from retinanet import RetinaNet
print('Loading pretrained ResNet50 model..')
d = torch.load('./model/resnet50.pth')
print('Loading into FPN50..')
fpn = FPN50()
dd = fpn.state_dict()
for k in d.keys():
if not k.startswith('fc'): # skip fc layers
dd[k] = d[k]
print('Saving RetinaNet..')
net = RetinaNet()
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.normal(m.weight, mean=0, std=0.01)
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
pi = 0.01
init.constant(net.cls_head[-1].bias, -math.log((1-pi)/pi))
net.fpn.load_state_dict(dd)
torch.save(net.state_dict(), 'net.pth')
print('Done!')
from fpn import FPN50
from retinanet import RetinaNet
print('Loading pretrained ResNet50 model..')
d = torch.load('./model/resnet50.pth')
print('Loading into FPN50..')
fpn = FPN50()
dd = fpn.state_dict()
for k in d.keys():
if not k.startswith('fc'): # skip fc layers
dd[k] = d[k]
print('Saving RetinaNet..')
net = RetinaNet()
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.normal(m.weight, mean=0, std=0.01)
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
pi = 0.01
init.constant(net.cls_head[-1].bias, -math.log((1 - pi) / pi))
net.fpn.load_state_dict(dd)
torch.save(net.state_dict(), 'net.pth')
print('Done!')
from retinanet import RetinaNet
print('Loading pretrained ResNet50 model..')
d = torch.load('./model/resnet50.pth')
print(d.keys())
print('Loading into FPN50..')
fpn = FPN50()
dd = fpn.state_dict()
for k in d.keys():
if not k.startswith('fc'): # skip fc layers
dd[k] = d[k]
print('Saving RetinaNet..')
net = RetinaNet()
print("net.modules():", net.modules())
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.normal(m.weight, mean=0, std=0.01)
if m.bias is not None:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
pi = 0.01
init.constant(net.cls_head[-1].bias, -math.log((1 - pi) / pi))
net.fpn.load_state_dict(dd)
torch.save(net.state_dict(), 'net.pth')
print('Done!')
