import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, NonLocal3d, build_activation_layer,
constant_init, kaiming_init)
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import _BatchNorm
from torch.nn.modules.utils import _ntuple, _triple
from ...utils import get_root_logger
from ..registry import BACKBONES
try:
from mmdet.models.builder import SHARED_HEADS as MMDET_SHARED_HEADS
from mmdet.models import BACKBONES as MMDET_BACKBONES
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
class BasicBlock3d(nn.Module):
"""BasicBlock 3d block for ResNet3D.
Args:
inplanes (int): Number of channels for the input in first conv3d layer.
planes (int): Number of channels produced by some norm/conv3d layers.
spatial_stride (int): Spatial stride in the conv3d layer. Default: 1.
temporal_stride (int): Temporal stride in the conv3d layer. Default: 1.
dilation (int): Spacing between kernel elements. Default: 1.
downsample (nn.Module | None): Downsample layer. Default: None.
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. Default: 'pytorch'.
inflate (bool): Whether to inflate kernel. Default: True.
non_local (bool): Determine whether to apply non-local module in this
block. Default: False.
non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
conv_cfg (dict): Config dict for convolution layer.
Default: ``dict(type='Conv3d')``.
norm_cfg (dict): Config for norm layers. required keys are ``type``,
Default: ``dict(type='BN3d')``.
act_cfg (dict): Config dict for activation layer.
Default: ``dict(type='ReLU')``.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
expansion = 1
def __init__(self,
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=1,
downsample=None,
style='pytorch',
inflate=True,
non_local=False,
non_local_cfg=dict(),
conv_cfg=dict(type='Conv3d'),
norm_cfg=dict(type='BN3d'),
act_cfg=dict(type='ReLU'),
with_cp=False,
**kwargs):
super().__init__()
assert style in ['pytorch', 'caffe']
# make sure that only ``inflate_style`` is passed into kwargs
assert set(kwargs).issubset(['inflate_style'])
self.inplanes = inplanes
self.planes = planes
self.spatial_stride = spatial_stride
self.temporal_stride = temporal_stride
self.dilation = dilation
self.style = style
self.inflate = inflate
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.with_cp = with_cp
self.non_local = non_local
self.non_local_cfg = non_local_cfg
self.conv1_stride_s = spatial_stride
self.conv2_stride_s = 1
self.conv1_stride_t = temporal_stride
self.conv2_stride_t = 1
if self.inflate:
conv1_kernel_size = (3, 3, 3)
conv1_padding = (1, dilation, dilation)
conv2_kernel_size = (3, 3, 3)
conv2_padding = (1, 1, 1)
else:
conv1_kernel_size = (1, 3, 3)
conv1_padding = (0, dilation, dilation)
conv2_kernel_size = (1, 3, 3)
conv2_padding = (0, 1, 1)
self.conv1 = ConvModule(
inplanes,
planes,
conv1_kernel_size,
stride=(self.conv1_stride_t, self.conv1_stride_s,
self.conv1_stride_s),
padding=conv1_padding,
dilation=(1, dilation, dilation),
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.conv2 = ConvModule(
planes,
planes * self.expansion,
conv2_kernel_size,
stride=(self.conv2_stride_t, self.conv2_stride_s,
self.conv2_stride_s),
padding=conv2_padding,
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=None)
self.downsample = downsample
self.relu = build_activation_layer(self.act_cfg)
if self.non_local:
self.non_local_block = NonLocal3d(self.conv2.norm.num_features,
**self.non_local_cfg)
def forward(self, x):
"""Defines the computation performed at every call."""
def _inner_forward(x):
"""Forward wrapper for utilizing checkpoint."""
identity = x
out = self.conv1(x)
out = self.conv2(out)
if self.downsample is not None:
identity = self.downsample(x)
out = out + identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
if self.non_local:
out = self.non_local_block(out)
return out
class Bottleneck3d(nn.Module):
"""Bottleneck 3d block for ResNet3D.
