# 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 class ResNet3dPathway(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, lateral_norm=False, speed_ratio=8, channel_ratio=8, fusion_kernel=5, **kwargs): self.lateral = lateral self.lateral_norm = lateral_norm 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=self.norm_cfg if self.lateral_norm else None, act_cfg=self.act_cfg if self.lateral_norm else 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=self.norm_cfg if self.lateral_norm else None, act_cfg=self.act_cfg if self.lateral_norm else 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': ResNet3dPathway, # 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 ResNet3dSlowFast(nn.Module): """Slowfast backbone. This module is proposed in `SlowFast Networks for Video Recognition `_ 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, 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) 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. """ 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 if mmdet_imported: MMDET_BACKBONES.register_module()(ResNet3dSlowFast)