# global configs Global: output_dir: ./output/ device: gpu model_dir: ./MobileNetV3_large_x1_0_infer model_filename: inference.pdmodel params_filename: inference.pdiparams input_name: inputs Distillation: alpha: 1.0 loss: soft_label Quantization: use_pact: true activation_bits: 8 is_full_quantize: false onnx_format: true activation_quantize_type: moving_average_abs_max weight_quantize_type: channel_wise_abs_max not_quant_pattern: - skip_quant quantize_op_types: - conv2d - depthwise_conv2d weight_bits: 8 TrainConfig: epochs: 2 eval_iter: 5000 learning_rate: 0.001 optimizer_builder: optimizer: type: Momentum weight_decay: 0.00005 origin_metric: 0.7532 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 - AutoAugment: - 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: 128 drop_last: False shuffle: True loader: num_workers: 8 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: 32 drop_last: False shuffle: False loader: num_workers: 4 use_shared_memory: True