Args:
inplanes (int): Number of channels for the input in first conv3d layer.
planes (int): Number of channels produced by some norm/conv3d layers.
spatial_stride (int): Spatial stride in the conv3d layer. Default: 1.
temporal_stride (int): Temporal stride in the conv3d layer. Default: 1.
dilation (int): Spacing between kernel elements. Default: 1.
downsample (nn.Module | None): Downsample layer. Default: None.
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. Default: 'pytorch'.
inflate (bool): Whether to inflate kernel. Default: True.
inflate_style (str): ``3x1x1`` or ``3x3x3``. which determines the
kernel sizes and padding strides for conv1 and conv2 in each block.
Default: '3x1x1'.
non_local (bool): Determine whether to apply non-local module in this
block. Default: False.
non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
conv_cfg (dict): Config dict for convolution layer.
Default: ``dict(type='Conv3d')``.
norm_cfg (dict): Config for norm layers. required keys are ``type``,
Default: ``dict(type='BN3d')``.
act_cfg (dict): Config dict for activation layer.
Default: ``dict(type='ReLU')``.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
"""
expansion = 4
def __init__(self,
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=1,
downsample=None,
style='pytorch',
inflate=True,
inflate_style='3x1x1',
non_local=False,
non_local_cfg=dict(),
conv_cfg=dict(type='Conv3d'),
norm_cfg=dict(type='BN3d'),
act_cfg=dict(type='ReLU'),
with_cp=False):
super().__init__()
assert style in ['pytorch', 'caffe']
assert inflate_style in ['3x1x1', '3x3x3']
self.inplanes = inplanes
self.planes = planes
self.spatial_stride = spatial_stride
self.temporal_stride = temporal_stride
self.dilation = dilation
self.style = style
self.inflate = inflate
self.inflate_style = inflate_style
self.norm_cfg = norm_cfg
self.conv_cfg = conv_cfg
self.act_cfg = act_cfg
self.with_cp = with_cp
self.non_local = non_local
self.non_local_cfg = non_local_cfg
if self.style == 'pytorch':
self.conv1_stride_s = 1
self.conv2_stride_s = spatial_stride
self.conv1_stride_t = 1
self.conv2_stride_t = temporal_stride
else:
self.conv1_stride_s = spatial_stride
self.conv2_stride_s = 1
self.conv1_stride_t = temporal_stride
self.conv2_stride_t = 1
if self.inflate:
if inflate_style == '3x1x1':
conv1_kernel_size = (3, 1, 1)
conv1_padding = (1, 0, 0)
conv2_kernel_size = (1, 3, 3)
conv2_padding = (0, dilation, dilation)
else:
conv1_kernel_size = (1, 1, 1)
conv1_padding = (0, 0, 0)
conv2_kernel_size = (3, 3, 3)
conv2_padding = (1, dilation, dilation)
else:
conv1_kernel_size = (1, 1, 1)
conv1_padding = (0, 0, 0)
conv2_kernel_size = (1, 3, 3)
conv2_padding = (0, dilation, dilation)
self.conv1 = ConvModule(
inplanes,
planes,
conv1_kernel_size,
stride=(self.conv1_stride_t, self.conv1_stride_s,
self.conv1_stride_s),
padding=conv1_padding,
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.conv2 = ConvModule(
planes,
planes,
conv2_kernel_size,
stride=(self.conv2_stride_t, self.conv2_stride_s,
self.conv2_stride_s),
padding=conv2_padding,
dilation=(1, dilation, dilation),
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.conv3 = ConvModule(
planes,
planes * self.expansion,
1,
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
# No activation in the third ConvModule for bottleneck
act_cfg=None)
self.downsample = downsample
self.relu = build_activation_layer(self.act_cfg)
if self.non_local:
self.non_local_block = NonLocal3d(self.conv3.norm.num_features,
**self.non_local_cfg)
def forward(self, x):
"""Defines the computation performed at every call."""
def _inner_forward(x):
"""Forward wrapper for utilizing checkpoint."""
identity = x
out = self.conv1(x)
out = self.conv2(out)
out = self.conv3(out)
if self.downsample is not None:
identity = self.downsample(x)
out = out + identity
return out
if self.with_cp and x.requires_grad:
out = cp.checkpoint(_inner_forward, x)
else:
out = _inner_forward(x)
out = self.relu(out)
if self.non_local:
out = self.non_local_block(out)
return out
[docs]@BACKBONES.register_module()
class ResNet3d(nn.Module):
"""ResNet 3d backbone.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
pretrained (str | None): Name of pretrained model.
pretrained2d (bool): Whether to load pretrained 2D model.
