def __init__(self, trunk_name, output_stride=8, pretrained=True):
super(get_resnet, self).__init__()
if trunk_name == "resnet18":
resnet = resnet18(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet34":
resnet = resnet34(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet50":
resnet = resnet50(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet101":
resnet = resnet101(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnet152":
resnet = resnet152(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "resnext101_32x8d":
resnet = resnext101_32x8d(pretrained=pretrained)
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == "se_resnet50":
resnet = se_resnext50_32x4d(pretrained=pretrained)
elif trunk_name == "se_resnet101":
resnet = se_resnext101_32x4d(pretrained=pretrained)
else:
raise KeyError("[*] Not a valid network arch")
self.layer0 = resnet.layer0
self.layer1, self.layer2, self.layer3, self.layer4 = \
resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4
if output_stride == 8:
for n, m in self.layer3.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
elif output_stride == 16:
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
else:
raise KeyError(
"[*] Unsupported output_stride {}".format(output_stride))
def __init__(self, trunk_name, output_stride=8):
super(get_resnet, self).__init__()
if trunk_name == 'seresnext-50':
resnet = se_resnext50_32x4d()
elif trunk_name == 'seresnext-101':
resnet = se_resnext101_32x4d()
elif trunk_name == 'resnet-50':
resnet = resnet50()
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
elif trunk_name == 'resnet-101':
resnet = resnet101()
resnet.layer0 = nn.Sequential(resnet.conv1, resnet.bn1,
resnet.relu, resnet.maxpool)
else:
raise ValueError("Not a valid network arch")
self.layer0 = resnet.layer0
self.layer1, self.layer2, self.layer3, self.layer4 = \
resnet.layer1, resnet.layer2, resnet.layer3, resnet.layer4
if output_stride == 8:
for n, m in self.layer3.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
elif output_stride == 16:
for n, m in self.layer4.named_modules():
if 'conv2' in n:
m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
elif 'downsample.0' in n:
m.stride = (1, 1)
else:
raise 'unsupported output_stride {}'.format(output_stride)