Source code for mmaction.models.heads.trn_head
# Copyright (c) OpenMMLab. All rights reserved.
import itertools
import numpy as np
import torch
import torch.nn as nn
from mmengine.model.weight_init import normal_init
from mmaction.registry import MODELS
from .base import BaseHead
class RelationModule(nn.Module):
"""Relation Module of TRN.
Args:
hidden_dim (int): The dimension of hidden layer of MLP in relation
module.
num_segments (int): Number of frame segments.
num_classes (int): Number of classes to be classified.
"""
def __init__(self, hidden_dim, num_segments, num_classes):
super().__init__()
self.hidden_dim = hidden_dim
self.num_segments = num_segments
self.num_classes = num_classes
bottleneck_dim = 512
self.classifier = nn.Sequential(
nn.ReLU(),
nn.Linear(self.num_segments * self.hidden_dim, bottleneck_dim),
nn.ReLU(), nn.Linear(bottleneck_dim, self.num_classes))
def init_weights(self):
"""Use the default kaiming_uniform for all nn.linear layers."""
pass
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, num_segs * hidden_dim]
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
class RelationModuleMultiScale(nn.Module):
"""Relation Module with Multi Scale of TRN.
Args:
hidden_dim (int): The dimension of hidden layer of MLP in relation
module.
num_segments (int): Number of frame segments.
num_classes (int): Number of classes to be classified.
"""
def __init__(self, hidden_dim, num_segments, num_classes):
super().__init__()
self.hidden_dim = hidden_dim
self.num_segments = num_segments
self.num_classes = num_classes
# generate the multiple frame relations
self.scales = range(num_segments, 1, -1)
self.relations_scales = []
self.subsample_scales = []
max_subsample = 3
for scale in self.scales:
# select the different frame features for different scales
relations_scale = list(
itertools.combinations(range(self.num_segments), scale))
self.relations_scales.append(relations_scale)
# sample `max_subsample` relation_scale at most
self.subsample_scales.append(
min(max_subsample, len(relations_scale)))
assert len(self.relations_scales[0]) == 1
bottleneck_dim = 256
self.fc_fusion_scales = nn.ModuleList()
for scale in self.scales:
fc_fusion = nn.Sequential(
nn.ReLU(), nn.Linear(scale * self.hidden_dim, bottleneck_dim),
nn.ReLU(), nn.Linear(bottleneck_dim, self.num_classes))
self.fc_fusion_scales.append(fc_fusion)
def init_weights(self):
"""Use the default kaiming_uniform for all nn.linear layers."""
pass
def forward(self, x):
# the first one is the largest scale
act_all = x[:, self.relations_scales[0][0], :]
act_all = act_all.view(
act_all.size(0), self.scales[0] * self.hidden_dim)
act_all = self.fc_fusion_scales[0](act_all)
for scaleID in range(1, len(self.scales)):
# iterate over the scales
idx_relations_randomsample = np.random.choice(
len(self.relations_scales[scaleID]),
self.subsample_scales[scaleID],
replace=False)
for idx in idx_relations_randomsample:
act_relation = x[:, self.relations_scales[scaleID][idx], :]
act_relation = act_relation.view(
act_relation.size(0),
self.scales[scaleID] * self.hidden_dim)
act_relation = self.fc_fusion_scales[scaleID](act_relation)
act_all += act_relation
return act_all
[docs]@MODELS.register_module()
class TRNHead(BaseHead):
"""Class head for TRN.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of channels in input feature.
num_segments (int): Number of frame segments. Default: 8.
loss_cls (dict): Config for building loss. Default:
dict(type='CrossEntropyLoss')
spatial_type (str): Pooling type in spatial dimension. Default: 'avg'.
relation_type (str): The relation module type. Choices are 'TRN' or
'TRNMultiScale'. Default: 'TRNMultiScale'.
hidden_dim (int): The dimension of hidden layer of MLP in relation
module. Default: 256.
dropout_ratio (float): Probability of dropout layer. Default: 0.8.
init_std (float): Std value for Initiation. Default: 0.001.
kwargs (dict, optional): Any keyword argument to be used to initialize
the head.
"""
def __init__(self,
num_classes,
in_channels,
num_segments=8,
loss_cls=dict(type='CrossEntropyLoss'),
spatial_type='avg',
relation_type='TRNMultiScale',
hidden_dim=256,
dropout_ratio=0.8,
init_std=0.001,
**kwargs):
super().__init__(num_classes, in_channels, loss_cls, **kwargs)
self.num_classes = num_classes
self.in_channels = in_channels
self.num_segments = num_segments
self.spatial_type = spatial_type
self.relation_type = relation_type
self.hidden_dim = hidden_dim
self.dropout_ratio = dropout_ratio
self.init_std = init_std
if self.relation_type == 'TRN':
self.consensus = RelationModule(self.hidden_dim, self.num_segments,
self.num_classes)
elif self.relation_type == 'TRNMultiScale':
self.consensus = RelationModuleMultiScale(self.hidden_dim,
self.num_segments,
self.num_classes)
else:
raise ValueError(f'Unknown Relation Type {self.relation_type}!')
if self.dropout_ratio != 0:
self.dropout = nn.Dropout(p=self.dropout_ratio)
else:
self.dropout = None
self.fc_cls = nn.Linear(self.in_channels, self.hidden_dim)
if self.spatial_type == 'avg':
# use `nn.AdaptiveAvgPool2d` to adaptively match the in_channels.
self.avg_pool = nn.AdaptiveAvgPool2d(1)
else:
self.avg_pool = None
[docs] def init_weights(self):
"""Initiate the parameters from scratch."""
normal_init(self.fc_cls, std=self.init_std)
self.consensus.init_weights()
[docs] def forward(self, x, num_segs, **kwargs):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
num_segs (int): Useless in TRNHead. By default, `num_segs`
is equal to `clip_len * num_clips * num_crops`, which is
automatically generated in Recognizer forward phase and
useless in TRN models. The `self.num_segments` we need is a
hyper parameter to build TRN models.
Returns:
torch.Tensor: The classification scores for input samples.
"""
# [N * num_segs, in_channels, 7, 7]
if self.avg_pool is not None:
x = self.avg_pool(x)
# [N * num_segs, in_channels, 1, 1]
x = torch.flatten(x, 1)
# [N * num_segs, in_channels]
if self.dropout is not None:
x = self.dropout(x)
# [N, num_segs, hidden_dim]
cls_score = self.fc_cls(x)
cls_score = cls_score.view((-1, self.num_segments) +
cls_score.size()[1:])
# [N, num_classes]
cls_score = self.consensus(cls_score)
return cls_score