Merge branch 'dev_mobilenetv2' into dev_shufflenetv1
lixiaojie
commit
2c6c2d9063
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from .mobilenet_v2 import MobileNetv2
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__all__ = [
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'MobileNetv2',
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]
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import logging
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from abc import ABCMeta, abstractmethod
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import torch.nn as nn
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from mmcv.runner import load_checkpoint
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class BaseBackbone(nn.Module, metaclass=ABCMeta):
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def __init__(self):
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super(BaseBackbone, self).__init__()
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def init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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logger = logging.getLogger()
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load_checkpoint(self, pretrained, strict=False, logger=logger)
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elif pretrained is None:
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pass
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else:
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raise TypeError('pretrained must be a str or None')
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@abstractmethod
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def forward(self, x):
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pass
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def train(self, mode=True):
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super(BaseBackbone, self).train(mode)
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import logging
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from ..runner import load_checkpoint
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from .base_backbone import BaseBackbone
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from .weight_init import constant_init, kaiming_init
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def conv3x3(in_planes, out_planes, stride=1, dilation=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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def conv_1x1_bn(inp, oup, activation=nn.ReLU6):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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activation(inplace=True)
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)
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class ConvBNReLU(nn.Sequential):
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def __init__(self,
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in_planes,
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out_planes,
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kernel_size=3,
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stride=1,
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groups=1,
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activation=nn.ReLU6):
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padding = (kernel_size - 1) // 2
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super(ConvBNReLU, self).__init__(
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nn.Conv2d(in_planes,
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out_planes,
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kernel_size,
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stride,
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padding,
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groups=groups,
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bias=False),
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nn.BatchNorm2d(out_planes),
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activation(inplace=True)
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)
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class InvertedResidual(nn.Module):
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def __init__(self,
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inplanes,
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outplanes,
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stride,
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expand_ratio,
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activation=nn.ReLU6,
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with_cp=False):
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super(InvertedResidual, self).__init__()
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self.stride = stride
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assert stride in [1, 2]
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self.with_cp = with_cp
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self.use_res_connect = self.stride == 1 and inplanes == outplanes
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hidden_dim = int(round(inplanes * expand_ratio))
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layers = []
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if expand_ratio != 1:
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# pw
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layers.append(ConvBNReLU(inplanes,
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hidden_dim,
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kernel_size=1,
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activation=activation))
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layers.extend([
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# dw
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ConvBNReLU(hidden_dim,
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hidden_dim,
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stride=stride,
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groups=hidden_dim,
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activation=activation),
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# pw-linear
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nn.Conv2d(hidden_dim, outplanes, 1, 1, 0, bias=False),
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nn.BatchNorm2d(outplanes),
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])
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self.conv = nn.Sequential(*layers)
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def forward(self, x):
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def _inner_forward(x):
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if self.use_res_connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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return out
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def make_inverted_res_layer(block,
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inplanes,
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planes,
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num_blocks,
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stride=1,
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expand_ratio=6,
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activation=nn.ReLU6,
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with_cp=False):
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layers = []
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for i in range(num_blocks):
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if i == 0:
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layers.append(block(inplanes, planes, stride,
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expand_ratio=expand_ratio,
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activation=activation,
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with_cp=with_cp))
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else:
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layers.append(block(inplanes, planes, 1,
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expand_ratio=expand_ratio,
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activation=activation,
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with_cp=with_cp))
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inplanes = planes
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return nn.Sequential(*layers)
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class MobileNetv2(BaseBackbone):
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"""MobileNetv2 backbone.
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Args:
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widen_factor (float): Config of widen_factor.
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activation (str): Activation type of the network.
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out_indices (Sequence[int]): Output from which stages.
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frozen_stages (int): Stages to be frozen (all param fixed). -1 means
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not freezing any parameters.
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bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
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running stats (mean and var).
