#!/usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################# # File: data_loader_VGGFace2HQ copy.py # Created Date: Sunday February 6th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com # Last Modified: Tuesday, 15th February 2022 1:35:06 am # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# import os import glob import torch import random import numpy as np from PIL import Image from torch.utils import data from torchvision import transforms as T # from StyleResize import StyleResize class InfiniteSampler(torch.utils.data.Sampler): def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): assert len(dataset) > 0 assert num_replicas > 0 assert 0 <= rank < num_replicas assert 0 <= window_size <= 1 super().__init__(dataset) self.dataset = dataset self.rank = rank self.num_replicas = num_replicas self.shuffle = shuffle self.seed = seed self.window_size = window_size def __iter__(self): order = np.arange(len(self.dataset)) rnd = None window = 0 if self.shuffle: rnd = np.random.RandomState(self.seed) rnd.shuffle(order) window = int(np.rint(order.size * self.window_size)) idx = 0 while True: i = idx % order.size if idx % self.num_replicas == self.rank: yield order[i] if window >= 2: j = (i - rnd.randint(window)) % order.size order[i], order[j] = order[j], order[i] idx += 1 class data_prefetcher(): def __init__(self, loader, cur_gpu): torch.cuda.set_device(cur_gpu) # must add this line to avoid excessive use of GPU 0 by the prefetcher self.loader = loader self.dataiter = iter(loader) self.stream = torch.cuda.Stream(device=cur_gpu) self.mean = torch.tensor([0.485, 0.456, 0.406]).to(cur_gpu).view(1,3,1,1) self.std = torch.tensor([0.229, 0.224, 0.225]).to(cur_gpu).view(1,3,1,1) self.cur_gpu = cur_gpu # 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 = loader.__len__() self.preload() def preload(self): # try: self.src_image1, self.src_image2 = next(self.dataiter) # except StopIteration: # self.dataiter = iter(self.loader) # self.src_image1, self.src_image2 = next(self.dataiter) with torch.cuda.stream(self.stream): self.src_image1 = self.src_image1.to(self.cur_gpu, non_blocking=True) self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std) self.src_image2 = self.src_image2.to(self.cur_gpu, non_blocking=True) self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std) # 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(device= self.cur_gpu,).wait_stream(self.stream) src_image1 = self.src_image1 src_image2 = self.src_image2 self.preload() return src_image1, src_image2 # def __len__(self): # """Return the number of images.""" # return self.num_images class VGGFace2HQDataset(data.Dataset): """Dataset class for the Artworks dataset and content dataset.""" def __init__(self, image_dir, img_transform, subffix='jpg', random_seed=1234): """Initialize and preprocess the VGGFace2 HQ dataset.""" self.image_dir = image_dir self.img_transform = img_transform self.subffix = subffix self.dataset = [] self.random_seed = random_seed self.preprocess() self.num_images = len(self.dataset) def preprocess(self): """Preprocess the VGGFace2 HQ dataset.""" print("processing VGGFace2 HQ dataset images...") temp_path = os.path.join(self.image_dir,'*/') pathes = glob.glob(temp_path) self.dataset = [] for dir_item in pathes: join_path = glob.glob(os.path.join(dir_item,'*.jpg')) print("processing %s"%dir_item,end='\r') temp_list = [] for item in join_path: temp_list.append(item) self.dataset.append(temp_list) random.seed(self.random_seed) random.shuffle(self.dataset) print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset)) def __getitem__(self, index): """Return two src domain images and two dst domain images.""" dir_tmp1 = self.dataset[index] dir_tmp1_len = len(dir_tmp1) filename1 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] filename2 = dir_tmp1[random.randint(0,dir_tmp1_len-1)] image1 = self.img_transform(Image.open(filename1)) image2 = self.img_transform(Image.open(filename2)) return image1, image2 def __len__(self): """Return the number of images.""" return self.num_images def GetLoader( dataset_roots, rank, num_gpus, batch_size=16, **kwargs ): """Build and return a data loader.""" data_root = dataset_roots random_seed = kwargs["random_seed"] num_workers = kwargs["dataloader_workers"] c_transforms = [] c_transforms.append(T.ToTensor()) c_transforms = T.Compose(c_transforms) content_dataset = VGGFace2HQDataset( data_root, c_transforms, "jpg", random_seed) device = torch.device('cuda', rank) sampler = InfiniteSampler(dataset=content_dataset, rank=rank, num_replicas=num_gpus, seed=random_seed) # content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, # drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True, sampler=sampler) prefetcher = data_prefetcher(content_data_loader,device) 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)