# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, kaiming_init
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import print_log
from ...utils import get_root_logger
from ..builder import BACKBONES
from .resnet3d import ResNet3d
try:
from mmdet.models import BACKBONES as MMDET_BACKBONES
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
# AMAGI
from ..segmentors.seg_hrnet_ocr import get_seg_model
from ..segmentors.config.default import update_config
from ..segmentors.config import config
import yaml
def load_seg_model(kfold=1):
#load model here
with open("mmaction/models/segmentors/config/ocr.yml", 'r') as f:
cfg = yaml.load(f)
#update_config(cfg, None)
if type(kfold) == str:
kfold = int(kfold)
seg_model = get_seg_model(cfg, kfold)
return seg_model
class AMAGIPathway(ResNet3d):
"""A pathway of Slowfast based on ResNet3d.
Args:
*args (arguments): Arguments same as :class:``ResNet3d``.
lateral (bool): Determines whether to enable the lateral connection
from another pathway. Default: False.
speed_ratio (int): Speed ratio indicating the ratio between time
dimension of the fast and slow pathway, corresponding to the
``alpha`` in the paper. Default: 8.
channel_ratio (int): Reduce the channel number of fast pathway
by ``channel_ratio``, corresponding to ``beta`` in the paper.
Default: 8.
fusion_kernel (int): The kernel size of lateral fusion.
Default: 5.
**kwargs (keyword arguments): Keywords arguments for ResNet3d.
"""
def __init__(self,
*args,
lateral=False,
speed_ratio=8,
channel_ratio=8,
fusion_kernel=5,
**kwargs):
self.lateral = lateral
self.speed_ratio = speed_ratio
self.channel_ratio = channel_ratio
self.fusion_kernel = fusion_kernel
super().__init__(*args, **kwargs)
self.inplanes = self.base_channels
if self.lateral:
self.conv1_lateral = ConvModule(
self.inplanes // self.channel_ratio,
# https://arxiv.org/abs/1812.03982, the
# third type of lateral connection has out_channel:
# 2 * \beta * C
self.inplanes * 2 // self.channel_ratio,
kernel_size=(fusion_kernel, 1, 1),
stride=(self.speed_ratio, 1, 1),
padding=((fusion_kernel - 1) // 2, 0, 0),
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=None,
act_cfg=None)
self.lateral_connections = []
for i in range(len(self.stage_blocks)):
planes = self.base_channels * 2**i
self.inplanes = planes * self.block.expansion
if lateral and i != self.num_stages - 1:
# no lateral connection needed in final stage
lateral_name = f'layer{(i + 1)}_lateral'
setattr(
self, lateral_name,
ConvModule(
self.inplanes // self.channel_ratio,
self.inplanes * 2 // self.channel_ratio,
kernel_size=(fusion_kernel, 1, 1),
stride=(self.speed_ratio, 1, 1),
padding=((fusion_kernel - 1) // 2, 0, 0),
bias=False,
conv_cfg=self.conv_cfg,
norm_cfg=None,
act_cfg=None))
self.lateral_connections.append(lateral_name)
def make_res_layer(self,
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(),
conv_cfg=None,
norm_cfg=None,
act_cfg=None,
with_cp=False):
"""Build residual layer for Slowfast.
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 ``3x3x3``. 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 conv 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): 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
if self.lateral:
lateral_inplanes = inplanes * 2 // self.channel_ratio
else:
lateral_inplanes = 0
if (spatial_stride != 1
or (inplanes + lateral_inplanes) != planes * block.expansion):
downsample = ConvModule(
inplanes + lateral_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)
else:
downsample = None
layers = []
layers.append(
block(
inplanes + lateral_inplanes,
planes,
spatial_stride,
temporal_stride,
dilation,
downsample,
style=style,
inflate=(inflate[0] == 1),
inflate_style=inflate_style,
non_local=(non_local[0] == 1),
non_local_cfg=non_local_cfg,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
with_cp=with_cp))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
inplanes,
planes,
1,
1,
dilation,
style=style,
inflate=(inflate[i] == 1),
inflate_style=inflate_style,
non_local=(non_local[i] == 1),
non_local_cfg=non_local_cfg,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg,
with_cp=with_cp))
return nn.Sequential(*layers)
def inflate_weights(self, logger):
"""Inflate the resnet2d parameters to resnet3d pathway.
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. For pathway the ``lateral_connection`` part should
not be inflated from 2d weights.
Args:
logger (logging.Logger): The logger used to print
debugging information.
"""
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 'lateral' in name:
continue
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:
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_conv_params(self, conv3d, state_dict_2d, module_name_2d,
inflated_param_names):
"""Inflate a conv module from 2d to 3d.
The differences of conv modules betweene 2d and 3d in Pathway
mainly lie in the inplanes due to lateral connections. To fit the
shapes of the lateral connection counterpart, it will expand
parameters by concatting conv2d parameters and extra zero paddings.
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]
old_shape = conv2d_weight.shape
new_shape = conv3d.weight.data.shape
kernel_t = new_shape[2]
if new_shape[1] != old_shape[1]:
if new_shape[1] < old_shape[1]:
warnings.warn(f'The parameter of {module_name_2d} is not'
'loaded due to incompatible shapes. ')
return
# Inplanes may be different due to lateral connections
new_channels = new_shape[1] - old_shape[1]
pad_shape = old_shape
pad_shape = pad_shape[:1] + (new_channels, ) + pad_shape[2:]
# Expand parameters by concat extra channels
conv2d_weight = torch.cat(
(conv2d_weight,
torch.zeros(pad_shape).type_as(conv2d_weight).to(
conv2d_weight.device)),
dim=1)
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)
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
if i != len(self.res_layers) and self.lateral:
# No fusion needed in the final stage
lateral_name = self.lateral_connections[i - 1]
conv_lateral = getattr(self, lateral_name)
conv_lateral.eval()
for param in conv_lateral.parameters():
param.requires_grad = False
def init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if pretrained:
self.pretrained = pretrained
# Override the init_weights of i3d
super().init_weights()
for module_name in self.lateral_connections:
layer = getattr(self, module_name)
for m in layer.modules():
if isinstance(m, (nn.Conv3d, nn.Conv2d)):
kaiming_init(m)
pathway_cfg = {
'resnet3d': AMAGIPathway,
# TODO: BNInceptionPathway
}
def build_pathway(cfg, *args, **kwargs):
"""Build pathway.
