Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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from mmcv.cnn import build_conv_layer, build_norm_layer
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from ..builder import BACKBONES
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from .resnet import Bottleneck as _Bottleneck
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from .resnet import ResLayer, ResNetV1d
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class RSoftmax(nn.Module):
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"""Radix Softmax module in ``SplitAttentionConv2d``.
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Args:
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radix (int): Radix of input.
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groups (int): Groups of input.
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"""
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def __init__(self, radix, groups):
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super().__init__()
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self.radix = radix
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self.groups = groups
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def forward(self, x):
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batch = x.size(0)
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if self.radix > 1:
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x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
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x = F.softmax(x, dim=1)
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x = x.reshape(batch, -1)
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else:
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x = torch.sigmoid(x)
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return x
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class SplitAttentionConv2d(nn.Module):
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"""Split-Attention Conv2d.
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Args:
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in_channels (int): Same as nn.Conv2d.
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out_channels (int): Same as nn.Conv2d.
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kernel_size (int | tuple[int]): Same as nn.Conv2d.
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stride (int | tuple[int]): Same as nn.Conv2d.
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padding (int | tuple[int]): Same as nn.Conv2d.
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dilation (int | tuple[int]): Same as nn.Conv2d.
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groups (int): Same as nn.Conv2d.
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of SplitAttentionConv2d.
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Default: 4.
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conv_cfg (dict): Config dict for convolution layer. Default: None,
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which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer. Default: None.
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"""
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def __init__(self,
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in_channels,
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channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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radix=2,
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reduction_factor=4,
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conv_cfg=None,
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norm_cfg=dict(type='BN')):
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super().__init__()
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inter_channels = max(in_channels * radix // reduction_factor, 32)
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self.radix = radix
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self.groups = groups
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self.channels = channels
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self.conv = build_conv_layer(
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conv_cfg,
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in_channels,
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channels * radix,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups * radix,
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bias=False)
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self.norm0_name, norm0 = build_norm_layer(
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norm_cfg, channels * radix, postfix=0)
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self.add_module(self.norm0_name, norm0)
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self.relu = nn.ReLU(inplace=True)
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self.fc1 = build_conv_layer(
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None, channels, inter_channels, 1, groups=self.groups)
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, inter_channels, postfix=1)
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self.add_module(self.norm1_name, norm1)
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self.fc2 = build_conv_layer(
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None, inter_channels, channels * radix, 1, groups=self.groups)
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self.rsoftmax = RSoftmax(radix, groups)
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@property
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def norm0(self):
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return getattr(self, self.norm0_name)
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@property
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def norm1(self):
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return getattr(self, self.norm1_name)
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def forward(self, x):
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x = self.conv(x)
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x = self.norm0(x)
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x = self.relu(x)
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batch, rchannel = x.shape[:2]
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if self.radix > 1:
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splits = x.view(batch, self.radix, -1, *x.shape[2:])
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gap = splits.sum(dim=1)
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else:
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gap = x
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gap = F.adaptive_avg_pool2d(gap, 1)
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gap = self.fc1(gap)
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gap = self.norm1(gap)
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gap = self.relu(gap)
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atten = self.fc2(gap)
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atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
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if self.radix > 1:
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attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
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out = torch.sum(attens * splits, dim=1)
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else:
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out = atten * x
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return out.contiguous()
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class Bottleneck(_Bottleneck):
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"""Bottleneck block for ResNeSt.
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Args:
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in_channels (int): Input channels of this block.
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out_channels (int): Output channels of this block.
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groups (int): Groups of conv2.
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width_per_group (int): Width per group of conv2. 64x4d indicates
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``groups=64, width_per_group=4`` and 32x8d indicates
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``groups=32, width_per_group=8``.
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of SplitAttentionConv2d.
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Default: 4.
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avg_down_stride (bool): Whether to use average pool for stride in
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Bottleneck. Default: True.
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stride (int): stride of the block. Default: 1
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dilation (int): dilation of convolution. Default: 1
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downsample (nn.Module): downsample operation on identity branch.
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Default: None
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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conv_cfg (dict): dictionary to construct and config conv layer.
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Default: None
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norm_cfg (dict): dictionary to construct and config norm layer.
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Default: dict(type='BN')
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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groups=1,
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width_per_group=4,
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base_channels=64,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True,
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**kwargs):
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super().__init__(in_channels, out_channels, **kwargs)
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self.groups = groups
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self.width_per_group = width_per_group
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# For ResNet bottleneck, middle channels are determined by expansion
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# and out_channels, but for ResNeXt bottleneck, it is determined by
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# groups and width_per_group and the stage it is located in.
