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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, kaiming_init
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet import Bottleneck, ResNet
from mmdet.models.builder import BACKBONES
class TridentConv(nn.Module):
"""Trident Convolution Module.
Args:
in_channels (int): Number of channels in input.
out_channels (int): Number of channels in output.
kernel_size (int): Size of convolution kernel.
stride (int, optional): Convolution stride. Default: 1.
trident_dilations (tuple[int, int, int], optional): Dilations of
different trident branch. Default: (1, 2, 3).
test_branch_idx (int, optional): In inference, all 3 branches will
be used if `test_branch_idx==-1`, otherwise only branch with
index `test_branch_idx` will be used. Default: 1.
bias (bool, optional): Whether to use bias in convolution or not.
Default: False.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
trident_dilations=(1, 2, 3),
test_branch_idx=1,
bias=False):
super(TridentConv, self).__init__()
self.num_branch = len(trident_dilations)
self.with_bias = bias
self.test_branch_idx = test_branch_idx
self.stride = _pair(stride)
self.kernel_size = _pair(kernel_size)
self.paddings = _pair(trident_dilations)
self.dilations = trident_dilations
self.in_channels = in_channels
self.out_channels = out_channels
self.bias = bias
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels, *self.kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_channels))
else:
self.bias = None
self.init_weights()
def init_weights(self):
kaiming_init(self, distribution='uniform', mode='fan_in')
def extra_repr(self):
tmpstr = f'in_channels={self.in_channels}'
tmpstr += f', out_channels={self.out_channels}'
tmpstr += f', kernel_size={self.kernel_size}'
tmpstr += f', num_branch={self.num_branch}'
tmpstr += f', test_branch_idx={self.test_branch_idx}'
tmpstr += f', stride={self.stride}'
tmpstr += f', paddings={self.paddings}'
tmpstr += f', dilations={self.dilations}'
tmpstr += f', bias={self.bias}'
return tmpstr
def forward(self, inputs):
if self.training or self.test_branch_idx == -1:
outputs = [
F.conv2d(input, self.weight, self.bias, self.stride, padding,
dilation) for input, dilation, padding in zip(
inputs, self.dilations, self.paddings)
]
else:
assert len(inputs) == 1
outputs = [
F.conv2d(inputs[0], self.weight, self.bias, self.stride,
self.paddings[self.test_branch_idx],
self.dilations[self.test_branch_idx])
]
return outputs
# Since TridentNet is defined over ResNet50 and ResNet101, here we
# only support TridentBottleneckBlock.
class TridentBottleneck(Bottleneck):
"""BottleBlock for TridentResNet.
Args:
trident_dilations (tuple[int, int, int]): Dilations of different
trident branch.
test_branch_idx (int): In inference, all 3 branches will be used
if `test_branch_idx==-1`, otherwise only branch with index
`test_branch_idx` will be used.
concat_output (bool): Whether to concat the output list to a Tensor.
`True` only in the last Block.
"""
def __init__(self, trident_dilations, test_branch_idx, concat_output,
**kwargs):
super(TridentBottleneck, self).__init__(**kwargs)
self.trident_dilations = trident_dilations
self.num_branch = len(trident_dilations)
self.concat_output = concat_output
self.test_branch_idx = test_branch_idx
self.conv2 = TridentConv(
self.planes,
self.planes,
kernel_size=3,
stride=self.conv2_stride,
bias=False,
trident_dilations=self.trident_dilations,
test_branch_idx=test_branch_idx)
def forward(self, x):
def _inner_forward(x):
num_branch = (
self.num_branch
if self.training or self.test_branch_idx == -1 else 1)
identity = x
if not isinstance(x, list):
x = (x, ) * num_branch
identity = x
if self.downsample is not None:
identity = [self.downsample(b) for b in x]
out = [self.conv1(b) for b in x]
out = [self.norm1(b) for b in out]
out = [self.relu(b) for b in out]
if self.with_plugins:
for k in range(len(out)):
out[k] = self.forward_plugin(out[k],
self.after_conv1_plugin_names)
out = self.conv2(out)
out = [self.norm2(b) for b in out]
out = [self.relu(b) for b in out]
if self.with_plugins:
for k in range(len(out)):
out[k] = self.forward_plugin(out[k],
self.after_conv2_plugin_names)
out = [self.conv3(b) for b in out]
out = [self.norm3(b) for b in out]
if self.with_plugins:
for k in range(len(out)):
out[k] = self.forward_plugin(out[k],
self.after_conv3_plugin_names)
out = [
out_b + identity_b for out_b, identity_b in zip(out, identity)
]
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = [self.relu(b) for b in out]
if self.concat_output:
out = torch.cat(out, dim=0)
return out
def make_trident_res_layer(block,
inplanes,
planes,
num_blocks,
stride=1,
trident_dilations=(1, 2, 3),
style='pytorch',
with_cp=False,
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None,
plugins=None,
test_branch_idx=-1):
"""Build Trident Res Layers."""
