312 lines
10 KiB
Python
312 lines
10 KiB
Python
|
import logging
|
||
|
|
from collections import OrderedDict
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.utils.checkpoint as cp
|
||
|
|
|
||
|
|
from ..runner import load_checkpoint
|
||
|
|
from .base_backbone import BaseBackbone
|
||
|
|
from .weight_init import constant_init, kaiming_init
|
||
|
|
|
||
|
|
|
||
|
|
def conv3x3(inplanes, planes, stride=1, padding=1, bias=False, groups=1):
|
||
|
|
"""3x3 convolution with padding
|
||
|
|
"""
|
||
|
|
return nn.Conv2d(
|
||
|
|
inplanes,
|
||
|
|
planes,
|
||
|
|
kernel_size=3,
|
||
|
|
stride=stride,
|
||
|
|
padding=padding,
|
||
|
|
bias=bias,
|
||
|
|
groups=groups)
|
||
|
|
|
||
|
|
|
||
|
|
def conv1x1(inplanes, planes, groups=1):
|
||
|
|
"""1x1 convolution with padding
|
||
|
|
- Normal pointwise convolution when groups == 1
|
||
|
|
- Grouped pointwise convolution when groups > 1
|
||
|
|
"""
|
||
|
|
return nn.Conv2d(
|
||
|
|
inplanes,
|
||
|
|
planes,
|
||
|
|
kernel_size=1,
|
||
|
|
groups=groups,
|
||
|
|
stride=1)
|
||
|
|
|
||
|
|
|
||
|
|
def channel_shuffle(x, groups):
|
||
|
|
batchsize, num_channels, height, width = x.data.size()
|
||
|
|
assert (num_channels % groups == 0)
|
||
|
|
channels_per_group = num_channels // groups
|
||
|
|
|
||
|
|
# reshape
|
||
|
|
x = x.view(batchsize, groups, channels_per_group, height, width)
|
||
|
|
|
||
|
|
# transpose
|
||
|
|
# - contiguous() required if transpose() is used before view().
|
||
|
|
# See https://github.com/pytorch/pytorch/issues/764
|
||
|
|
x = torch.transpose(x, 1, 2).contiguous()
|
||
|
|
|
||
|
|
# flatten
|
||
|
|
x = x.view(batchsize, -1, height, width)
|
||
|
|
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def _make_divisible(v, divisor, min_value=None):
|
||
|
|
if min_value is None:
|
||
|
|
min_value = divisor
|
||
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||
|
|
# Make sure that round down does not go down by more than 10%.
|
||
|
|
if new_v < 0.9 * v:
|
||
|
|
new_v += divisor
|
||
|
|
return new_v
|
||
|
|
|
||
|
|
|
||
|
|
# noinspection PyShadowingNames,PyShadowingNames
|
||
|
|
class ShuffleUnit(nn.Module):
|
||
|
|
def __init__(self,
|
||
|
|
inplanes,
|
||
|
|
planes,
|
||
|
|
groups=3,
|
||
|
|
first_block=True,
|
||
|
|
combine='add',
|
||
|
|
with_cp=False):
|
||
|
|
|
||
|
|
super(ShuffleUnit, self).__init__()
|
||
|
|
self.inplanes = inplanes
|
||
|
|
self.planes = planes
|
||
|
|
self.first_block = first_block
|
||
|
|
self.combine = combine
|
||
|
|
self.groups = groups
|
||
|
|
self.bottleneck_channels = self.planes // 4
|
||
|
|
self.with_cp = with_cp
|
||
|
|
|
||
|
|
if self.combine == 'add':
|
||
|
|
self.depthwise_stride = 1
|
||
|
|
self._combine_func = self._add
|
||
|
|
elif self.combine == 'concat':
|
||
|
|
self.depthwise_stride = 2
|
||
|
|
self._combine_func = self._concat
|
||
|
|
self.planes -= self.inplanes
|
||
|
|
else:
|
||
|
|
raise ValueError("Cannot combine tensors with \"{}\" "
|
||
|
|
"Only \"add\" and \"concat\" are "
|
||
|
|
"supported".format(self.combine))
|
||
|
|
|
||
|
|
