# Copyright (c) OpenMMLab. All rights reserved. import warnings from collections import OrderedDict from typing import Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from mmcv.cnn import ConvModule from mmengine.logging import MMLogger, print_log from mmengine.model import BaseModule from mmengine.model.weight_init import kaiming_init from mmengine.runner.checkpoint import _load_checkpoint, load_checkpoint from mmaction.registry import MODELS from .resnet3d import ResNet3d class DeConvModule(BaseModule): """A deconv module that bundles deconv/norm/activation layers. Args: in_channels (int): Number of channels in the input feature map. out_channels (int): Number of channels produced by the convolution. kernel_size (int | tuple[int]): Size of the convolving kernel. stride (int | tuple[int]): Stride of the convolution. padding (int | tuple[int]): Zero-padding added to both sides of the input. bias (bool): Whether to add a learnable bias to the output. Defaults to False. with_bn (bool): Whether to add a BN layer. Defaults to True. with_relu (bool): Whether to add a ReLU layer. Defaults to True. """ def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: Union[int, Tuple[int]] = (1, 1, 1), padding: Union[int, Tuple[int]] = 0, bias: bool = False, with_bn: bool = True, with_relu: bool = True) -> None: super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.bias = bias self.with_bn = with_bn self.with_relu = with_relu self.conv = nn.ConvTranspose3d( in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias) self.bn = nn.BatchNorm3d(out_channels) self.relu = nn.ReLU() def forward(self, x: torch.Tensor) -> torch.Tensor: """Defines the computation performed at every call.""" # x should be a 5-d tensor assert len(x.shape) == 5 N, C, T, H, W = x.shape out_shape = (N, self.out_channels, self.stride[0] * T, self.stride[1] * H, self.stride[2] * W) x = self.conv(x, output_size=out_shape) if self.with_bn: x = self.bn(x) if self.with_relu: x = self.relu(x) return x class ResNet3dPathway(ResNet3d): """A pathway of Slowfast based on ResNet3d. Args: lateral (bool): Determines whether to enable the lateral connection from another pathway. Defaults to False. lateral_inv (bool): Whether to use deconv to upscale the time dimension of features from another pathway. Defaults to False. lateral_norm (bool): Determines whether to enable the lateral norm in lateral layers. Defaults to 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. Defaults to 8. channel_ratio (int): Reduce the channel number of fast pathway by ``channel_ratio``, corresponding to ``beta`` in the paper. Defaults to 8. fusion_kernel (int): The kernel size of lateral fusion. Defaults to 5. lateral_infl (int): The ratio of the inflated channels. Defaults to 2. lateral_activate (list[int]): Flags for activating the lateral connection. Defaults to ``[1, 1, 1, 1]``. """ def __init__(self, lateral: bool = False, lateral_inv: bool = False, lateral_norm: bool = False, speed_ratio: int = 8, channel_ratio: int = 8, fusion_kernel: int = 5, lateral_infl: int = 2, lateral_activate: List[int] = [1, 1, 1, 1], **kwargs) -> None: self.lateral = lateral self.lateral_inv = lateral_inv self.lateral_norm = lateral_norm self.speed_ratio = speed_ratio self.channel_ratio = channel_ratio self.fusion_kernel = fusion_kernel self.lateral_infl = lateral_infl self.lateral_activate = lateral_activate self._calculate_lateral_inplanes(kwargs) super().__init__(**kwargs) self.inplanes = self.base_channels if self.lateral and self.lateral_activate[0] == 1: if self.lateral_inv: self.conv1_lateral = DeConvModule( self.inplanes * self.channel_ratio, self.inplanes * self.channel_ratio // lateral_infl, kernel_size=(fusion_kernel, 1, 1), stride=(self.speed_ratio, 1, 1), padding=((fusion_kernel - 1) // 2, 0, 0), with_bn=True, with_relu=True) else: self.conv1_lateral = ConvModule( self.inplanes // self.channel_ratio, self.inplanes * lateral_infl // 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 \ and self.lateral_activate[i + 1]: # no lateral connection needed in final stage lateral_name = f'layer{(i + 1)}_lateral' if self.lateral_inv: conv_module = DeConvModule( self.inplanes * self.channel_ratio, self.inplanes * self.channel_ratio // lateral_infl, kernel_size=(fusion_kernel, 1, 1), stride=(self.speed_ratio, 1, 1), padding=((fusion_kernel - 1) // 2, 0, 0), bias=False, with_bn=True, with_relu=True) else: conv_module = ConvModule( self.inplanes // self.channel_ratio, self.inplanes * lateral_infl // 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) setattr(self, lateral_name, conv_module) self.lateral_connections.append(lateral_name) def _calculate_lateral_inplanes(self, kwargs): """Calculate inplanes for lateral connection.""" depth = kwargs.get('depth', 50) expansion = 1 if depth < 50 else 4 base_channels = kwargs.get('base_channels', 64) lateral_inplanes = [] for i in range(kwargs.get('num_stages', 4)): if expansion % 2 == 0: planes = base_channels * (2 ** i) * \ ((expansion // 2) ** (i > 0)) else: planes = base_channels * (2**i) // (2**(i > 0)) if self.lateral and self.lateral_activate[i]: if self.lateral_inv: lateral_inplane = planes * \ self.channel_ratio // self.lateral_infl else: lateral_inplane = planes * \ self.lateral_infl // self.channel_ratio else: lateral_inplane = 0 lateral_inplanes.append(lateral_inplane) self.lateral_inplanes = lateral_inplanes def inflate_weights(self, logger: MMLogger) -> None: """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 (MMLogger): The logger used to print debugging information. """ state_dict_r2d = _load_checkpoint(self.pretrained, map_location='cpu') 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: nn.Module, state_dict_2d: OrderedDict, module_name_2d: str, inflated_param_names: List[str]) -> None: """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) -> None: """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: Optional[str] = None) -> 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: Dict, *args, **kwargs) -> nn.Module: """Build pathway. Args: cfg (dict): cfg should contain: - type (str): identify backbone 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 @MODELS.register_module() class ResNet3dSlowFast(BaseModule): """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. Defaults to 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. Defaults to 8. channel_ratio (int): Reduce the channel number of fast pathway by ``channel_ratio``, corresponding to :math:`\\beta` in the paper. Defaults to 8. slow_pathway (dict): Configuration of slow branch. Defaults to ``dict(type='resnet3d', depth=50, pretrained=None, lateral=True, conv1_kernel=(1, 7, 7), conv1_stride_t=1, pool1_stride_t=1, inflate=(0, 0, 1, 1))``. fast_pathway (dict): Configuration of fast branch. Defaults to ``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)``. init_cfg (dict or list[dict], optional): Initialization config dict. Defaults to None. """ def __init__(self, pretrained: Optional[str] = None, resample_rate: int = 8, speed_ratio: int = 8, channel_ratio: int = 8, slow_pathway: Dict = dict( type='resnet3d', depth=50, pretrained=None, lateral=True, conv1_kernel=(1, 7, 7), conv1_stride_t=1, pool1_stride_t=1, inflate=(0, 0, 1, 1)), fast_pathway: Dict = 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), init_cfg: Optional[Union[Dict, List[Dict]]] = None) -> None: super().__init__(init_cfg=init_cfg) 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: Optional[str] = None) -> None: """Initiate the parameters either from existing checkpoint or from scratch.""" if pretrained: self.pretrained = pretrained if isinstance(self.pretrained, str): logger = MMLogger.get_current_instance() 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: torch.Tensor) -> tuple: """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