def im_detect_ratio(net, im, target_size1, target_size2):
device = net.arm_conf[0].weight.device
h, w, _ = im.shape
scale = torch.Tensor([w, h, w, h])
scale = scale.to(device)
im_orig = im.astype(np.float32, copy=True)
if im_orig.shape[0] < im_orig.shape[1]:
target_size1, target_size2 = target_size2, target_size1
im = cv2.resize(im_orig,
None,
None,
fx=float(target_size2) / float(w),
fy=float(target_size1) / float(h),
interpolation=cv2.INTER_LINEAR)
x = (im - MEANS).astype(np.float32)
x = x[:, :, (2, 1, 0)] # to rgb
x = x.transpose(2, 0, 1)
x = torch.from_numpy(x).unsqueeze(0)
x = x.to(device)
arm_loc, arm_conf, adm_loc, adm_conf, feat_sizes = net(x)
priorbox = PriorBox(net.cfg,
feat_sizes, (target_size1, target_size2),
phase='test')
priors = priorbox.forward()
priors = priors.to(device)
det = detect.forward(arm_loc, arm_conf, adm_loc, adm_conf, priors, scale)
return det
def __init__(self,
config,
phase,
base,
extras,
head,
num_classes,
top_k=200):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
# TODO: implement __call__ in PriorBox
self.priorbox = PriorBox(config)
self.priors = Variable(self.priorbox.forward(), volatile=True)
# 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])
if phase == 'test':
self.softmax = nn.Softmax()
self.detect = Detect(num_classes,
0,
top_k,
0.01,
0.45,
variance=config['variance'])
def im_detect(net, im, target_size):
try:
device = net.arm_conf[0].weight.device
except:
device = net.odm_conf[0].weight.device
h, w, _ = im.shape
scale = torch.Tensor([w, h, w, h])
scale = scale.to(device)
im_orig = im.astype(np.float32, copy=True)
im = cv2.resize(im_orig, (target_size, target_size),
interpolation=cv2.INTER_LINEAR)
x = (im - MEANS).astype(np.float32)
x = x[:, :, (2, 1, 0)] # to rgb
x = x.transpose(2, 0, 1)
x = torch.from_numpy(x).unsqueeze(0)
x = x.to(device)
if args.wo_refined_anchor:
adm_loc, adm_conf, feat_sizes = net(x)
else:
arm_loc, arm_conf, adm_loc, adm_conf, feat_sizes = net(x)
priorbox = PriorBox(net.cfg,
feat_sizes, (target_size, target_size),
phase='test')
priors = priorbox.forward()
priors = priors.to(device)
if args.wo_refined_anchor:
det = detect.forward(adm_loc, adm_conf, priors, scale)
else:
det = detect.forward(arm_loc, arm_conf, adm_loc, adm_conf, priors,
scale)
return det
def __init__(self, phase, size, base, extras, head, num_classes):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = (coco, voc)[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])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
def __init__(self, num_classes, phase, pretrain=False, finetune=None):
super(SSD300, self).__init__()
self.num_classes = num_classes
self.phase = phase
self.base_net = self._base_net()
self.extra_net = self._extra_net()
self.loc_pred, self.cls_pred = self._predict_net()
self.L2Norm = L2Norm(512, 20)
self.priorbox = PriorBox(v2)
self.priors = Variable(self.priorbox.forward(), volatile=True)
if phase == 'test':
self.softmax = nn.Softmax()
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self._init_weight()
if pretrain:
self._load_weight()
if finetune is not None:
self._finetune(finetune)
def __init__(self, phase, size, base, extras, head, num_classes):
super(TBPP, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = {
'num_classes':
2,
'lr_steps': (80000, 100000, 120000),
'max_iter':
120000,
'feature_maps': [64, 32, 16, 8, 4, 2, 1],
'min_dim':
512,
'steps': [8, 16, 32, 64, 128, 256, 512],
'min_sizes': [20, 51, 133, 215, 296, 378, 460],
'max_sizes': [51, 133, 215, 296, 378, 460, 542],
'aspect_ratios': [[2, 3], [2, 3, 5], [2, 3, 5], [2, 3, 5],
[2, 3, 5], [2, 3], [2, 3]], # TODO
'variance': [0.1, 0.2],
'clip':
True,
'name':
'MINE'
}
self.priorbox = PriorBox(
self.cfg) # calculate the size of prior boxes, i.e. defaults boxes
self.priors = Variable(self.priorbox.forward(), volatile=True)
self.size = size
# TBPP network
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
def __init__(self, num_classes):
super(DSOD_64_16_1x1, self).__init__()
self.num_classes = num_classes
self.extractor = DenseNet_64_16_DSSD_s_Pred_D()
self.loc_layers = nn.ModuleList()
self.cls_layers = nn.ModuleList()
self.cfg = cfg_320_64_16
self.priorbox = PriorBox(self.cfg)
self.priors = self.priorbox.forward()
# in_channels = (768, 768, 768, 256, 256, 256) #pred C
in_channels = (256, 256, 256, 256, 256, 256) # pred D
num_anchors = (4, 6, 6, 6, 4, 4)
for inC, num_anchor in zip(in_channels, num_anchors):
# self.loc_layers += [nn.Conv2d(inC, num_anchor*4, kernel_size=3, padding=1)]
# self.cls_layers += [nn.Conv2d(inC, num_anchor* num_classes, kernel_size=3, padding=1)
# ]
self.loc_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * 4,
kernel_size=1,
padding=0,
bias=False), nn.BatchNorm2d(num_anchor * 4))
]
self.cls_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * num_classes,
kernel_size=1,
padding=0,
bias=False),
nn.BatchNorm2d(num_anchor * num_classes))
]
self.normalize = nn.ModuleList(
[L2Norm(chan, 20) for chan in in_channels])
self.reset_parameters()
def __init__(self, num_classes):
super(DSOD_64_16_GN, self).__init__()
self.num_classes = num_classes
self.extractor = DSSD_s_GN()
self.loc_layers = nn.ModuleList()
self.cls_layers = nn.ModuleList()
self.cfg = cfg_320_64_16
self.priorbox = PriorBox(self.cfg)
self.priors = self.priorbox.forward()
in_channels = channel_dict['DSSD']
num_anchors = (4, 6, 6, 6, 4, 4)
for inC, num_anchor in zip(in_channels, num_anchors):
# self.loc_layers += [nn.Conv2d(inC, num_anchor*4, kernel_size=3, padding=1)]
# self.cls_layers += [nn.Conv2d(inC, num_anchor* num_classes, kernel_size=3, padding=1)
# ]
self.loc_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * 4,
kernel_size=3,
padding=1,
bias=False), nn.GroupNorm(4, num_anchor * 4))
]
self.cls_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * num_classes,
kernel_size=3,
padding=1,
bias=False),
nn.GroupNorm(num_classes, num_anchor * num_classes))
]
self.normalize = nn.ModuleList(
[L2Norm(chan, 20) for chan in in_channels])
self.reset_parameters()
def forward(self, x):
img_size = x.size()[2:]
source = []
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(torch.cat((F.relu(x), F.relu(-x)), 1))
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(torch.cat((F.relu(x), F.relu(-x)), 1))
x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
x = self.inception1(x)
x = self.inception2(x)
x = self.inception3(x)
source.append(x)
x = self.conv3_1(x)
x = self.conv3_2(x)
source.append(x)
x = self.conv4_1(x)
x = self.conv4_2(x)
source.append(x)
feature_maps = []
for feat in source:
feature_maps.append([feat.size(2), feat.size(3)])
self.priors = Variable(PriorBox(img_size, feature_maps, cfg).forward())
loc_preds, conf_preds = self.multilbox(source)
if self.phase == 'test':
output = self.test_det(loc_preds, self.softmax(conf_preds),
self.priors)
else:
output = (loc_preds, conf_preds, self.priors)
return output
def train():
if args.visdom:
import visdom
viz = visdom.Visdom()
print('Loading the dataset...')
