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import logging
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BACKBONES.register_module()
class MobileNetV2(nn.Module):
"""MobileNetV2 backbone.
Args:
widen_factor (float): Width multiplier, multiply number of
channels in each layer by this amount. Default: 1.0.
strides (Sequence[int], optional): Strides of the first block of each
layer. If not specified, default config in ``arch_setting`` will
be used.
dilations (Sequence[int]): Dilation of each layer.
out_indices (None or Sequence[int]): Output from which stages.
Default: (7, ).
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='ReLU6').
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.
"""
# Parameters to build layers. 3 parameters are needed to construct a
# layer, from left to right: expand_ratio, channel, num_blocks.
arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
[6, 96, 3], [6, 160, 3], [6, 320, 1]]
def __init__(self,
widen_factor=1.,
strides=(1, 2, 2, 2, 1, 2, 1),
dilations=(1, 1, 1, 1, 1, 1, 1),
out_indices=(1, 2, 4, 6),
frozen_stages=-1,
conv_cfg=None,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU6'),
norm_eval=False,
with_cp=False):
super(MobileNetV2, self).__init__()
self.widen_factor = widen_factor
self.strides = strides
self.dilations = dilations
assert len(strides) == len(dilations) == len(self.arch_settings)
self.out_indices = out_indices
for index in out_indices:
if index not in range(0, 7):
raise ValueError('the item in out_indices must in '
f'range(0, 8). But received {index}')
if frozen_stages not in range(-1, 7):
raise ValueError('frozen_stages must be in range(-1, 7). '
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
self.in_channels = make_divisible(32 * widen_factor, 8)
self.conv1 = ConvModule(
in_channels=3,
out_channels=self.in_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg)
self.layers = []
for i, layer_cfg in enumerate(self.arch_settings):
expand_ratio, channel, num_blocks = layer_cfg
stride = self.strides[i]
dilation = self.dilations[i]
out_channels = make_divisible(channel * widen_factor, 8)
inverted_res_layer = self.make_layer(
out_channels=out_channels,
num_blocks=num_blocks,
stride=stride,
dilation=dilation,
expand_ratio=expand_ratio)
layer_name = f'layer{i + 1}'
self.add_module(layer_name, inverted_res_layer)
self.layers.append(layer_name)
def make_layer(self, out_channels, num_blocks, stride, dilation,
expand_ratio):
"""Stack InvertedResidual blocks to build a layer for MobileNetV2.
Args:
out_channels (int): out_channels of block.
num_blocks (int): Number of blocks.
stride (int): Stride of the first block.
dilation (int): Dilation of the first block.
expand_ratio (int): Expand the number of channels of the
hidden layer in InvertedResidual by this ratio.
"""
layers = []
for i in range(num_blocks):
layers.append(
InvertedResidual(
self.in_channels,
out_channels,
stride if i == 0 else 1,
expand_ratio=expand_ratio,
dilation=dilation if i == 0 else 1,
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 init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif 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)
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 _freeze_stages(self):
if self.frozen_stages >= 0:
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
layer = getattr(self, f'layer{i}')
layer.eval()
for param in layer.parameters():
param.requires_grad = False
def train(self, mode=True):
super(MobileNetV2, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
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