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mmcls.models.backbones.shufflenet_v2 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, normal_init
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm

from mmcls.models.utils import channel_shuffle
from ..builder import BACKBONES
from .base_backbone import BaseBackbone


class InvertedResidual(BaseModule):
    """InvertedResidual block for ShuffleNetV2 backbone.

    Args:
        in_channels (int): The input channels of the block.
        out_channels (int): The output channels of the block.
        stride (int): Stride of the 3x3 convolution layer. Default: 1
        conv_cfg (dict, optional): 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').
        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,
                 in_channels,
                 out_channels,
                 stride=1,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 with_cp=False,
                 init_cfg=None):
        super(InvertedResidual, self).__init__(init_cfg)
        self.stride = stride
        self.with_cp = with_cp

        branch_features = out_channels // 2
        if self.stride == 1:
            assert in_channels == branch_features * 2, (
                f'in_channels ({in_channels}) should equal to '
                f'branch_features * 2 ({branch_features * 2}) '
                'when stride is 1')

        if in_channels != branch_features * 2:
            assert self.stride != 1, (
                f'stride ({self.stride}) should not equal 1 when '
                f'in_channels != branch_features * 2')

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                ConvModule(
                    in_channels,
                    in_channels,
                    kernel_size=3,
                    stride=self.stride,
                    padding=1,
                    groups=in_channels,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=None),
                ConvModule(
                    in_channels,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=act_cfg),
            )

        self.branch2 = nn.Sequential(
            ConvModule(
                in_channels 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:
                out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
            else:
                # Channel Split operation. using these lines of code to replace
                # ``chunk(x, 2, dim=1)`` can make it easier to deploy a
                # shufflenetv2 model by using mmdeploy.
                channels = x.shape[1]
                c = channels // 2 + channels % 2
                x1 = x[:, :c, :, :]
                x2 = x[:, c:, :, :]

                out = torch.cat((x1, self.branch2(x2)), 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


[文档]@BACKBONES.register_module() class ShuffleNetV2(BaseBackbone): """ShuffleNetV2 backbone. Args: widen_factor (float): Width multiplier - adjusts the 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, optional): 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: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. """ def __init__(self, widen_factor=1.0, out_indices=(3, ), frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), norm_eval=False, with_cp=False, init_cfg=None): super(ShuffleNetV2, self).__init__(init_cfg) self.stage_blocks = [4, 8, 4] for index in out_indices: if index not in range(0, 4): raise ValueError('the item in out_indices must in ' f'range(0, 4). But received {index}') if frozen_stages not in range(-1, 4): raise ValueError('frozen_stages must be in range(-1, 4). ' f'But received {frozen_stages}') self.out_indices = out_indices self.frozen_stages = frozen_stages 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('widen_factor must be in [0.5, 1.0, 1.5, 2.0]. ' f'But received {widen_factor}') self.in_channels = 24 self.conv1 = ConvModule( in_channels=3, out_channels=self.in_channels, 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 = nn.ModuleList() for i, num_blocks in enumerate(self.stage_blocks): layer = self._make_layer(channels[i], num_blocks) self.layers.append(layer) output_channels = channels[-1] self.layers.append( ConvModule( in_channels=self.in_channels, out_channels=output_channels, kernel_size=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)) def _make_layer(self, out_channels, num_blocks): """Stack blocks to make a layer. Args: out_channels (int): out_channels of the block. num_blocks (int): number of blocks. """ layers = [] for i in range(num_blocks): stride = 2 if i == 0 else 1 layers.append( InvertedResidual( in_channels=self.in_channels, out_channels=out_channels, stride=stride, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=self.act_cfg, with_cp=self.with_cp)) self.in_channels = out_channels return nn.Sequential(*layers) def _freeze_stages(self): if self.frozen_stages >= 0: for param in self.conv1.parameters(): param.requires_grad = False for i in range(self.frozen_stages): m = self.layers[i] m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ShuffleNetV2, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress default init if use pretrained model. return for name, m in self.named_modules(): if isinstance(m, nn.Conv2d): if 'conv1' in name: normal_init(m, mean=0, std=0.01) else: normal_init(m, mean=0, std=1.0 / m.weight.shape[1]) elif isinstance(m, (_BatchNorm, nn.GroupNorm)): constant_init(m.weight, val=1, bias=0.0001) if isinstance(m, _BatchNorm): if m.running_mean is not None: nn.init.constant_(m.running_mean, 0) def forward(self, x): x = self.conv1(x) x = self.maxpool(x) outs = [] for i, layer in enumerate(self.layers): x = layer(x) if i in self.out_indices: outs.append(x) 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()
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