if args.dataset == 'COCO':
if args.dataset_root == VOC_ROOT:
if not os.path.exists(COCOroot):
parser.error('Must specify dataset_root if specifying dataset')
print("WARNING: Using default COCO dataset_root because " +
"--dataset_root was not specified.")
args.dataset_root = COCOroot
cfg = coco_refinedet[args.input_size]
train_sets = [('train2017')]
# train_sets = [('train2017', 'val2017')]
dataset = COCODetection(COCOroot, train_sets,
SSDAugmentation(cfg['min_dim'], MEANS))
elif args.dataset == 'VOC':
'''if args.dataset_root == COCO_ROOT:
parser.error('Must specify dataset if specifying dataset_root')'''
cfg = voc_refinedet[args.input_size]
dataset = VOCDetection(root=VOC_ROOT,
transform=SSDAugmentation(
cfg['min_dim'], MEANS))
print('Training RefineDet on:', dataset.name)
print('Using the specified args:')
print(args)
refinedet_net = build_refinedet('train', int(args.input_size),
cfg['num_classes'], backbone_dict)
net = refinedet_net
print(net)
device = torch.device('cuda:0' if args.cuda else 'cpu')
if args.ngpu > 1 and args.cuda:
net = torch.nn.DataParallel(refinedet_net,
device_ids=list(range(args.ngpu)))
cudnn.benchmark = True
net = net.to(device)
if args.resume:
print('Resuming training, loading {}...'.format(args.resume))
state_dict = torch.load(args.resume)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
refinedet_net.load_state_dict(new_state_dict)
else:
print('Initializing weights...')
refinedet_net.init_weights(pretrained=pretrained)
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
arm_criterion = RefineDetMultiBoxLoss(2, 0.5, True, 0, True, negpos_ratio,
0.5, False, args.cuda)
odm_criterion = RefineDetMultiBoxLoss(cfg['num_classes'],
0.5,
True,
0,
True,
negpos_ratio,
0.5,
False,
args.cuda,
use_ARM=True)
priorbox = PriorBox(cfg)
with torch.no_grad():
priors = priorbox.forward()
priors = priors.to(device)
net.train()
# loss counters
arm_loc_loss = 0
arm_conf_loss = 0
odm_loc_loss = 0
odm_conf_loss = 0
epoch = 0 + args.resume_epoch
epoch_size = math.ceil(len(dataset) / args.batch_size)
max_iter = args.max_epoch * epoch_size
stepvalues = (args.max_epoch * 2 // 3 * epoch_size,
args.max_epoch * 8 // 9 * epoch_size,
args.max_epoch * epoch_size)
if args.dataset == 'VOC':
stepvalues = (args.max_epoch * 2 // 3 * epoch_size,
args.max_epoch * 5 // 6 * epoch_size,
args.max_epoch * epoch_size)
step_index = 0
if args.resume_epoch > 0:
start_iter = args.resume_epoch * epoch_size
for step in stepvalues:
if step < start_iter:
step_index += 1
else:
start_iter = 0
if args.visdom:
vis_title = 'RefineDet.PyTorch on ' + dataset.name
vis_legend = ['Loc Loss', 'Conf Loss', 'Total Loss']
iter_plot = create_vis_plot(viz, 'Iteration', 'Loss', vis_title,
vis_legend)
epoch_plot = create_vis_plot(viz, 'Epoch', 'Loss', vis_title,
vis_legend)
data_loader = data.DataLoader(dataset,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
for iteration in range(start_iter, max_iter):
if iteration % epoch_size == 0:
if args.visdom and iteration != 0:
update_vis_plot(viz, epoch, arm_loc_loss, arm_conf_loss,
epoch_plot, None, 'append', epoch_size)
# reset epoch loss counters
arm_loc_loss = 0
arm_conf_loss = 0
odm_loc_loss = 0
odm_conf_loss = 0
# create batch iterator
batch_iterator = iter(data_loader)
if (epoch % 10 == 0 and epoch > 0) or (epoch % 5 == 0 and epoch >
(args.max_epoch * 2 // 3)):
torch.save(
net.state_dict(),
args.save_folder + 'RefineDet' + args.input_size + '_' +
args.dataset + '_epoches_' + repr(epoch) + '.pth')
epoch += 1
t0 = time.time()
if iteration in stepvalues:
step_index += 1
lr = adjust_learning_rate(optimizer, args.gamma, epoch, step_index,
iteration, epoch_size)
# load train data
images, targets = next(batch_iterator)
images = images.to(device)
targets = [ann.to(device) for ann in targets]
# for an in targets:
# for instance in an:
# for cor in instance[:-1]:
# if cor < 0 or cor > 1:
# raise StopIteration
# forward
out = net(images)
# backprop
optimizer.zero_grad()
arm_loss_l, arm_loss_c = arm_criterion(out, priors, targets)
odm_loss_l, odm_loss_c = odm_criterion(out, priors, targets)
arm_loss = arm_loss_l + arm_loss_c
odm_loss = odm_loss_l + odm_loss_c
loss = arm_loss + odm_loss
loss.backward()
optimizer.step()
arm_loc_loss += arm_loss_l.item()
arm_conf_loss += arm_loss_c.item()
odm_loc_loss += odm_loss_l.item()
odm_conf_loss += odm_loss_c.item()
t1 = time.time()
batch_time = t1 - t0
eta = int(batch_time * (max_iter - iteration))
print('Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || ARM_L Loss: {:.4f} ARM_C Loss: {:.4f} ODM_L Loss: {:.4f} ODM_C Loss: {:.4f} loss: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}'.\
format(epoch, args.max_epoch, (iteration % epoch_size) + 1, epoch_size, iteration + 1, max_iter, arm_loss_l.item(), arm_loss_c.item(), odm_loss_l.item(), odm_loss_c.item(), loss.item(), lr, batch_time, str(datetime.timedelta(seconds=eta))))
if args.visdom:
update_vis_plot(viz, iteration, arm_loss_l.item(),
arm_loss_c.item(), iter_plot, epoch_plot, 'append')
torch.save(
refinedet_net.state_dict(), args.save_folder +
'/RefineDet{}_{}_final.pth'.format(args.input_size, args.dataset))
def main():
global args
global minmum_loss
args.gpu = 0
args.world_size = 1
if args.distributed:
args.gpu = args.local_rank % torch.cuda.device_count()
torch.cuda.set_device(args.gpu)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
args.total_batch_size = args.world_size * args.batch_size
## DATA loading code
if args.dataset == 'COCO':
train_sets = [('2014', 'train'), ('2014', 'valminusminival')]
cfg = (COCO_300, COCO_512)[args.size == '512']
elif args.dataset == 'VOC':
train_sets = [('2007', 'trainval'), ('2012', 'trainval')]
cfg = (VOC_300, VOC_512)[args.size == '512']
# other impoort parmeters
img_dim = (300, 512)[args.size == '512']
rgb_means = ((104, 117, 123), (103.94, 116.78,
123.68))[args.version == 'RFB_mobile']
p = (0.6, 0.2)[args.version == 'RFB_mobile']
num_classes = (21, 81)[args.dataset == 'COCO']
if args.dataset == 'COCO':
dataset = COCODetection(root=cfg['coco_root'],
image_sets=train_sets,
preproc=preproc(img_dim, rgb_means, p))
elif args.dataset == 'VOC':
dataset = VOCDetection(root=cfg['voc_root'],
image_sets=train_sets,
preproc=preproc(img_dim, rgb_means, p),
target_transform=AnnotationTransform())
print('Training SSD on:', dataset.name)
print('Loading the dataset...')
