path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2...
python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data 此命令行将源域和目标域图像combine each pair of images (A,B) into a single image file,可以用于训练。 参考其他文章中的代码: python /home/xingxiangrui/pytorch-CycleGAN-and...
python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair of images (A,B) into a single image file, ready for training. Notes on Colorization No need to run combine_A_and_B.py for colorization....
python tools/process.py \ --input_dir a \ --b_dir b \ --operation combine \ --output_dir c This puts the images in a side-by-side combined image thatpix2pix.pyexpects. Colorization For colorization, your images should ideally all be the same aspect ratio. You can resize and crop ...
A.3. 训练 InstructPix2Pix B. 无分类器指导细节 完毕结语 翻译完成,接下来准备复现推理和训练,这篇论文的基础是扩散模型和我前面翻译的论文暨知:LDM(Latent Diffusion Model)论文翻译,这篇论文算是image2image的基础性论文之一,23年publish以来引用过千,star也不少,对于如何用diffusion做扩散应用有很好的帮助,翻译...
提取边缘, 然后用“python datasets/combine_A_and_B.py --fold_A datasets/cells/A --fold_B datasets/cells/B --fold_AB datasets/cells/AB”重新整合边缘。最后编辑于 :2019.06.12 06:38:12 ©著作权归作者所有,转载或内容合作请联系作者
# combine visualizations for A and B self.visual_names = visual_names_A + visual_names_B # specify the models you want to save to the disk. if self.isTrain: # 训练模式,定义4个网络其中G_B和D_B是新增 self.model_names = ['G_A', 'G_B', 'D_A', 'D_B'] ...
self.visual_names = ['real_A', 'fake_B', 'real_B'] # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks> if self.isTrain: self.model_names = ['G', 'D'] ...
/path/to/data/A/train/1.jpg is considered to correspond to /path/to/data/B/train/1.jpg. Once the data is formatted this way, call: python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair ...
python scripts/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data This will combine each pair of images (A,B) into a single image file, ready for training. Notes on Colorization ...