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if groups != 1:
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assert self.mid_channels % base_channels == 0
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self.mid_channels = (
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groups * width_per_group * self.mid_channels // base_channels)
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self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
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self.norm1_name, norm1 = build_norm_layer(
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self.norm_cfg, self.mid_channels, postfix=1)
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self.norm3_name, norm3 = build_norm_layer(
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self.norm_cfg, self.out_channels, postfix=3)
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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self.in_channels,
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self.mid_channels,
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kernel_size=1,
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stride=self.conv1_stride,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = SplitAttentionConv2d(
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self.mid_channels,
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self.mid_channels,
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kernel_size=3,
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stride=1 if self.avg_down_stride else self.conv2_stride,
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padding=self.dilation,
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dilation=self.dilation,
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groups=groups,
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radix=radix,
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reduction_factor=reduction_factor,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg)
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delattr(self, self.norm2_name)
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if self.avg_down_stride:
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self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
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self.conv3 = build_conv_layer(
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self.conv_cfg,
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self.mid_channels,
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self.out_channels,
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kernel_size=1,
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bias=False)
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self.add_module(self.norm3_name, norm3)
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def forward(self, x):
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def _inner_forward(x):
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identity = x
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out = self.conv1(x)
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out = self.norm1(out)
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out = self.relu(out)
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out = self.conv2(out)
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if self.avg_down_stride:
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out = self.avd_layer(out)
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out = self.conv3(out)
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out = self.norm3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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return out
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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out = self.relu(out)
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return out
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@BACKBONES.register_module()
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class ResNeSt(ResNetV1d):
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"""ResNeSt backbone.
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Please refer to the `paper <https://arxiv.org/pdf/2004.08955.pdf>`__
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for details.
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Args:
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depth (int): Network depth, from {50, 101, 152, 200}.
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groups (int): Groups of conv2 in Bottleneck. Default: 32.
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width_per_group (int): Width per group of conv2 in Bottleneck.
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Default: 4.
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radix (int): Radix of SpltAtConv2d. Default: 2
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reduction_factor (int): Reduction factor of SplitAttentionConv2d.
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Default: 4.
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avg_down_stride (bool): Whether to use average pool for stride in
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Bottleneck. Default: True.
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in_channels (int): Number of input image channels. Default: 3.
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stem_channels (int): Output channels of the stem layer. Default: 64.
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num_stages (int): Stages of the network. Default: 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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Default: ``(1, 2, 2, 2)``.
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dilations (Sequence[int]): Dilation of each stage.
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Default: ``(1, 1, 1, 1)``.
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out_indices (Sequence[int]): Output from which stages. If only one
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stage is specified, a single tensor (feature map) is returned,
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otherwise multiple stages are specified, a tuple of tensors will
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be returned. Default: ``(3, )``.
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.
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Default: False.
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottleneck. Default: False.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters. Default: -1.
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conv_cfg (dict | None): The config dict for conv layers. Default: None.
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norm_cfg (dict): The config dict for norm layers.
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only. Default: False.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed. Default: False.
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zero_init_residual (bool): Whether to use zero init for last norm layer
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in resblocks to let them behave as identity. Default: True.
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"""
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arch_settings = {
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50: (Bottleneck, (3, 4, 6, 3)),
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101: (Bottleneck, (3, 4, 23, 3)),
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152: (Bottleneck, (3, 8, 36, 3)),
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200: (Bottleneck, (3, 24, 36, 3)),
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269: (Bottleneck, (3, 30, 48, 8))
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}
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def __init__(self,
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depth,
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groups=1,
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width_per_group=4,
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radix=2,
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reduction_factor=4,
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avg_down_stride=True,
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**kwargs):
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self.groups = groups
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self.width_per_group = width_per_group
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self.radix = radix
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self.reduction_factor = reduction_factor
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self.avg_down_stride = avg_down_stride
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super().__init__(depth=depth, **kwargs)
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def make_res_layer(self, **kwargs):
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return ResLayer(
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groups=self.groups,
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width_per_group=self.width_per_group,
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base_channels=self.base_channels,
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radix=self.radix,
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reduction_factor=self.reduction_factor,
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avg_down_stride=self.avg_down_stride,
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**kwargs)
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