train_loader = data.DataLoader(dataset,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
print("Build RFB network")
if args.version == 'RFB_vgg':
model = RFB_Net_vgg('train', img_dim, num_classes)
elif args.version == 'RFB_E_vgg':
model = RFB_Net_E_vgg('train', img_dim, num_classes)
elif args.version == 'RFB_mobile':
model = RFB_Net_mobile('train', img_dim, num_classes)
else:
print('Unkown version!')
if args.pretrained:
base_weights = torch.load(args.save_folder + args.basenet)
print('Loading base network...')
model.base.load_state_dict(base_weights)
model = model.cuda()
# optimizer and loss function
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
criterion = MultiBoxLoss(cfg['num_classes'], 0.5, True, 0, True, 3, 0.5,
False)
## get the priorbox of ssd
priorbox = PriorBox(cfg)
with torch.no_grad():
priors = priorbox.forward()
priors = priors.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
args.resume,
map_location=lambda storage, loc: storage.cuda(args.gpu))
args.start_epoch = checkpoint['epoch']
minmum_loss = checkpoint['minmum_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
print('Initializing weights...')
# initialize newly added layers' weights with xavier method
model.extras.apply(weights_init)
model.loc.apply(weights_init)
model.conf.apply(weights_init)
model.Norm.apply(weights_init)
if args.version == 'RFB_E_vgg':
model.reduce.apply(weights_init)
model.up_reduce.apply(weights_init)
print('Using the specified args:')
print(args)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
loss = train(train_loader, model, priors, criterion, optimizer, epoch)
# remember best prec@1 and save checkpoint
if args.local_rank == 0:
is_best = loss < minmum_loss
minmum_loss = min(loss, minmum_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': minmum_loss,
'optimizer': optimizer.state_dict(),
}, is_best, epoch)
def SSD300(input_shape, num_classes=21):
"""SSD300 architecture.
# Arguments
input_shape: Shape of the input image,
expected to be either (300, 300, 3) or (3, 300, 300)(not tested).
num_classes: Number of classes including background.
# References
https://arxiv.org/abs/1512.02325
"""
print('begin building networks')
kernel_size = (3, 3)
net = {}
# Block 1
input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['input'] = input_tensor
net['conv1_1'] = Conv2D(64,
kernel_size,
activation='relu',
padding='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Conv2D(64,
kernel_size,
activation='relu',
padding='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Conv2D(128,
kernel_size,
activation='relu',
padding='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Conv2D(128,
kernel_size,
activation='relu',
padding='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Conv2D(256,
kernel_size,
activation='relu',
padding='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Conv2D(256,
kernel_size,
activation='relu',
padding='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Conv2D(256,
kernel_size,
activation='relu',
padding='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='pool3')(net['conv3_3'])
# Block 4
net['conv4_1'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv4_1')(net['pool3'])
net['conv4_2'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv4_2')(net['conv4_1'])
net['conv4_3'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv4_3')(net['conv4_2'])
net['pool4'] = MaxPooling2D((2, 2),
strides=(2, 2),
padding='same',
name='pool4')(net['conv4_3'])
# Block 5
net['conv5_1'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv5_1')(net['pool4'])
net['conv5_2'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv5_2')(net['conv5_1'])
net['conv5_3'] = Conv2D(512,
kernel_size,
activation='relu',
padding='same',
name='conv5_3')(net['conv5_2'])
net['pool5'] = MaxPooling2D((3, 3),
strides=(1, 1),
padding='same',
name='pool5')(net['conv5_3'])
# FC6
net['fc6'] = Conv2D(1024,
kernel_size,
dilation_rate=(6, 6),
activation='relu',
padding='same',
name='fc6')(net['pool5'])
# x = Dropout(0.5, name='drop6')(x)
# FC7
net['fc7'] = Conv2D(1024, (1, 1),
activation='relu',
padding='same',
name='fc7')(net['fc6'])
# x = Dropout(0.5, name='drop7')(x)
# Block 6
net['conv6_1'] = Conv2D(256, (1, 1),
activation='relu',
padding='same',
name='conv6_1')(net['fc7'])
net['conv6_2'] = Conv2D(512,
kernel_size,
strides=(2, 2),
activation='relu',
padding='same',
name='conv6_2')(net['conv6_1'])
# Block 7
net['conv7_1'] = Conv2D(128, (1, 1),
activation='relu',
padding='same',
name='conv7_1')(net['conv6_2'])
net['conv7_2'] = ZeroPadding2D()(net['conv7_1'])
net['conv7_2'] = Conv2D(256,
kernel_size,
strides=(2, 2),
activation='relu',
padding='valid',
name='conv7_2')(net['conv7_2'])
# Block 8
net['conv8_1'] = Conv2D(128, (1, 1),
activation='relu',
padding='same',
name='conv8_1')(net['conv7_2'])
net['conv8_2'] = Conv2D(256,
kernel_size,
strides=(2, 2),
activation='relu',
padding='same',
name='conv8_2')(net['conv8_1'])
# Last Pool
net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2'])
print('base network built')
# Prediction from conv4_3
net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3'])
num_priors = 3
x = Conv2D(num_priors * 4,
kernel_size,
padding='same',
name='conv4_3_norm_mbox_loc')(net['conv4_3_norm'])
net['conv4_3_norm_mbox_loc'] = x
flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')
net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc'])
name = 'conv4_3_norm_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Conv2D(num_priors * num_classes,
kernel_size,
padding='same',
name=name)(net['conv4_3_norm'])
net['conv4_3_norm_mbox_conf'] = x
flatten = Flatten(name='conv4_3_norm_mbox_conf_flat')
net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf'])
priorbox = PriorBox(img_size,
30.0,
aspect_ratios=[2],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv4_3_norm_mbox_priorbox')
net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])
print('conv4_3_norm_mbox_priorbox built')
# Prediction from fc7
num_priors = 6
net['fc7_mbox_loc'] = Conv2D(num_priors * 4,
kernel_size,
padding='same',
name='fc7_mbox_loc')(net['fc7'])
flatten = Flatten(name='fc7_mbox_loc_flat')
net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc'])
name = 'fc7_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
net['fc7_mbox_conf'] = Conv2D(num_priors * num_classes, (3, 3),
padding='same',
name=name)(net['fc7'])
