import torch import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import ConvModule, constant_init, kaiming_init from torch.nn.modules.batchnorm import _BatchNorm from .base_backbone import BaseBackbone def channel_shuffle(x, groups): """ Channel Shuffle operation. This function enables cross-group information flow for multiple group convolution layers. Args: x (Tensor): The input tensor. groups (int): The number of groups to divide the input tensor in channel dimension. Returns: Tensor: The output tensor after channel shuffle operation. """ batchsize, num_channels, height, width = x.size() assert (num_channels % groups == 0), ('num_channels should be ' 'divisible by groups') channels_per_group = num_channels // groups x = x.view(batchsize, groups, channels_per_group, height, width) x = torch.transpose(x, 1, 2).contiguous() x = x.view(batchsize, -1, height, width) return x def make_divisible(value, divisor, min_value=None): """ Make divisible function. This function ensures that all layers have a channel number that is divisible by divisor. Args: value (int): The original channel number. divisor (int): The divisor to fully divide the channel number. min_value (int, optional): the minimum value of the output channel. Returns: int: The modified output channel number """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return new_value class InvertedResidual(nn.Module): """InvertedResidual block for ShuffleNetV2 backbone. Args: inplanes (int): The input channels of the block. planes (int): The output channels of the block. stride (int): stride of the 3x3 convolution layer. Default: 1 conv_cfg (dict): Config dict for convolution layer. Default: None, which means using conv2d. norm_cfg (dict): Config dict for normalization layer. Default: dict(type='BN'). act_cfg (dict): Config dict for activation layer. Default: dict(type='ReLU'). norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: True. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. Returns: Tensor: The output tensor. """ def __init__(self, inplanes, planes, stride=1, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), with_cp=False): super(InvertedResidual, self).__init__() self.stride = stride self.with_cp = with_cp branch_features = planes // 2 assert (self.stride != 1) or (inplanes == branch_features << 1) if self.stride > 1: self.branch1 = nn.Sequential( ConvModule( inplanes, inplanes, kernel_size=3, stride=self.stride, padding=1, groups=inplanes, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None), ConvModule( inplanes, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg), ) else: self.branch1 = nn.Sequential() self.branch2 = nn.Sequential( ConvModule( inplanes if (self.stride > 1) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg), ConvModule( branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1, groups=branch_features, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None), ConvModule( branch_features, branch_features, kernel_size=1, stride=1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)) def forward(self, x): def _inner_forward(x): if self.stride == 1: x1, x2 = x.chunk(2, dim=1) out = torch.cat((x1, self.branch2(x2)), dim=1) else: out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) out = channel_shuffle(out, 2) return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) return out class ShuffleNetv2(BaseBackbone): """ShuffleNetv2 backbone. Args: groups (int): The number of groups to be used in grouped 1x1 convolutions in each ShuffleUnit. Default: 3. widen_factor (float): Width multiplier - adjusts number of channels in each layer by this amount. Default: 1.0. out_indices (Sequence[int]): Output from which stages. Default: (0, 1, 2, 3). frozen_stages (int): Stages to be frozen (all param fixed). Default: -1, which means not freezing any parameters. conv_cfg (dict): Config dict for convolution layer. Default: None, which means using conv2d. norm_cfg (dict): Config dict for normalization layer. Default: dict(type='BN'). act_cfg (dict): Config dict for activation layer. Default: dict(type='ReLU'). norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: True. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. """ def __init__(self, groups=3, widen_factor=1.0, out_indices=(0, 1, 2), frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), norm_eval=True, with_cp=False): super(ShuffleNetv2, self).__init__() self.stage_blocks = [4, 8, 4] self.groups = groups self.out_indices = out_indices assert max(out_indices) < len(self.stage_blocks) self.frozen_stages = frozen_stages assert frozen_stages < len(self.stage_blocks) 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 if widen_factor == 0.5: channels = [48, 96, 192, 1024] elif widen_factor == 1.0: channels = [116, 232, 464, 1024] elif widen_factor == 1.5: channels = [176, 352, 704, 1024] elif widen_factor == 2.0: channels = [244, 488, 976, 2048] else: raise ValueError(f'widen_factor must in [0.5, 1.0, 1.5, 2.0]. ' f'But received {widen_factor}.') self.inplanes = 24 self.conv1 = ConvModule( in_channels=3, out_channels=self.inplanes, kernel_size=3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layers = [] for i, num_blocks in enumerate(self.stage_blocks): layer = self._make_layer(channels[i], num_blocks) layer_name = f'layer{i + 1}' self.add_module(layer_name, layer) self.layers.append(layer_name) output_channels = channels[-1] self.conv2 = ConvModule( in_channels=self.inplanes, out_channels=output_channels, kernel_size=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) def _make_layer(self, planes, num_blocks): """ Stack blocks to make a layer. Args: planes (int): planes of block. num_blocks (int): number of blocks. """ layers = [] for i in range(num_blocks): stride = 2 if i == 0 else 1 layers.append( InvertedResidual( inplanes=self.inplanes, planes=planes, stride=stride, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg, with_cp=self.with_cp)) self.inplanes = planes return nn.Sequential(*layers) def _freeze_stages(self): if self.frozen_stages >= 0: for m in [self.conv1]: 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): 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) else: raise TypeError('pretrained must be a str or None') def forward(self, x): x = self.conv1(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.layers): layer = getattr(self, layer_name) x = layer(x) if i in self.out_indices: outs.append(x) if len(outs) == 1: return outs[0] else: return tuple(outs) def train(self, mode=True): super(ShuffleNetv2, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): if isinstance(m, nn.BatchNorm2d): m.eval()