| general: | |
| project_name: '' | |
| output: '' | |
| saved_models_dir: saved_models | |
| display_figures: False | |
| seed: 42 | |
| gpu_memory_limit: 3 | |
| workers: 4 | |
| log_interval: 50 | |
| recovery_interval: 0 | |
| checkpoint_hist: 10 | |
| save_images: False | |
| amp: false | |
| amp_dtype: "float16" | |
| amp_impl: "native" | |
| no_ddp_bb: false | |
| synchronize_step: false | |
| pin_mem: false | |
| no_prefetcher: true | |
| eval_metric: "top1" | |
| tta: 0 | |
| local_rank: 0 | |
| use_multi_epochs_loader: false | |
| log_wandb: false | |
| log_tb: false | |
| operation_mode: training | |
| # quantization: | |
| # quantizer: Onnx_quantizer | |
| # quantization_type: PTQ | |
| # quantization_input_type: uint8 | |
| # quantization_output_type: float | |
| # export_dir: quantized_models | |
| dataset: | |
| dataset_name: "imagenet" # options "flowers102", "food101", "imagenet" | |
| class_names: '' # how is class_names used, torch especially imagenet requires a dict called class_map | |
| classes_file_path: ./datasets/deployment_labels_imagenet.txt | |
| num_classes: 1000 # change according to your dataset | |
| data_dir: 'local/datasets/' # provide root folder which cotnains imagenet folder and this can also be used for quantization as a fall back | |
| #train_split: "train" # folder name under root (for imagenet) Optional for standard imagenet | |
| #val_split: "validation" # folder name under root (for imagenet) Optional for standard imagenet | |
| #test_path: '/local/datasets/ic_imagenet_2012/val/' | |
| #quantization_path: '/local/datasets/ic_imagenet_2012/val/' | |
| #prediction_path: '/local/datasets/ic_imagenet_2012/n01440764/' | |
| preprocessing: | |
| rescaling: | |
| scale: 1/255.0 # TODO scale node is already present under data_augmentation | |
| offset: 0 | |
| resizing: | |
| interpolation: nearest # nearest 'Image resize interpolation type (overrides model)' | |
| aspect_ratio: fit | |
| color_mode: rgb | |
| mean: [0.485, 0.456, 0.406] # 'Override mean pixel value of dataset' | |
| std: [0.229, 0.224, 0.225] # 'Override std deviation of dataset' | |
| data_augmentation: | |
| no_aug: False | |
| scale: [0.08, 1.0] # TODO scale node is already present under data_augmentation | |
| ratio: [0.75, 1.33] | |
| horizontal_flip: 0.5 | |
| vertical_flip: 0.0 | |
| hflip: 0.5 | |
| vflip: 0.0 | |
| color_jitter: 0.4 | |
| aa: null | |
| aug_repeats: 0 | |
| aug_splits: 0 | |
| jsd_loss: False | |
| bce_loss: False | |
| bce_target_thresh: null | |
| reprob: 0 | |
| remode: 'pixel' | |
| recount: 1 | |
| resplit: False | |
| mixup: 0.0 | |
| cutmix: 0.0 | |
| cutmix_minmax: null # Example: [0.3, 0.8] | |
| mixup_prob: 1.0 | |
| mixup_switch_prob: 0.5 | |
| mixup_mode: "batch" | |
| smoothing: 0.1 | |
| train_interpolation: "random" | |
| drop: 0.0 | |
| drop_connect: null | |
| drop_path: null | |
| drop_block: null | |
| model: | |
| model_name: 'mobilenetv2_w035_pt' | |
| pretrained: True | |
| pretrained_dataset: "imagenet" | |
| input_shape: [3, 224, 224] | |
| training: | |
| epochs: 2 | |
| batch_size: 256 | |
| validation_batch_size: null | |
| optimizer: | |
| opt: 'sgd' | |
| opt-eps: null | |
| opt-betas: null | |
| momentum: 0.9 | |
| weight_decay: !!float 2e-5 | |
| clip_grad: null | |
| clip_mode: 'norm' | |
| layer_decay: null | |
| lr_scheduler: | |
| sched: 'cosine' | |
| sched_on_updates: False | |
| lr: null | |
| lr_base: 0.1 | |
| lr_base_size: 256 | |
| lr_base_scale: '' | |
| lr_noise: null | |
| lr_noise_pct: 0.67 | |
| lr_noise_std: 1.0 | |
| lr_cycle_mul: 1.0 | |
| lr_cycle_decay: 0.5 | |
| lr_cycle_limit: 1 | |
| lr_k_decay: 1.0 | |
| warmup_lr: !!float 1e-5 | |
| min_lr: 0 | |
| epoch_repeats: 0 | |
| start_epoch: 0 | |
| decay_milestones: [90, 180, 270] | |
| decay_epochs: 90 | |
| warmup_epochs: 5 | |
| warmup_prefix: False | |
| cooldown_epochs: 0 | |
| patience_epochs: 10 | |
| decay_rate: 0.1 | |
| bn_momentum: null | |
| bn_eps: null | |
| sync_bn: false | |
| dist_bn: "reduce" | |
| split_bn: false | |
| #distributed: True | |
| model_ema: false | |
| model_ema_force_cpu: false | |
| model_ema_decay: 0.9998 | |
| worker_seeding: all | |
| mlflow: | |
| uri: ./pt/src/experiments_outputs/mlruns | |
| hydra: | |
| run: | |
| dir: ./pt/src/experiments_outputs/${now:%Y_%m_%d_%H_%M_%S} |