init
This commit is contained in:
chenxuanhong
@@ -0,0 +1,36 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: StyleResize.py
|
||||
# Created Date: Friday April 17th 2020
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Saturday, 18th April 2020 1:39:53 pm
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2020 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
from PIL import Image
|
||||
import torchvision.transforms.functional as F
|
||||
|
||||
class StyleResize(object):
|
||||
|
||||
def __call__(self, images):
|
||||
th, tw = images.size # target height, width
|
||||
if max(th,tw) > 1800:
|
||||
alpha = 1800. / float(min(th,tw))
|
||||
h = int(th*alpha)
|
||||
w = int(tw*alpha)
|
||||
images = F.resize(images, (h, w))
|
||||
if max(th,tw) < 800:
|
||||
# Resize the smallest side of the image to 800px
|
||||
alpha = 800. / float(min(th,tw))
|
||||
if alpha < 4.:
|
||||
h = int(th*alpha)
|
||||
w = int(tw*alpha)
|
||||
images = F.resize(images, (h, w))
|
||||
else:
|
||||
images = F.resize(images, (800, 800))
|
||||
return images
|
||||
|
||||
def __repr__(self):
|
||||
return self.__class__.__name__ + '()'
|
||||
@@ -0,0 +1,269 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: data_loader.py
|
||||
# Created Date: Saturday April 4th 2020
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Tuesday, 5th January 2021 2:12:29 pm
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2020 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
|
||||
import os
|
||||
import glob
|
||||
import torch
|
||||
import random
|
||||
from PIL import Image
|
||||
from pathlib import Path
|
||||
from torch.utils import data
|
||||
import torchvision.datasets as dsets
|
||||
from torchvision import transforms as T
|
||||
import torchvision.transforms.functional as F
|
||||
|
||||
|
||||
class StyleResize(object):
|
||||
def __call__(self, images):
|
||||
th, tw = images.size # target height, width
|
||||
if max(th,tw) > 1800:
|
||||
alpha = 1800. / float(min(th,tw))
|
||||
h = int(th*alpha)
|
||||
w = int(tw*alpha)
|
||||
images = F.resize(images, (h, w))
|
||||
if max(th,tw) < 800:
|
||||
# Resize the smallest side of the image to 800px
|
||||
alpha = 800. / float(min(th,tw))
|
||||
if alpha < 4.:
|
||||
h = int(th*alpha)
|
||||
w = int(tw*alpha)
|
||||
images = F.resize(images, (h, w))
|
||||
else:
|
||||
images = F.resize(images, (800, 800))
|
||||
return images
|
||||
|
||||
def __repr__(self):
|
||||
return self.__class__.__name__ + '()'
|
||||
|
||||
class DataPrefetcher():
|
||||
def __init__(self, loader):
|
||||
self.loader = loader
|
||||
self.dataiter = iter(loader)
|
||||
self.stream = torch.cuda.Stream()
|
||||
# self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)
|
||||
# self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.mean = self.mean.half()
|
||||
# self.std = self.std.half()
|
||||
self.__preload__()
|
||||
|
||||
def __preload__(self):
|
||||
try:
|
||||
self.content, self.style, self.label = next(self.dataiter)
|
||||
except StopIteration:
|
||||
self.dataiter = iter(self.loader)
|
||||
self.content, self.style, self.label = next(self.dataiter)
|
||||
|
||||
with torch.cuda.stream(self.stream):
|
||||
self.content= self.content.cuda(non_blocking=True)
|
||||
self.style = self.style.cuda(non_blocking=True)
|
||||
self.label = self.label.cuda(non_blocking=True)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.next_input = self.next_input.half()
|
||||
# else:
|
||||
# self.next_input = self.next_input.float()
|
||||
# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
|
||||
|
||||
def next(self):
|
||||
torch.cuda.current_stream().wait_stream(self.stream)
|
||||
content = self.content
|
||||
style = self.style
|
||||
label = self.label
|
||||
self.__preload__()
|
||||
return content, style, label
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of images."""
|
||||
return len(self.loader)
|
||||
|
||||
class TotalDataset(data.Dataset):
|
||||
"""Dataset class for the Artworks dataset and content dataset."""
