support multi-gpu
This commit is contained in:
chenxuanhong
@@ -8,13 +8,15 @@ from torch.utils import data
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from torchvision import transforms as T
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# from StyleResize import StyleResize
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class data_prefetcher():
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def __init__(self, loader):
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def __init__(self, loader, cur_gpu):
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self.loader = loader
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self.dataiter = iter(loader)
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self.stream = torch.cuda.Stream()
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self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(1,3,1,1)
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self.std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(1,3,1,1)
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self.stream = torch.cuda.Stream(device=cur_gpu)
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self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1)
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self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1)
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self.cur_gpu = cur_gpu
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.mean = self.mean.half()
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@@ -30,9 +32,9 @@ class data_prefetcher():
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self.src_image1, self.src_image2 = next(self.dataiter)
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with torch.cuda.stream(self.stream):
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self.src_image1 = self.src_image1.cuda(non_blocking=True)
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self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True)
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self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std)
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self.src_image2 = self.src_image2.cuda(non_blocking=True)
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self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True)
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self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std)
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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@@ -41,7 +43,7 @@ class data_prefetcher():
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# self.next_input = self.next_input.float()
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# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
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def next(self):
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torch.cuda.current_stream().wait_stream(self.stream)
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torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream)
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src_image1 = self.src_image1
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src_image2 = self.src_image2
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self.preload()
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@@ -102,6 +104,7 @@ class VGGFace2HQDataset(data.Dataset):
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return self.num_images
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def GetLoader( dataset_roots,
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cur_gpu,
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batch_size=16,
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**kwargs
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):
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@@ -123,7 +126,7 @@ def GetLoader( dataset_roots,
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random_seed)
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content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
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drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
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prefetcher = data_prefetcher(content_data_loader)
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prefetcher = data_prefetcher(content_data_loader,cur_gpu)
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return prefetcher
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def denorm(x):
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@@ -0,0 +1,195 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: data_loader_VGGFace2HQ copy.py
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# Created Date: Saturday January 29th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Saturday, 29th January 2022 3:39:14 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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import os
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import glob
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import torch
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import random
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from PIL import Image
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from pathlib import Path
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from torch.utils import data
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from torchvision import transforms as T
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# from StyleResize import StyleResize
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class data_prefetcher():
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def __init__(self, loader):
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self.loader = loader
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self.dataiter = iter(loader)
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self.stream = torch.cuda.Stream()
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self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(1,3,1,1)
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self.std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(1,3,1,1)
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.mean = self.mean.half()
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# self.std = self.std.half()
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self.num_images = len(loader)
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self.preload()
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def preload(self):
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try:
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self.src_image1 = next(self.dataiter)
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except StopIteration:
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self.dataiter = iter(self.loader)
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self.src_image1 = next(self.dataiter)
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with torch.cuda.stream(self.stream):
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self.src_image1 = self.src_image1.cuda(non_blocking=True)
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self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std)
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.next_input = self.next_input.half()
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# else:
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# self.next_input = self.next_input.float()
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# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
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def next(self):
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torch.cuda.current_stream().wait_stream(self.stream)
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src_image1 = self.src_image1
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self.preload()
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return src_image1
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def __len__(self):
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"""Return the number of images."""
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return self.num_images
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class VGGFace2HQDataset(data.Dataset):
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"""Dataset class for the Artworks dataset and content dataset."""
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def __init__(self,
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image_dir,
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img_transform,
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subffix='jpg',
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random_seed=1234):
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"""Initialize and preprocess the VGGFace2 HQ dataset."""
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self.image_dir = image_dir
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self.img_transform = img_transform
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self.subffix = subffix
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self.dataset = []
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self.random_seed = random_seed
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self.preprocess()
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self.num_images = len(self.dataset)
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def preprocess(self):
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"""Preprocess the VGGFace2 HQ dataset."""
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print("processing VGGFace2 HQ dataset images...")
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temp_path = os.path.join(self.image_dir,'*/*')
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pathes = glob.glob(temp_path)
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self.dataset = []
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for dir_item in pathes:
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print("processing %s"%dir_item,end='\r')
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self.dataset.append(dir_item)
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random.seed(self.random_seed)
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random.shuffle(self.dataset)
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print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset))
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def __getitem__(self, index):
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"""Return two src domain images and two dst domain images."""
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dir_tmp1 = self.dataset[index]
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image1 = self.img_transform(Image.open(dir_tmp1))
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return image1
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def __len__(self):
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"""Return the number of images."""
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return self.num_images
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def GetLoader( dataset_roots,
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batch_size=16,
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**kwargs
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):
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"""Build and return a data loader."""
