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# Copyright (c) OpenMMLab. All rights reserved.
import copy as cp
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer, build_norm_layer
from mmengine.model import BaseModule, ModuleList, Sequential
class unit_gcn(BaseModule):
"""The basic unit of graph convolutional network.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
A (torch.Tensor): The adjacency matrix defined in the graph
with shape of `(num_subsets, num_nodes, num_nodes)`.
adaptive (str): The strategy for adapting the weights of the
adjacency matrix. Defaults to ``'importance'``.
conv_pos (str): The position of the 1x1 2D conv.
Defaults to ``'pre'``.
with_res (bool): Whether to use residual connection.
Defaults to False.
norm (str): The name of norm layer. Defaults to ``'BN'``.
act (str): The name of activation layer. Defaults to ``'Relu'``.
init_cfg (dict or list[dict], optional): Initialization config dict.
Defaults to None.
"""
def __init__(self,
in_channels: int,
out_channels: int,
A: torch.Tensor,
adaptive: str = 'importance',
conv_pos: str = 'pre',
with_res: bool = False,
norm: str = 'BN',
act: str = 'ReLU',
init_cfg: Optional[Union[Dict, List[Dict]]] = None) -> None:
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_subsets = A.size(0)
assert adaptive in [None, 'init', 'offset', 'importance']
self.adaptive = adaptive
assert conv_pos in ['pre', 'post']
self.conv_pos = conv_pos
self.with_res = with_res
self.norm_cfg = norm if isinstance(norm, dict) else dict(type=norm)
self.act_cfg = act if isinstance(act, dict) else dict(type=act)
self.bn = build_norm_layer(self.norm_cfg, out_channels)[1]
self.act = build_activation_layer(self.act_cfg)
if self.adaptive == 'init':
self.A = nn.Parameter(A.clone())
else:
self.register_buffer('A', A)
if self.adaptive in ['offset', 'importance']:
self.PA = nn.Parameter(A.clone())
if self.adaptive == 'offset':
nn.init.uniform_(self.PA, -1e-6, 1e-6)
elif self.adaptive == 'importance':
nn.init.constant_(self.PA, 1)
if self.conv_pos == 'pre':
self.conv = nn.Conv2d(in_channels, out_channels * A.size(0), 1)
elif self.conv_pos == 'post':
self.conv = nn.Conv2d(A.size(0) * in_channels, out_channels, 1)
if self.with_res:
if in_channels != out_channels:
self.down = Sequential(
nn.Conv2d(in_channels, out_channels, 1),
build_norm_layer(self.norm_cfg, out_channels)[1])
else:
self.down = lambda x: x
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
n, c, t, v = x.shape
res = self.down(x) if self.with_res else 0
A_switch = {None: self.A, 'init': self.A}
if hasattr(self, 'PA'):
A_switch.update({
'offset': self.A + self.PA,
'importance': self.A * self.PA
})
A = A_switch[self.adaptive]
if self.conv_pos == 'pre':
x = self.conv(x)
x = x.view(n, self.num_subsets, -1, t, v)
x = torch.einsum('nkctv,kvw->nctw', (x, A)).contiguous()
elif self.conv_pos == 'post':
x = torch.einsum('nctv,kvw->nkctw', (x, A)).contiguous()
x = x.view(n, -1, t, v)
x = self.conv(x)
return self.act(self.bn(x) + res)
class unit_aagcn(BaseModule):
"""The graph convolution unit of AAGCN.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
A (torch.Tensor): The adjacency matrix defined in the graph
with shape of `(num_subsets, num_joints, num_joints)`.
coff_embedding (int): The coefficient for downscaling the embedding
dimension. Defaults to 4.
adaptive (bool): Whether to use adaptive graph convolutional layer.
Defaults to True.
attention (bool): Whether to use the STC-attention module.