flatten = Flatten(name='fc7_mbox_conf_flat')
net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf'])
priorbox = PriorBox(img_size,
60.0,
max_size=114.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='fc7_mbox_priorbox')
net['fc7_mbox_priorbox'] = priorbox(net['fc7'])
print('fc7_mbox_priorbox built')
# Prediction from conv6_2
num_priors = 6
x = Conv2D(num_priors * 4,
kernel_size,
padding='same',
name='conv6_2_mbox_loc')(net['conv6_2'])
net['conv6_2_mbox_loc'] = x
flatten = Flatten(name='conv6_2_mbox_loc_flat')
net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc'])
name = 'conv6_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Conv2D(num_priors * num_classes,
kernel_size,
padding='same',
name=name)(net['conv6_2'])
net['conv6_2_mbox_conf'] = x
flatten = Flatten(name='conv6_2_mbox_conf_flat')
net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf'])
priorbox = PriorBox(img_size,
114.0,
max_size=168.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv6_2_mbox_priorbox')
net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2'])
print('conv6_2_mbox_priorbox built')
# Prediction from conv7_2
num_priors = 6
x = Conv2D(num_priors * 4,
kernel_size,
padding='same',
name='conv7_2_mbox_loc')(net['conv7_2'])
net['conv7_2_mbox_loc'] = x
flatten = Flatten(name='conv7_2_mbox_loc_flat')
net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc'])
name = 'conv7_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Conv2D(num_priors * num_classes,
kernel_size,
padding='same',
name=name)(net['conv7_2'])
net['conv7_2_mbox_conf'] = x
flatten = Flatten(name='conv7_2_mbox_conf_flat')
net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf'])
priorbox = PriorBox(img_size,
168.0,
max_size=222.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv7_2_mbox_priorbox')
net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2'])
print('conv7_2_mbox_priorbox built')
# Prediction from conv8_2
num_priors = 6
x = Conv2D(num_priors * 4,
kernel_size,
padding='same',
name='conv8_2_mbox_loc')(net['conv8_2'])
net['conv8_2_mbox_loc'] = x
flatten = Flatten(name='conv8_2_mbox_loc_flat')
net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc'])
name = 'conv8_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Conv2D(num_priors * num_classes,
kernel_size,
padding='same',
name=name)(net['conv8_2'])
net['conv8_2_mbox_conf'] = x
flatten = Flatten(name='conv8_2_mbox_conf_flat')
net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf'])
priorbox = PriorBox(img_size,
222.0,
max_size=276.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv8_2_mbox_priorbox')
net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2'])
print('conv8_2_mbox_priorbox built')
# Prediction from pool6
num_priors = 6
x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6'])
net['pool6_mbox_loc_flat'] = x
name = 'pool6_mbox_conf_flat'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Dense(num_priors * num_classes, name=name)(net['pool6'])
net['pool6_mbox_conf_flat'] = x
priorbox = PriorBox(img_size,
276.0,
max_size=330.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='pool6_mbox_priorbox')
if K.image_dim_ordering() == 'tf':
target_shape = (1, 1, 256)
else:
target_shape = (256, 1, 1)
net['pool6_reshaped'] = Reshape(target_shape,
name='pool6_reshaped')(net['pool6'])
net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped'])
print('pool6_mbox_priorbox built')
# Gather all predictions
net['mbox_loc'] = concatenate([
net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'],
net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'],
net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat']
],
axis=1,
name='mbox_loc')
net['mbox_conf'] = concatenate([
net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'],
net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'],
net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat']
],
axis=1,
name='mbox_conf')
net['mbox_priorbox'] = concatenate([
net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'],
net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'],
net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox']
],
axis=1,
name='mbox_priorbox')
print('gathering all prediction layers built')
if hasattr(net['mbox_loc'], '_keras_shape'):
# divide 4 for [xmin, ymin, xmax, ymax]
num_boxes = net['mbox_loc']._keras_shape[-1] // 4
elif hasattr(net['mbox_loc'], 'int_shape'):
num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4
net['mbox_loc'] = Reshape((num_boxes, 4),
name='mbox_loc_final')(net['mbox_loc'])
net['mbox_conf'] = Reshape((num_boxes, num_classes),
name='mbox_conf_logits')(net['mbox_conf'])
net['mbox_conf'] = Activation('softmax',
name='mbox_conf_final')(net['mbox_conf'])
net['predictions'] = concatenate(
[net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox']],
axis=2,
name='predictions')
print('prediction layers built')
model = Model(net['input'], net['predictions'])
return model
class TBPP(nn.Module):
def __init__(self, phase, size, base, extras, head, num_classes):
super(TBPP, self).__init__()
self.phase = phase
self.num_classes = num_classes
self.cfg = {
'num_classes':
2,
'lr_steps': (80000, 100000, 120000),
'max_iter':
120000,
'feature_maps': [64, 32, 16, 8, 4, 2, 1],
'min_dim':
512,
'steps': [8, 16, 32, 64, 128, 256, 512],
'min_sizes': [20, 51, 133, 215, 296, 378, 460],
'max_sizes': [51, 133, 215, 296, 378, 460, 542],
'aspect_ratios': [[2, 3], [2, 3, 5], [2, 3, 5], [2, 3, 5],
[2, 3, 5], [2, 3], [2, 3]], # TODO
'variance': [0.1, 0.2],
'clip':
True,
'name':
'MINE'
}
self.priorbox = PriorBox(
self.cfg) # calculate the size of prior boxes, i.e. defaults boxes
self.priors = Variable(self.priorbox.forward(), volatile=True)
self.size = size
# TBPP network
self.vgg = nn.ModuleList(base)
self.L2Norm = L2Norm(512, 20)
self.extras = nn.ModuleList(extras)
self.loc = nn.ModuleList(head[0])
self.conf = nn.ModuleList(head[1])
if phase == 'test':
self.softmax = nn.Softmax(dim=-1)
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
def forward(self, x):
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)
if self.phase == "test":
output = self.detect(
loc.view(loc.size(0), -1, 4), # loc predictions
self.softmax(conf.view(conf.size(0), -1,
self.num_classes)), # conf predictions
self.priors.type(type(
x.data))) # prior boxes, i.e. 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('Loaded!')
