def __init__(self, version='mobile', feature_levels=(3, 4, 5), pretrained=True, **kwargs):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
name = 'proxylessnas_%s' % version
backbone = ptcv_get_model(name, pretrained=pretrained)
del backbone.output
features = backbone.features
self.layer1 = nn.Sequential(
features.init_block,
features.stage1,
features.stage2.unit1.body.bc_conv,
)
self.layer2 = nn.Sequential(
features.stage2.unit1.body.dw_conv,
features.stage2.unit1.body.pw_conv,
features.stage2[1:],
features.stage3.unit1.body.bc_conv,
)
self.layer3 = nn.Sequential(
features.stage3.unit1.body.dw_conv,
features.stage3.unit1.body.pw_conv,
features.stage3[1:],
features.stage4.unit1.body.bc_conv,
)
self.layer4 = nn.Sequential(
features.stage4.unit1.body.dw_conv,
features.stage4.unit1.body.pw_conv,
features.stage4[1:],
features.stage5.unit1.body.bc_conv,
)
self.layer5 = nn.Sequential(
features.stage5.unit1.body.dw_conv,
features.stage5.unit1.body.pw_conv,
features.stage5[1:],
features.final_block
)
self.out_channels = [
get_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]
def __init__(self, mult=1.0, feature_levels=(3, 4, 5), pretrained=True, **kwargs):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
net = ptcv_get_model(self.mult2name[float(mult)], pretrained=pretrained)
del net.output
net = net.features
self.layer1 = nn.Sequential(
net.init_block,
net.stage1,
net.stage2.unit1.conv1,
)
self.layer2 = nn.Sequential(
net.stage2.unit1.conv2,
net.stage2.unit1.conv3,
net.stage2.unit2,
net.stage3.unit1.conv1,
)
self.layer3 = nn.Sequential(
net.stage3.unit1.conv2,
net.stage3.unit1.conv3,
*net.stage3[1:],
net.stage4.unit1.conv1,
)
self.layer4 = nn.Sequential(
net.stage4.unit1.conv2,
net.stage4.unit1.conv3,
*net.stage4[1:],
net.stage5.unit1.conv1,
)
self.layer5 = nn.Sequential(
net.stage5.unit1.conv2,
net.stage5.unit1.conv3,
*net.stage5[1:],
net.final_block,
)
self.out_channels = [
get_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]
def __init__(self, feature_levels=(3, 4, 5), pretrained=True, **kwargs):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
net = ptcv_get_model("darknet53", pretrained=pretrained)
del net.output
net = net.features
self.layer1 = nn.Sequential(
net.init_block,
net.stage1,
)
self.layer2 = net.stage2
self.layer3 = net.stage3
self.layer4 = net.stage4
self.layer5 = net.stage5
self.out_channels = [
get_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]
def __init__(self, name, feature_levels=(3, 4, 5), pretrained=True):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
from torchvision.models import resnet18
net = resnet18(pretrained=pretrained)
del net.fc
self.layer1 = nn.Sequential(
net.conv1,
net.bn1,
net.relu,
)
self.layer2 = net.maxpool
self.layer3 = net.stage2
self.layer4 = net.stage3
self.layer5 = net.layer4
self.out_channels = [
calc_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]
def __init__(self, name, feature_levels=(3, 4, 5), pretrained=True):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
net = torch.hub.load('zhanghang1989/ResNeSt', name, pretrained=pretrained)
del net.fc
self.layer1 = nn.Sequential(
net.conv1,
net.bn1,
net.relu,
)
self.layer2 = nn.Sequential(
net.maxpool,
net.layer1,
)
self.layer3 = net.layer2
self.layer4 = net.layer3
self.layer5 = net.layer4
self.out_channels = np.array([
get_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
])
def __init__(self,
version=27,
feature_levels=(3, 4, 5),
pretrained=True,
no_down=0,
atrous=False,
**kwargs):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.feature_levels = feature_levels
if no_down != 0:
assert feature_levels == (3, 4) and no_down == -1
backbone = self.version2name[version](pretrained=pretrained)
del backbone.classifier
self.layer1 = backbone.stem[:6]
self.layer2 = nn.Sequential(
backbone.stem[6:],
backbone.stage2,
)
self.layer3 = backbone.stage3
if no_down:
del backbone.stage5.Pooling
if atrous:
conv_to_atrous(backbone.stage5, rate=2)
self.layer4 = nn.Sequential(backbone.stage4, backbone.stage5)
else:
self.layer4 = backbone.stage4
self.layer5 = backbone.stage5
self.out_channels = [
get_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]