Default: True.
in_channels (int): Channel num of input features. Default: 3.
base_channels (int): Channel num of stem output features. Default: 64.
out_indices (Sequence[int]): Indices of output feature. Default: (3, ).
num_stages (int): Resnet stages. Default: 4.
spatial_strides (Sequence[int]):
Spatial strides of residual blocks of each stage.
Default: ``(1, 2, 2, 2)``.
temporal_strides (Sequence[int]):
Temporal strides of residual blocks of each stage.
Default: ``(1, 1, 1, 1)``.
dilations (Sequence[int]): Dilation of each stage.
Default: ``(1, 1, 1, 1)``.
conv1_kernel (Sequence[int]): Kernel size of the first conv layer.
Default: ``(5, 7, 7)``.
conv1_stride_t (int): Temporal stride of the first conv layer.
Default: 2.
pool1_stride_t (int): Temporal stride of the first pooling layer.
Default: 2.
with_pool2 (bool): Whether to use pool2. Default: True.
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. Default: 'pytorch'.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters. Default: -1.
inflate (Sequence[int]): Inflate Dims of each block.
Default: (1, 1, 1, 1).
inflate_style (str): ``3x1x1`` or ``1x1x1``. which determines the
kernel sizes and padding strides for conv1 and conv2 in each block.
Default: '3x1x1'.
conv_cfg (dict): Config for conv layers. required keys are ``type``
Default: ``dict(type='Conv3d')``.
norm_cfg (dict): Config for norm layers. required keys are ``type`` and
``requires_grad``.
Default: ``dict(type='BN3d', requires_grad=True)``.
act_cfg (dict): Config dict for activation layer.
Default: ``dict(type='ReLU', inplace=True)``.
norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze
running stats (mean and var). Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed. Default: False.
non_local (Sequence[int]): Determine whether to apply non-local module
in the corresponding block of each stages. Default: (0, 0, 0, 0).
non_local_cfg (dict): Config for non-local module. Default: ``dict()``.
zero_init_residual (bool):
Whether to use zero initialization for residual block,
Default: True.
kwargs (dict, optional): Key arguments for "make_res_layer".
"""
arch_settings = {
18: (BasicBlock3d, (2, 2, 2, 2)),
34: (BasicBlock3d, (3, 4, 6, 3)),
50: (Bottleneck3d, (3, 4, 6, 3)),
101: (Bottleneck3d, (3, 4, 23, 3)),
152: (Bottleneck3d, (3, 8, 36, 3))
}
def __init__(self,
depth,
pretrained,
pretrained2d=True,
in_channels=3,
num_stages=4,
base_channels=64,
out_indices=(3, ),
spatial_strides=(1, 2, 2, 2),
temporal_strides=(1, 1, 1, 1),
dilations=(1, 1, 1, 1),
conv1_kernel=(5, 7, 7),
conv1_stride_t=2,
pool1_stride_t=2,
with_pool2=True,
style='pytorch',
frozen_stages=-1,
inflate=(1, 1, 1, 1),
inflate_style='3x1x1',
conv_cfg=dict(type='Conv3d'),
norm_cfg=dict(type='BN3d', requires_grad=True),
act_cfg=dict(type='ReLU', inplace=True),
norm_eval=False,
with_cp=False,
non_local=(0, 0, 0, 0),
non_local_cfg=dict(),
zero_init_residual=True,
**kwargs):
super().__init__()
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for resnet')
self.depth = depth
self.pretrained = pretrained
self.pretrained2d = pretrained2d
self.in_channels = in_channels
self.base_channels = base_channels
self.num_stages = num_stages
assert 1 <= num_stages <= 4
self.out_indices = out_indices