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data_root = dataset_roots
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random_seed = kwargs["random_seed"]
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num_workers = kwargs["dataloader_workers"]
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c_transforms = []
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c_transforms.append(T.Resize((112,112)))
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c_transforms.append(T.ToTensor())
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c_transforms = T.Compose(c_transforms)
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content_dataset = VGGFace2HQDataset(
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data_root,
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c_transforms,
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"jpg",
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random_seed)
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content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size,
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drop_last=True,shuffle=True,num_workers=num_workers,pin_memory=True)
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prefetcher = data_prefetcher(content_data_loader)
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return prefetcher
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def denorm(x):
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out = (x + 1) / 2
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return out.clamp_(0, 1)
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if __name__ == "__main__":
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from torchvision.utils import save_image
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style_class = ["vangogh","picasso","samuel"]
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categories_names = \
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['a/abbey', 'a/arch', 'a/amphitheater', 'a/aqueduct', 'a/arena/rodeo', 'a/athletic_field/outdoor',
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'b/badlands', 'b/balcony/exterior', 'b/bamboo_forest', 'b/barn', 'b/barndoor', 'b/baseball_field',
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'b/basilica', 'b/bayou', 'b/beach', 'b/beach_house', 'b/beer_garden', 'b/boardwalk', 'b/boathouse',
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'b/botanical_garden', 'b/bullring', 'b/butte', 'c/cabin/outdoor', 'c/campsite', 'c/campus',
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'c/canal/natural', 'c/canal/urban', 'c/canyon', 'c/castle', 'c/church/outdoor', 'c/chalet',
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'c/cliff', 'c/coast', 'c/corn_field', 'c/corral', 'c/cottage', 'c/courtyard', 'c/crevasse',
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'd/dam', 'd/desert/vegetation', 'd/desert_road', 'd/doorway/outdoor', 'f/farm', 'f/fairway',
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'f/field/cultivated', 'f/field/wild', 'f/field_road', 'f/fishpond', 'f/florist_shop/indoor',
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'f/forest/broadleaf', 'f/forest_path', 'f/forest_road', 'f/formal_garden', 'g/gazebo/exterior',
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'g/glacier', 'g/golf_course', 'g/greenhouse/indoor', 'g/greenhouse/outdoor', 'g/grotto', 'g/gorge',
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'h/hayfield', 'h/herb_garden', 'h/hot_spring', 'h/house', 'h/hunting_lodge/outdoor', 'i/ice_floe',
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'i/ice_shelf', 'i/iceberg', 'i/inn/outdoor', 'i/islet', 'j/japanese_garden', 'k/kasbah',
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'k/kennel/outdoor', 'l/lagoon', 'l/lake/natural', 'l/lawn', 'l/library/outdoor', 'l/lighthouse',
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'm/mansion', 'm/marsh', 'm/mausoleum', 'm/moat/water', 'm/mosque/outdoor', 'm/mountain',
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'm/mountain_path', 'm/mountain_snowy', 'o/oast_house', 'o/ocean', 'o/orchard', 'p/park',
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'p/pasture', 'p/pavilion', 'p/picnic_area', 'p/pier', 'p/pond', 'r/raft', 'r/railroad_track',
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'r/rainforest', 'r/rice_paddy', 'r/river', 'r/rock_arch', 'r/roof_garden', 'r/rope_bridge',
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'r/ruin', 's/schoolhouse', 's/sky', 's/snowfield', 's/swamp', 's/swimming_hole',
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's/synagogue/outdoor', 't/temple/asia', 't/topiary_garden', 't/tree_farm', 't/tree_house',
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'u/underwater/ocean_deep', 'u/utility_room', 'v/valley', 'v/vegetable_garden', 'v/viaduct',
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'v/village', 'v/vineyard', 'v/volcano', 'w/waterfall', 'w/watering_hole', 'w/wave',
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'w/wheat_field', 'z/zen_garden', 'a/alcove', 'a/apartment-building/outdoor', 'a/artists_loft',
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'b/building_facade', 'c/cemetery']
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s_datapath = "D:\\F_Disk\\data_set\\Art_Data\\data_art_backup"
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c_datapath = "D:\\Downloads\\data_large"
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savepath = "D:\\PatchFace\\PleaseWork\\multi-style-gan\\StyleTransfer\\dataloader_test"
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imsize = 512
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s_datasetloader= getLoader(s_datapath,c_datapath,
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style_class, categories_names,
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crop_size=imsize, batch_size=16, num_workers=4)
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wocao = iter(s_datasetloader)
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for i in range(500):
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print("new batch")
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s_image,c_image,label = next(wocao)
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print(label)
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# print(label)
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# saved_image1 = torch.cat([denorm(image.data),denorm(hahh.data)],3)
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# save_image(denorm(image), "%s\\%d-label-%d.jpg"%(savepath,i), nrow=1, padding=1)
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pass
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# import cv2
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# import os
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# for dir_item in categories_names:
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# join_path = Path(contentdatapath,dir_item)
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# if join_path.exists():
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# print("processing %s"%dir_item,end='\r')
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# images = join_path.glob('*.%s'%("jpg"))
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# for item in images:
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# temp_path = str(item)
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# # temp = cv2.imread(temp_path)
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# temp = Image.open(temp_path)
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# if temp.layers<3:
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# print("remove broken image...")