Args:
cfg (None or dict): cfg should contain:
- type (str): identify conv layer type.
Returns:
nn.Module: Created pathway.
"""
if not (isinstance(cfg, dict) and 'type' in cfg):
raise TypeError('cfg must be a dict containing the key "type"')
cfg_ = cfg.copy()
pathway_type = cfg_.pop('type')
if pathway_type not in pathway_cfg:
raise KeyError(f'Unrecognized pathway type {pathway_type}')
pathway_cls = pathway_cfg[pathway_type]
pathway = pathway_cls(*args, **kwargs, **cfg_)
return pathway
@BACKBONES.register_module()
class AMAGI(nn.Module):
"""Slowfast backbone.
This module is proposed in `SlowFast Networks for Video Recognition
<https://arxiv.org/abs/1812.03982>`_
Args:
pretrained (str): The file path to a pretrained model.
resample_rate (int): A large temporal stride ``resample_rate``
on input frames. The actual resample rate is calculated by
multipling the ``interval`` in ``SampleFrames`` in the
pipeline with ``resample_rate``, equivalent to the :math:`\\tau`
in the paper, i.e. it processes only one out of
``resample_rate * interval`` frames. Default: 8.
speed_ratio (int): Speed ratio indicating the ratio between time
dimension of the fast and slow pathway, corresponding to the
:math:`\\alpha` in the paper. Default: 8.
channel_ratio (int): Reduce the channel number of fast pathway
by ``channel_ratio``, corresponding to :math:`\\beta` in the paper.
Default: 8.
slow_pathway (dict): Configuration of slow branch, should contain
necessary arguments for building the specific type of pathway
and:
type (str): type of backbone the pathway bases on.
lateral (bool): determine whether to build lateral connection
for the pathway.Default:
.. code-block:: Python
dict(type='ResNetPathway',
lateral=True, depth=50, pretrained=None,
conv1_kernel=(1, 7, 7), dilations=(1, 1, 1, 1),
conv1_stride_t=1, pool1_stride_t=1, inflate=(0, 0, 1, 1))
fast_pathway (dict): Configuration of fast branch, similar to
`slow_pathway`. Default:
.. code-block:: Python
dict(type='ResNetPathway',
lateral=False, depth=50, pretrained=None, base_channels=8,
conv1_kernel=(5, 7, 7), conv1_stride_t=1, pool1_stride_t=1)
"""
def __init__(self,
pretrained,
resample_rate=8,
speed_ratio=8,
channel_ratio=8,
kfold=1,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1)):
super().__init__()
self.pretrained = pretrained
self.resample_rate = resample_rate
self.speed_ratio = speed_ratio
self.channel_ratio = channel_ratio
if slow_pathway['lateral']:
slow_pathway['speed_ratio'] = speed_ratio
slow_pathway['channel_ratio'] = channel_ratio
self.slow_path = build_pathway(slow_pathway)
self.fast_path = build_pathway(fast_pathway)
self.seg_model = load_seg_model(kfold)
def init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if pretrained:
self.pretrained = pretrained
if isinstance(self.pretrained, str):
logger = get_root_logger()
msg = f'load model from: {self.pretrained}'
print_log(msg, logger=logger)
# Directly load 3D model.
load_checkpoint(self, self.pretrained, strict=True, logger=logger)
elif self.pretrained is None:
# Init two branch separately.
self.fast_path.init_weights()
self.slow_path.init_weights()
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
tuple[torch.Tensor]: The feature of the input samples extracted
by the backbone.
"""
###############
time_len = len(x[0,0])
seg_input = x[:,:,time_len//2] # segmentation inference on center frame
seg_out = self.seg_model(seg_input)
##############
x_slow = nn.functional.interpolate(
x,
mode='nearest',
scale_factor=(1.0 / self.resample_rate, 1.0, 1.0))
x_slow = self.slow_path.conv1(x_slow)
x_slow = self.slow_path.maxpool(x_slow)
x_fast = nn.functional.interpolate(
x,
mode='nearest',
scale_factor=(1.0 / (self.resample_rate // self.speed_ratio), 1.0,
1.0))
x_fast = self.fast_path.conv1(x_fast)
x_fast = self.fast_path.maxpool(x_fast)
if self.slow_path.lateral:
x_fast_lateral = self.slow_path.conv1_lateral(x_fast)
x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
for i, layer_name in enumerate(self.slow_path.res_layers):
res_layer = getattr(self.slow_path, layer_name)
x_slow = res_layer(x_slow)
res_layer_fast = getattr(self.fast_path, layer_name)
x_fast = res_layer_fast(x_fast)
if (i != len(self.slow_path.res_layers) - 1
and self.slow_path.lateral):
# No fusion needed in the final stage
lateral_name = self.slow_path.lateral_connections[i]
conv_lateral = getattr(self.slow_path, lateral_name)
x_fast_lateral = conv_lateral(x_fast)
x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
out = (x_slow, x_fast)
return out, seg_out
if mmdet_imported:
MMDET_BACKBONES.register_module()(AMAGI)
Datasets
Models