assert max(out_indices) < num_stages
self.spatial_strides = spatial_strides
self.temporal_strides = temporal_strides
self.dilations = dilations
assert len(spatial_strides) == len(temporal_strides) == len(
dilations) == num_stages
self.conv1_kernel = conv1_kernel
self.conv1_stride_t = conv1_stride_t
self.pool1_stride_t = pool1_stride_t
self.with_pool2 = with_pool2
self.style = style
self.frozen_stages = frozen_stages
self.stage_inflations = _ntuple(num_stages)(inflate)
self.non_local_stages = _ntuple(num_stages)(non_local)
self.inflate_style = inflate_style
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = self.base_channels
self.non_local_cfg = non_local_cfg
self._make_stem_layer()
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
spatial_stride = spatial_strides[i]
temporal_stride = temporal_strides[i]
dilation = dilations[i]
planes = self.base_channels * 2**i
res_layer = self.make_res_layer(
self.block,
self.inplanes,
planes,
num_blocks,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
dilation=dilation,
style=self.style,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
act_cfg=self.act_cfg,
non_local=self.non_local_stages[i],
non_local_cfg=self.non_local_cfg,
inflate=self.stage_inflations[i],
inflate_style=self.inflate_style,
with_cp=with_cp,
**kwargs)
self.inplanes = planes * self.block.expansion
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
self.feat_dim = self.block.expansion * self.base_channels * 2**(
len(self.stage_blocks) - 1)
[docs] @staticmethod
def make_res_layer(block,
inplanes,
planes,
blocks,
spatial_stride=1,
temporal_stride=1,
dilation=1,
style='pytorch',
inflate=1,
inflate_style='3x1x1',
non_local=0,
non_local_cfg=dict(),
norm_cfg=None,
act_cfg=None,
conv_cfg=None,
with_cp=False,
**kwargs):
"""Build residual layer for ResNet3D.
Args:
block (nn.Module): Residual module to be built.
inplanes (int): Number of channels for the input feature
in each block.
planes (int): Number of channels for the output feature
in each block.
blocks (int): Number of residual blocks.
spatial_stride (int | Sequence[int]): Spatial strides in
residual and conv layers. Default: 1.
temporal_stride (int | Sequence[int]): Temporal strides in
residual and conv layers. Default: 1.
dilation (int): Spacing between kernel elements. Default: 1.
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.
Default: ``pytorch``.
inflate (int | Sequence[int]): Determine whether to inflate
for each block. Default: 1.
inflate_style (str): ``3x1x1`` or ``1x1x1``. which determines
the kernel sizes and padding strides for conv1 and conv2
in each block. Default: '3x1x1'.
non_local (int | Sequence[int]): Determine whether to apply
non-local module in the corresponding block of each stages.
Default: 0.
non_local_cfg (dict): Config for non-local module.
Default: ``dict()``.
conv_cfg (dict | None): Config for norm layers. Default: None.
norm_cfg (dict | None): Config for norm layers. Default: None.
act_cfg (dict | None): Config for activate layers. Default: None.
with_cp (bool | None): Use checkpoint or not. Using checkpoint
will save some memory while slowing down the training speed.
Default: False.
Returns:
nn.Module: A residual layer for the given config.
