File size: 5,116 Bytes
e8f2571 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
@MODELS.register_module()
class ChannelMapper(BaseModule):
"""Channel Mapper to reduce/increase channels of backbone features.
This is used to reduce/increase channels of backbone features.
Args:
in_channels (List[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale).
kernel_size (int, optional): kernel_size for reducing channels (used
at each scale). Default: 3.
conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
convolution layer. Default: None.
norm_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
normalization layer. Default: None.
act_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
activation layer in ConvModule. Default: dict(type='ReLU').
num_outs (int, optional): Number of output feature maps. There would
be extra_convs when num_outs larger than the length of in_channels.
init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or 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 = ChannelMapper(in_channels, 11, 3).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: List[int],
out_channels: int,
kernel_size: int = 3,
conv_cfg: OptConfigType = None,
norm_cfg: OptConfigType = None,
act_cfg: OptConfigType = dict(type='ReLU'),
num_outs: int = None,
init_cfg: OptMultiConfig = dict(
type='Xavier', layer='Conv2d', distribution='uniform')
) -> None:
super().__init__(init_cfg=init_cfg)
assert isinstance(in_channels, list)
self.extra_convs = None
if num_outs is None:
num_outs = len(in_channels)
self.convs = nn.ModuleList()
for in_channel in in_channels:
self.convs.append(
ConvModule(
in_channel,
out_channels,
kernel_size,
padding=(kernel_size - 1) // 2,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
if num_outs > len(in_channels):
self.extra_convs = nn.ModuleList()
for i in range(len(in_channels), num_outs):
if i == len(in_channels):
in_channel = in_channels[-1]
else:
in_channel = out_channels
# self.extra_convs.append(
# ConvModule(
# in_channel,
# out_channels,
# 3,
# stride=2,
# padding=1,
# conv_cfg=conv_cfg,
# norm_cfg=norm_cfg,
# act_cfg=act_cfg))
self.extra_convs.append(
ConvModule(
in_channel,
out_channels,
32,
stride=32,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.extra_convs.append(
ConvModule(
out_channels,
out_channels,
4,
stride=4,
padding=0,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
def forward(self, inputs: Tuple[Tensor]) -> Tuple[Tensor]:
"""Forward function."""
assert len(inputs) == len(self.convs)
outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
if self.extra_convs:
outs.append(self.extra_convs[1](self.extra_convs[0](inputs[-1])))
# for i in range(len(self.extra_convs)):
# if i == 0:
# outs.append(self.extra_convs[0](inputs[-1]))
# else:
# outs.append(self.extra_convs[i](outs[-1]))
return tuple(outs)
|