# global configs Global: checkpoints: null pretrained_model: null output_dir: ./output/ device: gpu class_num: 1000 save_interval: 1 eval_during_train: True eval_interval: 1 epochs: 120 print_batch_step: 10 use_visualdl: False # used for static mode and model export image_shape: [3, 224, 224] save_inference_dir: ./inference # model architecture Arch: name: DenseNet161 # loss function config for traing/eval process Loss: Train: - CELoss: weight: 1.0 Eval: - CELoss: weight: 1.0 Optimizer: name: Momentum momentum: 0.9 lr: name: Piecewise learning_rate: 0.1 decay_epochs: [30, 60, 90] values: [0.1, 0.01, 0.001, 0.0001] regularizer: name: 'L2' coeff: 0.0001 # data loader for train and eval DataLoader: Train: dataset: name: ImageNetDataset image_root: ./dataset/ILSVRC2012/ cls_label_path: ./dataset/ILSVRC2012/train_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - RandCropImage: size: 224 - RandFlipImage: flip_code: 1 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 64 drop_last: False shuffle: True loader: num_workers: 4 use_shared_memory: True Eval: dataset: name: ImageNetDataset image_root: ./dataset/ILSVRC2012/ cls_label_path: ./dataset/ILSVRC2012/val_list.txt transform_ops: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' sampler: name: DistributedBatchSampler batch_size: 64 drop_last: False shuffle: False loader: num_workers: 4 use_shared_memory: True Infer: infer_imgs: docs/images/whl/demo.jpg batch_size: 10 transforms: - DecodeImage: to_rgb: True channel_first: False - ResizeImage: resize_short: 256 - CropImage: size: 224 - NormalizeImage: scale: 1.0/255.0 mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: '' - ToCHWImage: PostProcess: name: Topk topk: 5 class_id_map_file: ppcls/utils/imagenet1k_label_list.txt Metric: Train: - TopkAcc: topk: [1, 5] Eval: - TopkAcc: topk: [1, 5]