Source code for mmaction.models.backbones.stgcn
# 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, mstcn, unit_gcn, unit_tcn
EPS = 1e-4
class STGCNBlock(BaseModule):
"""The basic block of STGCN.
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_gcn')
assert gcn_type in ['unit_gcn']
self.gcn = unit_gcn(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)
elif tcn_type == 'mstcn':
self.tcn = mstcn(
out_channels, out_channels, 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."""
res = self.residual(x)
x = self.tcn(self.gcn(x)) + res
return self.relu(x)
[docs]@MODELS.register_module()
class STGCN(BaseModule):
"""STGCN backbone.
Spatial Temporal Graph Convolutional
Networks for Skeleton-Based Action Recognition.
More details can be found in the `paper
<https://arxiv.org/abs/1801.07455>`__ .
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 ``'VC'``.
ch_ratio (int): Inflation ratio of the number of channels.
Defaults to 2.
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]``.
stage_cfgs (dict): Extra config dict for each stage.
Defaults to ``dict()``.
init_cfg (dict or list[dict], optional): Config to control
the initialization. Defaults to None.
Examples:
>>> import torch
>>> from mmaction.models import STGCN
>>>
>>> mode = 'stgcn_spatial'
>>> batch_size, num_person, num_frames = 2, 2, 150
>>>
>>> # openpose-18 layout
>>> num_joints = 18
>>> model = STGCN(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 = STGCN(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 = STGCN(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
>>> # instantiate STGCN++
>>> model = STGCN(graph_cfg=dict(layout='coco', mode='spatial'),
... gcn_adaptive='init', gcn_with_res=True,
... tcn_type='mstcn')
>>> 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 = 'VC',
ch_ratio: int = 2,
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.data_bn_type = data_bn_type
if data_bn_type == 'MVC':
self.data_bn = nn.BatchNorm1d(num_person * in_channels * A.size(1))
elif 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, list)) and len(v) == num_stages:
for i in range(num_stages):
lw_kwargs[i][k] = v[i]
lw_kwargs[0].pop('tcn_dropout', None)
self.in_channels = in_channels
self.base_channels = base_channels
self.ch_ratio = ch_ratio
self.inflate_stages = inflate_stages
self.down_stages = down_stages
modules = []
if self.in_channels != self.base_channels:
modules = [
STGCNBlock(
in_channels,
base_channels,
A.clone(),
1,
residual=False,
**lw_kwargs[0])
]
inflate_times = 0
for i in range(2, num_stages + 1):
stride = 1 + (i in down_stages)
in_channels = base_channels
if i in inflate_stages:
inflate_times += 1
out_channels = int(self.base_channels *
self.ch_ratio**inflate_times + EPS)
base_channels = out_channels
modules.append(
STGCNBlock(in_channels, out_channels, A.clone(), stride,
**lw_kwargs[i - 1]))
if self.in_channels == self.base_channels:
num_stages -= 1
self.num_stages = num_stages
self.gcn = ModuleList(modules)
[docs] 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