|
||||
def __init__(self, content_image_dir,style_image_dir,
|
||||
selectedContent,selectedStyle,
|
||||
content_transform,style_transform,
|
||||
subffix='jpg', random_seed=1234):
|
||||
"""Initialize and preprocess the CelebA dataset."""
|
||||
self.content_image_dir= content_image_dir
|
||||
self.style_image_dir = style_image_dir
|
||||
self.content_transform= content_transform
|
||||
self.style_transform = style_transform
|
||||
self.selectedContent = selectedContent
|
||||
self.selectedStyle = selectedStyle
|
||||
self.subffix = subffix
|
||||
self.content_dataset = []
|
||||
self.art_dataset = []
|
||||
self.random_seed= random_seed
|
||||
self.__preprocess__()
|
||||
self.num_images = len(self.content_dataset)
|
||||
self.art_num = len(self.art_dataset)
|
||||
|
||||
def __preprocess__(self):
|
||||
"""Preprocess the Artworks dataset."""
|
||||
print("processing content images...")
|
||||
for dir_item in self.selectedContent:
|
||||
join_path = Path(self.content_image_dir,dir_item)#.replace('/','_'))
|
||||
if join_path.exists():
|
||||
print("processing %s"%dir_item)
|
||||
images = join_path.glob('*.%s'%(self.subffix))
|
||||
for item in images:
|
||||
self.content_dataset.append(item)
|
||||
else:
|
||||
print("%s dir does not exist!"%dir_item)
|
||||
label_index = 0
|
||||
print("processing style images...")
|
||||
for class_item in self.selectedStyle:
|
||||
images = Path(self.style_image_dir).glob('%s/*.%s'%(class_item, self.subffix))
|
||||
for item in images:
|
||||
self.art_dataset.append([item, label_index])
|
||||
label_index += 1
|
||||
random.seed(self.random_seed)
|
||||
random.shuffle(self.content_dataset)
|
||||
random.shuffle(self.art_dataset)
|
||||
# self.dataset = images
|
||||
print('Finished preprocessing the Art Works dataset, total image number: %d...'%len(self.art_dataset))
|
||||
print('Finished preprocessing the Content dataset, total image number: %d...'%len(self.content_dataset))
|
||||
|
||||
def __getitem__(self, index):
|
||||
"""Return one image and its corresponding attribute label."""
|
||||
filename = self.content_dataset[index]
|
||||
image = Image.open(filename)
|
||||
content = self.content_transform(image)
|
||||
art_index = random.randint(0,self.art_num-1)
|
||||
filename,label = self.art_dataset[art_index]
|
||||
image = Image.open(filename)
|
||||
style = self.style_transform(image)
|
||||
return content,style,label
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of images."""
|
||||
return self.num_images
|
||||
|
||||
def denorm(x):
|
||||
out = (x + 1) / 2
|
||||
return out.clamp_(0, 1)
|
||||
|
||||
def GetLoader(s_image_dir,c_image_dir,
|
||||
style_selected_dir, content_selected_dir,
|
||||
crop_size=178, batch_size=16, num_workers=8,
|
||||
colorJitterEnable=True, colorConfig={"brightness":0.05,"contrast":0.05,"saturation":0.05,"hue":0.05}):
|
||||
"""Build and return a data loader."""
|
||||
|
||||
s_transforms = []
|
||||
c_transforms = []
|
||||
|
||||
s_transforms.append(T.Resize(768))
|
||||
# s_transforms.append(T.Resize(900))
|
||||
c_transforms.append(T.Resize(768))
|
||||
|
||||
s_transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect'))
|
||||
c_transforms.append(T.RandomCrop(crop_size))
|
||||
|
||||
s_transforms.append(T.RandomHorizontalFlip())
|
||||
c_transforms.append(T.RandomHorizontalFlip())
|
||||
|
||||
s_transforms.append(T.RandomVerticalFlip())
|
||||
c_transforms.append(T.RandomVerticalFlip())
|
||||
|
||||
if colorJitterEnable:
|
||||
if colorConfig is not None:
|
||||
print("Enable color jitter!")
