import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc300, voc512, coco300, coco512
import os
import warnings
import math
import numpy as np
import cv2
class SSD_CON(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a base VGG network followed by the
added multibox conv layers. Each multibox layer branches into
1) conv2d for class conf scores
2) conv2d for localization predictions
3) associated priorbox layer to produce default bounding
boxes specific to the layer's feature map size.
See: https://arxiv.org/pdf/1512.02325.pdf for more details.
Args:
phase: (string) Can be "test" or "train"
size: input image size
base: VGG16 layers for input, size of either 300 or 500
extras: extra layers that feed to multibox loc and conf layers
head: "multibox head" consists of loc and conf conv layers
"""
def __init__(self, phase, size, base, extras, head, num_classes):
super(SSD_CON, self).__init__()
self.phase = phase
self.num_classes = num_classes
if(size==300):
self.cfg = (coco300, voc300)[num_classes == 21]
else:
self.cfg = (coco512, voc512)[num_classes == 21]
self.priorbox = PriorBox(self.cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True)
self.size = size
# SSD network
self.vgg = nn.ModuleList(base)
# Layer learns to scale the l2 normalized features from conv4_3
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
self.softmax = nn.Softmax(dim=-1)
if phase == 'test':
# self.softmax = nn.Softmax(dim=-1)
# self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self.detect = Detect()
def forward(self, x):
"""Applies network layers and ops on input image(s) x.
Args:
x: input image or batch of images. Shape: [batch,3,300,300].
Return:
Depending on phase:
test:
Variable(tensor) of output class label predictions,
confidence score, and corresponding location predictions for
each object detected. Shape: [batch,topk,7]
train:
list of concat outputs from:
1: confidence layers, Shape: [batch*num_priors,num_classes]
2: localization layers, Shape: [batch,num_priors*4]
3: priorbox layers, Shape: [2,num_priors*4]
"""
x_flip = x.clone()
x_flip = flip(x_flip,3)
sources = list()
loc = list()
conf = list()
# apply vgg up to conv4_3 relu
for k in range(23):
x = self.vgg[k](x)
s = self.L2Norm(x)
sources.append(s)
# apply vgg up to fc7
for k in range(23, len(self.vgg)):
x = self.vgg[k](x)
sources.append(x)
# apply extra layers and cache source layer outputs
for k, v in enumerate(self.extras):
x = F.relu(v(x), inplace=True)
if k % 2 == 1:
sources.append(x)
# apply multibox head to source layers
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)
# zero_mask = torch.cat([o.view(o.size(0), -1) for o in zero_mask], 1)
if self.phase == "test":
# output = self.detect(
output = self.detect.apply(self.num_classes, 0, 200, 0.01, 0.45,
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf preds
self.priors.type(type(x.data)) # default boxes
)
else:
output = (
loc.view(loc.size(0), -1, 4),
conf.view(conf.size(0), -1, self.num_classes),
self.priors
)
loc = loc.view(loc.size(0), -1, 4)
conf = self.softmax(conf.view(conf.size(0), -1, self.num_classes))
# basic
sources_flip = list()
loc_flip = list()
conf_flip = list()
# apply vgg up to conv4_3 relu
for k in range(23):
x_flip = self.vgg[k](x_flip)
s_flip = self.L2Norm(x_flip)
sources_flip.append(s_flip)
# apply vgg up to fc7
for k in range(23, len(self.vgg)):
x_flip = self.vgg[k](x_flip)
sources_flip.append(x_flip)
# apply extra layers and cache source layer outputs
for k, v in enumerate(self.extras):
x_flip = F.relu(v(x_flip), inplace=True)
if k % 2 == 1:
sources_flip.append(x_flip)
# apply multibox head to source layers
for (x_flip, l, c) in zip(sources_flip, self.loc, self.conf):
append_loc = l(x_flip).permute(0, 2, 3, 1).contiguous()
append_conf = c(x_flip).permute(0, 2, 3, 1).contiguous()
append_loc = flip(append_loc,2)
append_conf = flip(append_conf,2)
loc_flip.append(append_loc)
conf_flip.append(append_conf)
loc_flip = torch.cat([o.view(o.size(0), -1) for o in loc_flip], 1)
conf_flip = torch.cat([o.view(o.size(0), -1) for o in conf_flip], 1)
loc_flip = loc_flip.view(loc_flip.size(0), -1, 4)
conf_flip = self.softmax(conf_flip.view(conf.size(0), -1, self.num_classes))
if self.phase == "test":
return output
else:
return output, conf, conf_flip, loc, loc_flip
def load_weights(self, base_file):
other, ext = os.path.splitext(base_file)
if ext == '.pkl' or '.pth':
print('Loading weights into state dict...')
