Shortcuts

Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Source code for mmcls.models.backbones.shufflenet_v2

# 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


[docs]@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
[docs] 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)
[docs] 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)
[docs] 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()
Read the Docs v: latest
Versions
master
latest
1.x
dev-1.x
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.