|
||||
colorBrightness = colorConfig["brightness"]
|
||||
colorContrast = colorConfig["contrast"]
|
||||
colorSaturation = colorConfig["saturation"]
|
||||
colorHue = (-colorConfig["hue"],colorConfig["hue"])
|
||||
s_transforms.append(T.ColorJitter(brightness=colorBrightness,\
|
||||
contrast=colorContrast,saturation=colorSaturation, hue=colorHue))
|
||||
c_transforms.append(T.ColorJitter(brightness=colorBrightness,\
|
||||
contrast=colorContrast,saturation=colorSaturation, hue=colorHue))
|
||||
s_transforms.append(T.ToTensor())
|
||||
c_transforms.append(T.ToTensor())
|
||||
|
||||
s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
|
||||
c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
|
||||
|
||||
s_transforms = T.Compose(s_transforms)
|
||||
c_transforms = T.Compose(c_transforms)
|
||||
|
||||
content_dataset = TotalDataset(c_image_dir,s_image_dir, content_selected_dir, style_selected_dir
|
||||
, c_transforms,s_transforms)
|
||||
content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
|
||||
drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
|
||||
prefetcher = DataPrefetcher(content_data_loader)
|
||||
return prefetcher
|
||||
|
||||
def GetValiDataTensors(
|
||||
image_dir=None,
|
||||
selected_imgs=[],
|
||||
crop_size=178,
|
||||
mean = (0.5, 0.5, 0.5),
|
||||
std=(0.5, 0.5, 0.5)
|
||||
):
|
||||
|
||||
transforms = []
|
||||
|
||||
transforms.append(T.Resize(768))
|
||||
|
||||
transforms.append(T.RandomCrop(crop_size,pad_if_needed=True,padding_mode='reflect'))
|
||||
|
||||
transforms.append(T.ToTensor())
|
||||
|
||||
transforms.append(T.Normalize(mean=mean, std=std))
|
||||
|
||||
transforms = T.Compose(transforms)
|
||||
|
||||
result_img = []
|
||||
print("Start to read validation data......")
|
||||
if len(selected_imgs) != 0:
|
||||
for s_img in selected_imgs:
|
||||
if image_dir == None:
|
||||
temp_img = s_img
|
||||
else:
|
||||
temp_img = os.path.join(image_dir, s_img)
|
||||
temp_img = Image.open(temp_img)
|
||||
temp_img = transforms(temp_img).cuda().unsqueeze(0)
|
||||
result_img.append(temp_img)
|
||||
else:
|
||||
s_imgs = glob.glob(os.path.join(image_dir, '*.jpg'))
|
||||
s_imgs = s_imgs + glob.glob(os.path.join(image_dir, '*.png'))
|
||||
for s_img in s_imgs:
|
||||
temp_img = os.path.join(image_dir, s_img)
|
||||
temp_img = Image.open(temp_img)
|
||||
temp_img = transforms(temp_img).cuda().unsqueeze(0)
|
||||
result_img.append(temp_img)
|
||||
print("Finish to read validation data......")
|
||||
print("Total validation images: %d"%len(result_img))
|
||||
return result_img
|
||||
|
||||
def ScanAbnormalImg(image_dir, selected_imgs):
|
||||
"""Scan the dataset, this function is designed to exclude or remove the non-RGB images."""
|
||||
print("processing images...")
|
||||
subffix = "jpg"
|
||||
for dir_item in selected_imgs:
|
||||
join_path = Path(image_dir,dir_item)#.replace('/','_'))
|
||||
if join_path.exists():
|
||||
print("processing %s"%dir_item)
|
||||
images = join_path.glob('*.%s'%(subffix))
|
||||
for item in images:
|
||||
# print(str(item.name)[0:6])
|
||||
# temp = cv2.imread(str(item))
|
||||
temp = Image.open(item)
|
||||
# exclude the abnormal images
|
||||
if temp.mode!="RGB":
|
||||
print(temp.mode)
|
||||
print("Found one abnormal image!")
