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m2det.py
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m2det.py
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'''
This code is based on pytorch_ssd and RFBNet.
Details about the modules:
TUM - Thinned U-shaped Module
MLFPN - Multi-Level Feature Pyramid Network
M2Det - Multi-level Multi-scale single-shot object Detector
Author: Qijie Zhao (zhaoqijie@pku.edu.cn)
Finished Date: 01/17/2019
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import warnings
warnings.filterwarnings('ignore')
import torchvision.transforms as transforms
import torchvision.models as models
import torch.backends.cudnn as cudnn
import os,sys,time
from layers.nn_utils import *
from torch.nn import init as init
from utils.core import print_info
class M2Det(nn.Module):
def __init__(self, phase, size, config = None):
'''
M2Det: Multi-level Multi-scale single-shot object Detector
'''
super(M2Det,self).__init__()
self.phase = phase
self.size = size
self.init_params(config)
print_info('===> Constructing M2Det model', ['yellow','bold'])
self.construct_modules()
def init_params(self, config=None): # Directly read the config
assert config is not None, 'Error: no config'
for key,value in config.items():
if check_argu(key,value):
setattr(self, key, value)
def construct_modules(self,):
# construct tums
for i in range(self.num_levels):
if i == 0:
setattr(self,
'unet{}'.format(i+1),
TUM(first_level=True,
input_planes=self.planes//2,
is_smooth=self.smooth,
scales=self.num_scales,
side_channel=512)) #side channel isn't fixed.
else:
setattr(self,
'unet{}'.format(i+1),
TUM(first_level=False,
input_planes=self.planes//2,
is_smooth=self.smooth,
scales=self.num_scales,
side_channel=self.planes))
# construct base features
if 'vgg' in self.net_family:
self.base = nn.ModuleList(get_backbone(self.backbone))
shallow_in, shallow_out = 512,256
deep_in, deep_out = 1024,512
elif 'res' in self.net_family: # Including ResNet series and ResNeXt series
self.base = get_backbone(self.backbone)
shallow_in, shallow_out = 512,256
deep_in, deep_out = 2048,512
self.reduce= BasicConv(shallow_in, shallow_out, kernel_size=3, stride=1, padding=1)
self.up_reduce= BasicConv(deep_in, deep_out, kernel_size=1, stride=1)
# construct others
if self.phase == 'test':
self.softmax = nn.Softmax()
self.Norm = nn.BatchNorm2d(256*8)
self.leach = nn.ModuleList([BasicConv(
deep_out+shallow_out,
self.planes//2,
kernel_size=(1,1),stride=(1,1))]*self.num_levels)
# construct localization and recognition layers
loc_ = list()
conf_ = list()
for i in range(self.num_scales):
loc_.append(nn.Conv2d(self.planes*self.num_levels,
4 * 6, # 4 is coordinates, 6 is anchors for each pixels,
3, 1, 1))
conf_.append(nn.Conv2d(self.planes*self.num_levels,
self.num_classes * 6, #6 is anchors for each pixels,
3, 1, 1))
self.loc = nn.ModuleList(loc_)
self.conf = nn.ModuleList(conf_)
# construct SFAM module
if self.sfam:
self.sfam_module = SFAM(self.planes, self.num_levels, self.num_scales, compress_ratio=16)
def forward(self,x):
loc,conf = list(),list()
base_feats = list()
if 'vgg' in self.net_family:
for k in range(len(self.base)):
x = self.base[k](x)
if k in self.base_out:
base_feats.append(x)
elif 'res' in self.net_family:
base_feats = self.base(x, self.base_out)
base_feature = torch.cat(
(self.reduce(base_feats[0]), F.interpolate(self.up_reduce(base_feats[1]),scale_factor=2,mode='nearest')),1
)
# tum_outs is the multi-level multi-scale feature
tum_outs = [getattr(self, 'unet{}'.format(1))(self.leach[0](base_feature), 'none')]
for i in range(1,self.num_levels,1):
tum_outs.append(
getattr(self, 'unet{}'.format(i+1))(
self.leach[i](base_feature), tum_outs[i-1][-1]
)
)
# concat with same scales
sources = [torch.cat([_fx[i-1] for _fx in tum_outs],1) for i in range(self.num_scales, 0, -1)]
# forward_sfam
if self.sfam:
sources = self.sfam_module(sources)
sources[0] = self.Norm(sources[0])
for (x,l,c) in zip(sources, self.loc, self.conf):
loc.append(l(x).permute(0, 2, 3, 1).contiguous())
conf.append(c(x).permute(0, 2, 3, 1).contiguous())
loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
if self.phase == "test":
output = (
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(-1, self.num_classes)), # conf preds
)
else:
output = (
loc.view(loc.size(0), -1, 4),
conf.view(conf.size(0), -1, self.num_classes),
)
return output
def init_model(self, base_model_path):
if self.backbone == 'vgg16':
if isinstance(base_model_path, str):
base_weights = torch.load(base_model_path)
print_info('Loading base network...')
self.base.load_state_dict(base_weights)
elif 'res' in self.backbone:
pass # pretrained seresnet models are initially loaded when defining them.
def weights_init(m):
for key in m.state_dict():
if key.split('.')[-1] == 'weight':
if 'conv' in key:
init.kaiming_normal_(m.state_dict()[key], mode='fan_out')
if 'bn' in key:
m.state_dict()[key][...] = 1
elif key.split('.')[-1] == 'bias':
m.state_dict()[key][...] = 0
print_info('Initializing weights for [tums, reduce, up_reduce, leach, loc, conf]...')
for i in range(self.num_levels):
getattr(self,'unet{}'.format(i+1)).apply(weights_init)
self.reduce.apply(weights_init)
self.up_reduce.apply(weights_init)
self.leach.apply(weights_init)
self.loc.apply(weights_init)
self.conf.apply(weights_init)
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
if ext == '.pkl' or '.pth':
print_info('Loading weights into state dict...')
self.load_state_dict(torch.load(base_file))
print_info('Finished!')
else:
print_info('Sorry only .pth and .pkl files supported.')
def build_net(phase='train', size=320, config = None):
if not phase in ['test','train']:
raise ValueError("Error: Phase not recognized")
if not size in [320, 512, 704, 800]:
raise NotImplementedError("Error: Sorry only M2Det320,M2Det512 M2Det704 or M2Det800 are supported!")
return M2Det(phase, size, config)