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 copy
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
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constant_init, kaiming_init)
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from mmcv.utils.parrots_wrapper import _BatchNorm
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from ..builder import BACKBONES
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from .base_backbone import BaseBackbone
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class BasicBlock(nn.Module):
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"""BasicBlock for ResNet.
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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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expansion (int): The ratio of ``out_channels/mid_channels`` where
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``mid_channels`` is the output channels of conv1. This is a
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reserved argument in BasicBlock and should always be 1. Default: 1.
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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`. It is unused and reserved for
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unified API with Bottleneck.
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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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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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"""
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def __init__(self,
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in_channels,
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out_channels,
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expansion=1,
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stride=1,
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dilation=1,
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downsample=None,
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style='pytorch',
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with_cp=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN')):
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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.expansion = expansion
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assert self.expansion == 1
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assert out_channels % expansion == 0
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self.mid_channels = out_channels // expansion
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self.stride = stride
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self.dilation = dilation
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self.style = style
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self.with_cp = with_cp
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, self.mid_channels, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(
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norm_cfg, out_channels, postfix=2)
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self.conv1 = build_conv_layer(
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conv_cfg,
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in_channels,
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self.mid_channels,
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3,
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stride=stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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conv_cfg,
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self.mid_channels,
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out_channels,
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3,
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padding=1,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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@property
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def norm1(self):
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"""nn.Module: the normalization layer named "norm1" """
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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"""nn.Module: the normalization layer named "norm2" """
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return getattr(self, self.norm2_name)
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def forward(self, x):
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"""Forward function."""
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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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out = self.norm2(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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class Bottleneck(nn.Module):
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"""Bottleneck block for ResNet.
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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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expansion (int): The ratio of ``out_channels/mid_channels`` where
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``mid_channels`` is the input/output channels of conv2. Default: 4.
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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
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stride-two layer is the 3x3 conv layer, otherwise the stride-two
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layer is the first 1x1 conv layer. Default: "pytorch".
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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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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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"""
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def __init__(self,
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in_channels,
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out_channels,
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expansion=4,
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stride=1,
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dilation=1,
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downsample=None,
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style='pytorch',
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with_cp=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN')):
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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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super().__init__()
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assert style in ['pytorch', 'caffe']
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.expansion = expansion
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assert out_channels % expansion == 0
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self.mid_channels = out_channels // expansion
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self.stride = stride
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self.dilation = dilation
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self.style = style
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self.with_cp = with_cp
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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if self.style == 'pytorch':
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self.conv1_stride = 1
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self.conv2_stride = stride
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else:
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self.conv1_stride = stride
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self.conv2_stride = 1
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, self.mid_channels, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(
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norm_cfg, self.mid_channels, postfix=2)
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self.norm3_name, norm3 = build_norm_layer(
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norm_cfg, out_channels, postfix=3)
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self.conv1 = build_conv_layer(
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conv_cfg,
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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 = build_conv_layer(
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conv_cfg,
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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=self.conv2_stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.conv3 = build_conv_layer(
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conv_cfg,
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self.mid_channels,
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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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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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@property
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def norm1(self):
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"""nn.Module: the normalization layer named "norm1" """
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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"""nn.Module: the normalization layer named "norm2" """
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return getattr(self, self.norm2_name)
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@property
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def norm3(self):
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"""nn.Module: the normalization layer named "norm3" """
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return getattr(self, self.norm3_name)
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def forward(self, x):
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"""Forward function."""
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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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out = self.norm2(out)
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out = self.relu(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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def get_expansion(block, expansion=None):
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"""Get the expansion of a residual block.
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The block expansion will be obtained by the following order:
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1. If ``expansion`` is given, just return it.
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2. If ``block`` has the attribute ``expansion``, then return
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``block.expansion``.
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3. Return the default value according the the block type:
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1 for ``BasicBlock`` and 4 for ``Bottleneck``.