|
||||
print(item)
|
||||
os.remove(str(item))
|
||||
|
||||
else:
|
||||
print("%s dir does not exist!"%dir_item)
|
||||
@@ -0,0 +1,253 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: data_loader_modify.py
|
||||
# Created Date: Saturday April 4th 2020
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Sunday, 4th July 2021 11:12:42 pm
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2020 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
|
||||
import os
|
||||
import torch
|
||||
import random
|
||||
from PIL import Image
|
||||
from pathlib import Path
|
||||
from torch.utils import data
|
||||
import torchvision.datasets as dsets
|
||||
from torchvision import transforms as T
|
||||
from data_tools.StyleResize import StyleResize
|
||||
# from StyleResize import StyleResize
|
||||
|
||||
class data_prefetcher():
|
||||
def __init__(self, loader):
|
||||
self.loader = loader
|
||||
self.dataiter = iter(loader)
|
||||
self.stream = torch.cuda.Stream()
|
||||
# self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)
|
||||
# self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.mean = self.mean.half()
|
||||
# self.std = self.std.half()
|
||||
self.preload()
|
||||
|
||||
def preload(self):
|
||||
try:
|
||||
self.content, self.style, self.label = next(self.dataiter)
|
||||
except StopIteration:
|
||||
self.dataiter = iter(self.loader)
|
||||
self.content, self.style, self.label = next(self.dataiter)
|
||||
|
||||
with torch.cuda.stream(self.stream):
|
||||
self.content= self.content.cuda(non_blocking=True)
|
||||
self.style = self.style.cuda(non_blocking=True)
|
||||
self.label = self.label.cuda(non_blocking=True)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.next_input = self.next_input.half()
|
||||
# else:
|
||||
# self.next_input = self.next_input.float()
|
||||
# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
|
||||
def next(self):
|
||||
torch.cuda.current_stream().wait_stream(self.stream)
|
||||
content = self.content
|
||||
style = self.style
|
||||
label = self.label
|
||||
self.preload()
|
||||
return content, style, label
|
||||
|
||||
class TotalDataset(data.Dataset):
|
||||
"""Dataset class for the Artworks dataset and content dataset."""
|
||||
|
||||
def __init__(self, content_image_dir,style_image_dir,
|
||||
selectedContent,selectedStyle,
|
||||
content_transform,style_transform,
|
||||
subffix='jpg', random_seed=1234):
|
||||
"""Initialize and preprocess the CelebA dataset."""
|
||||
self.content_image_dir = content_image_dir
|
||||
self.style_image_dir = style_image_dir
|
||||
self.content_transform = content_transform
|
||||
self.style_transform = style_transform
|
||||
self.selectedContent = selectedContent
|
||||
self.selectedStyle = selectedStyle
|
||||
self.subffix = subffix
|
||||
self.content_dataset = []
|
||||
self.art_dataset = []
|
||||
self.random_seed = random_seed
|
||||
self.preprocess()
|
||||
self.num_images = len(self.content_dataset)
|
||||
self.art_num = len(self.art_dataset)
|
||||
|
||||
def preprocess(self):
|
||||
"""Preprocess the Artworks dataset."""
|
||||
print("processing content images...")
|
||||
for dir_item in self.selectedContent:
|
||||
join_path = Path(self.content_image_dir,dir_item)
|
||||
if join_path.exists():
|
||||
print("processing %s"%dir_item,end='\r')
|
||||
images = join_path.glob('*.%s'%(self.subffix))
|
||||
for item in images:
|
||||
self.content_dataset.append(item)
|
||||
else:
|
||||
print("%s dir does not exist!"%dir_item,end='\r')
|
||||
label_index = 0
|
||||
print("processing style images...")
|
||||
for class_item in self.selectedStyle:
|
||||
images = Path(self.style_image_dir).glob('%s/*.%s'%(class_item, self.subffix))
|
||||
for item in images:
|
||||
self.art_dataset.append([item, label_index])
|
||||
label_index += 1
|
||||
random.seed(self.random_seed)
|
||||
random.shuffle(self.content_dataset)
|
||||
random.shuffle(self.art_dataset)
|
||||
# self.dataset = images
|
||||
print('Finished preprocessing the Art Works dataset, total image number: %d...'%len(self.art_dataset))
|
||||
print('Finished preprocessing the Content dataset, total image number: %d...'%len(self.content_dataset))
|
||||
|
||||
def __getitem__(self, index):
|
||||
"""Return one image and its corresponding attribute label."""
|
||||
filename = self.content_dataset[index]
|
||||
image = Image.open(filename)
|
||||
content = self.content_transform(image)
|
||||
art_index = random.randint(0,self.art_num-1)
|
||||
filename,label = self.art_dataset[art_index]
|
||||
image = Image.open(filename)
|
||||
style = self.style_transform(image)
|
||||
return content,style,label
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of images."""