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bn_frozen (bool): Whether to freeze weight and bias of BN layers.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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"""
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def __init__(self,
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widen_factor=1.,
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activation=nn.ReLU6,
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out_indices=(0, 1, 2, 3, 4, 5, 6),
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frozen_stages=-1,
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bn_eval=True,
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bn_frozen=False,
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with_cp=False):
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super(MobileNetv2, self).__init__()
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block = InvertedResidual
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# expand_ratio, out_channel, n, stride
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inverted_residual_setting = [
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[1, 16, 1, 1],
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[6, 24, 2, 2],
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[6, 32, 3, 2],
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[6, 64, 4, 2],
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[6, 96, 3, 1],
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[6, 160, 3, 2],
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[6, 320, 1, 1]
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]
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self.widen_factor = widen_factor
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if isinstance(activation, str):
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activation = eval(activation)
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self.activation = activation(inplace=True)
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self.out_indices = out_indices
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self.frozen_stages = frozen_stages
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self.bn_eval = bn_eval
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self.bn_frozen = bn_frozen
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self.with_cp = with_cp
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self.inplanes = 32
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self.inplanes = _make_divisible(self.inplanes * widen_factor, 8)
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self.conv1 = conv3x3(3, self.inplanes, stride=2)
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self.bn1 = nn.BatchNorm2d(self.inplanes)
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self.inverted_res_layers = []
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for i, layer_cfg in enumerate(inverted_residual_setting):
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t, c, n, s = layer_cfg
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planes = _make_divisible(c * widen_factor, 8)
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inverted_res_layer = make_inverted_res_layer(
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block,
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self.inplanes,
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planes,
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num_blocks=n,
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stride=s,
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expand_ratio=t,
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activation=activation,
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with_cp=self.with_cp)
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self.inplanes = planes
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layer_name = 'layer{}'.format(i + 1)
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self.add_module(layer_name, inverted_res_layer)
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self.inverted_res_layers.append(layer_name)
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self.out_channel = 1280
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self.out_channel = int(self.out_channel * widen_factor) \
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if widen_factor > 1.0 else self.out_channel
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self.conv_last = nn.Conv2d(self.inplanes,
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self.out_channel,
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1, 1, 0, bias=False)
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self.bn_last = nn.BatchNorm2d(self.out_channel)
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self.feat_dim = self.out_channel
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def init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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logger = logging.getLogger()
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load_checkpoint(self, pretrained, strict=False, logger=logger)
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elif pretrained is None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m)
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elif isinstance(m, nn.BatchNorm2d):
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constant_init(m, 1)
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else:
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raise TypeError('pretrained must be a str or None')
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def forward(self, x):
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x = self.conv1(x)
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x = self.activation(x)
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outs = []
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for i, layer_name in enumerate(self.inverted_res_layers):
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inverted_res_layer = getattr(self, layer_name)
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x = inverted_res_layer(x)
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if i in self.out_indices:
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outs.append(x)
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x = self.conv_last(x)
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x = self.bn_last(x)
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x = self.activation(x)
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outs.append(x)
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if len(outs) == 1:
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return outs[0]
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else:
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return tuple(outs)
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def train(self, mode=True):
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super(MobileNetv2, self).train(mode)
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if self.bn_eval:
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for m in self.modules():
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if isinstance(m, nn.BatchNorm2d):
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m.eval()
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if self.bn_frozen:
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for params in m.parameters():
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params.requires_grad = False
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if mode and self.frozen_stages >= 0:
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for param in self.conv1.parameters():
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param.requires_grad = False
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for param in self.bn1.parameters():
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param.requires_grad = False
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self.bn1.eval()
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self.bn1.weight.requires_grad = False
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self.bn1.bias.requires_grad = False
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for i in range(1, self.frozen_stages + 1):
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mod = getattr(self, 'layer{}'.format(i))
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mod.eval()
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for param in mod.parameters():
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param.requires_grad = False
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@ -0,0 +1,66 @@
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# Copyright (c) Open-MMLab. All rights reserved.
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import numpy as np
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import torch.nn as nn
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def constant_init(module, val, bias=0):
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if hasattr(module, 'weight') and module.weight is not None:
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nn.init.constant_(module.weight, val)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, bias)
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def xavier_init(module, gain=1, bias=0, distribution='normal'):
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assert distribution in ['uniform', 'normal']
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if distribution == 'uniform':
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nn.init.xavier_uniform_(module.weight, gain=gain)
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else:
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nn.init.xavier_normal_(module.weight, gain=gain)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, bias)
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def normal_init(module, mean=0, std=1, bias=0):
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nn.init.normal_(module.weight, mean, std)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, bias)
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def uniform_init(module, a=0, b=1, bias=0):
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nn.init.uniform_(module.weight, a, b)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, bias)
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def kaiming_init(module,
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a=0,
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mode='fan_out',
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nonlinearity='relu',
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bias=0,
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distribution='normal'):
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assert distribution in ['uniform', 'normal']
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if distribution == 'uniform':
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nn.init.kaiming_uniform_(
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module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
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else:
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nn.init.kaiming_normal_(
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module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
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if hasattr(module, 'bias') and module.bias is not None:
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nn.init.constant_(module.bias, bias)
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def caffe2_xavier_init(module, bias=0):
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# `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
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# Acknowledgment to FAIR's internal code
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kaiming_init(
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module,
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a=1,
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mode='fan_in',
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nonlinearity='leaky_relu',
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distribution='uniform')
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def bias_init_with_prob(prior_prob):
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""" initialize conv/fc bias value according to giving probablity"""
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bias_init = float(-np.log((1 - prior_prob) / prior_prob))
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return bias_init
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@ -0,0 +1,22 @@
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import torch
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import torch.nn as nn
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from mmcls.models.backbones import MobileNetv2
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def test_mobilenetv2_backbone():
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# Test MobileNetv2 with widen_factor 1.0, activation nn.ReLU6
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model = MobileNetv2(widen_factor=1.0, activation=nn.ReLU6)
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model.init_weights()
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert len(feat) == 8
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assert feat[0].shape == torch.Size([1, 16, 112, 112])
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assert feat[1].shape == torch.Size([1, 24, 56, 56])
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assert feat[2].shape == torch.Size([1, 32, 28, 28])
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assert feat[3].shape == torch.Size([1, 64, 14, 14])
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assert feat[4].shape == torch.Size([1, 96, 14, 14])
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assert feat[5].shape == torch.Size([1, 160, 7, 7])
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assert feat[6].shape == torch.Size([1, 320, 7, 7])
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