128, 2), (256, 8), (512, 8), (1024, 4)],
'url': 'https://github.com/frgfm/Holocron/releases/download/v0.1.2/darknet53_256-f57b8429.pth'},
}
class ResBlock(_ResBlock):
def __init__(
self,
planes: int,
mid_planes: int,
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
) -> None:
super().__init__(
conv_sequence(planes, mid_planes, act_layer, norm_layer, drop_layer, conv_layer,
kernel_size=1, bias=False) +
conv_sequence(mid_planes, planes, act_layer, norm_layer, drop_layer, conv_layer,
kernel_size=3, padding=1, bias=False),
None, None)
if drop_layer is not None:
self.dropblock = DropBlock2d(0.1, 7, inplace=True)
# The backpropagation does not seem to appreciate inplace activation on the residual branch
if hasattr(self.conv[-1], 'inplace'):
self.conv[-1].inplace = False
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = super().forward(x)
if hasattr(self, 'dropblock'):
out = self.dropblock(out)
return out
class DarknetBodyV3(nn.Sequential):
def __init__(
self,
layout: List[Tuple[int, int]],
in_channels: int = 3,
stem_channels: int = 32,
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
) -> None:
if act_layer is None:
act_layer = nn.LeakyReLU(0.1, inplace=True)
if norm_layer is None:
norm_layer = nn.BatchNorm2d
in_chans = [stem_channels] + [_layout[0] for _layout in layout[:-1]]
super().__init__(OrderedDict([
('stem', nn.Sequential(*conv_sequence(in_channels, stem_channels,
act_layer, norm_layer, drop_layer, conv_layer,
kernel_size=3, padding=1, bias=False))),
('layers', nn.Sequential(*[self._make_layer(num_blocks, _in_chans, out_chans,
act_layer, norm_layer, drop_layer, conv_layer)
for _in_chans, (out_chans, num_blocks) in zip(in_chans, layout)]))])
)
@staticmethod
def _make_layer(
num_blocks: int,
in_planes: int,
out_planes: int,
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
) -> nn.Sequential:
layers = conv_sequence(in_planes, out_planes, act_layer, norm_layer, drop_layer, conv_layer,
kernel_size=3, padding=1, stride=2, bias=False)
layers.extend([ResBlock(out_planes, out_planes // 2, act_layer, norm_layer, drop_layer, conv_layer)
for _ in range(num_blocks)])
return nn.Sequential(*layers)
class DarknetV3(nn.Sequential):
def __init__(
self,
layout: List[Tuple[int, int]],
num_classes: int = 10,
in_channels: int = 3,
stem_channels: int = 32,
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
) -> None:
super().__init__(OrderedDict([
('features', DarknetBodyV3(layout, in_channels, stem_channels,
act_layer, norm_layer, drop_layer, conv_layer)),
('pool', GlobalAvgPool2d(flatten=True)),
('classifier', nn.Linear(layout[-1][0], num_classes))]))
init_module(self, 'leaky_relu')
def _darknet(arch: str, pretrained: bool, progress: bool, **kwargs: Any) -> DarknetV3:
# Build the model
model = DarknetV3(default_cfgs[arch]['layout'], **kwargs)
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]['url'], progress)
return model
[docs]
def darknet53(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DarknetV3:
"""Darknet-53 from
`"YOLOv3: An Incremental Improvement" <https://pjreddie.com/media/files/papers/YOLOv3.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 _darknet('darknet53', pretrained, progress, **kwargs) # type: ignore[return-value]