self.load_state_dict(torch.load(base_file,
map_location=lambda storage, loc: storage))
print('Finished!')
else:
print('Sorry only .pth and .pkl files supported.')
# This function is derived from torchvision VGG make_layers()
# https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
def vgg(cfg, i, batch_norm=False):
layers = []
in_channels = i
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
elif v == 'C':
layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)
conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
layers += [pool5, conv6,
nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)]
return layers
def add_extras(cfg, i, batch_norm=False):
# Extra layers added to VGG for feature scaling
layers = []
in_channels = i
flag = False
for k, v in enumerate(cfg):
if in_channels != 'S':
if v == 'S':
layers += [nn.Conv2d(in_channels, cfg[k + 1],
kernel_size=(1, 3)[flag], stride=2, padding=1)]
elif v=='K':
layers += [nn.Conv2d(in_channels, 256,
kernel_size=4, stride=1, padding=1)]
else:
layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
flag = not flag
in_channels = v
return layers
def multibox(vgg, extra_layers, cfg, num_classes):
loc_layers = []
conf_layers = []
vgg_source = [21, -2]
for k, v in enumerate(vgg_source):
loc_layers += [nn.Conv2d(vgg[v].out_channels,
cfg[k] * 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(vgg[v].out_channels,
cfg[k] * num_classes, kernel_size=3, padding=1)]
for k, v in enumerate(extra_layers[1::2], 2):
loc_layers += [nn.Conv2d(v.out_channels, cfg[k]
* 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(v.out_channels, cfg[k]
* num_classes, kernel_size=3, padding=1)]
return vgg, extra_layers, (loc_layers, conf_layers)
base = {
'300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
512, 512, 512],
'512': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M',
512, 512, 512],
}
extras = {
'300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
'512': [256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128, 'K'],
}
mbox = {
'300': [4, 6, 6, 6, 4, 4], # number of boxes per feature map location
'512': [4, 6, 6, 6, 6, 4, 4],
}
def flip(x, dim):
dim = x.dim() + dim if dim < 0 else dim
return x[tuple(slice(None, None) if i != dim
else torch.arange(x.size(i)-1, -1, -1).long()
for i in range(x.dim()))]
class GaussianNoise(nn.Module):
def __init__(self, batch_size, input_size=(3, 300, 300), mean=0, std=0.15):
super(GaussianNoise, self).__init__()
self.shape = (batch_size, ) + input_size
self.noise = Variable(torch.zeros(self.shape).cuda())
self.mean = mean
self.std = std
def forward(self, x):
self.noise.data.normal_(self.mean, std=self.std)
if x.size(0) == self.noise.size(0):
return x + self.noise
else:
#print('---- Noise Size ')
return x + self.noise[:x.size(0)]
def build_ssd_con(phase, size=300, num_classes=21):
if phase != "test" and phase != "train":
print("ERROR: Phase: " + phase + " not recognized")
return
# if size != 300:
# print("ERROR: You specified size " + repr(size) + ". However, " +
# "currently only SSD300 (size=300) is supported!")
# return
base_, extras_, head_ = multibox(vgg(base[str(size)], 3),
add_extras(extras[str(size)], 1024),
mbox[str(size)], num_classes)
return SSD_CON(phase, size, base_, extras_, head_, num_classes)
