# Copyright (C) 2020-2024, François-Guillaume Fernandez.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
from collections import OrderedDict
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from holocron.nn import DropBlock2d, GlobalAvgPool2d
from holocron.nn.init import init_module
from ..checkpoints import Checkpoint, _handle_legacy_pretrained
from ..presets import IMAGENETTE
from ..utils import _checkpoint, _configure_model, conv_sequence
from .darknetv3 import ResBlock
__all__ = ["CSPDarknet53_Checkpoint", "CSPDarknet53_Mish_Checkpoint", "DarknetV4", "cspdarknet53", "cspdarknet53_mish"]
default_cfgs: Dict[str, Dict[str, Any]] = {
"cspdarknet53": {
**IMAGENETTE.__dict__,
"input_shape": (3, 224, 224),
"url": "https://github.com/frgfm/Holocron/releases/download/v0.1.3/cspdarknet53_224-d2a17b18.pt",
},
"cspdarknet53_mish": {
**IMAGENETTE.__dict__,
"input_shape": (3, 224, 224),
"url": "https://github.com/frgfm/Holocron/releases/download/v0.1.3/cspdarknet53_mish_256-32d8ec68.pt",
},
}
class CSPStage(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
num_blocks: 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,
) -> None:
super().__init__()
compression = 2 if num_blocks > 1 else 1
self.base_layer = nn.Sequential(
*conv_sequence(
in_channels,
out_channels,
act_layer,
norm_layer,
drop_layer,
conv_layer,
kernel_size=3,
padding=1,
stride=2,
bias=(norm_layer is None),
),
# Share the conv
*conv_sequence(
out_channels,
2 * out_channels // compression,
act_layer,
norm_layer,
drop_layer,
conv_layer,
kernel_size=1,
bias=(norm_layer is None),
),
)
self.main = nn.Sequential(
*[
ResBlock(
out_channels // compression,
out_channels // compression if num_blocks > 1 else in_channels,
act_layer,
norm_layer,
drop_layer,
conv_layer,
)
for _ in range(num_blocks)
],
*conv_sequence(
out_channels // compression,
out_channels // compression,
act_layer,
norm_layer,
drop_layer,
conv_layer,
kernel_size=1,
bias=(norm_layer is None),
),
)
self.transition = nn.Sequential(
*conv_sequence(
2 * out_channels // compression,
out_channels,
act_layer,
norm_layer,
drop_layer,
conv_layer,
kernel_size=1,
bias=(norm_layer is None),
)
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.base_layer(x)
x1, x2 = x.chunk(2, dim=1)
return self.transition(torch.cat([x1, self.main(x2)], dim=1))
class DarknetBodyV4(nn.Sequential):
def __init__(
self,
layout: List[Tuple[int, int]],
in_channels: int = 3,
stem_channels: int = 32,
num_features: 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,
) -> None:
super().__init__()
if act_layer is None:
act_layer = nn.LeakyReLU(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=(norm_layer is None),
)
),
),
(
"stages",
nn.Sequential(*[
CSPStage(_in_chans, out_chans, num_blocks, act_layer, norm_layer, drop_layer, conv_layer)
for _in_chans, (out_chans, num_blocks) in zip(in_chans, layout)
]),
),
])
)
self.num_features = num_features
def forward(self, x: torch.Tensor) -> Union[torch.Tensor, List[torch.Tensor]]:
if self.num_features == 1:
return super().forward(x)
self.stem: nn.Sequential
self.stages: nn.Sequential
x = self.stem(x)
features = []
for idx, stage in enumerate(self.stages):
x = stage(x)
if idx >= (len(self.stages) - self.num_features):
features.append(x)
return features
class DarknetV4(nn.Sequential):
def __init__(
self,
layout: List[Tuple[int, int]],
num_classes: int = 10,
in_channels: int = 3,
stem_channels: int = 32,
num_features: 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,
) -> None:
super().__init__(
OrderedDict([
(
"features",
DarknetBodyV4(
layout,
