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from collections import namedtuple
import torch
from torchvision import models
from src.utils import utils
"""
More detail about the VGG architecture (if you want to understand magic/hardcoded numbers) can be found here:
https://github.com/pytorch/vision/blob/3c254fb7af5f8af252c24e89949c54a3461ff0be/torchvision/models/vgg.py
"""
class Vgg16(torch.nn.Module):
"""Only those layers are exposed which have already proven to work nicely."""
def __init__(self, requires_grad=False, show_progress=False):
super().__init__()
vgg_pretrained_features = models.vgg16(pretrained=True,
progress=show_progress).features
self.layer_names = {'relu1_2': 1, 'relu2_2': 2,
'relu3_3': 3, 'relu4_3': 4}
self.content_feature_maps_index = self.layer_names[
utils.yamlGet('contentLayer')]-1 # relu2_2
self.style_feature_maps_indices = list(range(len(
self.layer_names))) # all layers used for style representation
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
for x in range(4):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(4, 9):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(9, 16):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(16, 23):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, x):
x = self.slice1(x)
relu1_2 = x
x = self.slice2(x)
relu2_2 = x
x = self.slice3(x)
relu3_3 = x
x = self.slice4(x)
relu4_3 = x
vgg_outputs = namedtuple("VggOutputs", self.layer_names.keys())
out = vgg_outputs(relu1_2, relu2_2, relu3_3, relu4_3)
return out
class Vgg16Experimental(torch.nn.Module):
"""Everything exposed so you can play with different combinations for style and content representation"""
def __init__(self, requires_grad=False, show_progress=False):
super().__init__()
vgg_pretrained_features = models.vgg16(pretrained=True,
progress=show_progress).features
self.layer_names = [
'relu1_1', 'relu2_1', 'relu2_2', 'relu3_1', 'relu3_2', 'relu4_1',
'relu4_3', 'relu5_1'
]
self.content_feature_maps_index = 4
self.style_feature_maps_indices = list(range(len(
self.layer_names))) # all layers used for style representation
self.conv1_1 = vgg_pretrained_features[0]
self.relu1_1 = vgg_pretrained_features[1]
self.conv1_2 = vgg_pretrained_features[2]
self.relu1_2 = vgg_pretrained_features[3]
self.max_pooling1 = vgg_pretrained_features[4]
self.conv2_1 = vgg_pretrained_features[5]
self.relu2_1 = vgg_pretrained_features[6]
self.conv2_2 = vgg_pretrained_features[7]
self.relu2_2 = vgg_pretrained_features[8]
self.max_pooling2 = vgg_pretrained_features[9]
self.conv3_1 = vgg_pretrained_features[10]
self.relu3_1 = vgg_pretrained_features[11]
self.conv3_2 = vgg_pretrained_features[12]
self.relu3_2 = vgg_pretrained_features[13]
self.conv3_3 = vgg_pretrained_features[14]
self.relu3_3 = vgg_pretrained_features[15]
self.max_pooling3 = vgg_pretrained_features[16]
self.conv4_1 = vgg_pretrained_features[17]
self.relu4_1 = vgg_pretrained_features[18]
self.conv4_2 = vgg_pretrained_features[19]
self.relu4_2 = vgg_pretrained_features[20]
self.conv4_3 = vgg_pretrained_features[21]
self.relu4_3 = vgg_pretrained_features[22]
self.max_pooling4 = vgg_pretrained_features[23]
self.conv5_1 = vgg_pretrained_features[24]
self.relu5_1 = vgg_pretrained_features[25]
self.conv5_2 = vgg_pretrained_features[26]
self.relu5_2 = vgg_pretrained_features[27]
self.conv5_3 = vgg_pretrained_features[28]
self.relu5_3 = vgg_pretrained_features[29]
self.max_pooling5 = vgg_pretrained_features[30]
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, x):
x = self.conv1_1(x)
conv1_1 = x
x = self.relu1_1(x)
relu1_1 = x
x = self.conv1_2(x)
conv1_2 = x
x = self.relu1_2(x)
relu1_2 = x
x = self.max_pooling1(x)
x = self.conv2_1(x)
conv2_1 = x
x = self.relu2_1(x)
relu2_1 = x
x = self.conv2_2(x)
conv2_2 = x
x = self.relu2_2(x)
relu2_2 = x
x = self.max_pooling2(x)
x = self.conv3_1(x)
conv3_1 = x
x = self.relu3_1(x)
relu3_1 = x
x = self.conv3_2(x)
conv3_2 = x
x = self.relu3_2(x)
relu3_2 = x
x = self.conv3_3(x)
conv3_3 = x
x = self.relu3_3(x)
relu3_3 = x
x = self.max_pooling3(x)
x = self.conv4_1(x)
conv4_1 = x
x = self.relu4_1(x)
relu4_1 = x
x = self.conv4_2(x)
conv4_2 = x
x = self.relu4_2(x)
relu4_2 = x
x = self.conv4_3(x)
conv4_3 = x
x = self.relu4_3(x)
relu4_3 = x
x = self.max_pooling4(x)
x = self.conv5_1(x)
conv5_1 = x
x = self.relu5_1(x)
relu5_1 = x
x = self.conv5_2(x)
conv5_2 = x
x = self.relu5_2(x)
relu5_2 = x
x = self.conv5_3(x)
conv5_3 = x
x = self.relu5_3(x)
relu5_3 = x
x = self.max_pooling5(x)
# expose only the layers that you want to experiment with here
vgg_outputs = namedtuple("VggOutputs", self.layer_names)
out = vgg_outputs(relu1_1, relu2_1, relu2_2, relu3_1, relu3_2, relu4_1,
relu4_3, relu5_1)
return out
class Vgg19(torch.nn.Module):
"""
Used in the original NST paper, only those layers are exposed which were used in the original paper
'conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1' were used for style representation
'conv4_2' was used for content representation (although they did some experiments with conv2_2 and conv5_2)
"""
def __init__(self,
requires_grad=False,
show_progress=False,
use_relu=True):
super().__init__()
vgg_pretrained_features = models.vgg19(pretrained=True,
progress=show_progress).features
if use_relu: # use relu or as in original paper conv layers
self.layer_names = [
'relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1'
]
self.offset = 1
else:
self.layer_names = [
'conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv4_2',
'conv5_1'
]
self.offset = 0
self.content_feature_maps_index = 4 # conv4_2
# all layers used for style representation except conv4_2
self.style_feature_maps_indices = list(range(len(self.layer_names)))
self.style_feature_maps_indices.remove(4) # conv4_2
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.nn.Sequential()
self.slice5 = torch.nn.Sequential()
self.slice6 = torch.nn.Sequential()
for x in range(1 + self.offset):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(1 + self.offset, 6 + self.offset):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(6 + self.offset, 11 + self.offset):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(11 + self.offset, 20 + self.offset):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(20 + self.offset, 22):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
for x in range(22, 29 + +self.offset):
self.slice6.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, x):
x = self.slice1(x)
layer1_1 = x
x = self.slice2(x)
layer2_1 = x
x = self.slice3(x)
layer3_1 = x
x = self.slice4(x)
layer4_1 = x
x = self.slice5(x)
conv4_2 = x
x = self.slice6(x)
layer5_1 = x
vgg_outputs = namedtuple("VggOutputs", self.layer_names)
out = vgg_outputs(layer1_1, layer2_1, layer3_1, layer4_1, conv4_2,
layer5_1)
return out
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