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ssd_mobilenet.py
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ssd_mobilenet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
from data import voc, coco, cub
from nets import mobilenet
import os
class SSDMobileNetV2(nn.Module):
"""Single Shot Multibox Architecture
The network is composed of a base MobileNetV2 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
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, extras, head, top_down, final_features, cfg):
super(SSDMobileNetV2, self).__init__()
self.phase = phase
self.num_classes = cfg['num_classes']
self.cfg = cfg
self.priorbox = PriorBox(self.cfg)
self.priors = Variable(self.priorbox.forward(), volatile=True)
self.size = size
# SSD network
self.backbone = nn.ModuleList(mobilenet.MobileNetV2(num_classes=self.num_classes, width_mult=0.75).features)
self.norm = L2Norm(int(96 * 0.75), 20)
self.extras = nn.ModuleList(extras)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
self.top_down = nn.ModuleList(top_down)
self.final_features = nn.ModuleList(final_features)
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(self.num_classes, 0, 200, 0.01, 0.45)
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]
"""
sources = list()
loc = list()
conf = list()
for k in range(len(self.backbone)):
x = self.backbone[k](x)
if k in [13, 17]:
if len(sources) == 0:
s = self.norm(x)
sources.append(s)
else:
sources.append(x)
# apply extra layers and cache source layer outputs
for k, v in enumerate(self.extras):
x = F.celu(v(x), inplace=True)
#x = v(x)
#if k % 2 == 1:
sources.append(x)
top_down_x = []
for k, v in enumerate(sources):
x = self.top_down[k](v)
top_down_x.append(x)
top_down_x = top_down_x[::-1]
pyramids = [self.final_features[0](top_down_x[0])]
for k, v in enumerate(top_down_x[:-1]):
size = top_down_x[k+1].shape[2:]
x = F.upsample(v, size=size) + top_down_x[k+1]
x = self.final_features[k+1](x)
pyramids.append(x)
pyramids = pyramids[::-1]
# apply multibox head to source layers
for (x, l, c) in zip(pyramids, 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 = self.detect(
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
)
return output
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.')
'''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)]
else:
layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])]
flag = not flag
in_channels = v
return layers'''
def add_extras(cfg):
layers = []
block = mobilenet.InvertedResidual
layers.append(block(int(320*0.75), 512, 2, 512.0/(320.0*0.75)))
layers.append(block(512, 256, 2, 0.5))
layers.append(block(256, 256, 2, 1))
layers.append(block(256, 128, 2, 0.5))
return layers
def multibox(extra_layers, cfg, num_classes):
loc_layers = []
conf_layers = []
top_down_layers = []
final_features = []
mobilenet_channels = [int(96*0.75), int(320*0.75)]
for k, channel in enumerate(mobilenet_channels):
loc_layers += [nn.Conv2d(256,
cfg[k] * 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(256,
cfg[k] * num_classes, kernel_size=3, padding=1)]
out_channels = mobilenet_channels + [512, 256, 256, 128]
for k, v in enumerate(extra_layers, 2):
loc_layers += [nn.Conv2d(256, cfg[k]
* 4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(256, cfg[k]
* num_classes, kernel_size=3, padding=1)]
for k, v in enumerate(out_channels):
top_down_layers += [nn.Conv2d(v, 256, kernel_size=1)]
final_features += [nn.Conv2d(256, 256, kernel_size=1, padding=0)]
return extra_layers, (loc_layers, conf_layers), top_down_layers, final_features
extras = {
'300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
'512': [],
}
mbox = {
'300': [6, 6, 6, 6, 4, 4], # number of boxes per feature map location
'512': [],
}
def build_ssd_mobilenet(phase, cfg):
size = cfg['min_dim']
num_classes = cfg['num_classes']
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
extras_, head_, top_down_, final_features_ = multibox(add_extras(extras[str(size)]),
mbox[str(size)], num_classes)
return SSDMobileNetV2(phase, size, extras_, head_, top_down_, final_features_, cfg)