| import torch | |
| # removed: numpy, nn, FrozenBatchNorm2d, logging, random, json, os, pathlib, dataset_split | |
| # — all dead after removing training helpers; only do_mixup and interpolate used by htsat.py | |
| # removed: freeze_batch_norm_2d — no longer imported after removing ModifiedResNet/timm_model | |
| # removed: exist, get_tar_path_from_dataset_name, get_tar_path_from_txts, get_mix_lambda | |
| # — dataset/tar path helpers, training data utilities, not used in inference | |
| def do_mixup(x, mixup_lambda): | |
| """ | |
| Args: | |
| x: (batch_size , ...) | |
| mixup_lambda: (batch_size,) | |
| Returns: | |
| out: (batch_size, ...) | |
| """ | |
| out = ( | |
| x.transpose(0, -1) * mixup_lambda | |
| + torch.flip(x, dims=[0]).transpose(0, -1) * (1 - mixup_lambda) | |
| ).transpose(0, -1) | |
| return out | |
| def interpolate(x, ratio): | |
| """Interpolate data in time domain. This is used to compensate the | |
| resolution reduction in downsampling of a CNN. | |
| Args: | |
| x: (batch_size, time_steps, classes_num) | |
| ratio: int, ratio to interpolate | |
| Returns: | |
| upsampled: (batch_size, time_steps * ratio, classes_num) | |
| """ | |
| (batch_size, time_steps, classes_num) = x.shape | |
| upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1) | |
| upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num) | |
| return upsampled | |
| # removed: pad_framewise_output — only used by pann_model (deleted) | |
| # removed: process_ipc, save_to_dict, get_data_from_log, save_p, load_p, save_json, load_json, | |
| # load_class_label, get_optimizer — training/logging/IO helpers, not used in inference | |