else:
print('Sorry, only .pth and .pkl files are supported.')
class SSD(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"
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,
config,
phase,
base,
extras,
head,
num_classes,
top_k=200):
super(SSD, self).__init__()
self.phase = phase
self.num_classes = num_classes
# TODO: implement __call__ in PriorBox
self.priorbox = PriorBox(config)
self.priors = Variable(self.priorbox.forward(), volatile=True)
# 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])
if phase == 'test':
self.softmax = nn.Softmax()
self.detect = Detect(num_classes,
0,
top_k,
0.01,
0.45,
variance=config['variance'])
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*batch,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()
# 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)
if self.phase == "test":
output = self.detect(
loc.view(loc.size(0), -1, 4), # loc preds
self.softmax(conf.view(-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 simple_SSD(input_shape, num_classes, min_size, num_priors, max_size,
aspect_ratios, variances):
input_tensor = Input(shape=input_shape)
body = Convolution2D(16, 7, 7)(input_tensor)
body = Activation('relu')(body)
body = MaxPooling2D(2, 2, border_mode='valid')(body)
body = Convolution2D(32, 5, 5)(body)
body = Activation('relu')(body)
branch_1 = MaxPooling2D(2, 2, border_mode='valid')(body)
body = Convolution2D(64, 3, 3)(branch_1)
body = Activation('relu')(body)
branch_2 = MaxPooling2D(2, 2, border_mode='valid')(body)
# first branch
norm_1 = Normalize(20)(branch_1)
localization_1 = Convolution2D(num_priors * 4, 3, 3,
border_mode='same')(norm_1)
localization_1 = Flatten(localization_1)
classification_1 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same')(norm_1)
classification_1 = Flatten()(classification_1)
prior_boxes_1 = PriorBox(input_shape[0:2], min_size, max_size,
aspect_ratios)
# second branch
norm_2 = Normalize(20)(branch_2)
localization_2 = Convolution2D(num_priors * 4, 3, 3,
border_mode='same')(norm_2)
localization_2 = Flatten(localization_2)
classification_2 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='same')(norm_2)
classification_2 = Flatten()(classification_2)
prior_boxes_2 = PriorBox(input_shape[0:2], min_size, max_size,
aspect_ratios)
localization_head = Merge([localization_1, localization_2],
mode='concat',
concat_axis=1)
classification_head = Merge([classification_1, classification_2],
mode='concat',
concat_axis=1)
prior_boxes_head = Merge([prior_boxes_1, prior_boxes_2],
mode='concat',
concat_axis=1)
if hasattr(localization_head, '_keras_shape'):
num_boxes = localization_head._keras_shape[-1] // 4
elif hasattr(localization_head, 'int_shape'):
num_boxes = K.int_shape(localization_head)[-1] // 4
localization_head = Reshape((num_boxes, 4))(localization_head)
classification_head = Reshape(
(num_boxes, num_classes))(classification_head)
classification_head = Activation('softmax')(classification_head)
predictions = Merge(localization_head,
classification_head,
prior_boxes_head,
mode='concat',
concat_axis=2)
model = Model(input_tensor, predictions)
return model
class SSD300(nn.Module):
def __init__(self, num_classes, phase, pretrain=False, finetune=None):
super(SSD300, self).__init__()
self.num_classes = num_classes
self.phase = phase
self.base_net = self._base_net()
self.extra_net = self._extra_net()
self.loc_pred, self.cls_pred = self._predict_net()
self.L2Norm = L2Norm(512, 20)
self.priorbox = PriorBox(v2)
self.priors = Variable(self.priorbox.forward(), volatile=True)
if phase == 'test':
self.softmax = nn.Softmax()
self.detect = Detect(num_classes, 0, 200, 0.01, 0.45)
self._init_weight()
if pretrain:
self._load_weight()
if finetune is not None:
self._finetune(finetune)
def _base_net(self):
"""Use vgg16 as basenet.
Refer https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py.
Returns:
basenet: (ModuleList)
"""
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
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
cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C',
512, 512, 512, 'M', 512, 512, 512]
return nn.ModuleList(make_layers(cfg))
def _extra_net(self):
"""Extra layers in SSD300, conv8,9,10,11
Refer https://arxiv.org/pdf/1512.02325.pdf
Returns:
extra_net: (ModuleList)
"""
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 1024
flag = False
for i, v in enumerate(cfg):
if in_channels == 'S':
in_channels = v
continue
_kerner_size = (1,3)[flag]
if v == 'S':
conv = nn.Conv2d(in_channels, cfg[i+1],
_kerner_size, stride=2, padding=1)
else:
conv = nn.Conv2d(in_channels, v, _kerner_size)
layers += [conv]
in_channels = v
flag = not flag
return layers
cfg = [256, 'S', 512,
128, 'S', 256,
128, 256,
128, 256]
return nn.ModuleList(make_layers(cfg))
def _predict_net(self):
"""Predict layer, cls and loc
Returns:
loc_layers: [list], len=6
conf_layers: [list], len=6
"""
loc_layers = []
conf_layers = []
in_channels = [512, 1024, 512, 256, 256, 256]
mboxes = [4, 6, 6, 6, 4, 4] # number of boxes per feature map location
for (in_channels, mbox) in zip(in_channels, mboxes):
loc_layers += [nn.Conv2d(in_channels, mbox*4, kernel_size=3, padding=1)]
conf_layers += [nn.Conv2d(in_channels, mbox*self.num_classes, kernel_size=3, padding=1)]
return nn.ModuleList(loc_layers), nn.ModuleList(conf_layers)
def forward(self, x):
"""Apply network layers and ops on input image(s) x.
Args:
x (tensor): input image or batch of image.
Shape: [batch, 3, 300, 300]
Returns:
Depending on phase;
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]
test:
Variale(tensor) of input class label predictions
..
"""
sources = [] # feature maps where to make predictions
conf = []
loc = []
# apply vgg, without BatchNorm
pred_index = [22,] # conv4_3 relu
for k, v in enumerate(self.base_net):
x = v(x)
if k in pred_index:
sources.append(self.L2Norm(x))
sources.append(x)
# apply extra_net and cache source layer outputs
pred_index = [1,3,5,7]
for k, v in enumerate(self.extra_net):
x = v(x)
if k in pred_index:
sources.append(x)
# apply predict_net to source layers
for (x, l, c) in zip(sources, self.loc_pred, self.cls_pred):
loc.append(l(x).permute(0,2,3,1).contiguous()) # [B,C,H,W] -> [B,H,W,C]
conf.append(c(x).permute(0,2,3,1).contiguous())
loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1) # to concat pred from many layers
conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
if self.phase == 'train':
output = (
loc.view(loc.size(0), -1, 4),
conf.view(loc.size(0), -1, self.num_classes),
self.priors
)
else:
output = self.detect(
loc.view(loc.size(0), -1, 4),
self.softmax(conf.view(-1, self.num_classes)),
self.priors.type(type(x.data))
)
return output
def _init_weight(self):
def weight_init(m):
if isinstance(m, nn.Conv2d):
init.xavier_uniform(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.zero_()
self.apply(weight_init)
def _fetch_weight(self):
"""Fetch pretrain model using torchvision.
