272 lines
10 KiB
Python
272 lines
10 KiB
Python
from functools import partial
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from timm.models.layers import trunc_normal_, DropPath
|
|
from detectron2.modeling.backbone import Backbone
|
|
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
|
|
from detectron2.modeling.backbone.fpn import FPN
|
|
from detectron2.layers import ShapeSpec
|
|
|
|
|
|
class Block(nn.Module):
|
|
r""" ConvNeXt Block. There are two equivalent implementations:
|
|
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
|
|
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
|
|
We use (2) as we find it slightly faster in PyTorch
|
|
|
|
Args:
|
|
dim (int): Number of input channels.
|
|
drop_path (float): Stochastic depth rate. Default: 0.0
|
|
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
|
|
"""
|
|
|
|
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
|
|
super().__init__()
|
|
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
|
|
self.norm = LayerNorm(dim, eps=1e-6)
|
|
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
|
|
self.act = nn.GELU()
|
|
self.pwconv2 = nn.Linear(4 * dim, dim)
|
|
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
|
|
requires_grad=True) if layer_scale_init_value > 0 else None
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
|
|
def forward(self, x):
|
|
input = x
|
|
x = self.dwconv(x)
|
|
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
|
|
x = self.norm(x)
|
|
x = self.pwconv1(x)
|
|
x = self.act(x)
|
|
x = self.pwconv2(x)
|
|
if self.gamma is not None:
|
|
x = self.gamma * x
|
|
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
|
|
|
|
x = input + self.drop_path(x)
|
|
return x
|
|
|
|
|
|
class LayerNorm(nn.Module):
|
|
r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
|
|
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
|
|
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
|
|
with shape (batch_size, channels, height, width).
|
|
"""
|
|
|
|
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(normalized_shape))
|
|
self.bias = nn.Parameter(torch.zeros(normalized_shape))
|
|
self.eps = eps
|
|
self.data_format = data_format
|
|
if self.data_format not in ["channels_last", "channels_first"]:
|
|
raise NotImplementedError
|
|
self.normalized_shape = (normalized_shape,)
|
|
|
|
def forward(self, x):
|
|
if self.data_format == "channels_last":
|
|
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
|
elif self.data_format == "channels_first":
|
|
u = x.mean(1, keepdim=True)
|
|
s = (x - u).pow(2).mean(1, keepdim=True)
|
|
x = (x - u) / torch.sqrt(s + self.eps)
|
|
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
|
return x
|
|
|
|
|
|
class ConvNeXt(Backbone):
|
|
r""" ConvNeXt
|
|
A PyTorch impl of : `A ConvNet for the 2020s` -
|
|
https://arxiv.org/pdf/2201.03545.pdf
|
|
Args:
|
|
in_chans (int): Number of input image channels. Default: 3
|
|
num_classes (int): Number of classes for classification head. Default: 1000
|
|
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
|
|
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
|
|
drop_path_rate (float): Stochastic depth rate. Default: 0.
|
|
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
|
|
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
|
|
out_features (tuple(int)): Stage numbers of the outputs given to the Neck.
|
|
"""
|
|
|
|
def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
|
|
drop_path_rate=0., layer_scale_init_value=1e-6, out_features=None):
|
|
super().__init__()
|
|
|
|
self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
|
|
stem = nn.Sequential(
|
|
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
|
|
LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
|
|
)
|
|
|
|
self.downsample_layers.append(stem)
|
|
for i in range(3):
|
|
downsample_layer = nn.Sequential(
|
|
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
|
|
nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
|
|
)
|
|
self.downsample_layers.append(downsample_layer)
|
|
|
|
self.num_layers = len(depths)
|
|
num_features = [int(dims[i] * 2 ** i) for i in range(self.num_layers)]
|
|
self.num_features = num_features
|
|
self._out_features = out_features
|
|
|