"""
inflate = inflate if not isinstance(inflate,
int) else (inflate, ) * blocks
non_local = non_local if not isinstance(
non_local, int) else (non_local, ) * blocks
assert len(inflate) == blocks and len(non_local) == blocks
downsample = None
if spatial_stride != 1 or inplanes != planes * block.expansion:
downsample = ConvModule(
inplanes,
planes * block.expansion,
kernel_size=1,
stride=(temporal_stride, spatial_stride, spatial_stride),
bias=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=None)
layers = []
layers.append(
block(
inplanes,
planes,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
dilation=dilation,
downsample=downsample,
style=style,
inflate=(inflate[0] == 1),
inflate_style=inflate_style,
non_local=(non_local[0] == 1),
non_local_cfg=non_local_cfg,
norm_cfg=norm_cfg,
conv_cfg=conv_cfg,
act_cfg=act_cfg,
with_cp=with_cp,
**kwargs))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=dilation,
style=style,
inflate=(inflate[i] == 1),
inflate_style=inflate_style,
non_local=(non_local[i] == 1),
non_local_cfg=non_local_cfg,
norm_cfg=norm_cfg,
conv_cfg=conv_cfg,
act_cfg=act_cfg,
with_cp=with_cp,
**kwargs))
return nn.Sequential(*layers)
@staticmethod
def _inflate_conv_params(conv3d, state_dict_2d, module_name_2d,
inflated_param_names):
"""Inflate a conv module from 2d to 3d.
Args:
conv3d (nn.Module): The destination conv3d module.
state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
module_name_2d (str): The name of corresponding conv module in the
2d model.
inflated_param_names (list[str]): List of parameters that have been
inflated.
"""
weight_2d_name = module_name_2d + '.weight'
conv2d_weight = state_dict_2d[weight_2d_name]
kernel_t = conv3d.weight.data.shape[2]
new_weight = conv2d_weight.data.unsqueeze(2).expand_as(
conv3d.weight) / kernel_t
conv3d.weight.data.copy_(new_weight)
inflated_param_names.append(weight_2d_name)
if getattr(conv3d, 'bias') is not None:
bias_2d_name = module_name_2d + '.bias'
conv3d.bias.data.copy_(state_dict_2d[bias_2d_name])
inflated_param_names.append(bias_2d_name)
@staticmethod
def _inflate_bn_params(bn3d, state_dict_2d, module_name_2d,
inflated_param_names):
"""Inflate a norm module from 2d to 3d.
Args:
bn3d (nn.Module): The destination bn3d module.
state_dict_2d (OrderedDict): The state dict of pretrained 2d model.
module_name_2d (str): The name of corresponding bn module in the
2d model.
inflated_param_names (list[str]): List of parameters that have been
inflated.
"""
for param_name, param in bn3d.named_parameters():
param_2d_name = f'{module_name_2d}.{param_name}'
param_2d = state_dict_2d[param_2d_name]
param.data.copy_(param_2d)
inflated_param_names.append(param_2d_name)
for param_name, param in bn3d.named_buffers():
param_2d_name = f'{module_name_2d}.{param_name}'
# some buffers like num_batches_tracked may not exist in old
# checkpoints
if param_2d_name in state_dict_2d:
param_2d = state_dict_2d[param_2d_name]
param.data.copy_(param_2d)
inflated_param_names.append(param_2d_name)
@staticmethod
def _inflate_weights(self, logger):
"""Inflate the resnet2d parameters to resnet3d.
The differences between resnet3d and resnet2d mainly lie in an extra
axis of conv kernel. To utilize the pretrained parameters in 2d model,
the weight of conv2d models should be inflated to fit in the shapes of
the 3d counterpart.
Args:
logger (logging.Logger): The logger used to print
debugging infomation.