|
||||
return self.num_images
|
||||
|
||||
def GetLoader( dataset_roots,
|
||||
batch_size=16,
|
||||
crop_size=512,
|
||||
**kwargs
|
||||
):
|
||||
"""Build and return a data loader."""
|
||||
if not kwargs:
|
||||
a = "Input params error!"
|
||||
raise ValueError(print(a))
|
||||
|
||||
colorJitterEnable = kwargs["color_jitter"]
|
||||
colorConfig = kwargs["color_config"]
|
||||
num_workers = kwargs["dataloader_workers"]
|
||||
num_workers = kwargs["dataloader_workers"]
|
||||
place365_root = dataset_roots["Place365_big"]
|
||||
wikiart_root = dataset_roots["WikiArt"]
|
||||
selected_c_dir = kwargs["selected_content_dir"]
|
||||
selected_s_dir = kwargs["selected_style_dir"]
|
||||
random_seed = kwargs["random_seed"]
|
||||
|
||||
s_transforms = []
|
||||
c_transforms = []
|
||||
|
||||
s_transforms.append(StyleResize())
|
||||
# s_transforms.append(T.Resize(900))
|
||||
c_transforms.append(T.Resize(900))
|
||||
|
||||
s_transforms.append(T.RandomCrop(crop_size, pad_if_needed=True, padding_mode='reflect'))
|
||||
c_transforms.append(T.RandomCrop(crop_size))
|
||||
|
||||
s_transforms.append(T.RandomHorizontalFlip())
|
||||
c_transforms.append(T.RandomHorizontalFlip())
|
||||
|
||||
s_transforms.append(T.RandomVerticalFlip())
|
||||
c_transforms.append(T.RandomVerticalFlip())
|
||||
|
||||
if colorJitterEnable:
|
||||
if colorConfig is not None:
|
||||
print("Enable color jitter!")
|
||||
colorBrightness = colorConfig["brightness"]
|
||||
colorContrast = colorConfig["contrast"]
|
||||
colorSaturation = colorConfig["saturation"]
|
||||
colorHue = (-colorConfig["hue"],colorConfig["hue"])
|
||||
s_transforms.append(T.ColorJitter(brightness=colorBrightness,\
|
||||
contrast=colorContrast,saturation=colorSaturation, hue=colorHue))
|
||||
c_transforms.append(T.ColorJitter(brightness=colorBrightness,\
|
||||
contrast=colorContrast,saturation=colorSaturation, hue=colorHue))
|
||||
s_transforms.append(T.ToTensor())
|
||||
c_transforms.append(T.ToTensor())
|
||||
|
||||
s_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
|
||||
c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
|
||||
|
||||
s_transforms = T.Compose(s_transforms)
|
||||
c_transforms = T.Compose(c_transforms)
|
||||
|
||||
content_dataset = TotalDataset(place365_root,wikiart_root,
|
||||
selected_c_dir, selected_s_dir,
|
||||
c_transforms, s_transforms, "jpg", random_seed)
|
||||
content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
|
||||
drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
|
||||
prefetcher = data_prefetcher(content_data_loader)
|
||||
return prefetcher
|
||||
|
||||
def denorm(x):
|
||||
out = (x + 1) / 2
|
||||
return out.clamp_(0, 1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
from torchvision.utils import save_image
|
||||
style_class = ["vangogh","picasso","samuel"]
|
||||
categories_names = \
|
||||
['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor',
|
||||
'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field',
|
||||
'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse',
|
||||
'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus',
|
||||
'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet',
|
||||
'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse',
|
||||
'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway',
|
||||
'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor',
|
||||
'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior',
|
||||
'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge',
|
||||
'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe',
|
||||
'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah',
|
||||
'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse',
|
||||
'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain',
|
||||
'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park',
|
||||
'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track',
|
||||
'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge',
|
||||
'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole',
|
||||
's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house',
|
||||
'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct',
|
||||
'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave',
|
||||
'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft',
|
||||
'b/building_facade', 'c/cemetery']
|
||||
|
||||
s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup"
|
||||
c_datapath = "D:\\Downloads\\data_large"
|
||||
savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test"
|
||||
|
||||
imsize = 512
|
||||
s_datasetloader= getLoader(s_datapath,c_datapath,
|
||||
style_class, categories_names,
|
||||
crop_size=imsize, batch_size=16, num_workers=4)
|
||||
wocao = iter(s_datasetloader)
|
||||
for i in range(500):
|
||||
print("new batch")
|
||||
s_image,c_image,label = next(wocao)
|
||||
print(label)