|
self._out_feature_strides = {}
|
|
self._out_feature_channels = {}
|
|
|
|
self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
|
|
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
|
|
cur = 0
|
|
strides = [4, 4, 4, 4]
|
|
for i in range(4):
|
|
stage = nn.Sequential(
|
|
*[Block(dim=dims[i], drop_path=dp_rates[cur + j],
|
|
layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
|
|
)
|
|
self.stages.append(stage)
|
|
cur += depths[i]
|
|
|
|
self._out_feature_channels[i] = dims[i]
|
|
self._out_feature_strides[i] = strides[i] * 2 ** i
|
|
|
|
norm_layer = partial(LayerNorm, eps=1e-6, data_format="channels_first")
|
|
for i_layer in range(4):
|
|
layer = norm_layer(dims[i_layer])
|
|
layer_name = f'norm{i_layer}'
|
|
self.add_module(layer_name, layer)
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
def _init_weights(self, m):
|
|
if isinstance(m, (nn.Conv2d, nn.Linear)):
|
|
trunc_normal_(m.weight, std=.02)
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
|
|
def _init_weights(m):
|
|
if isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.LayerNorm):
|
|
nn.init.constant_(m.bias, 0)
|
|
nn.init.constant_(m.weight, 1.0)
|
|
|
|
self.apply(_init_weights)
|
|
|
|
def forward_features(self, x):
|
|
outs = {}
|
|
for i in range(4):
|
|
x = self.downsample_layers[i](x)
|
|
x = self.stages[i](x)
|
|
if i in self._out_features:
|
|
norm_layer = getattr(self, f'norm{i}')
|
|
x_out = norm_layer(x)
|
|
out = x_out.contiguous()
|
|
stage_name = i
|
|
outs[stage_name] = out
|
|
|
|
return outs # {"stage%d" % (i+2,): out for i, out in enumerate(outs)} #tuple(outs)
|
|
|
|
def forward(self, x):
|
|
x = self.forward_features(x)
|
|
return x
|
|
|
|
|
|
model_urls = {
|
|
"convnext_tiny_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
|
|
"convnext_small_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth",
|
|
"convnext_base_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth",
|
|
"convnext_large_1k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth",
|
|
"convnext_tiny_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth",
|
|
"convnext_small_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth",
|
|
"convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth",
|
|
"convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth",
|
|
"convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth",
|
|
}
|
|
|
|
size2config = {
|
|
"N": {
|
|
"DEPTHS": [2, 2, 8, 2],
|
|
"DIMS": [80, 160, 320, 640]
|
|
},
|
|
'T': {
|
|
"DEPTHS": [3, 3, 9, 3],
|
|
"DIMS": [96, 192, 384, 768]
|
|
},
|
|
'S': {
|
|
"DEPTHS": [3, 3, 27, 3],
|
|
"DIMS": [96, 192, 384, 768]
|
|
},
|
|
'B': {
|
|
"DEPTHS": [3, 3, 27, 3],
|
|
"DIMS": [128, 256, 512, 1024]
|
|
},
|
|
'L': {
|
|
"DEPTHS": [3, 3, 27, 3],
|
|
"DIMS": [192, 384, 768, 1536]
|
|
},
|
|
'XL': {
|
|
"DEPTHS": [3, 3, 27, 3],
|
|
"DIMS": [256, 512, 1024, 2048]
|
|
}
|
|
}
|
|
|
|
|
|
@BACKBONE_REGISTRY.register()
|
|
def build_convnext_backbone(cfg, input_shape):
|
|
"""
|
|
Create a ConvNeXt instance from config.
|
|
|
|
Returns:
|
|
VoVNet: a :class:`VoVNet` instance.
|
|
"""
|
|
size = cfg.MODEL.CONVNEXT.SIZE
|
|
if size in size2config:
|
|
depth = size2config[size]['DEPTHS']
|
|
dims = size2config[size]['DIMS']
|
|
else:
|
|
depth = cfg.MODEL.CONVNEXT.DEPTHS
|
|
dims = cfg.MODEL.CONVNEXT.DIMS
|
|
|
|
return ConvNeXt(
|
|
in_chans=input_shape.channels,
|
|
depths=depth,
|
|
dims=dims,
|
|
drop_path_rate=cfg.MODEL.CONVNEXT.DROP_PATH_RATE,
|
|
layer_scale_init_value=cfg.MODEL.CONVNEXT.LAYER_SCALE_INIT_VALUE,
|
|
out_features=cfg.MODEL.CONVNEXT.OUT_FEATURES
|
|
)
|
|
|
|
|
|
@BACKBONE_REGISTRY.register()
|
|
def build_convnext_fpn_backbone(cfg, input_shape: ShapeSpec):
|
|
"""
|
|
Args:
|
|
cfg: a detectron2 CfgNode
|
|
|
|
Returns:
|
|
backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.
|
|
"""
|
|
bottom_up = build_convnext_backbone(cfg, input_shape)
|
|
in_features = cfg.MODEL.FPN.IN_FEATURES
|
|
out_channels = cfg.MODEL.FPN.OUT_CHANNELS
|
|
backbone = FPN(
|
|
bottom_up=bottom_up,
|
|
in_features=in_features,
|
|
out_channels=out_channels,
|
|
norm=cfg.MODEL.FPN.NORM,
|
|
top_block=None,
|
|
fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
|
|
)
|
|
return backbone
|