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net_cutmix.py
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net_cutmix.py
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import torch
import torch.nn as nn
from function import adaptive_instance_normalization as adain
from IPython import embed
import numpy as np
decoder = nn.Sequential(
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 256, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 128, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 64, (3, 3)),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 3, (3, 3)),
)
vgg = nn.Sequential(
nn.Conv2d(3, 3, (1, 1)),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(3, 64, (3, 3)),
nn.ReLU(), # relu1-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(), # relu1-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 128, (3, 3)),
nn.ReLU(), # relu2-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(), # relu2-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 256, (3, 3)),
nn.ReLU(), # relu3-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 512, (3, 3)),
nn.ReLU(), # relu4-1, this is the last layer used
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU() # relu5-4
)
class Net_E(nn.Module):
def __init__(self, encoder):
super(Net_E, self).__init__()
enc_layers = list(encoder.children())
self.enc_1 = nn.Sequential(*enc_layers[:4]) # input -> relu1_1
self.enc_2 = nn.Sequential(*enc_layers[4:11]) # relu1_1 -> relu2_1
self.enc_3 = nn.Sequential(*enc_layers[11:18]) # relu2_1 -> relu3_1
self.enc_4 = nn.Sequential(*enc_layers[18:31]) # relu3_1 -> relu4_1
# fix the encoder
for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(4):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
# extract relu4_1 from input image
def encode(self, input):
for i in range(4):
input = getattr(self, 'enc_{:d}'.format(i + 1))(input)
return input
def forward(self, x):
x_feat = self.encode(x)
return x_feat
class Net_D(nn.Module):
def __init__(self, encoder, decoder):
super(Net_D, self).__init__()
enc_layers = list(encoder.children())
self.enc_1 = nn.Sequential(*enc_layers[:4]) # input -> relu1_1
self.enc_2 = nn.Sequential(*enc_layers[4:11]) # relu1_1 -> relu2_1
self.enc_3 = nn.Sequential(*enc_layers[11:18]) # relu2_1 -> relu3_1
self.enc_4 = nn.Sequential(*enc_layers[18:31]) # relu3_1 -> relu4_1
self.decoder = decoder
# fix the encoder
for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(4):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
# extract relu4_1 from input image
def encode(self, input):
for i in range(4):
input = getattr(self, 'enc_{:d}'.format(i + 1))(input)
return input
def forward(self, x1, x2, x1_feat, x2_feat, rs, gamma, bbx1, bby1, bbx2, bby2):
if torch.is_tensor(gamma):
gamma = gamma.view(-1,1,1,1)
g11 = x1
g22 = x2
g12 = gamma*x1 + (1.-gamma)*self.decoder(adain(x1_feat, x2_feat))
g21 = gamma*x2 + (1.-gamma)*self.decoder(adain(x2_feat, x1_feat))
Rc = torch.zeros(1, 1, 224, 224).cuda()
Rc[:,:,bbx1:bbx2,bby1:bby2] = 1
Rs = rs * torch.ones(1, 1, 224, 224).cuda()
zero = torch.zeros(1, 1, 224 ,224).cuda()
T = torch.max(zero,Rc+Rs-1)
output = T*g11 + (1.0-Rc-Rs+T)*g22 + (Rc-T)*g12 + (Rs-T)*g21
return output