Defaults to True.
init_cfg (dict or list[dict]): Initialization config dict. Defaults to
``[
dict(type='Constant', layer='BatchNorm2d', val=1,
override=dict(type='Constant', name='bn', val=1e-6)),
dict(type='Kaiming', layer='Conv2d', mode='fan_out'),
dict(type='ConvBranch', name='conv_d')
]``.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
A: torch.Tensor,
coff_embedding: int = 4,
adaptive: bool = True,
attention: bool = True,
init_cfg: Optional[Union[Dict, List[Dict]]] = [
dict(
type='Constant',
layer='BatchNorm2d',
val=1,
override=dict(type='Constant', name='bn', val=1e-6)),
dict(type='Kaiming', layer='Conv2d', mode='fan_out'),
dict(type='ConvBranch', name='conv_d')
]
) -> None:
if attention:
attention_init_cfg = [
dict(
type='Constant',
layer='Conv1d',
val=0,
override=dict(type='Xavier', name='conv_sa')),
dict(
type='Kaiming',
layer='Linear',
mode='fan_in',
override=dict(type='Constant', val=0, name='fc2c'))
]
init_cfg = cp.copy(init_cfg)
init_cfg.extend(attention_init_cfg)
super(unit_aagcn, self).__init__(init_cfg=init_cfg)
inter_channels = out_channels // coff_embedding
self.inter_c = inter_channels
self.out_c = out_channels
self.in_c = in_channels
self.num_subset = A.shape[0]
self.adaptive = adaptive
self.attention = attention
num_joints = A.shape[-1]
self.conv_d = ModuleList()
for i in range(self.num_subset):
self.conv_d.append(nn.Conv2d(in_channels, out_channels, 1))
if self.adaptive:
self.A = nn.Parameter(A)
self.alpha = nn.Parameter(torch.zeros(1))
self.conv_a = ModuleList()
self.conv_b = ModuleList()
for i in range(self.num_subset):
self.conv_a.append(nn.Conv2d(in_channels, inter_channels, 1))
self.conv_b.append(nn.Conv2d(in_channels, inter_channels, 1))
else:
self.register_buffer('A', A)
if self.attention:
self.conv_ta = nn.Conv1d(out_channels, 1, 9, padding=4)
# s attention
ker_joint = num_joints if num_joints % 2 else num_joints - 1
pad = (ker_joint - 1) // 2
self.conv_sa = nn.Conv1d(out_channels, 1, ker_joint, padding=pad)
# channel attention
rr = 2
self.fc1c = nn.Linear(out_channels, out_channels // rr)
self.fc2c = nn.Linear(out_channels // rr, out_channels)
self.down = lambda x: x
if in_channels != out_channels:
self.down = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1),
nn.BatchNorm2d(out_channels))
self.bn = nn.BatchNorm2d(out_channels)
self.tan = nn.Tanh()
self.sigmoid = nn.Sigmoid()
self.relu = nn.ReLU(inplace=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
N, C, T, V = x.size()
y = None
if self.adaptive:
for i in range(self.num_subset):
A1 = self.conv_a[i](x).permute(0, 3, 1, 2).contiguous().view(
N, V, self.inter_c * T)
A2 = self.conv_b[i](x).view(N, self.inter_c * T, V)
A1 = self.tan(torch.matmul(A1, A2) / A1.size(-1)) # N V V
A1 = self.A[i] + A1 * self.alpha
A2 = x.view(N, C * T, V)
z = self.conv_d[i](torch.matmul(A2, A1).view(N, C, T, V))
y = z + y if y is not None else z
else:
for i in range(self.num_subset):
A1 = self.A[i]
A2 = x.view(N, C * T, V)
z = self.conv_d[i](torch.matmul(A2, A1).view(N, C, T, V))
y = z + y if y is not None else z
y = self.relu(self.bn(y) + self.down(x))
if self.attention:
# spatial attention first
se = y.mean(-2) # N C V
se1 = self.sigmoid(self.conv_sa(se)) # N 1 V
y = y * se1.unsqueeze(-2) + y
# then temporal attention
se = y.mean(-1) # N C T
se1 = self.sigmoid(self.conv_ta(se)) # N 1 T
y = y * se1.unsqueeze(-1) + y
# then spatial temporal attention ??
se = y.mean(-1).mean(-1) # N C
se1 = self.relu(self.fc1c(se))
se2 = self.sigmoid(self.fc2c(se1)) # N C
y = y * se2.unsqueeze(-1).unsqueeze(-1) + y
# A little bit weird
return y
class unit_tcn(BaseModule):
"""The basic unit of temporal convolutional network.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
kernel_size (int): Size of the temporal convolution kernel.
Defaults to 9.
stride (int): Stride of the temporal convolution. Defaults to 1.
dilation (int): Spacing between temporal kernel elements.
