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Added support for resnet architectures #290

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Added support for resnet architectures #290

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8 changes: 7 additions & 1 deletion lpips/networks_basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
from pdb import set_trace as st
from skimage import color
from IPython import embed
from functools import partial
from . import pretrained_networks as pn

import lpips as util
Expand All @@ -25,7 +26,7 @@ def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W

# Learned perceptual metric
class PNetLin(nn.Module):
def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True):
def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True, num=None):
super(PNetLin, self).__init__()

self.pnet_type = pnet_type
Expand All @@ -45,6 +46,11 @@ def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropou
elif(self.pnet_type=='squeeze'):
net_type = pn.squeezenet
self.chns = [64,128,256,384,384,512,512]
elif(self.pnet_type== 'resnet'):
if num is None:
raise ValueError(f'\'num\' must be specified for pnet_type == {self.pnet_type}')
net_type = partial(pn.resnet, num=num)
self.chns = [64, 256, 512, 1024, 2048]
self.L = len(self.chns)

self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
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