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Separate feature extractors out, add resnet feature extractor

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This commit is contained in:
James Betker 2020-05-28 20:26:30 -06:00
parent 156cee240a
commit 57682ebee3
3 changed files with 80 additions and 36 deletions
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28
codes/models/archs/discriminator_vgg_arch.py
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@ -62,31 +62,3 @@ class Discriminator_VGG_128(nn.Module):
out = self.linear2(fea)
return out
class VGGFeatureExtractor(nn.Module):
def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True,
device=torch.device('cpu')):
super(VGGFeatureExtractor, self).__init__()
self.use_input_norm = use_input_norm
if use_bn:
model = torchvision.models.vgg19_bn(pretrained=True)
else:
model = torchvision.models.vgg19(pretrained=True)
if self.use_input_norm:
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
self.register_buffer('mean', mean)
self.register_buffer('std', std)
self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
# No need to BP to variable
for k, v in self.features.named_parameters():
v.requires_grad = False
def forward(self, x):
# Assume input range is [0, 1]
if self.use_input_norm:
x = (x - self.mean) / self.std
output = self.features(x)
return output

68
codes/models/archs/feature_arch.py Normal file
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@ -0,0 +1,68 @@
import torch
import torch.nn as nn
import torchvision
# Utilizes pretrained torchvision modules for feature extraction
class VGGFeatureExtractor(nn.Module):
def __init__(self, feature_layer=34, use_bn=False, use_input_norm=True,
device=torch.device('cpu')):
super(VGGFeatureExtractor, self).__init__()
self.use_input_norm = use_input_norm
if use_bn:
model = torchvision.models.vgg19_bn(pretrained=True)
else:
model = torchvision.models.vgg19(pretrained=True)
if self.use_input_norm:
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
self.register_buffer('mean', mean)
self.register_buffer('std', std)
self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
# No need to BP to variable
for k, v in self.features.named_parameters():
v.requires_grad = False
def forward(self, x):
# Assume input range is [0, 1]
if self.use_input_norm:
x = (x - self.mean) / self.std
output = self.features(x)
return output
class WideResnetFeatureExtractor(nn.Module):
def __init__(self, use_input_norm=True, device=torch.device('cpu')):
print("Using wide resnet extractor.")
super(WideResnetFeatureExtractor, self).__init__()
self.use_input_norm = use_input_norm
self.model = torchvision.models.wide_resnet50_2(pretrained=True)
if self.use_input_norm:
mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
# [0.485 - 1, 0.456 - 1, 0.406 - 1] if input in range [-1, 1]
std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
# [0.229 * 2, 0.224 * 2, 0.225 * 2] if input in range [-1, 1]
self.register_buffer('mean', mean)
self.register_buffer('std', std)
# No need to BP to variable
for p in self.model.parameters():
p.requires_grad = False
def forward(self, x):
# Assume input range is [0, 1]
if self.use_input_norm:
x = (x - self.mean) / self.std
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
x = self.model.layer1(x)
x = self.model.layer2(x)
x = self.model.layer3(x)
return x
w = WideResnetFeatureExtractor()
w.forward(torch.randn(3,64,64))

8
codes/models/networks.py
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@ -8,7 +8,7 @@ import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.HighToLowResNet as HighToLowResNet
import models.archs.ResGen_arch as ResGen_arch
import models.archs.biggan_gen_arch as biggan_arch
import math
import models.archs.feature_arch as feature_arch
# Generator
def define_G(opt, net_key='network_G'):
@ -83,12 +83,16 @@ def define_D(opt):
def define_F(opt, use_bn=False):
gpu_ids = opt['gpu_ids']
device = torch.device('cuda' if gpu_ids else 'cpu')
if 'which_model_F' not in opt['train'].keys() or opt['train']['which_model_F'] == 'vgg':
# PyTorch pretrained VGG19-54, before ReLU.
if use_bn:
feature_layer = 49
else:
feature_layer = 34
netF = SRGAN_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
netF = feature_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
use_input_norm=True, device=device)
elif opt['train']['which_model_F'] == 'wide_resnet':
netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True, device=device)
netF.eval() # No need to train
return netF