"""
state_dict_r2d = _load_checkpoint(self.pretrained)
if 'state_dict' in state_dict_r2d:
state_dict_r2d = state_dict_r2d['state_dict']
inflated_param_names = []
for name, module in self.named_modules():
if isinstance(module, ConvModule):
# we use a ConvModule to wrap conv+bn+relu layers, thus the
# name mapping is needed
if 'downsample' in name:
# layer{X}.{Y}.downsample.conv->layer{X}.{Y}.downsample.0
original_conv_name = name + '.0'
# layer{X}.{Y}.downsample.bn->layer{X}.{Y}.downsample.1
original_bn_name = name + '.1'
else:
# layer{X}.{Y}.conv{n}.conv->layer{X}.{Y}.conv{n}
original_conv_name = name
# layer{X}.{Y}.conv{n}.bn->layer{X}.{Y}.bn{n}
original_bn_name = name.replace('conv', 'bn')
if original_conv_name + '.weight' not in state_dict_r2d:
logger.warning(f'Module not exist in the state_dict_r2d'
f': {original_conv_name}')
else:
shape_2d = state_dict_r2d[original_conv_name +
'.weight'].shape
shape_3d = module.conv.weight.data.shape
if shape_2d != shape_3d[:2] + shape_3d[3:]:
logger.warning(f'Weight shape mismatch for '
f': {original_conv_name} : '
f'3d weight shape: {shape_3d}; '
f'2d weight shape: {shape_2d}. ')
else:
self._inflate_conv_params(module.conv, state_dict_r2d,
original_conv_name,
inflated_param_names)
if original_bn_name + '.weight' not in state_dict_r2d:
logger.warning(f'Module not exist in the state_dict_r2d'
f': {original_bn_name}')
else:
self._inflate_bn_params(module.bn, state_dict_r2d,
original_bn_name,
inflated_param_names)
# check if any parameters in the 2d checkpoint are not loaded
remaining_names = set(
state_dict_r2d.keys()) - set(inflated_param_names)
if remaining_names:
logger.info(f'These parameters in the 2d checkpoint are not loaded'
f': {remaining_names}')
def inflate_weights(self, logger):
self._inflate_weights(self, logger)
def _make_stem_layer(self):
"""Construct the stem layers consists of a conv+norm+act module and a
pooling layer."""
self.conv1 = ConvModule(
self.in_channels,
self.base_channels,
kernel_size=self.conv1_kernel,
stride=(self.conv1_stride_t, 2, 2),
padding=tuple([(k - 1) // 2 for k in _triple(self.conv1_kernel)]),
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.maxpool = nn.MaxPool3d(
kernel_size=(1, 3, 3),
stride=(self.pool1_stride_t, 2, 2),
padding=(0, 1, 1))
self.pool2 = nn.MaxPool3d(kernel_size=(2, 1, 1), stride=(2, 1, 1))
def _freeze_stages(self):
"""Prevent all the parameters from being optimized before
``self.frozen_stages``."""
if self.frozen_stages >= 0:
self.conv1.eval()
for param in self.conv1.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
@staticmethod
def _init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch.
Args:
pretrained (str | None): The path of the pretrained weight. Will
override the original `pretrained` if set. The arg is added to
be compatible with mmdet. Default: None.
"""
if pretrained:
self.pretrained = pretrained
if isinstance(self.pretrained, str):
logger = get_root_logger()
logger.info(f'load model from: {self.pretrained}')
if self.pretrained2d:
# Inflate 2D model into 3D model.
self.inflate_weights(logger)
else:
# Directly load 3D model.
load_checkpoint(
self, self.pretrained, strict=False, logger=logger)
elif self.pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv3d):
kaiming_init(m)
elif isinstance(m, _BatchNorm):
constant_init(m, 1)
if self.zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck3d):
constant_init(m.conv3.bn, 0)
elif isinstance(m, BasicBlock3d):
constant_init(m.conv2.bn, 0)
else:
raise TypeError('pretrained must be a str or None')
def init_weights(self, pretrained=None):
self._init_weights(self, pretrained)
[docs] def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The feature of the input
samples extracted by the backbone.
"""
x = self.conv1(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 == 0 and self.with_pool2:
x = self.pool2(x)
if i in self.out_indices:
outs.append(x)
if len(outs) == 1:
return outs[0]
return tuple(outs)
[docs] def train(self, mode=True):
"""Set the optimization status when training."""
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
[docs]@BACKBONES.register_module()
class ResNet3dLayer(nn.Module):
"""ResNet 3d Layer.
Args:
depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
pretrained (str | None): Name of pretrained model.
pretrained2d (bool): Whether to load pretrained 2D model.