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Args:
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block (class): The block class.
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expansion (int | None): The given expansion ratio.
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Returns:
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int: The expansion of the block.
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"""
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if isinstance(expansion, int):
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assert expansion > 0
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elif expansion is None:
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if hasattr(block, 'expansion'):
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expansion = block.expansion
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elif issubclass(block, BasicBlock):
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expansion = 1
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elif issubclass(block, Bottleneck):
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expansion = 4
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else:
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raise TypeError(f'expansion is not specified for {block.__name__}')
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else:
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raise TypeError('expansion must be an integer or None')
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return expansion
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class ResLayer(nn.Sequential):
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"""ResLayer to build ResNet style backbone.
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Args:
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block (nn.Module): Residual block used to build ResLayer.
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num_blocks (int): Number of blocks.
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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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expansion (int, optional): The expansion for BasicBlock/Bottleneck.
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If not specified, it will firstly be obtained via
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``block.expansion``. If the block has no attribute "expansion",
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the following default values will be used: 1 for BasicBlock and
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4 for Bottleneck. Default: None.
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stride (int): stride of the first block. Default: 1.
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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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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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downsample_first (bool): Downsample at the first block or last block.
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False for Hourglass, True for ResNet. Default: True
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"""
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def __init__(self,
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block,
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num_blocks,
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in_channels,
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out_channels,
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expansion=None,
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stride=1,
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avg_down=False,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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downsample_first=True,
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**kwargs):
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# Protect mutable default arguments
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norm_cfg = copy.deepcopy(norm_cfg)
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self.block = block
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self.expansion = get_expansion(block, expansion)
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downsample = None
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if stride != 1 or in_channels != out_channels:
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downsample = []
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conv_stride = stride
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if avg_down and stride != 1:
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conv_stride = 1
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downsample.append(
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nn.AvgPool2d(
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kernel_size=stride,
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stride=stride,
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ceil_mode=True,
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count_include_pad=False))
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downsample.extend([
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build_conv_layer(
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conv_cfg,
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in_channels,
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out_channels,
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kernel_size=1,
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stride=conv_stride,
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bias=False),
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build_norm_layer(norm_cfg, out_channels)[1]
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])
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downsample = nn.Sequential(*downsample)
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layers = []
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if downsample_first:
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layers.append(
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block(
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in_channels=in_channels,
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out_channels=out_channels,
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expansion=self.expansion,
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stride=stride,
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downsample=downsample,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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in_channels = out_channels
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for _ in range(1, num_blocks):
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layers.append(
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block(
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in_channels=in_channels,
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out_channels=out_channels,
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expansion=self.expansion,
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stride=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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else: # downsample_first=False is for HourglassModule
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for i in range(0, num_blocks - 1):
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layers.append(
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block(
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in_channels=in_channels,
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out_channels=in_channels,
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expansion=self.expansion,
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stride=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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layers.append(
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block(
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in_channels=in_channels,
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out_channels=out_channels,
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expansion=self.expansion,
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stride=stride,
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downsample=downsample,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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**kwargs))
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super().__init__(*layers)
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@BACKBONES.register_module()
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class ResNet(BaseBackbone):
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"""ResNet backbone.
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Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for
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details.
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Args:
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depth (int): Network depth, from {18, 34, 50, 101, 152}.
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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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base_channels (int): Middle channels of the first stage. 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.
|
||||
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
|
||||
layer is the 3x3 conv layer, otherwise the stride-two layer is
|
||||
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
|
||||
memory while slowing down the training speed. Default: False.