Returns:
weight_file: (str) pretrained weight file path
"""
print('Fetching pretrained model...')
vgg16 = models.vgg16(pretrained=True)
model_file = os.path.join(os.environ['HOME'], '.torch/models', 'vgg16-*.pth')
return glob.glob(model_file)[0]
def _load_weight(self, weight_file=None):
"""Load pretrained model.
source: features.[0-28].[weight,bias], classifier.[0,3,6].[weight,bias]
target: base_net.[0-28].[weight,bias], base_net.[31,33].[weight,bias], -> (load pretrained model)
extra_net.[0-7].[xx], loc_pred.[0-5].[xx], cls_pred.[0-5].[xx] -> (init)
Kwargs:
weight_file (str): *.pth file path
Returns: None
"""
if weight_file == None:
weight_file = self._fetch_weight()
_, ext = os.path.splitext(weight_file)
def downsample(fc, layer):
"""
downsample weight and bias in fc6,fc7 to conv6,conv7
w: [512,7,7,4096] -> [512,3,3,1024] fc6
[4096, 4096] -> [1024, 1, 1, 1024] fc7
b: [4096] -> [1024]
"""
fc = fc.view(4, 1024, -1)[0] # [4096, 512*7*7] -> [4, 1024, -1][0],
if fc.size(1) > 1: # weight
if layer == 'fc6':
fc = fc.view(1024, 512, 7, 7)[:, :, 0::3, 0::3]
elif layer == 'fc7':
fc = fc.view(4, 1024, 1024, 1, 1)[0]
else:
fc = fc[:,0]
return fc
if ext == '.pkl' or '.pth':
source_dict = torch.load(weight_file)
# features -> base_net, remove
target_dict = {}
for key in source_dict.keys():
if 'features' in key: # conv1-5
target_dict['base_net'+key[8:]] = source_dict[key]
elif 'classifier.0' in key: # conv6
target_dict['base_net.31'+key[12:]] = downsample(source_dict[key], 'fc6')
elif 'classifier.3' in key: # conv7
target_dict['base_net.33'+key[12:]] = downsample(source_dict[key], 'fc7')
source_dict = target_dict
# add
for (key, value) in self.state_dict().items():
if key not in target_dict.keys():
target_dict[key] = value
self.load_state_dict(target_dict)
print('Loading imagenet weight successfully!')
else:
print('Sorry, only .pth and .pkl')
def _finetune(self, weight_file):
_, ext = os.path.splitext(weight_file)
if ext == '.pkl' or '.pth':
source_dict = torch.load(weight_file)
# remove num_classes-awared layers
target_dict = {}
for key in source_dict.keys():
if 'cls_pred' not in key: # conv1-5
target_dict[key] = source_dict[key]
# add
for (key, value) in self.state_dict().items():
if key not in target_dict.keys():
target_dict[key] = value
self.load_state_dict(target_dict)
print('Loading finetune weight successfully!')
else:
print('Sorry, only .pth and .pkl')
class DSOD_64_16(nn.Module):
def __init__(self, num_classes):
super(DSOD_64_16, self).__init__()
self.num_classes = num_classes
self.extractor = DenseNet_64_16()
self.loc_layers = nn.ModuleList()
self.cls_layers = nn.ModuleList()
self.cfg = cfg_320_64_16
self.priorbox = PriorBox(self.cfg)
self.priors = self.priorbox.forward()
in_channels = channel_dict['6416']
num_anchors = (4, 6, 6, 6, 4, 4)
for inC, num_anchor in zip(in_channels, num_anchors):
# self.loc_layers += [nn.Conv2d(inC, num_anchor*4, kernel_size=3, padding=1)]
# self.cls_layers += [nn.Conv2d(inC, num_anchor* num_classes, kernel_size=3, padding=1)
# ]
self.loc_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * 4,
kernel_size=3,
padding=1,
bias=False), nn.BatchNorm2d(num_anchor * 4))
]
self.cls_layers += [
nn.Sequential(
nn.Conv2d(inC,
num_anchor * num_classes,
kernel_size=3,
padding=1,
bias=False),
nn.BatchNorm2d(num_anchor * num_classes))
]
self.normalize = nn.ModuleList(
[L2Norm(chan, 20) for chan in in_channels])
self.reset_parameters()
def forward(self, x):
loc_preds = []
cls_preds = []
xs = self.extractor(x)
for i, x in enumerate(xs):
x = self.normalize[i](x)
loc_pred = self.loc_layers[i](x)
loc_pred = loc_pred.permute(0, 2, 3, 1).contiguous()
loc_preds.append(loc_pred.view(loc_pred.size(0), -1, 4))
cls_pred = self.cls_layers[i](x)
cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous()
cls_preds.append(
cls_pred.view(cls_pred.size(0), -1, self.num_classes))
# loc_preds = torch.cat(loc_preds, 1)
# cls_preds = torch.cat(cls_preds, 1)
loc = torch.cat([o.view(o.size(0), -1) for o in loc_preds], 1)
conf = torch.cat([o.view(o.size(0), -1) for o in cls_preds], 1)
output = (loc.view(loc.size(0), -1,
4), conf.view(conf.size(0), -1,
self.num_classes), self.priors)
return output
def reset_parameters(self):
for name, param in self.extractor.named_parameters():
if hasattr(param, 'weight'):
nn.init.xavier_uniform(param.weight.data,
gain=nn.init.calculate_gain('relu'))
for name, param in self.loc_layers.named_parameters():
if hasattr(param, 'weight'):
nn.init.normal(param.weight.data, std=0.01)
for name, param in self.cls_layers.named_parameters():
if hasattr(param, 'weight'):
nn.init.normal(param.weight.data, std=0.01)
cv2.rectangle(img, (p1[0] - 2 // 2, p1[1] - 2 - baseline),
(p1[0] + text_size[0], p1[1] + text_size[1]),
[255, 0, 0], -1)
cv2.putText(img, conf, (p1[0], p1[1] + baseline),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1, 8)
t2 = time.time()
print('detect:{} timer:{}'.format(img_path, t2 - t1))
cv2.imwrite(os.path.join(args.save_dir, os.path.basename(img_path)), img)
if __name__ == '__main__':
# load PriorBox
with torch.no_grad():
priorbox = PriorBox(input_size=[640, 640], cfg=cfg)
priors = priorbox.forward()
priors = priors.cuda()
net = build_net('test', cfg.NUM_CLASSES)
net.load_state_dict(torch.load(args.model))
net.eval()
if use_cuda:
net.cuda()
cudnn.benckmark = True
img_path = './img'
img_list = [
os.path.join(img_path, x) for x in os.listdir(img_path)
if x.endswith('jpg')
def main():
global args
global minmum_loss
args.gpu = 0
args.world_size = 1
if args.distributed:
args.gpu = args.local_rank % torch.cuda.device_count()
torch.cuda.set_device(args.gpu)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
args.total_batch_size = args.world_size * args.batch_size
# build dsfd network
print("Building net...")