|
||||
# print(label)
|
||||
# saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3)
|
||||
# save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1)
|
||||
pass
|
||||
# import cv2
|
||||
# import os
|
||||
# for dir_item in categories_names:
|
||||
# join_path = Path(contentdatapath,dir_item)
|
||||
# if join_path.exists():
|
||||
# print("processing %s"%dir_item,end='\r')
|
||||
# images = join_path.glob('*.%s'%("jpg"))
|
||||
# for item in images:
|
||||
# temp_path = str(item)
|
||||
# # temp = cv2.imread(temp_path)
|
||||
# temp = Image.open(temp_path)
|
||||
# if temp.layers<3:
|
||||
# print("remove broken image...")
|
||||
# print("image name:%s"%temp_path)
|
||||
# del temp
|
||||
# os.remove(item)
|
||||
@@ -0,0 +1,223 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: data_loader_modify.py
|
||||
# Created Date: Saturday April 4th 2020
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Monday, 11th October 2021 12:17:58 am
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2020 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
|
||||
import os
|
||||
import torch
|
||||
import random
|
||||
from PIL import Image
|
||||
from pathlib import Path
|
||||
from torch.utils import data
|
||||
import torchvision.datasets as dsets
|
||||
from torchvision import transforms as T
|
||||
from data_tools.StyleResize import StyleResize
|
||||
# from StyleResize import StyleResize
|
||||
|
||||
class data_prefetcher():
|
||||
def __init__(self, loader):
|
||||
self.loader = loader
|
||||
self.dataiter = iter(loader)
|
||||
self.stream = torch.cuda.Stream()
|
||||
# self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)
|
||||
# self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.mean = self.mean.half()
|
||||
# self.std = self.std.half()
|
||||
self.num_images = len(loader)
|
||||
self.preload()
|
||||
|
||||
def preload(self):
|
||||
try:
|
||||
self.content = next(self.dataiter)
|
||||
except StopIteration:
|
||||
self.dataiter = iter(self.loader)
|
||||
self.content = next(self.dataiter)
|
||||
|
||||
with torch.cuda.stream(self.stream):
|
||||
self.content= self.content.cuda(non_blocking=True)
|
||||
# With Amp, it isn't necessary to manually convert data to half.
|
||||
# if args.fp16:
|
||||
# self.next_input = self.next_input.half()
|
||||
# else:
|
||||
# self.next_input = self.next_input.float()
|
||||
# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
|
||||
def next(self):
|
||||
torch.cuda.current_stream().wait_stream(self.stream)
|
||||
content = self.content
|
||||
self.preload()
|
||||
return content
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of images."""
|
||||
return self.num_images
|
||||
|
||||
class Place365Dataset(data.Dataset):
|
||||
"""Dataset class for the Artworks dataset and content dataset."""
|
||||
|
||||
def __init__(self,
|
||||
content_image_dir,
|
||||
selectedContent,
|
||||
content_transform,
|
||||
subffix='jpg',
|
||||
random_seed=1234):
|
||||
"""Initialize and preprocess the CelebA dataset."""
|
||||
self.content_image_dir = content_image_dir
|
||||
self.content_transform = content_transform
|
||||
self.selectedContent = selectedContent
|
||||
self.subffix = subffix
|
||||
self.content_dataset = []
|
||||
self.random_seed = random_seed
|
||||
self.preprocess()
|
||||
self.num_images = len(self.content_dataset)
|
||||
|
||||
def preprocess(self):
|
||||
"""Preprocess the Artworks dataset."""
|
||||
print("processing content images...")
|
||||
for dir_item in self.selectedContent:
|
||||
join_path = Path(self.content_image_dir,dir_item)
|
||||
if join_path.exists():
|
||||
print("processing %s"%dir_item,end='\r')
|
||||
images = join_path.glob('*.%s'%(self.subffix))
|
||||
for item in images:
|
||||
self.content_dataset.append(item)
|
||||
else:
|
||||
print("%s dir does not exist!"%dir_item,end='\r')
|
||||
random.seed(self.random_seed)
|
||||
random.shuffle(self.content_dataset)
|
||||
print('Finished preprocessing the Content dataset, total image number: %d...'%len(self.content_dataset))
|
||||
|
||||
def __getitem__(self, index):
|
||||
"""Return one image and its corresponding attribute label."""
