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import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmdet.registry import MODELS
from mmengine.model import BaseModule
class Norm2d(nn.Module):
def __init__(self, embed_dim):
super().__init__()
self.ln = nn.LayerNorm(embed_dim, eps=1e-6)
def forward(self, x):
x = x.permute(0, 2, 3, 1)
x = self.ln(x)
x = x.permute(0, 3, 1, 2).contiguous()
return x
@MODELS.register_module()
class SimpleFPN(BaseModule):
r"""Simplified Feature Pyramid Network.
This is an implementation of Simple FPN used in ViT Det.
Args:
in_channels (List[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale)
num_outs (int): Number of output scales.
start_level (int): Index of the start input backbone level used to
build the feature pyramid. Default: 0.
end_level (int): Index of the end input backbone level (exclusive) to
build the feature pyramid. Default: -1, which means the last level.
add_extra_convs (bool | str): If bool, it decides whether to add conv
layers on top of the original feature maps. Default to False.
If True, it is equivalent to `add_extra_convs='on_input'`.
If str, it specifies the source feature map of the extra convs.
Only the following options are allowed
- 'on_input': Last feat map of neck inputs (i.e. backbone feature).
- 'on_lateral': Last feature map after lateral convs.
- 'on_output': The last output feature map after fpn convs.
relu_before_extra_convs (bool): Whether to apply relu before the extra
conv. Default: False.
no_norm_on_lateral (bool): Whether to apply norm on lateral.
Default: False.
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Config dict for normalization layer. Default: None.
act_cfg (str): Config dict for activation layer in ConvModule.
Default: None.
upsample_cfg (dict): Config dict for interpolate layer.
Default: `dict(mode='nearest')`
init_cfg (dict or list[dict], optional): Initialization config dict.
Example:
>>> import torch
>>> in_channels = [2, 3, 5, 7]
>>> scales = [340, 170, 84, 43]
>>> inputs = [torch.rand(1, c, s, s)
... for c, s in zip(in_channels, scales)]
>>> self = FPN(in_channels, 11, len(in_channels)).eval()
>>> outputs = self.forward(inputs)
>>> for i in range(len(outputs)):
... print(f'outputs[{i}].shape = {outputs[i].shape}')
outputs[0].shape = torch.Size([1, 11, 340, 340])
outputs[1].shape = torch.Size([1, 11, 170, 170])
outputs[2].shape = torch.Size([1, 11, 84, 84])
outputs[3].shape = torch.Size([1, 11, 43, 43])
"""
def __init__(
self,
in_channels,
out_channels,
num_outs,
start_level=0,
end_level=-1,
add_extra_convs=False,
relu_before_extra_convs=False,
no_norm_on_lateral=False,
conv_cfg=None,
norm_cfg=None,
act_cfg=None,
use_residual=True,
upsample_cfg=dict(mode="nearest"),
init_cfg=dict(type="Xavier", layer="Conv2d", distribution="uniform"),
):
super(SimpleFPN, self).__init__(init_cfg)
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
self.num_outs = num_outs
self.relu_before_extra_convs = relu_before_extra_convs
self.no_norm_on_lateral = no_norm_on_lateral
self.fp16_enabled = False
self.upsample_cfg = upsample_cfg.copy()
self.use_residual = use_residual
if end_level == -1:
self.backbone_end_level = self.num_ins
assert num_outs >= self.num_ins - start_level
else:
# if end_level < inputs, no extra level is allowed
self.backbone_end_level = end_level
assert end_level <= len(in_channels)
assert num_outs == end_level - start_level
self.start_level = start_level
self.end_level = end_level
self.add_extra_convs = add_extra_convs
assert isinstance(add_extra_convs, (str, bool))
if isinstance(add_extra_convs, str):
# Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
assert add_extra_convs in ("on_input", "on_lateral", "on_output")
elif add_extra_convs: # True
self.add_extra_convs = "on_input"
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
for i in range(self.start_level, self.backbone_end_level):
l_conv = ConvModule(
in_channels[i],
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
act_cfg=act_cfg,
inplace=False,
)
fpn_conv = ConvModule(
out_channels,
out_channels,
3,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False,
)
# l_conv = checkpoint_wrapper(l_conv)
# fpn_conv = checkpoint_wrapper(fpn_conv)
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
# add extra conv layers (e.g., RetinaNet)
extra_levels = num_outs - self.backbone_end_level + self.start_level
if self.add_extra_convs and extra_levels >= 1:
for i in range(extra_levels):
if i == 0 and self.add_extra_convs == "on_input":
in_channels = self.in_channels[self.backbone_end_level - 1]
else:
in_channels = out_channels
extra_fpn_conv = ConvModule(
in_channels,
out_channels,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
inplace=False,
)
self.fpn_convs.append(extra_fpn_conv)
self.fpn1 = nn.Sequential(
nn.ConvTranspose2d(
self.in_channels[0], self.in_channels[0], kernel_size=2, stride=2
),
Norm2d(self.in_channels[0]),
nn.GELU(),
nn.ConvTranspose2d(
self.in_channels[0], self.in_channels[0], kernel_size=2, stride=2
),
)
self.fpn2 = nn.Sequential(
nn.ConvTranspose2d(
self.in_channels[0], self.in_channels[0], kernel_size=2, stride=2
),
)
self.fpn3 = nn.Identity()
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, inputs):
"""Forward function."""
features = []
ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
if isinstance(inputs, list):
assert len(inputs) == len(ops)
for i in range(len(ops)):
features.append(ops[i](inputs[i]))
else:
for i in range(len(ops)):
features.append(ops[i](inputs))
assert len(features) == len(self.in_channels)
# build laterals
laterals = [
lateral_conv(features[i + self.start_level])
for i, lateral_conv in enumerate(self.lateral_convs)
]
# build top-down path
used_backbone_levels = len(laterals)
if self.use_residual:
for i in range(used_backbone_levels - 1, 0, -1):
# In some cases, fixing `scale factor` (e.g. 2) is preferred, but
# it cannot co-exist with `size` in `F.interpolate`.
if "scale_factor" in self.upsample_cfg:
# fix runtime error of "+=" inplace operation in PyTorch 1.10
laterals[i - 1] = laterals[i - 1] + F.interpolate(
laterals[i], **self.upsample_cfg
)
else:
prev_shape = laterals[i - 1].shape[2:]
laterals[i - 1] = laterals[i - 1] + F.interpolate(
laterals[i], size=prev_shape, **self.upsample_cfg
)
# build outputs
# part 1: from original levels
outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)]
# part 2: add extra levels
if self.num_outs > len(outs):
# use max pool to get more levels on top of outputs
# (e.g., Faster R-CNN, Mask R-CNN)
if not self.add_extra_convs:
for i in range(self.num_outs - used_backbone_levels):
outs.append(F.max_pool2d(outs[-1], 1, stride=2))
# add conv layers on top of original feature maps (RetinaNet)
else:
if self.add_extra_convs == "on_input":
extra_source = features[self.backbone_end_level - 1]
elif self.add_extra_convs == "on_lateral":
extra_source = laterals[-1]
elif self.add_extra_convs == "on_output":
extra_source = outs[-1]
else:
raise NotImplementedError
outs.append(self.fpn_convs[used_backbone_levels](extra_source))
for i in range(used_backbone_levels + 1, self.num_outs):
if self.relu_before_extra_convs:
outs.append(self.fpn_convs[i](F.relu(outs[-1])))
else:
outs.append(self.fpn_convs[i](outs[-1]))
return tuple(outs)
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