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = []
conv_stride = stride
downsample.extend([
build_conv_layer(
conv_cfg,
inplanes,
planes * block.expansion,
kernel_size=1,
stride=conv_stride,
bias=False),
build_norm_layer(norm_cfg, planes * block.expansion)[1]
])
downsample = nn.Sequential(*downsample)
layers = []
for i in range(num_blocks):
layers.append(
block(
inplanes=inplanes,
planes=planes,
stride=stride if i == 0 else 1,
trident_dilations=trident_dilations,
downsample=downsample if i == 0 else None,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
dcn=dcn,
plugins=plugins,
test_branch_idx=test_branch_idx,
concat_output=True if i == num_blocks - 1 else False))
inplanes = planes * block.expansion
return nn.Sequential(*layers)
@BACKBONES.register_module()
class TridentResNet(ResNet):
"""The stem layer, stage 1 and stage 2 in Trident ResNet are identical to
ResNet, while in stage 3, Trident BottleBlock is utilized to replace the
normal BottleBlock to yield trident output. Different branch shares the
convolution weight but uses different dilations to achieve multi-scale
output.
/ stage3(b0) \
x - stem - stage1 - stage2 - stage3(b1) - output
\ stage3(b2) /
Args:
depth (int): Depth of resnet, from {50, 101, 152}.
num_branch (int): Number of branches in TridentNet.
test_branch_idx (int): In inference, all 3 branches will be used
if `test_branch_idx==-1`, otherwise only branch with index
`test_branch_idx` will be used.
trident_dilations (tuple[int]): Dilations of different trident branch.
len(trident_dilations) should be equal to num_branch.
""" # noqa
def __init__(self, depth, num_branch, test_branch_idx, trident_dilations,
**kwargs):
assert num_branch == len(trident_dilations)
assert depth in (50, 101, 152)
super(TridentResNet, self).__init__(depth, **kwargs)
assert self.num_stages == 3
self.test_branch_idx = test_branch_idx
self.num_branch = num_branch
last_stage_idx = self.num_stages - 1
stride = self.strides[last_stage_idx]
dilation = trident_dilations
dcn = self.dcn if self.stage_with_dcn[last_stage_idx] else None
if self.plugins is not None:
stage_plugins = self.make_stage_plugins(self.plugins,
last_stage_idx)
else:
stage_plugins = None
planes = self.base_channels * 2**last_stage_idx
res_layer = make_trident_res_layer(
TridentBottleneck,
inplanes=(self.block.expansion * self.base_channels *
2**(last_stage_idx - 1)),
planes=planes,
num_blocks=self.stage_blocks[last_stage_idx],
stride=stride,
trident_dilations=dilation,
style=self.style,
with_cp=self.with_cp,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
dcn=dcn,
plugins=stage_plugins,
test_branch_idx=self.test_branch_idx)
layer_name = f'layer{last_stage_idx + 1}'
self.__setattr__(layer_name, res_layer)
self.res_layers.pop(last_stage_idx)
self.res_layers.insert(last_stage_idx, layer_name)
self._freeze_stages()
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