in_channels,
stem_channels,
num_features,
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(
checkpoint: Union[Checkpoint, None],
progress: bool,
layout: List[Tuple[int, int]],
**kwargs: Any,
) -> DarknetV4:
# Build the model
model = DarknetV4(layout, **kwargs)
return _configure_model(model, checkpoint, progress=progress)
[docs]
class CSPDarknet53_Checkpoint(Enum):
IMAGENETTE = _checkpoint(
arch="cspdarknet53",
url="https://github.com/frgfm/Holocron/releases/download/v0.2.1/cspdarknet53_224-7a69463a.pth",
acc1=0.9450,
acc5=0.9964,
sha256="7a69463a4bd445beb6691dfd6ef7378efcf941f75d07d60034106ebedfcb82f8",
size=106732575,
num_params=26627434,
commit="6e32c5b578711a2ef3731a8f8c61760ed9f03e58",
train_args=(
"./imagenette2-320/ --arch cspdarknet53 --batch-size 64 --mixup-alpha 0.2 --amp --device 0 --epochs 100"
" --lr 1e-3 --label-smoothing 0.1 --random-erase 0.1 --train-crop-size 176 --val-resize-size 232"
" --opt adamw --weight-decay 5e-2"
),
)
DEFAULT = IMAGENETTE
[docs]
def cspdarknet53(
pretrained: bool = False,
checkpoint: Union[Checkpoint, None] = None,
progress: bool = True,
**kwargs: Any,
) -> DarknetV4:
"""CSP-Darknet-53 from
`"CSPNet: A New Backbone that can Enhance Learning Capability of CNN" <https://arxiv.org/pdf/1911.11929.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
checkpoint: If specified, the model's parameters will be set to the checkpoint's values
progress (bool): If True, displays a progress bar of the download to stderr
kwargs: keyword args of _darknet
Returns:
torch.nn.Module: classification model
.. autoclass:: holocron.models.CSPDarknet53_Checkpoint
:members:
"""
checkpoint = _handle_legacy_pretrained(
pretrained,
checkpoint,
CSPDarknet53_Checkpoint.DEFAULT.value,
)
return _darknet(checkpoint, progress, [(64, 1), (128, 2), (256, 8), (512, 8), (1024, 4)], **kwargs)
[docs]
class CSPDarknet53_Mish_Checkpoint(Enum):
IMAGENETTE = _checkpoint(
arch="cspdarknet53_mish",
url="https://github.com/frgfm/Holocron/releases/download/v0.2.1/cspdarknet53_mish_224-1b660b3c.pth",
acc1=0.9465,
acc5=0.9969,
sha256="1b660b3cb144195100c99ee3b9b863c37a5b5a59619c8de8c588b3d2af954b15",
size=106737530,
num_params=26627434,
commit="6e32c5b578711a2ef3731a8f8c61760ed9f03e58",
train_args=(
"./imagenette2-320/ --arch cspdarknet53_mish --batch-size 32 --grad-acc 2 --mixup-alpha 0.2 --amp"
" --device 0 --epochs 100 --lr 1e-3 --label-smoothing 0.1 --random-erase 0.1 --train-crop-size 176"
" --val-resize-size 232 --opt adamw --weight-decay 5e-2"
),
)
DEFAULT = IMAGENETTE
[docs]
def cspdarknet53_mish(
pretrained: bool = False,
checkpoint: Union[Checkpoint, None] = None,
progress: bool = True,
**kwargs: Any,
) -> DarknetV4:
"""Modified version of CSP-Darknet-53 from
`"CSPNet: A New Backbone that can Enhance Learning Capability of CNN" <https://arxiv.org/pdf/1911.11929.pdf>`_
with Mish as activation layer and DropBlock as regularization layer.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
checkpoint: If specified, the model's parameters will be set to the checkpoint's values
progress (bool): If True, displays a progress bar of the download to stderr
kwargs: keyword args of _darknet
Returns:
torch.nn.Module: classification model
.. autoclass:: holocron.models.CSPDarknet53_Mish_Checkpoint
:members:
"""
kwargs["act_layer"] = nn.Mish(inplace=True)
kwargs["drop_layer"] = DropBlock2d
checkpoint = _handle_legacy_pretrained(
pretrained,
checkpoint,
CSPDarknet53_Mish_Checkpoint.DEFAULT.value,
)
return _darknet(checkpoint, progress, [(64, 1), (128, 2), (256, 8), (512, 8), (1024, 4)], **kwargs)