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# print("image name:%s"%temp_path)
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# del temp
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# os.remove(item)
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@@ -0,0 +1,246 @@
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: data_loader_VGGFace2HQ copy.py
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# Created Date: Sunday February 6th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Tuesday, 8th February 2022 1:50:00 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 Shanghai Jiao Tong University
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#############################################################
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import os
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import glob
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import torch
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import random
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import numpy as np
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from PIL import Image
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from torch.utils import data
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from torchvision import transforms as T
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# from StyleResize import StyleResize
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class InfiniteSampler(torch.utils.data.Sampler):
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def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
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assert len(dataset) > 0
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assert num_replicas > 0
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assert 0 <= rank < num_replicas
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assert 0 <= window_size <= 1
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super().__init__(dataset)
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self.dataset = dataset
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self.rank = rank
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self.num_replicas = num_replicas
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self.shuffle = shuffle
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self.seed = seed
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self.window_size = window_size
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def __iter__(self):
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order = np.arange(len(self.dataset))
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rnd = None
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window = 0
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if self.shuffle:
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rnd = np.random.RandomState(self.seed)
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rnd.shuffle(order)
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window = int(np.rint(order.size * self.window_size))
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idx = 0
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while True:
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i = idx % order.size
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if idx % self.num_replicas == self.rank:
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yield order[i]
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if window >= 2:
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j = (i - rnd.randint(window)) % order.size
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order[i], order[j] = order[j], order[i]
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idx += 1
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class data_prefetcher():
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def __init__(self, loader, cur_gpu):
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self.loader = loader
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self.dataiter = iter(loader)
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self.stream = torch.cuda.Stream(device=cur_gpu)
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self.mean = torch.tensor([0.485, 0.456, 0.406]).cuda(device=cur_gpu).view(1,3,1,1)
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self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=cur_gpu).view(1,3,1,1)
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self.cur_gpu = cur_gpu
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.mean = self.mean.half()
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# self.std = self.std.half()
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# self.num_images = loader.__len__()
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self.preload()
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def preload(self):
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# try:
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self.src_image1, self.src_image2 = next(self.dataiter)
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# except StopIteration:
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# self.dataiter = iter(self.loader)
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# self.src_image1, self.src_image2 = next(self.dataiter)
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with torch.cuda.stream(self.stream):
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self.src_image1 = self.src_image1.cuda(device= self.cur_gpu, non_blocking=True)
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self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std)
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self.src_image2 = self.src_image2.cuda(device= self.cur_gpu, non_blocking=True)
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self.src_image2 = self.src_image2.sub_(self.mean).div_(self.std)
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.next_input = self.next_input.half()
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# else:
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# self.next_input = self.next_input.float()
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# self.next_input = self.next_input.sub_(self.mean).div_(self.std)
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def next(self):
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torch.cuda.current_stream(device= self.cur_gpu,).wait_stream(self.stream)
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src_image1 = self.src_image1
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src_image2 = self.src_image2
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self.preload()
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return src_image1, src_image2
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# def __len__(self):
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# """Return the number of images."""
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# return self.num_images
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class VGGFace2HQDataset(data.Dataset):
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"""Dataset class for the Artworks dataset and content dataset."""
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def __init__(self,
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image_dir,
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img_transform,
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subffix='jpg',
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random_seed=1234):
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"""Initialize and preprocess the VGGFace2 HQ dataset."""
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self.image_dir = image_dir
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self.img_transform = img_transform
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self.subffix = subffix
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self.dataset = []
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self.random_seed = random_seed
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self.preprocess()
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self.num_images = len(self.dataset)
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def preprocess(self):
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"""Preprocess the VGGFace2 HQ dataset."""
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print("processing VGGFace2 HQ dataset images...")
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temp_path = os.path.join(self.image_dir,'*/')
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pathes = glob.glob(temp_path)
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self.dataset = []
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for dir_item in pathes:
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join_path = glob.glob(os.path.join(dir_item,'*.jpg'))
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print("processing %s"%dir_item,end='\r')
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temp_list = []
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for item in join_path:
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temp_list.append(item)
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self.dataset.append(temp_list)
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random.seed(self.random_seed)
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random.shuffle(self.dataset)
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print('Finished preprocessing the VGGFace2 HQ dataset, total dirs number: %d...'%len(self.dataset))
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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=True,num_workers=num_workers,pin_memory=True)
|
||||
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)
|
||||
@@ -0,0 +1,246 @@
|
||||
#!/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, 8th February 2022 1:24:27 pm
|
||||
# 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):
|
||||
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]).cuda(device=cur_gpu).view(1,3,1,1)
|
||||
self.std = torch.tensor([0.229, 0.224, 0.225]).cuda(device=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.cuda(device= self.cur_gpu, non_blocking=True)
|
||||
self.src_image1 = self.src_image1.sub_(self.mean).div_(self.std)
|
||||
self.src_image2 = self.src_image2.cuda(device= 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=True,num_workers=num_workers,pin_memory=True)
|
||||
# prefetcher = data_prefetcher(content_data_loader,device)
|
||||
return content_data_loader
|
||||
|
||||
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)
|
||||
Reference in New Issue
Block a user