num_gpu: 使用的 GPU 数量。 manual_seed: 随机种子。 数据集和数据加载器设置 (Dataset and Data Loader Settings): datasets: 包括训练和验证数据集的设置。 gt_size: 地面真值的大小。 use_hflip, use_rot: 数据增强选项。 batch_size_per_gpu: 每个 GPU 的批量大小
在option 里面加上: # network structuresnetwork_g:type:ExampleArch# the class name# --- the followings are the arguments of ExampleArch --- #num_in_ch:3num_out_ch:3num_feat:64upscale:4 ③ model 原作者的例子里面写了一个新的models/example_model.py,包含模型的训练过程。 在这个文件中,通过...
num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 # dataset and data loader settings datasets: train: name: face_dataset type: PairedImageDataset dataroot_gt: data/hq dataroot_lq: data/lq filename_tmpl: '{}' io_backend: type: disk gt_size: 384 use_flip: true use_rot:...
num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=train_sampler, seed=opt['manual_seed']) num_iter_per_epoch = math.ceil( len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) ...
name: IMDN_x4_test_DIV2K model_type: SRModel scale: 4 num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 0 datasets: test_1: name: DIV2K100 type: PairedImageDataset dataroot_gt: D:/Datasets/SISR/DIV2K/DIV2K_valid_HR dataroot_lq: D:/Datasets/SISR/DIV2K/DIV2K_valid_LR_...
true use_rot: true # --- arguments of data loader --- # use_shuffle: true num_worker_per_gpu: 3 batch_size_per_gpu: 16 dataset_enlarge_ratio: 10 prefetch_mode: ~ val: # validation dataset name: ExampleSet5 type: ExampleDataset dataroot_gt: datasets/example/Set5 io_backend: type:...
num_worker_per_gpu: 6 batch_size_per_gpu: 3 dataset_enlarge_ratio: 100 prefetch_mode: ~ # network structures network_g: type: StyleGAN2Generator out_size: 256 num_style_feat: 512 num_mlp: 8 channel_multiplier: 2 resample_kernel: [1, 3, 3, 1] lr_mlp: 0.01 network_d: type: Styl...
true#Number of workers of reading data for each GPUnum_worker:6#per GPU#Total training batch sizebatch_size:16#total#THe ratio of enlarging dataset. For example, it will repeat 1000 times for a dataset with 15 images#So that after one epoch, it will read 15000 times. It is used ...
prefetch_mode: cpu num_prefetch_queue: 1 # 1 by default Image Super-Resolution It is recommended to symlink the dataset root to datasets with the command ln -s xxx yyy. If your folder structure is different, you may need to change the corresponding paths in config files. DIV2...
num_half_frames * interval while (start_frame_idx < 0) or (end_frame_idx > 99): center_frame_idx = random.randint(0, 99) start_frame_idx = center_frame_idx - self.num_half_frames * interval start_frame_idx = ( center_frame_idx - self.num_half_frames * interval) end_frame_...