# encoding: utf-8
|
"""
|
@author: xingyu liao
|
@contact: liaoxingyu5@jd.com
|
"""
|
|
# based on:
|
# https://github.com/XingangPan/IBN-Net/blob/master/models/imagenet/resnext_ibn_a.py
|
|
import math
|
import logging
|
import torch.nn as nn
|
import torch.nn.functional as F
|
from torch.nn import init
|
import torch
|
from layers import IBN
|
from .build import BACKBONE_REGISTRY
|
|
|
class Bottleneck(nn.Module):
|
"""
|
RexNeXt bottleneck type C
|
"""
|
expansion = 4
|
|
def __init__(self, inplanes, planes, with_ibn, baseWidth, cardinality, stride=1, downsample=None):
|
""" Constructor
|
Args:
|
inplanes: input channel dimensionality
|
planes: output channel dimensionality
|
baseWidth: base width.
|
cardinality: num of convolution groups.
|
stride: conv stride. Replaces pooling layer.
|
"""
|
super(Bottleneck, self).__init__()
|
|
D = int(math.floor(planes * (baseWidth / 64)))
|
C = cardinality
|
self.conv1 = nn.Conv2d(inplanes, D * C, kernel_size=1, stride=1, padding=0, bias=False)
|
if with_ibn:
|
self.bn1 = IBN(D * C)
|
else:
|
self.bn1 = nn.BatchNorm2d(D * C)
|
self.conv2 = nn.Conv2d(D * C, D * C, kernel_size=3, stride=stride, padding=1, groups=C, bias=False)
|
self.bn2 = nn.BatchNorm2d(D * C)
|
self.conv3 = nn.Conv2d(D * C, planes * 4, kernel_size=1, stride=1, padding=0, bias=False)
|
self.bn3 = nn.BatchNorm2d(planes * 4)
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = downsample
|
|
def forward(self, x):
|
residual = x
|
|
out = self.conv1(x)
|
out = self.bn1(out)
|
out = self.relu(out)
|
|
out = self.conv2(out)
|
out = self.bn2(out)
|
out = self.relu(out)
|
|
out = self.conv3(out)
|
out = self.bn3(out)
|
|
if self.downsample is not None:
|
residual = self.downsample(x)
|
|
out += residual
|
out = self.relu(out)
|
|
return out
|
|
|
class ResNeXt(nn.Module):
|
"""
|
ResNext optimized for the ImageNet dataset, as specified in
|
https://arxiv.org/pdf/1611.05431.pdf
|
"""
|
|
def __init__(self, last_stride, with_ibn, block, layers, baseWidth=4, cardinality=32):
|
""" Constructor
|
Args:
|
baseWidth: baseWidth for ResNeXt.
|
cardinality: number of convolution groups.
|
layers: config of layers, e.g., [3, 4, 6, 3]
|
num_classes: number of classes
|
"""
|
super(ResNeXt, self).__init__()
|
|
self.cardinality = cardinality
|
self.baseWidth = baseWidth
|
self.inplanes = 64
|
self.output_size = 64
|
|
self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False)
|
self.bn1 = nn.BatchNorm2d(64)
|
self.relu = nn.ReLU(inplace=True)
|
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
self.layer1 = self._make_layer(block, 64, layers[0], with_ibn=with_ibn)
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, with_ibn=with_ibn)
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, with_ibn=with_ibn)
|
self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride, with_ibn=with_ibn)
|
|
self.random_init()
|
|
def _make_layer(self, block, planes, blocks, stride=1, with_ibn=False):
|
""" Stack n bottleneck modules where n is inferred from the depth of the network.
|
Args:
|
block: block type used to construct ResNext
|
planes: number of output channels (need to multiply by block.expansion)
|
blocks: number of blocks to be built
|
stride: factor to reduce the spatial dimensionality in the first bottleneck of the block.
|
Returns: a Module consisting of n sequential bottlenecks.
|
"""
|
downsample = None
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
downsample = nn.Sequential(
|
nn.Conv2d(self.inplanes, planes * block.expansion,
|
kernel_size=1, stride=stride, bias=False),
|
nn.BatchNorm2d(planes * block.expansion),
|
)
|
|
layers = []
|
if planes == 512:
|
with_ibn = False
|
layers.append(block(self.inplanes, planes, with_ibn, self.baseWidth, self.cardinality, stride, downsample))
|
self.inplanes = planes * block.expansion
|
for i in range(1, blocks):
|
layers.append(block(self.inplanes, planes, with_ibn, self.baseWidth, self.cardinality, 1, None))
|
|
return nn.Sequential(*layers)
|
|
def forward(self, x):
|
x = self.conv1(x)
|
x = self.bn1(x)
|
x = self.relu(x)
|
x = self.maxpool1(x)
|
x = self.layer1(x)
|
x = self.layer2(x)
|
x = self.layer3(x)
|
x = self.layer4(x)
|
|
return x
|
|
def random_init(self):
|
self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
|
for m in self.modules():
|
if isinstance(m, nn.Conv2d):
|
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
m.weight.data.normal_(0, math.sqrt(2. / n))
|
elif isinstance(m, nn.BatchNorm2d):
|
m.weight.data.fill_(1)
|
m.bias.data.zero_()
|
elif isinstance(m, nn.InstanceNorm2d):
|
m.weight.data.fill_(1)
|
m.bias.data.zero_()
|
|
|
@BACKBONE_REGISTRY.register()
|
def build_resnext_backbone(cfg):
|
"""
|
Create a ResNeXt instance from config.
|
Returns:
|
ResNeXt: a :class:`ResNeXt` instance.
|
"""
|
|
# fmt: off
|
pretrain = cfg.MODEL.BACKBONE.PRETRAIN
|
pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
|
last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
|
with_ibn = cfg.MODEL.BACKBONE.WITH_IBN
|
with_se = cfg.MODEL.BACKBONE.WITH_SE
|
with_nl = cfg.MODEL.BACKBONE.WITH_NL
|
depth = cfg.MODEL.BACKBONE.DEPTH
|
|
num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], }[depth]
|
nl_layers_per_stage = {50: [0, 2, 3, 0], 101: [0, 2, 3, 0]}[depth]
|
model = ResNeXt(last_stride, with_ibn, Bottleneck, num_blocks_per_stage)
|
if pretrain:
|
# if not with_ibn:
|
# original resnet
|
# state_dict = model_zoo.load_url(model_urls[depth])
|
# else:
|
# ibn resnet
|
state_dict = torch.load(pretrain_path)['state_dict']
|
# remove module in name
|
new_state_dict = {}
|
for k in state_dict:
|
new_k = '.'.join(k.split('.')[1:])
|
if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
|
new_state_dict[new_k] = state_dict[k]
|
state_dict = new_state_dict
|
res = model.load_state_dict(state_dict, strict=False)
|
logger = logging.getLogger(__name__)
|
logger.info('missing keys is {}'.format(res.missing_keys))
|
logger.info('unexpected keys is {}'.format(res.unexpected_keys))
|
return model
|