|
||||
filename = self.content_dataset[index]
|
||||
image = Image.open(filename)
|
||||
content = self.content_transform(image)
|
||||
return content
|
||||
|
||||
def __len__(self):
|
||||
"""Return the number of images."""
|
||||
return self.num_images
|
||||
|
||||
def GetLoader( dataset_roots,
|
||||
batch_size=16,
|
||||
crop_size=512,
|
||||
**kwargs
|
||||
):
|
||||
"""Build and return a data loader."""
|
||||
if not kwargs:
|
||||
a = "Input params error!"
|
||||
raise ValueError(print(a))
|
||||
|
||||
colorJitterEnable = kwargs["color_jitter"]
|
||||
colorConfig = kwargs["color_config"]
|
||||
num_workers = kwargs["dataloader_workers"]
|
||||
num_workers = kwargs["dataloader_workers"]
|
||||
place365_root = dataset_roots["Place365_big"]
|
||||
selected_c_dir = kwargs["selected_content_dir"]
|
||||
random_seed = kwargs["random_seed"]
|
||||
|
||||
c_transforms = []
|
||||
|
||||
# s_transforms.append(T.Resize(900))
|
||||
c_transforms.append(T.Resize(900))
|
||||
c_transforms.append(T.RandomCrop(crop_size))
|
||||
c_transforms.append(T.RandomHorizontalFlip())
|
||||
c_transforms.append(T.RandomVerticalFlip())
|
||||
|
||||
if colorJitterEnable:
|
||||
if colorConfig is not None:
|
||||
print("Enable color jitter!")
|
||||
colorBrightness = colorConfig["brightness"]
|
||||
colorContrast = colorConfig["contrast"]
|
||||
colorSaturation = colorConfig["saturation"]
|
||||
colorHue = (-colorConfig["hue"],colorConfig["hue"])
|
||||
c_transforms.append(T.ColorJitter(brightness=colorBrightness,\
|
||||
contrast=colorContrast,saturation=colorSaturation, hue=colorHue))
|
||||
c_transforms.append(T.ToTensor())
|
||||
c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
|
||||
c_transforms = T.Compose(c_transforms)
|
||||
|
||||
content_dataset = Place365Dataset(
|
||||
place365_root,
|
||||
selected_c_dir,
|
||||
c_transforms,
|
||||
"jpg",
|
||||
random_seed)
|
||||
content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
|
||||
drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
|
||||
prefetcher = data_prefetcher(content_data_loader)
|
||||
return prefetcher
|
||||
|
||||
def denorm(x):
|
||||
out = (x + 1) / 2
|
||||
return out.clamp_(0, 1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
from torchvision.utils import save_image
|
||||
style_class = ["vangogh","picasso","samuel"]
|
||||
categories_names = \
|
||||
['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor',
|
||||
'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field',
|
||||
'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse',
|
||||
'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus',
|
||||
'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet',
|
||||
'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse',
|
||||
'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway',
|
||||
'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor',
|
||||
'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior',
|
||||
'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge',
|
||||
'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe',
|
||||
'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah',
|
||||
'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse',
|
||||
'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain',
|
||||
'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park',
|
||||
'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track',
|
||||
'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge',
|
||||
'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole',
|
||||
's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house',
|
||||
'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct',
|
||||
'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave',
|
||||
'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft',
|
||||
'b/building_facade', 'c/cemetery']
|
||||
|
||||
s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup"
|
||||
c_datapath = "D:\\Downloads\\data_large"
|
||||
savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test"
|
||||
|
||||
imsize = 512
|
||||
s_datasetloader= getLoader(s_datapath,c_datapath,
|
||||
style_class, categories_names,
|
||||
crop_size=imsize, batch_size=16, num_workers=4)
|
||||
wocao = iter(s_datasetloader)
|
||||
for i in range(500):
|
||||
print("new batch")
|
||||
s_image,c_image,label = next(wocao)
|
||||
print(label)
