def __init__(self, bn=False, mid_feat=False):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(Conv2d(3, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2, ceil_mode=True))
self.conv2 = nn.Sequential(
Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2, ceil_mode=True))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(
Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2, ceil_mode=True))
self.conv4 = nn.Sequential(
Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
nn.MaxPool2d(2, ceil_mode=True))
self.conv5 = nn.Sequential(
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
network.set_trainable(self.conv3, requires_grad=False)
network.set_trainable(self.conv4, requires_grad=False)
network.set_trainable(self.conv5, requires_grad=False)
self.mid_feat = mid_feat
def __init__(self, bn=False, channel_in=3):
super(VGG16, self).__init__()
self.conv1 = nn.Sequential(
Conv2d(channel_in, 64, 3, same_padding=True, bn=bn),
Conv2d(64, 64, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
self.conv2 = nn.Sequential(
Conv2d(64, 128, 3, same_padding=True, bn=bn),
Conv2d(128, 128, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
network.set_trainable(self.conv1, requires_grad=False)
network.set_trainable(self.conv2, requires_grad=False)
self.conv3 = nn.Sequential(
Conv2d(128, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn),
Conv2d(256, 256, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
self.conv4 = nn.Sequential(
Conv2d(256, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn), nn.MaxPool2d(2))
self.conv5 = nn.Sequential(
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn),
Conv2d(512, 512, 3, same_padding=True, bn=bn))
train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=8, pin_memory=True)
net = RPN(not args.use_normal_anchors)
#if args.resume_training:
# print 'Resume training from: {}'.format(args.res ume_model)
# if len(args.resume_model) == 0:
# raise Exception('[resume_model] not specified')
# network.load_net(args.resume_model, net)
# optimizer = torch.optim.SGD([
# {'params': list(net.parameters())[26:]},
# ], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
print 'Training from scratch...Initializing network...'
optimizer = torch.optim.SGD(list(net.parameters())[26:], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
network.set_trainable(net.features, requires_grad=False)
#net.cuda()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
best_recall = np.array([0.0, 0.0])
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, net, optimizer, epoch)
# Testing
recall = test(test_loader, net)
print('Epoch[{epoch:d}]: '