def __init__(self,
mult=1.0,
feature_levels=(3, 4, 5),
pretrained=True,
include_final=False,
**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[mult], pretrained=pretrained)
del net.output
net = net.features
self.layer1 = net.init_block.conv
self.layer2 = net.init_block.pool
self.layer3 = net.stage1
self.layer4 = net.stage2
if include_final:
self.layer5 = nn.Sequential(
net.stage3,
net.final_block,
)
else:
self.layer5 = net.stage3
out_channels = [
get_out_channels(self.layer1),
get_out_channels(self.layer1),
calc_out_channels(self.layer3),
calc_out_channels(self.layer4),
calc_out_channels(self.layer5),
]
self.out_channels = [out_channels[i - 1] 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 = ptcv_get_model(name, pretrained=pretrained)
del net.output
net = net.features
self.layer1 = net.init_block.conv
self.layer2 = nn.Sequential(
net.init_block.pool,
net.stage1,
)
self.layer3 = net.stage2
self.layer4 = net.stage3
if hasattr(net, "post_activ"):
self.layer5 = nn.Sequential(
net.stage4,
net.post_activ,
)
else:
self.layer5 = net.stage4
self.out_channels = [
calc_out_channels(getattr(self, ("layer%d" % i)))
for i in feature_levels
]
def __init__(self,
mult=1.0,
feature_levels=(3, 4, 5),
pretrained=False,
**kwargs):
super().__init__()
_check_levels(feature_levels)
self.forward_levels = tuple(range(1, feature_levels[-1] + 1))
self.out_levels = feature_levels
if pretrained:
net = ptcv_get_model(self.mult2name[mult], pretrained=True)
del net.output
net = net.features
self.layer1 = net.init_block.conv
self.layer2 = net.init_block.pool
self.layer3 = net.stage1
self.layer4 = net.stage2
self.layer5 = nn.Sequential(
net.stage3,
net.final_block,
)
out_channels = [
get_out_channels(self.layer1),
get_out_channels(self.layer1),
calc_out_channels(self.layer3),
calc_out_channels(self.layer4),
calc_out_channels(self.layer5),
]
else:
from horch.models.re.shufflenet import shufflenet_v2 as BShuffleNetV2
net = BShuffleNetV2(mult=mult, **kwargs)
channels = net.out_channels
del net.fc
self.layer1 = net.conv1
self.layer2 = net.maxpool
self.layer3 = net.stage2
self.layer4 = net.stage3
self.layer5 = nn.Sequential(
net.stage4,
net.conv5,
)
out_channels = [channels[0]] + channels[:3] + [channels[-1]]
self.out_channels = [out_channels[i - 1] 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 = ptcv_get_model(name, pretrained=pretrained)
del net.output
net = net.features
self.layer1 = net.init_block
self.layer2 = net.stage1
self.layer3 = net.stage2
self.layer4 = net.stage3
self.layer5 = net.stage4
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
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
]
