File size: 9,074 Bytes
d670799 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
# 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 mmengine.model import BaseModule, ModuleList
from mmaction.registry import MODELS
from ..utils import Graph, unit_aagcn, unit_tcn
class AAGCNBlock(BaseModule):
"""The basic block 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_nodes, num_nodes)`.
stride (int): Stride of the temporal convolution. Defaults to 1.
residual (bool): Whether to use residual connection. Defaults to True.
init_cfg (dict or list[dict], optional): Config to control
the initialization. Defaults to None.
"""
def __init__(self,
in_channels: int,
out_channels: int,
A: torch.Tensor,
stride: int = 1,
residual: bool = True,
init_cfg: Optional[Union[Dict, List[Dict]]] = None,
**kwargs) -> None:
super().__init__(init_cfg=init_cfg)
gcn_kwargs = {k[4:]: v for k, v in kwargs.items() if k[:4] == 'gcn_'}
tcn_kwargs = {k[4:]: v for k, v in kwargs.items() if k[:4] == 'tcn_'}
kwargs = {
k: v
for k, v in kwargs.items() if k[:4] not in ['gcn_', 'tcn_']
}
assert len(kwargs) == 0, f'Invalid arguments: {kwargs}'
tcn_type = tcn_kwargs.pop('type', 'unit_tcn')
assert tcn_type in ['unit_tcn', 'mstcn']
gcn_type = gcn_kwargs.pop('type', 'unit_aagcn')
assert gcn_type in ['unit_aagcn']
self.gcn = unit_aagcn(in_channels, out_channels, A, **gcn_kwargs)
if tcn_type == 'unit_tcn':
self.tcn = unit_tcn(
out_channels, out_channels, 9, stride=stride, **tcn_kwargs)
self.relu = nn.ReLU()
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)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
return self.relu(self.tcn(self.gcn(x)) + self.residual(x))
@MODELS.register_module()
class AAGCN(BaseModule):
"""AAGCN backbone, the attention-enhanced version of 2s-AGCN.
Skeleton-Based Action Recognition with Multi-Stream
Adaptive Graph Convolutional Networks.
More details can be found in the `paper
<https://arxiv.org/abs/1912.06971>`__ .
Two-Stream Adaptive Graph Convolutional Networks for
Skeleton-Based Action Recognition.
More details can be found in the `paper
<https://arxiv.org/abs/1805.07694>`__ .
Args:
graph_cfg (dict): Config for building the graph.
in_channels (int): Number of input channels. Defaults to 3.
base_channels (int): Number of base channels. Defaults to 64.
data_bn_type (str): Type of the data bn layer. Defaults to ``'MVC'``.
num_person (int): Maximum number of people. Only used when
data_bn_type == 'MVC'. Defaults to 2.
num_stages (int): Total number of stages. Defaults to 10.
inflate_stages (list[int]): Stages to inflate the number of channels.
Defaults to ``[5, 8]``.
down_stages (list[int]): Stages to perform downsampling in
the time dimension. Defaults to ``[5, 8]``.
init_cfg (dict or list[dict], optional): Config to control
the initialization. Defaults to None.
Examples:
>>> import torch
>>> from mmaction.models import AAGCN
>>> from mmaction.utils import register_all_modules
>>>
>>> register_all_modules()
>>> mode = 'stgcn_spatial'
>>> batch_size, num_person, num_frames = 2, 2, 150
>>>
>>> # openpose-18 layout
>>> num_joints = 18
>>> model = AAGCN(graph_cfg=dict(layout='openpose', mode=mode))
>>> model.init_weights()
>>> inputs = torch.randn(batch_size, num_person,
... num_frames, num_joints, 3)
>>> output = model(inputs)
>>> print(output.shape)
>>>
>>> # nturgb+d layout
>>> num_joints = 25
>>> model = AAGCN(graph_cfg=dict(layout='nturgb+d', mode=mode))
>>> model.init_weights()
>>> inputs = torch.randn(batch_size, num_person,
... num_frames, num_joints, 3)
>>> output = model(inputs)
>>> print(output.shape)
>>>
>>> # coco layout
>>> num_joints = 17
>>> model = AAGCN(graph_cfg=dict(layout='coco', mode=mode))
>>> model.init_weights()
>>> inputs = torch.randn(batch_size, num_person,
... num_frames, num_joints, 3)
>>> output = model(inputs)
>>> print(output.shape)
>>>
>>> # custom settings
>>> # disable the attention module to degenerate AAGCN to AGCN
>>> model = AAGCN(graph_cfg=dict(layout='coco', mode=mode),
... gcn_attention=False)
>>> model.init_weights()
>>> output = model(inputs)
>>> print(output.shape)
torch.Size([2, 2, 256, 38, 18])
torch.Size([2, 2, 256, 38, 25])
torch.Size([2, 2, 256, 38, 17])
torch.Size([2, 2, 256, 38, 17])
"""
def __init__(self,
graph_cfg: Dict,
in_channels: int = 3,
base_channels: int = 64,
data_bn_type: str = 'MVC',
num_person: int = 2,
num_stages: int = 10,
inflate_stages: List[int] = [5, 8],
down_stages: List[int] = [5, 8],
init_cfg: Optional[Union[Dict, List[Dict]]] = None,
**kwargs) -> None:
super().__init__(init_cfg=init_cfg)
self.graph = Graph(**graph_cfg)
A = torch.tensor(
self.graph.A, dtype=torch.float32, requires_grad=False)
self.register_buffer('A', A)
assert data_bn_type in ['MVC', 'VC', None]
self.data_bn_type = data_bn_type
self.in_channels = in_channels
self.base_channels = base_channels
self.num_person = num_person
self.num_stages = num_stages
self.inflate_stages = inflate_stages
self.down_stages = down_stages
if self.data_bn_type == 'MVC':
self.data_bn = nn.BatchNorm1d(num_person * in_channels * A.size(1))
elif self.data_bn_type == 'VC':
self.data_bn = nn.BatchNorm1d(in_channels * A.size(1))
else:
self.data_bn = nn.Identity()
lw_kwargs = [cp.deepcopy(kwargs) for i in range(num_stages)]
for k, v in kwargs.items():
if isinstance(v, tuple) and len(v) == num_stages:
for i in range(num_stages):
lw_kwargs[i][k] = v[i]
lw_kwargs[0].pop('tcn_dropout', None)
modules = []
if self.in_channels != self.base_channels:
modules = [
AAGCNBlock(
in_channels,
base_channels,
A.clone(),
1,
residual=False,
**lw_kwargs[0])
]
for i in range(2, num_stages + 1):
in_channels = base_channels
out_channels = base_channels * (1 + (i in inflate_stages))
stride = 1 + (i in down_stages)
modules.append(
AAGCNBlock(
base_channels,
out_channels,
A.clone(),
stride=stride,
**lw_kwargs[i - 1]))
base_channels = out_channels
if self.in_channels == self.base_channels:
self.num_stages -= 1
self.gcn = ModuleList(modules)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
N, M, T, V, C = x.size()
x = x.permute(0, 1, 3, 4, 2).contiguous()
if self.data_bn_type == 'MVC':
x = self.data_bn(x.view(N, M * V * C, T))
else:
x = self.data_bn(x.view(N * M, V * C, T))
x = x.view(N, M, V, C, T).permute(0, 1, 3, 4,
2).contiguous().view(N * M, C, T, V)
for i in range(self.num_stages):
x = self.gcn[i](x)
x = x.reshape((N, M) + x.shape[1:])
return x
|