self.first_1x1_groups = self.groups if first_block else 1
|
||
|
|
self.g_conv_1x1_compress = self._make_grouped_conv1x1(
|
||
|
|
self.inplanes,
|
||
|
|
self.bottleneck_channels,
|
||
|
|
self.first_1x1_groups,
|
||
|
|
batch_norm=True,
|
||
|
|
relu=True
|
||
|
|
)
|
||
|
|
|
||
|
|
self.depthwise_conv3x3 = conv3x3(self.bottleneck_channels,
|
||
|
|
self.bottleneck_channels,
|
||
|
|
stride=self.depthwise_stride,
|
||
|
|
groups=self.bottleneck_channels)
|
||
|
|
self.nn.BatchNorm2d_after_depthwise = \
|
||
|
|
nn.BatchNorm2d(self.bottleneck_channels)
|
||
|
|
|
||
|
|
self.g_conv_1x1_expand = \
|
||
|
|
self._make_grouped_conv1x1(self.bottleneck_channels,
|
||
|
|
self.planes,
|
||
|
|
self.groups,
|
||
|
|
batch_norm=True,
|
||
|
|
relu=False)
|
||
|
|
self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
|
||
|
|
self.relu = nn.ReLU(inplace=True)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _add(x, out):
|
||
|
|
# residual connection
|
||
|
|
return x + out
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _concat(x, out):
|
||
|
|
# concatenate along channel axis
|
||
|
|
return torch.cat((x, out), 1)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _make_grouped_conv1x1(inplanes, planes, groups,
|
||
|
|
batch_norm=True, relu=False):
|
||
|
|
|
||
|
|
modules = OrderedDict()
|
||
|
|
|
||
|
|
conv = conv1x1(inplanes, planes, groups=groups)
|
||
|
|
modules['conv1x1'] = conv
|
||
|
|
|
||
|
|
if batch_norm:
|
||
|
|
modules['batch_norm'] = nn.BatchNorm2d(planes)
|
||
|
|
if relu:
|
||
|
|
modules['relu'] = nn.ReLU()
|
||
|
|
if len(modules) > 1:
|
||
|
|
return nn.Sequential(modules)
|
||
|
|
else:
|
||
|
|
return conv
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
def _inner_forward(x):
|
||
|
|
residual = x
|
||
|
|
|
||
|
|
if self.combine == 'concat':
|
||
|
|
residual = self.avgpool(residual)
|
||
|
|
|
||
|
|
out = self.g_conv_1x1_compress(x)
|
||
|
|
out = channel_shuffle(out, self.groups)
|
||
|
|
out = self.depthwise_conv3x3(out)
|
||
|
|
out = self.nn.BatchNorm2d_after_depthwise(out)
|
||
|
|
out = self.g_conv_1x1_expand(out)
|
||
|
|
|
||
|
|
out = self._combine_func(residual, out)
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
if self.with_cp and x.requires_grad:
|
||
|
|
out = cp.checkpoint(_inner_forward, x)
|
||
|
|
else:
|
||
|
|
out = _inner_forward(x)
|
||
|
|
|
||
|
|
out = self.relu(out)
|
||
|
|
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class ShuffleNetv1(BaseBackbone):
|
||
|
|
"""ShuffleNetv1 backbone.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
groups (int): number of groups to be used in grouped
|
||
|
|
1x1 convolutions in each ShuffleUnit. Default is 3 for best
|
||
|
|
performance according to original paper.
|
||
|
|
widen_factor (float): Config of widen_factor.
|
||
|
|
out_indices (Sequence[int]): Output from which stages.
|
||
|
|
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
|
||
|
|
not freezing any parameters.
|
||
|
|
bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
|
||
|
|
running stats (mean and var).
|
||
|
|
bn_frozen (bool): Whether to freeze weight and bias of BN layers.
