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ECV_Generator.py
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import glob
import random
import os
import math
import itertools
import sys
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from PIL import Image
import argparse
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from torchvision.utils import save_image, make_grid
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torchvision.models import vgg19
import util
class BaseColor(nn.Module):
def __init__(self):
super(BaseColor, self).__init__()
self.l_cent = 50.
self.l_norm = 100.
self.ab_norm = 110.
def normalize_l(self, in_l):
return (in_l-self.l_cent)/self.l_norm
def unnormalize_l(self, in_l):
return in_l*self.l_norm + self.l_cent
def normalize_ab(self, in_ab):
return in_ab/self.ab_norm
def unnormalize_ab(self, in_ab):
return in_ab*self.ab_norm
class ECCVGenerator(BaseColor):
def __init__(self, norm_layer=nn.BatchNorm2d):
super(ECCVGenerator, self).__init__()
model1=[nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=True),]
model1+=[nn.ReLU(True),]
model1+=[nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=True),]
model1+=[nn.ReLU(True),]
model1+=[norm_layer(64),]
model2=[nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=True),]
model2+=[nn.ReLU(True),]
model2+=[nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1, bias=True),]
model2+=[nn.ReLU(True),]
model2+=[norm_layer(128),]
model3=[nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1, bias=True),]
model3+=[nn.ReLU(True),]
model3+=[norm_layer(256),]
model4=[nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model4+=[nn.ReLU(True),]
model4+=[norm_layer(512),]
model5=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model5+=[nn.ReLU(True),]
model5+=[norm_layer(512),]
model6=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),]
model6+=[nn.ReLU(True),]
model6+=[norm_layer(512),]
model7=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),]
model7+=[nn.ReLU(True),]
model7+=[norm_layer(512),]
model8=[nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),]
model8+=[nn.ReLU(True),]
model8+=[nn.Conv2d(256, 313, kernel_size=1, stride=1, padding=0, bias=True),]
self.model1 = nn.Sequential(*model1)
self.model2 = nn.Sequential(*model2)
self.model3 = nn.Sequential(*model3)
self.model4 = nn.Sequential(*model4)
self.model5 = nn.Sequential(*model5)
self.model6 = nn.Sequential(*model6)
self.model7 = nn.Sequential(*model7)
self.model8 = nn.Sequential(*model8)
self.softmax = nn.Softmax(dim=1)
self.model_out = nn.Conv2d(313, 2, kernel_size=1, padding=0, dilation=1, stride=1, bias=False)
self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, input_l):
conv1_2 = self.model1(self.normalize_l(input_l))
conv2_2 = self.model2(conv1_2)
conv3_3 = self.model3(conv2_2)
conv4_3 = self.model4(conv3_3)
conv5_3 = self.model5(conv4_3)
conv6_3 = self.model6(conv5_3)
conv7_3 = self.model7(conv6_3)
conv8_3 = self.model8(conv7_3)
out_reg = self.model_out(self.softmax(conv8_3))
return self.unnormalize_ab(self.upsample4(out_reg))
def eccv16(pretrained=True):
model = ECCVGenerator()
if(pretrained):
import torch.utils.model_zoo as model_zoo
model.load_state_dict(model_zoo.load_url('https://colorizers.s3.us-east-2.amazonaws.com/colorization_release_v2-9b330a0b.pth',map_location='cpu',check_hash=True))
return model
class color_ecv(nn.Module):
def __init__(self, in_channels=3):
super(color_ecv, self).__init__()
self.model = eccv16(pretrained=True)
def forward(self, x):
ecv_output = self.model(x)
return ecv_output