def ela_skresnext50_32x4d(num_classes=4, pretrained=True, dropout=0):
encoder = skresnext50_32x4d(stem_type="deep", in_chans=6)
del encoder.fc
if pretrained:
donor = skresnext50_32x4d(pretrained=True)
transfer_weights(encoder, donor.state_dict())
return TimmRgbElaModel(encoder, num_classes=num_classes, dropout=dropout)
def __init__(self, pretrained=True, layers=None):
if layers is None:
layers = [1, 2, 3, 4]
from timm.models import skresnext50_32x4d
encoder = skresnext50_32x4d(pretrained=pretrained)
super().__init__([64, 256, 512, 1024, 2048], [2, 4, 8, 16, 32], layers)
self.stem = nn.Sequential(
OrderedDict([("conv1", encoder.conv1), ("bn1", encoder.bn1), ("act1", encoder.act1)])
)
self.layer1 = nn.Sequential(encoder.maxpool, encoder.layer1)
self.layer2 = encoder.layer2
self.layer3 = encoder.layer3
self.layer4 = encoder.layer4
def ela_s2d_skresnext50_32x4d(num_classes=4, pretrained=True, dropout=0):
encoder = skresnext50_32x4d(stem_type="deep", in_chans=64)
del encoder.fc
return ELAYCbCcS2DModel(encoder, num_classes=num_classes, dropout=dropout)
def rgb_skresnext50_32x4d(num_classes=4, pretrained=True, dropout=0):
encoder = skresnext50_32x4d(pretrained=pretrained)
del encoder.fc
return TimmRgbModel(encoder, num_classes=num_classes, dropout=dropout)
def ycrcb_skresnext50_32x4d(num_classes=4, pretrained=True, dropout=0):
encoder = skresnext50_32x4d(stem_type="deep", in_chans=6)
del encoder.fc
# encoder.conv1 = make_n_channel_input(encoder.conv1, 6, "auto")
return YCrCbModel(encoder, num_classes=num_classes, dropout=dropout)