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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, build_conv_layer, constant_init, kaiming_init
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmpose.core import WeightNormClipHook
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
class BasicTemporalBlock(nn.Module):
"""Basic block for VideoPose3D.
Args:
in_channels (int): Input channels of this block.
out_channels (int): Output channels of this block.
mid_channels (int): The output channels of conv1. Default: 1024.
kernel_size (int): Size of the convolving kernel. Default: 3.
dilation (int): Spacing between kernel elements. Default: 3.
dropout (float): Dropout rate. Default: 0.25.
causal (bool): Use causal convolutions instead of symmetric
convolutions (for real-time applications). Default: False.
residual (bool): Use residual connection. Default: True.
use_stride_conv (bool): Use optimized TCN that designed
specifically for single-frame batching, i.e. where batches have
input length = receptive field, and output length = 1. This
implementation replaces dilated convolutions with strided
convolutions to avoid generating unused intermediate results.
Default: False.
conv_cfg (dict): dictionary to construct and config conv layer.
Default: dict(type='Conv1d').
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN1d').
"""
def __init__(self,
in_channels,
out_channels,
mid_channels=1024,
kernel_size=3,
dilation=3,
dropout=0.25,
causal=False,
residual=True,
use_stride_conv=False,
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d')):
# Protect mutable default arguments
conv_cfg = copy.deepcopy(conv_cfg)
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.mid_channels = mid_channels
self.kernel_size = kernel_size
self.dilation = dilation
self.dropout = dropout
self.causal = causal
self.residual = residual
self.use_stride_conv = use_stride_conv
self.pad = (kernel_size - 1) * dilation // 2
if use_stride_conv:
self.stride = kernel_size
self.causal_shift = kernel_size // 2 if causal else 0
self.dilation = 1
else:
self.stride = 1
self.causal_shift = kernel_size // 2 * dilation if causal else 0
self.conv1 = nn.Sequential(
ConvModule(
in_channels,
mid_channels,
kernel_size=kernel_size,
stride=self.stride,
dilation=self.dilation,
bias='auto',
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
self.conv2 = nn.Sequential(
ConvModule(
mid_channels,
out_channels,
kernel_size=1,
bias='auto',
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
if residual and in_channels != out_channels:
self.short_cut = build_conv_layer(conv_cfg, in_channels,
out_channels, 1)
else:
self.short_cut = None
self.dropout = nn.Dropout(dropout) if dropout > 0 else None
def forward(self, x):
"""Forward function."""
if self.use_stride_conv:
assert self.causal_shift + self.kernel_size // 2 < x.shape[2]
else:
assert 0 <= self.pad + self.causal_shift < x.shape[2] - \
self.pad + self.causal_shift <= x.shape[2]
out = self.conv1(x)
if self.dropout is not None:
out = self.dropout(out)
out = self.conv2(out)
if self.dropout is not None:
out = self.dropout(out)
if self.residual:
if self.use_stride_conv:
res = x[:, :, self.causal_shift +
self.kernel_size // 2::self.kernel_size]
else:
res = x[:, :,
(self.pad + self.causal_shift):(x.shape[2] - self.pad +
self.causal_shift)]
if self.short_cut is not None:
res = self.short_cut(res)
out = out + res
return out
@BACKBONES.register_module()
class TCN(BaseBackbone):
"""TCN backbone.
Temporal Convolutional Networks.
More details can be found in the
`paper <https://arxiv.org/abs/1811.11742>`__ .
Args:
in_channels (int): Number of input channels, which equals to
num_keypoints * num_features.
stem_channels (int): Number of feature channels. Default: 1024.
num_blocks (int): NUmber of basic temporal convolutional blocks.
Default: 2.
kernel_sizes (Sequence[int]): Sizes of the convolving kernel of
each basic block. Default: ``(3, 3, 3)``.
dropout (float): Dropout rate. Default: 0.25.
causal (bool): Use causal convolutions instead of symmetric
convolutions (for real-time applications).
Default: False.
residual (bool): Use residual connection. Default: True.
use_stride_conv (bool): Use TCN backbone optimized for
single-frame batching, i.e. where batches have input length =
receptive field, and output length = 1. This implementation
replaces dilated convolutions with strided convolutions to avoid
generating unused intermediate results. The weights are
interchangeable with the reference implementation. Default: False
conv_cfg (dict): dictionary to construct and config conv layer.
Default: dict(type='Conv1d').
norm_cfg (dict): dictionary to construct and config norm layer.
Default: dict(type='BN1d').
max_norm (float|None): if not None, the weight of convolution layers
will be clipped to have a maximum norm of max_norm.
Example:
>>> from mmpose.models import TCN
>>> import torch
>>> self = TCN(in_channels=34)
>>> self.eval()
>>> inputs = torch.rand(1, 34, 243)
>>> level_outputs = self.forward(inputs)
>>> for level_out in level_outputs:
... print(tuple(level_out.shape))
(1, 1024, 235)
(1, 1024, 217)
"""
def __init__(self,
in_channels,
stem_channels=1024,
num_blocks=2,
kernel_sizes=(3, 3, 3),
dropout=0.25,
causal=False,
residual=True,
use_stride_conv=False,
conv_cfg=dict(type='Conv1d'),
norm_cfg=dict(type='BN1d'),
max_norm=None):
# Protect mutable default arguments
conv_cfg = copy.deepcopy(conv_cfg)
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.in_channels = in_channels
self.stem_channels = stem_channels
self.num_blocks = num_blocks
self.kernel_sizes = kernel_sizes
self.dropout = dropout
self.causal = causal
self.residual = residual
self.use_stride_conv = use_stride_conv
self.max_norm = max_norm
assert num_blocks == len(kernel_sizes) - 1
for ks in kernel_sizes:
assert ks % 2 == 1, 'Only odd filter widths are supported.'
self.expand_conv = ConvModule(
in_channels,
stem_channels,
kernel_size=kernel_sizes[0],
stride=kernel_sizes[0] if use_stride_conv else 1,
bias='auto',
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
dilation = kernel_sizes[0]
self.tcn_blocks = nn.ModuleList()
for i in range(1, num_blocks + 1):
self.tcn_blocks.append(
BasicTemporalBlock(
in_channels=stem_channels,
out_channels=stem_channels,
mid_channels=stem_channels,
kernel_size=kernel_sizes[i],
dilation=dilation,
dropout=dropout,
causal=causal,
residual=residual,
use_stride_conv=use_stride_conv,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg))
dilation *= kernel_sizes[i]
if self.max_norm is not None:
# Apply weight norm clip to conv layers
weight_clip = WeightNormClipHook(self.max_norm)
for module in self.modules():
if isinstance(module, nn.modules.conv._ConvNd):
weight_clip.register(module)
self.dropout = nn.Dropout(dropout) if dropout > 0 else None
def forward(self, x):
"""Forward function."""
x = self.expand_conv(x)
if self.dropout is not None:
x = self.dropout(x)
outs = []
for i in range(self.num_blocks):
x = self.tcn_blocks[i](x)
outs.append(x)
return tuple(outs)
def init_weights(self, pretrained=None):
"""Initialize the weights."""
super().init_weights(pretrained)
if pretrained is None:
for m in self.modules():
if isinstance(m, nn.modules.conv._ConvNd):
kaiming_init(m, mode='fan_in', nonlinearity='relu')
elif isinstance(m, _BatchNorm):
constant_init(m, 1)
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