File size: 6,656 Bytes
d3dbf03 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer
from mmengine.model import BaseModule, ModuleList, Sequential
from mmaction.models.utils import unit_tcn
# ! Notice: The implementation of MSTCN in
# MS-G3D is not the same as our implementation.
class MSTCN(BaseModule):
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
stride=1,
dilations=[1, 2, 3, 4],
residual=True,
act_cfg=dict(type='ReLU'),
init_cfg=[
dict(type='Constant', layer='BatchNorm2d', val=1),
dict(type='Kaiming', layer='Conv2d', mode='fan_out')
],
tcn_dropout=0):
super().__init__(init_cfg=init_cfg)
# Multiple branches of temporal convolution
self.num_branches = len(dilations) + 2
branch_channels = out_channels // self.num_branches
branch_channels_rem = out_channels - branch_channels * (
self.num_branches - 1)
if type(kernel_size) == list:
assert len(kernel_size) == len(dilations)
else:
kernel_size = [kernel_size] * len(dilations)
self.branches = ModuleList([
Sequential(
nn.Conv2d(
in_channels, branch_channels, kernel_size=1, padding=0),
nn.BatchNorm2d(branch_channels),
build_activation_layer(act_cfg),
unit_tcn(
branch_channels,
branch_channels,
kernel_size=ks,
stride=stride,
dilation=dilation),
) for ks, dilation in zip(kernel_size, dilations)
])
# Additional Max & 1x1 branch
self.branches.append(
Sequential(
nn.Conv2d(
in_channels, branch_channels, kernel_size=1, padding=0),
nn.BatchNorm2d(branch_channels),
build_activation_layer(act_cfg),
nn.MaxPool2d(
kernel_size=(3, 1), stride=(stride, 1), padding=(1, 0)),
nn.BatchNorm2d(branch_channels)))
self.branches.append(
Sequential(
nn.Conv2d(
in_channels,
branch_channels_rem,
kernel_size=1,
padding=0,
stride=(stride, 1)), nn.BatchNorm2d(branch_channels_rem)))
# Residual connection
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels) and (stride == 1):
self.residual = lambda x: x
else:
self.residual = unit_tcn(
in_channels, out_channels, kernel_size=1, stride=stride)
self.act = build_activation_layer(act_cfg)
self.drop = nn.Dropout(tcn_dropout)
def forward(self, x):
# Input dim: (N,C,T,V)
res = self.residual(x)
branch_outs = []
for tempconv in self.branches:
out = tempconv(x)
branch_outs.append(out)
out = torch.cat(branch_outs, dim=1)
out += res
out = self.act(out)
out = self.drop(out)
return out
class CTRGC(BaseModule):
def __init__(self,
in_channels,
out_channels,
rel_reduction=8,
init_cfg=[
dict(type='Constant', layer='BatchNorm2d', val=1),
dict(type='Kaiming', layer='Conv2d', mode='fan_out')
]):
super(CTRGC, self).__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
if in_channels <= 16:
self.rel_channels = 8
else:
self.rel_channels = in_channels // rel_reduction
self.conv1 = nn.Conv2d(
self.in_channels, self.rel_channels, kernel_size=1)
self.conv2 = nn.Conv2d(
self.in_channels, self.rel_channels, kernel_size=1)
self.conv3 = nn.Conv2d(
self.in_channels, self.out_channels, kernel_size=1)
self.conv4 = nn.Conv2d(
self.rel_channels, self.out_channels, kernel_size=1)
self.tanh = nn.Tanh()
def forward(self, x, A=None, alpha=1):
# Input: N, C, T, V
x1, x2, x3 = self.conv1(x).mean(-2), self.conv2(x).mean(
-2), self.conv3(x)
# X1, X2: N, R, V
# N, R, V, 1 - N, R, 1, V
x1 = self.tanh(x1.unsqueeze(-1) - x2.unsqueeze(-2))
# N, R, V, V
x1 = self.conv4(x1) * alpha + (A[None, None] if A is not None else 0
) # N,C,V,V
x1 = torch.einsum('ncuv,nctu->nctv', x1, x3)
return x1
class unit_ctrgcn(BaseModule):
def __init__(self,
in_channels,
out_channels,
A,
init_cfg=[
dict(
type='Constant',
layer='BatchNorm2d',
val=1,
override=dict(type='Constant', name='bn', val=1e-6)),
dict(type='Kaiming', layer='Conv2d', mode='fan_out')
]):
super(unit_ctrgcn, self).__init__(init_cfg=init_cfg)
inter_channels = out_channels // 4
self.inter_c = inter_channels
self.out_c = out_channels
self.in_c = in_channels
self.num_subset = A.shape[0]
self.convs = ModuleList()
for i in range(self.num_subset):
self.convs.append(CTRGC(in_channels, out_channels))
if in_channels != out_channels:
self.down = Sequential(
nn.Conv2d(in_channels, out_channels, 1),
nn.BatchNorm2d(out_channels))
else:
self.down = lambda x: x
self.A = nn.Parameter(A.clone())
self.alpha = nn.Parameter(torch.zeros(1))
self.bn = nn.BatchNorm2d(out_channels)
self.soft = nn.Softmax(-2)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
y = None
for i in range(self.num_subset):
z = self.convs[i](x, self.A[i], self.alpha)
y = z + y if y is not None else z
y = self.bn(y)
y += self.down(x)
return self.relu(y)
|