pyramidbox = build_net('train', cfg.NUM_CLASSES)
model = pyramidbox
if args.pretrained:
vgg_weights = torch.load(args.save_folder + args.basenet)
print('Load base network....')
model.vgg.load_state_dict(vgg_weights)
# for multi gpu
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model)
model = model.cuda()
# optimizer and loss function
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
criterion1 = MultiBoxLoss(cfg, True)
criterion2 = MultiBoxLoss(cfg, True, use_head_loss=True)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
args.resume,
map_location=lambda storage, loc: storage.cuda(args.gpu))
args.start_epoch = checkpoint['epoch']
minmum_loss = checkpoint['minmum_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
print('Initializing weights...')
pyramidbox.extras.apply(pyramidbox.weights_init)
pyramidbox.lfpn_topdown.apply(pyramidbox.weights_init)
pyramidbox.lfpn_later.apply(pyramidbox.weights_init)
pyramidbox.cpm.apply(pyramidbox.weights_init)
pyramidbox.loc_layers.apply(pyramidbox.weights_init)
pyramidbox.conf_layers.apply(pyramidbox.weights_init)
print('Loading wider dataset...')
train_dataset = WIDERDetection(cfg.FACE.TRAIN_FILE, mode='train')
val_dataset = WIDERDetection(cfg.FACE.VAL_FILE, mode='val')
train_loader = data.DataLoader(train_dataset,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
val_batchsize = args.batch_size // 2
val_loader = data.DataLoader(val_dataset,
val_batchsize,
num_workers=args.num_workers,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True)
print('Using the specified args:')
print(args)
# load PriorBox
with torch.no_grad():
priorbox = PriorBox(input_size=[640, 640], cfg=cfg)
priors = priorbox.forward()
priors = priors.cuda()
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
end = time.time()
train_loss = train(train_loader, model, priors, criterion1, criterion2,
optimizer, epoch)
val_loss = val(val_loader, model, priors, criterion1, criterion2)
if args.local_rank == 0:
is_best = val_loss < minmum_loss
minmum_loss = min(val_loss, minmum_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': minmum_loss,
'optimizer': optimizer.state_dict(),
}, is_best, epoch)
epoch_time = time.time() - end
print('Epoch %s time cost %f' % (epoch, epoch_time))
def main():
global args
global minmum_loss
args.gpu = 0
args.world_size = 1
if args.distributed:
args.gpu = args.local_rank % torch.cuda.device_count()
torch.cuda.set_device(args.gpu)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
args.total_batch_size = args.world_size * args.batch_size
# build dsfd network
print("Building net...")
model = RetinaFace(cfg=cfg)
print("Printing net...")
# for multi gpu
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model)
model = model.cuda()
# optimizer and loss function
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
criterion = MultiBoxLoss(cfg['num_classes'], 0.35, True, 0, True, 7, 0.35,
False)
## dataset
print("loading dataset")
train_dataset = WiderFaceDetection(
args.training_dataset, preproc(cfg['image_size'], cfg['rgb_mean']))
train_loader = data.DataLoader(train_dataset,
args.batch_size,
num_workers=args.num_workers,
shuffle=True,
collate_fn=detection_collate,
pin_memory=True)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(
args.resume,
map_location=lambda storage, loc: storage.cuda(args.gpu))
args.start_epoch = checkpoint['epoch']
minmum_loss = checkpoint['minmum_loss']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
print('Using the specified args:')
print(args)
# load PriorBox
print("Load priorbox")
with torch.no_grad():
priorbox = PriorBox(cfg=cfg,
image_size=(cfg['image_size'], cfg['image_size']))
priors = priorbox.forward()
priors = priors.cuda()
print("start traing")
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train_loss = train(train_loader, model, priors, criterion, optimizer,
epoch)
if args.local_rank == 0:
is_best = train_loss < minmum_loss
minmum_loss = min(train_loss, minmum_loss)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': minmum_loss,
'optimizer': optimizer.state_dict(),
}, is_best, epoch)
def mini_SSD300(input_shape=(300,300,3), num_classes=21):
"""SSD300 architecture.
# Arguments
input_shape: Shape of the input image,
expected to be either (300, 300, 3) or (3, 300, 300)(not tested).
num_classes: Number of classes including background.
# References
https://arxiv.org/abs/1512.02325
"""
net = {}
# Block 1
input_tensor = input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['input'] = input_tensor
net['conv1_1'] = Convolution2D(64, 3, 3,
activation='relu',
border_mode='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Convolution2D(64, 3, 3,
activation='relu',
border_mode='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Convolution2D(128, 3, 3,
activation='relu',
border_mode='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Convolution2D(128, 3, 3,
activation='relu',
border_mode='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool3')(net['conv3_3'])
# Block 4
net['conv4_1'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv4_1')(net['pool3'])
net['conv4_2'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv4_2')(net['conv4_1'])
net['conv4_3'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv4_3')(net['conv4_2'])
net['pool4'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool4')(net['conv4_3'])
# Block 5
net['conv5_1'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv5_1')(net['pool4'])
net['conv5_2'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv5_2')(net['conv5_1'])
net['conv5_3'] = Convolution2D(512, 3, 3,
activation='relu',
border_mode='same',
name='conv5_3')(net['conv5_2'])
net['pool5'] = MaxPooling2D((3, 3), strides=(1, 1), border_mode='same',
name='pool5')(net['conv5_3'])
# FC6
net['fc6'] = AtrousConvolution2D(1024, 3, 3, atrous_rate=(6, 6),
activation='relu', border_mode='same',
name='fc6')(net['pool5'])
# x = Dropout(0.5, name='drop6')(x)
# FC7
net['fc7'] = Convolution2D(1024, 1, 1, activation='relu',
border_mode='same', name='fc7')(net['fc6'])
# x = Dropout(0.5, name='drop7')(x)
# Block 6
# deleted
net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3'])
num_priors = 3
x = Convolution2D(num_priors * 4, 3, 3, border_mode='same',
name='conv4_3_norm_mbox_loc')(net['conv4_3_norm'])
net['conv4_3_norm_mbox_loc'] = x
flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')
net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc'])
name = 'conv4_3_norm_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same',