|
||||
# print(label)
|
||||
# saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3)
|
||||
# save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1)
|
||||
pass
|
||||
# import cv2
|
||||
# import os
|
||||
# for dir_item in categories_names:
|
||||
# join_path = Path(contentdatapath,dir_item)
|
||||
# if join_path.exists():
|
||||
# print("processing %s"%dir_item,end='\r')
|
||||
# images = join_path.glob('*.%s'%("jpg"))
|
||||
# for item in images:
|
||||
# temp_path = str(item)
|
||||
# # temp = cv2.imread(temp_path)
|
||||
# temp = Image.open(temp_path)
|
||||
# if temp.layers<3:
|
||||
# print("remove broken image...")
|
||||
# print("image name:%s"%temp_path)
|
||||
# del temp
|
||||
# os.remove(item)
|
||||
@@ -0,0 +1,81 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
#############################################################
|
||||
# File: eval_dataloader_DIV2K.py
|
||||
# Created Date: Tuesday January 12th 2021
|
||||
# Author: Chen Xuanhong
|
||||
# Email: chenxuanhongzju@outlook.com
|
||||
# Last Modified: Tuesday, 12th October 2021 8:29:51 pm
|
||||
# Modified By: Chen Xuanhong
|
||||
# Copyright (c) 2021 Shanghai Jiao Tong University
|
||||
#############################################################
|
||||
|
||||
import os
|
||||
import cv2
|
||||
import glob
|
||||
import torch
|
||||
|
||||
class TestDataset:
|
||||
def __init__( self,
|
||||
path,
|
||||
batch_size = 16,
|
||||
subffix=['png','jpg']):
|
||||
"""Initialize and preprocess the setX dataset."""
|
||||
self.path = path
|
||||
|
||||
self.subffix = subffix
|
||||
self.dataset = []
|
||||
self.pointer = 0
|
||||
self.batch_size = batch_size
|
||||
self.__preprocess__()
|
||||
self.num_images = len(self.dataset)
|
||||
|
||||
def __preprocess__(self):
|
||||
"""Preprocess the SetX dataset."""
|
||||
|
||||
print("processing content images...")
|
||||
for i_suf in self.subffix:
|
||||
temp_path = os.path.join(self.path,'*.%s'%(i_suf))
|
||||
images = glob.glob(temp_path)
|
||||
for item in images:
|
||||
file_name = os.path.basename(item)
|
||||
file_name = os.path.splitext(file_name)
|
||||
file_name = file_name[0]
|
||||
# lr_name = os.path.join(set5lr_path, file_name)
|
||||
self.dataset.append([item,file_name])
|
||||
# self.dataset = images
|
||||
print('Finished preprocessing the content dataset, total image number: %d...'%len(self.dataset))
|
||||
|
||||
def __call__(self):
|
||||
"""Return one batch images."""
|
||||
if self.pointer>=self.num_images:
|
||||
self.pointer = 0
|
||||
a = "The end of the story!"
|
||||
raise StopIteration(print(a))
|
||||
elif (self.pointer+self.batch_size) > self.num_images:
|
||||
end = self.num_images
|
||||
else:
|
||||
end = self.pointer+self.batch_size
|
||||
for i in range(self.pointer, end):
|
||||
filename = self.dataset[i][0]
|
||||
hr_img = cv2.imread(filename)
|
||||
hr_img = cv2.cvtColor(hr_img,cv2.COLOR_BGR2RGB)
|
||||
hr_img = hr_img.transpose((2,0,1))#.astype(np.float)
|
||||
|
||||
hr_img = torch.from_numpy(hr_img)
|
||||
hr_img = hr_img/255.0
|
||||
hr_img = 2 * (hr_img - 0.5)
|
||||
if (i-self.pointer) == 0:
|
||||
hr_ls = hr_img.unsqueeze(0)
|
||||
nm_ls = [self.dataset[i][1],]
|
||||
else:
|
||||
hr_ls = torch.cat((hr_ls,hr_img.unsqueeze(0)),0)
|
||||
nm_ls += [self.dataset[i][1],]
|
||||
self.pointer = end
|
||||
return hr_ls, nm_ls
|
||||
|
||||
def __len__(self):
|
||||
return self.num_images
|
||||
|
||||
def __repr__(self):
|
||||
return self.__class__.__name__ + ' (' + self.path + ')'
|
||||
Reference in New Issue
Block a user