Default: True.
stage (int): The index of Resnet stage. Default: 3.
base_channels (int): Channel num of stem output features. Default: 64.
spatial_stride (int): The 1st res block's spatial stride. Default 2.
temporal_stride (int): The 1st res block's temporal stride. Default 1.
dilation (int): The dilation. Default: 1.
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. Default: 'pytorch'.
all_frozen (bool): Frozen all modules in the layer. Default: False.
inflate (int): Inflate Dims of each block. Default: 1.
inflate_style (str): ``3x1x1`` or ``1x1x1``. which determines the
kernel sizes and padding strides for conv1 and conv2 in each block.
Default: '3x1x1'.
conv_cfg (dict): Config for conv layers. required keys are ``type``
Default: ``dict(type='Conv3d')``.
norm_cfg (dict): Config for norm layers. required keys are ``type`` and
``requires_grad``.
Default: ``dict(type='BN3d', requires_grad=True)``.
act_cfg (dict): Config dict for activation layer.
Default: ``dict(type='ReLU', inplace=True)``.
norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze
running stats (mean and var). Default: False.
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 initialization for residual block,
Default: True.
kwargs (dict, optional): Key arguments for "make_res_layer".
"""
def __init__(self,
depth,
pretrained,
pretrained2d=True,
stage=3,
base_channels=64,
spatial_stride=2,
temporal_stride=1,
dilation=1,
style='pytorch',
all_frozen=False,
inflate=1,
inflate_style='3x1x1',
conv_cfg=dict(type='Conv3d'),
norm_cfg=dict(type='BN3d', requires_grad=True),
act_cfg=dict(type='ReLU', inplace=True),
norm_eval=False,
with_cp=False,
zero_init_residual=True,
**kwargs):
super().__init__()
self.arch_settings = ResNet3d.arch_settings
assert depth in self.arch_settings
self.make_res_layer = ResNet3d.make_res_layer
self._inflate_conv_params = ResNet3d._inflate_conv_params
self._inflate_bn_params = ResNet3d._inflate_bn_params
self._inflate_weights = ResNet3d._inflate_weights
self._init_weights = ResNet3d._init_weights
self.depth = depth
self.pretrained = pretrained
self.pretrained2d = pretrained2d
self.stage = stage
# stage index is 0 based
assert stage >= 0 and stage <= 3
self.base_channels = base_channels
self.spatial_stride = spatial_stride
self.temporal_stride = temporal_stride
self.dilation = dilation
self.style = style
self.all_frozen = all_frozen
self.stage_inflation = inflate
self.inflate_style = inflate_style
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.norm_eval = norm_eval
self.with_cp = with_cp
self.zero_init_residual = zero_init_residual
block, stage_blocks = self.arch_settings[depth]
stage_block = stage_blocks[stage]
planes = 64 * 2**stage
inplanes = 64 * 2**(stage - 1) * block.expansion
res_layer = self.make_res_layer(
block,
inplanes,
planes,
stage_block,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
dilation=dilation,
style=self.style,
norm_cfg=self.norm_cfg,
conv_cfg=self.conv_cfg,
act_cfg=self.act_cfg,
inflate=self.stage_inflation,
inflate_style=self.inflate_style,
with_cp=with_cp,
**kwargs)
self.layer_name = f'layer{stage + 1}'
self.add_module(self.layer_name, res_layer)
def inflate_weights(self, logger):
self._inflate_weights(self, logger)
def _freeze_stages(self):
"""Prevent all the parameters from being optimized before
``self.frozen_stages``."""
if self.all_frozen:
layer = getattr(self, self.layer_name)
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
self._init_weights(self, pretrained)
[docs] def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The feature of the input
samples extracted by the backbone.
"""
res_layer = getattr(self, self.layer_name)
out = res_layer(x)
return out
[docs] def train(self, mode=True):
"""Set the optimization status when training."""
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
if mmdet_imported:
MMDET_SHARED_HEADS.register_module()(ResNet3dLayer)
MMDET_BACKBONES.register_module()(ResNet3d)