|
||
|
|
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
||
|
|
memory while slowing down the training speed.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
groups=3,
|
||
|
|
widen_factor=1.0,
|
||
|
|
out_indices=(0, 1, 2, 3),
|
||
|
|
frozen_stages=-1,
|
||
|
|
bn_eval=True,
|
||
|
|
bn_frozen=False,
|
||
|
|
with_cp=False):
|
||
|
|
super(ShuffleNetv1, self).__init__()
|
||
|
|
blocks = [3, 7, 3]
|
||
|
|
self.groups = groups
|
||
|
|
self.out_indices = out_indices
|
||
|
|
self.frozen_stages = frozen_stages
|
||
|
|
self.bn_eval = bn_eval
|
||
|
|
self.bn_frozen = bn_frozen
|
||
|
|
self.with_cp = with_cp
|
||
|
|
|
||
|
|
if groups == 1:
|
||
|
|
channels = [144, 288, 576]
|
||
|
|
elif groups == 2:
|
||
|
|
channels = [200, 400, 800]
|
||
|
|
elif groups == 3:
|
||
|
|
channels = [240, 480, 960]
|
||
|
|
elif groups == 4:
|
||
|
|
channels = [272, 544, 1088]
|
||
|
|
elif groups == 8:
|
||
|
|
channels = [384, 768, 1536]
|
||
|
|
else:
|
||
|
|
raise ValueError("{} groups is not supported for "
|
||
|
|
"1x1 Grouped Convolutions".format(groups))
|
||
|
|
channels = [_make_divisible(ch * widen_factor, 8) for ch in channels]
|
||
|
|
|
||
|
|
self.inplanes = int(24 * widen_factor)
|
||
|
|
self.conv1 = conv3x3(3, self.inplanes, stride=2)
|
||
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||
|
|
|
||
|
|
self.layer2 = self._make_layer(channels[0], blocks[0],
|
||
|
|
first_block=False, with_cp=with_cp)
|
||
|
|
self.layer3 = self._make_layer(channels[1], blocks[1], with_cp=with_cp)
|
||
|
|
self.layer4 = self._make_layer(channels[2], blocks[2], with_cp=with_cp)
|
||
|
|
|
||
|
|
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, nn.BatchNorm2d):
|
||
|
|
constant_init(m, 1)
|
||
|
|
else:
|
||
|
|
raise TypeError('pretrained must be a str or None')
|
||
|
|
|
||
|
|
def _make_layer(self,
|
||
|
|
outplanes,
|
||
|
|
blocks,
|
||
|
|
first_block=True,
|
||
|
|
with_cp=False):
|
||
|
|
layers = []
|
||
|
|
for i in range(blocks):
|
||
|
|
if i == 0:
|
||
|
|
layers.append(ShuffleUnit(self.inplanes, outplanes,
|
||
|
|
groups=self.groups,
|
||
|
|
first_block=first_block,
|
||
|
|
combine='concat',
|
||
|
|
with_cp=with_cp))
|
||
|
|
else:
|
||
|
|
layers.append(ShuffleUnit(self.inplanes, outplanes,
|
||
|
|
groups=self.groups,
|
||
|
|
first_block=True,
|
||
|
|
combine='add',
|
||
|
|
with_cp=with_cp))
|
||
|
|
self.inplanes = outplanes
|
||
|
|
|
||
|
|
return nn.Sequential(*layers)
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.conv1(x)
|
||
|
|
x = self.maxpool(x)
|
||
|
|
outs = []
|
||
|
|
if 0 in self.out_indices:
|
||
|
|
outs.append(x)
|
||
|
|
x = self.layer2(x)
|
||
|
|
if 1 in self.out_indices:
|
||
|
|
outs.append(x)
|
||
|
|
x = self.layer3(x)
|
||
|
|
if 2 in self.out_indices:
|
||
|
|
outs.append(x)
|
||
|
|
x = self.layer4(x)
|
||
|
|
if 3 in self.out_indices:
|
||
|
|
outs.append(x)
|
||
|
|
|
||
|
|
if len(outs) == 1:
|
||
|
|
return outs[0]
|
||
|
|
else:
|
||
|
|
return tuple(outs)
|
||
|
|
|
||
|
|
def train(self, mode=True):
|
||
|
|
super(ShuffleNetv1, self).train(mode)
|
||
|
|
if self.bn_eval:
|
||
|
|
for m in self.modules():
|
||
|
|
if isinstance(m, nn.BatchNorm2d):
|
||
|
|
m.eval()
|
||
|
|
if self.bn_frozen:
|
||
|
|
for params in m.parameters():
|
||
|
|
params.requires_grad = False
|
||
|
|
if mode and self.frozen_stages >= 0:
|
||
|
|
for param in self.conv1.parameters():
|
||
|
|
param.requires_grad = False
|
||
|
|
for i in range(1, self.frozen_stages + 1):
|
||
|
|
mod = getattr(self, 'layer{}'.format(i))
|
||
|
|
mod.eval()
|
||
|
|
for param in mod.parameters():
|
||
|
|
param.requires_grad = False
|