|
||||
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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||||
Example:
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>>> from mmpose.models import ResNet
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>>> import torch
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>>> self = ResNet(depth=18, out_indices=(0, 1, 2, 3))
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>>> self.eval()
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>>> inputs = torch.rand(1, 3, 32, 32)
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>>> level_outputs = self.forward(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 64, 8, 8)
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(1, 128, 4, 4)
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(1, 256, 2, 2)
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(1, 512, 1, 1)
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"""
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arch_settings = {
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18: (BasicBlock, (2, 2, 2, 2)),
|
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34: (BasicBlock, (3, 4, 6, 3)),
|
||||
50: (Bottleneck, (3, 4, 6, 3)),
|
||||
101: (Bottleneck, (3, 4, 23, 3)),
|
||||
152: (Bottleneck, (3, 8, 36, 3))
|
||||
}
|
||||
|
||||
def __init__(self,
|
||||
depth,
|
||||
in_channels=3,
|
||||
stem_channels=64,
|
||||
base_channels=64,
|
||||
expansion=None,
|
||||
num_stages=4,
|
||||
strides=(1, 2, 2, 2),
|
||||
dilations=(1, 1, 1, 1),
|
||||
out_indices=(3, ),
|
||||
style='pytorch',
|
||||
deep_stem=False,
|
||||
avg_down=False,
|
||||
frozen_stages=-1,
|
||||
conv_cfg=None,
|
||||
norm_cfg=dict(type='BN', requires_grad=True),
|
||||
norm_eval=False,
|
||||
with_cp=False,
|
||||
zero_init_residual=True):
|
||||
# Protect mutable default arguments
|
||||
norm_cfg = copy.deepcopy(norm_cfg)
|
||||
super().__init__()
|
||||
if depth not in self.arch_settings:
|
||||
raise KeyError(f'invalid depth {depth} for resnet')
|
||||
self.depth = depth
|
||||
self.stem_channels = stem_channels
|
||||
self.base_channels = base_channels
|
||||
self.num_stages = num_stages
|
||||
assert 1 <= num_stages <= 4
|
||||
self.strides = strides
|
||||
self.dilations = dilations
|
||||
assert len(strides) == len(dilations) == num_stages
|
||||
self.out_indices = out_indices
|
||||
assert max(out_indices) < num_stages
|
||||
self.style = style
|
||||
self.deep_stem = deep_stem
|
||||
self.avg_down = avg_down
|
||||
self.frozen_stages = frozen_stages
|
||||
self.conv_cfg = conv_cfg
|
||||
self.norm_cfg = norm_cfg
|
||||
self.with_cp = with_cp
|
||||
self.norm_eval = norm_eval
|
||||
self.zero_init_residual = zero_init_residual
|
||||
self.block, stage_blocks = self.arch_settings[depth]
|
||||
self.stage_blocks = stage_blocks[:num_stages]
|
||||
self.expansion = get_expansion(self.block, expansion)
|
||||
|
||||
self._make_stem_layer(in_channels, stem_channels)
|
||||
|
||||
self.res_layers = []
|
||||
_in_channels = stem_channels
|
||||
_out_channels = base_channels * self.expansion
|
||||
for i, num_blocks in enumerate(self.stage_blocks):
|
||||
stride = strides[i]
|
||||
dilation = dilations[i]
|
||||
res_layer = self.make_res_layer(
|
||||
block=self.block,
|
||||
num_blocks=num_blocks,
|
||||
in_channels=_in_channels,
|
||||
out_channels=_out_channels,
|
||||
expansion=self.expansion,
|
||||
stride=stride,
|
||||
dilation=dilation,
|
||||
style=self.style,
|
||||
avg_down=self.avg_down,
|
||||
with_cp=with_cp,
|
||||
conv_cfg=conv_cfg,
|
||||
norm_cfg=norm_cfg)
|
||||
_in_channels = _out_channels
|
||||
_out_channels *= 2
|
||||
layer_name = f'layer{i + 1}'
|
||||
self.add_module(layer_name, res_layer)
|
||||
self.res_layers.append(layer_name)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
self.feat_dim = res_layer[-1].out_channels
|
||||
|
||||
def make_res_layer(self, **kwargs):
|
||||
"""Make a ResLayer."""