Defaults to 1.
norm (str): The name of norm layer. Defaults to ``'BN'``.
dropout (float): Dropout probability. Defaults to 0.
init_cfg (dict or list[dict]): Initialization config dict. Defaults to
``[
dict(type='Constant', layer='BatchNorm2d', val=1),
dict(type='Kaiming', layer='Conv2d', mode='fan_out')
]``.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 9,
stride: int = 1,
dilation: int = 1,
norm: str = 'BN',
dropout: float = 0,
init_cfg: Union[Dict, List[Dict]] = [
dict(type='Constant', layer='BatchNorm2d', val=1),
dict(type='Kaiming', layer='Conv2d', mode='fan_out')
]
) -> None:
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
self.norm_cfg = norm if isinstance(norm, dict) else dict(type=norm)
pad = (kernel_size + (kernel_size - 1) * (dilation - 1) - 1) // 2
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size=(kernel_size, 1),
padding=(pad, 0),
stride=(stride, 1),
dilation=(dilation, 1))
self.bn = build_norm_layer(self.norm_cfg, out_channels)[1] \
if norm is not None else nn.Identity()
self.drop = nn.Dropout(dropout, inplace=True)
self.stride = stride
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
return self.drop(self.bn(self.conv(x)))
class mstcn(BaseModule):
"""The multi-scale temporal convolutional network.
Args:
in_channels (int): Number of input channels.
out_channels (int): Number of output channels.
mid_channels (int): Number of middle channels. Defaults to None.
dropout (float): Dropout probability. Defaults to 0.
ms_cfg (list): The config of multi-scale branches. Defaults to
``[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1']``.
stride (int): Stride of the temporal convolution. Defaults to 1.
init_cfg (dict or list[dict]): Initialization config dict.
Defaults to None.
"""
def __init__(self,
in_channels: int,
out_channels: int,
mid_channels: int = None,
dropout: float = 0.,
ms_cfg: List = [(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3),
'1x1'],
stride: int = 1,
init_cfg: Union[Dict, List[Dict]] = None) -> None:
super().__init__(init_cfg=init_cfg)
# Multiple branches of temporal convolution
self.ms_cfg = ms_cfg
num_branches = len(ms_cfg)
self.num_branches = num_branches
self.in_channels = in_channels
self.out_channels = out_channels
self.act = nn.ReLU()
if mid_channels is None:
mid_channels = out_channels // num_branches
rem_mid_channels = out_channels - mid_channels * (num_branches - 1)
else:
assert isinstance(mid_channels, float) and mid_channels > 0
mid_channels = int(out_channels * mid_channels)
rem_mid_channels = mid_channels
self.mid_channels = mid_channels
self.rem_mid_channels = rem_mid_channels
branches = []
for i, cfg in enumerate(ms_cfg):
branch_c = rem_mid_channels if i == 0 else mid_channels
if cfg == '1x1':
branches.append(
nn.Conv2d(
in_channels,
branch_c,
kernel_size=1,
stride=(stride, 1)))
continue
assert isinstance(cfg, tuple)
if cfg[0] == 'max':
branches.append(
Sequential(
nn.Conv2d(in_channels, branch_c, kernel_size=1),
nn.BatchNorm2d(branch_c), self.act,
nn.MaxPool2d(
kernel_size=(cfg[1], 1),
stride=(stride, 1),
padding=(1, 0))))
continue
assert isinstance(cfg[0], int) and isinstance(cfg[1], int)
branch = Sequential(
nn.Conv2d(in_channels, branch_c, kernel_size=1),
nn.BatchNorm2d(branch_c), self.act,
unit_tcn(
branch_c,
branch_c,
kernel_size=cfg[0],
stride=stride,
dilation=cfg[1],
norm=None))
branches.append(branch)
self.branches = ModuleList(branches)
tin_channels = mid_channels * (num_branches - 1) + rem_mid_channels
self.transform = Sequential(
nn.BatchNorm2d(tin_channels), self.act,
nn.Conv2d(tin_channels, out_channels, kernel_size=1))
self.bn = nn.BatchNorm2d(out_channels)
self.drop = nn.Dropout(dropout, inplace=True)
def inner_forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
N, C, T, V = x.shape
branch_outs = []
for tempconv in self.branches:
out = tempconv(x)
branch_outs.append(out)
feat = torch.cat(branch_outs, dim=1)
feat = self.transform(feat)
return feat
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
out = self.inner_forward(x)
out = self.bn(out)
return self.drop(out)
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