name=name)(net['conv4_3_norm'])
net['conv4_3_norm_mbox_conf'] = x
flatten = Flatten(name='conv4_3_norm_mbox_conf_flat')
net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf'])
priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv4_3_norm_mbox_priorbox')
net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])
# Prediction from fc7
num_priors = 6
net['fc7_mbox_loc'] = Convolution2D(num_priors * 4, 3, 3,
border_mode='same',
name='fc7_mbox_loc')(net['fc7'])
flatten = Flatten(name='fc7_mbox_loc_flat')
net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc'])
name = 'fc7_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
net['fc7_mbox_conf'] = Convolution2D(num_priors * num_classes, 3, 3,
border_mode='same',
name=name)(net['fc7'])
flatten = Flatten(name='fc7_mbox_conf_flat')
net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf'])
priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='fc7_mbox_priorbox')
net['fc7_mbox_priorbox'] = priorbox(net['fc7'])
# Gather all predictions
net['mbox_loc'] = merge([net['conv4_3_norm_mbox_loc_flat'],
net['fc7_mbox_loc_flat']],
mode='concat', concat_axis=1, name='mbox_loc')
net['mbox_conf'] = merge([net['conv4_3_norm_mbox_conf_flat'],
net['fc7_mbox_conf_flat']],
mode='concat', concat_axis=1, name='mbox_conf')
if hasattr(net['mbox_loc'], '_keras_shape'):
num_boxes = net['mbox_loc']._keras_shape[-1] // 4
elif hasattr(net['mbox_loc'], 'int_shape'):
num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4
net['mbox_loc'] = Reshape((num_boxes, 4),
name='mbox_loc_final')(net['mbox_loc'])
net['mbox_conf'] = Reshape((num_boxes, num_classes),
name='mbox_conf_logits')(net['mbox_conf'])
net['mbox_conf'] = Activation('softmax',
name='mbox_conf_final')(net['mbox_conf'])
net['mbox_priorbox'] = merge([net['conv4_3_norm_mbox_priorbox'],
net['fc7_mbox_priorbox']],
mode='concat', concat_axis=1,
name='mbox_priorbox')
net['predictions'] = merge([net['mbox_loc'],
net['mbox_conf'],
net['mbox_priorbox']],
mode='concat', concat_axis=2,
name='predictions')
model = Model(net['input'], net['predictions'])
return model
def mini_SSD(num_classes=21):
base_kernel_size = 4 + num_classes
aspect_ratios = (1, 2, 1 / 2)
num_aspect_ratios = len(aspect_ratios)
base_model = VGG16(weights='imagenet')
base_model.layers[0].name = 'input_1'
input_tensor = base_model.input
#input_tensor = base_model
#input_tensor.name = 'image_array'
for layer in base_model.layers:
layer.trainable = False
body = base_model.get_layer('block4_pool').output
body = Convolution2D((base_kernel_size * num_aspect_ratios),
3,
3,
border_mode='same')(body)
branch_1 = PriorBox(aspect_ratios)(body)
body = Convolution2D(32, 3, 3, border_mode='same')(branch_1)
body = Activation('relu')(body)
body = MaxPooling2D((2, 2))(body)
body = Dropout(.5)(body)
body = Convolution2D((base_kernel_size * num_aspect_ratios),
3,
3,
border_mode='same')(body)
branch_2 = PriorBox(aspect_ratios)(body)
body = Convolution2D(64, 3, 3, border_mode='same')(branch_2)
body = Activation('relu')(body)
body = MaxPooling2D((3, 3))(body)
body = Dropout(.5)(body)
body = Convolution2D((base_kernel_size * num_aspect_ratios),
3,
3,
border_mode='same')(body)
branch_3 = PriorBox(aspect_ratios)(body)
branch_1 = Reshape((-1, 4 + num_classes))(branch_1)
local_1 = Lambda(lambda x: x[:, :, :4])(branch_1)
class_1 = Lambda(lambda x: K.softmax(x[:, :, 4:]))(branch_1)
branch_2 = Reshape((-1, 4 + num_classes))(branch_2)
local_2 = Lambda(lambda x: x[:, :, :4])(branch_2)
class_2 = Lambda(lambda x: K.softmax(x[:, :, 4:]))(branch_2)
branch_3 = Reshape((-1, 4 + num_classes))(branch_3)
local_3 = Lambda(lambda x: x[:, :, :4])(branch_3)
class_3 = Lambda(lambda x: K.softmax(x[:, :, 4:]))(branch_3)
classification_tensor = merge([class_1, class_2, class_3],
mode='concat',
concat_axis=1,
name='classes')
localization_tensor = merge([local_1, local_2, local_3],
mode='concat',
concat_axis=1,
name='encoded_box')
output_tensor = merge([localization_tensor, classification_tensor],
mode='concat',
concat_axis=-1,
name='predictions')
model = Model(input_tensor, output_tensor)
return model
def mini_SSD300(input_shape=(300, 300, 3), num_classes=21):
net = {}
# Block 1
input_tensor = input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['input'] = input_tensor
net['conv1_1'] = Convolution2D(64, 3, 3,
activation='relu',
border_mode='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Convolution2D(64, 3, 3,
activation='relu',
border_mode='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Convolution2D(128, 3, 3,
activation='relu',
border_mode='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Convolution2D(128, 3, 3,
activation='relu',
border_mode='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Convolution2D(256, 3, 3,
activation='relu',
border_mode='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), border_mode='same',
name='pool3')(net['conv3_3'])
net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv3_3'])
num_priors = 6
x = Convolution2D(num_priors * 4, 3, 3, border_mode='same',
name='conv4_3_norm_mbox_loc')(net['conv4_3_norm'])
net['conv4_3_norm_mbox_loc'] = x
flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')
net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc'])
name = 'conv4_3_norm_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
x = Convolution2D(num_priors * num_classes, 3, 3, border_mode='same',
name=name)(net['conv4_3_norm'])
net['conv4_3_norm_mbox_conf'] = x
flatten = Flatten(name='conv4_3_norm_mbox_conf_flat')
net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf'])
priorbox = PriorBox(img_size, 30.0, max_size=60, aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2],
name='conv4_3_norm_mbox_priorbox')
net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm'])
# Prediction from fc7
if hasattr(net['conv4_3_norm_mbox_loc_flat'], '_keras_shape'):
num_boxes = net['conv4_3_norm_mbox_loc_flat']._keras_shape[-1] // 4
elif hasattr(net['mbox_loc'], 'int_shape'):
num_boxes = K.int_shape(net['conv4_3_norm_mbox_loc_flat'])[-1] // 4
net['mbox_loc'] = Reshape((num_boxes, 4),
name='mbox_loc_final')(net['conv4_3_norm_mbox_loc_flat'])
net['mbox_conf'] = Reshape((num_boxes, num_classes),
name='mbox_conf_logits')(net['conv4_3_norm_mbox_conf_flat'])
net['mbox_conf'] = Activation('softmax',
name='mbox_conf_final')(net['mbox_conf'])
net['predictions'] = merge([net['mbox_loc'],
net['mbox_conf'],
net['conv4_3_norm_mbox_priorbox']],
mode='concat', concat_axis=2,
name='predictions')
model = Model(net['input'], net['predictions'])
return model