|
||||
return ResLayer(**kwargs)
|
||||
|
||||
@property
|
||||
def norm1(self):
|
||||
"""nn.Module: the normalization layer named "norm1" """
|
||||
return getattr(self, self.norm1_name)
|
||||
|
||||
def _make_stem_layer(self, in_channels, stem_channels):
|
||||
"""Make stem layer."""
|
||||
if self.deep_stem:
|
||||
self.stem = nn.Sequential(
|
||||
ConvModule(
|
||||
in_channels,
|
||||
stem_channels // 2,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
inplace=True),
|
||||
ConvModule(
|
||||
stem_channels // 2,
|
||||
stem_channels // 2,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
inplace=True),
|
||||
ConvModule(
|
||||
stem_channels // 2,
|
||||
stem_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
conv_cfg=self.conv_cfg,
|
||||
norm_cfg=self.norm_cfg,
|
||||
inplace=True))
|
||||
else:
|
||||
self.conv1 = build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels,
|
||||
stem_channels,
|
||||
kernel_size=7,
|
||||
stride=2,
|
||||
padding=3,
|
||||
bias=False)
|
||||
self.norm1_name, norm1 = build_norm_layer(
|
||||
self.norm_cfg, stem_channels, postfix=1)
|
||||
self.add_module(self.norm1_name, norm1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
|
||||
def _freeze_stages(self):
|
||||
"""Freeze parameters."""
|
||||
if self.frozen_stages >= 0:
|
||||
if self.deep_stem:
|
||||
self.stem.eval()
|
||||
for param in self.stem.parameters():
|
||||
param.requires_grad = False
|
||||
else:
|
||||
self.norm1.eval()
|
||||
for m in [self.conv1, self.norm1]:
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
for i in range(1, self.frozen_stages + 1):
|
||||
m = getattr(self, f'layer{i}')
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
"""Initialize the weights in backbone.
|
||||
|
||||
Args:
|
||||
pretrained (str, optional): Path to pre-trained weights.
|
||||
Defaults to None.
|
||||
"""
|
||||
super().init_weights(pretrained)
|
||||
if pretrained is None:
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
kaiming_init(m)
|
||||
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
|
||||
constant_init(m, 1)
|
||||
|
||||
if self.zero_init_residual:
|
||||
for m in self.modules():
|
||||
if isinstance(m, Bottleneck):
|
||||
constant_init(m.norm3, 0)
|
||||
elif isinstance(m, BasicBlock):
|
||||
constant_init(m.norm2, 0)
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
if self.deep_stem:
|
||||
x = self.stem(x)
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.relu(x)
|
||||
x = self.maxpool(x)
|
||||
outs = []
|
||||
for i, layer_name in enumerate(self.res_layers):
|
||||
res_layer = getattr(self, layer_name)
|
||||
x = res_layer(x)
|
||||
if i in self.out_indices:
|
||||
outs.append(x)
|
||||
if len(outs) == 1:
|
||||
return outs[0]
|
||||
return tuple(outs)
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode."""
|
||||
super().train(mode)
|
||||
self._freeze_stages()
|
||||
if mode and self.norm_eval:
|
||||
for m in self.modules():
|
||||
# trick: eval have effect on BatchNorm only
|
||||
if isinstance(m, _BatchNorm):
|
||||
m.eval()
|
||||
|
||||
|
||||
@BACKBONES.register_module()
|
||||
class ResNetV1d(ResNet):
|
||||
r"""ResNetV1d variant described in `Bag of Tricks
|
||||
<https://arxiv.org/pdf/1812.01187.pdf>`__.
|
||||
|
||||
Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
|
||||
the input stem with three 3x3 convs. And in the downsampling block, a 2x2
|
||||
avg_pool with stride 2 is added before conv, whose stride is changed to 1.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(deep_stem=True, avg_down=True, **kwargs)
|
||||
Reference in New Issue
Block a user