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| import numpy as np | |
| import os | |
| import datetime | |
| def mk_dir(path): | |
| if not os.path.isdir(path): | |
| os.makedirs(path) | |
| def get_params(make_dirs=False, exps_dir='../../experiments/'): | |
| params = {} | |
| params['model_name'] = 'Net2DFast' # Net2DFast, Net2DSkip, Net2DSimple, Net2DSkipDS, Net2DRN | |
| params['num_filters'] = 128 | |
| now_str = datetime.datetime.now().strftime("%Y_%m_%d__%H_%M_%S") | |
| model_name = now_str + '.pth.tar' | |
| params['experiment'] = os.path.join(exps_dir, now_str, '') | |
| params['model_file_name'] = os.path.join(params['experiment'], model_name) | |
| params['op_im_dir'] = os.path.join(params['experiment'], 'op_ims', '') | |
| params['op_im_dir_test'] = os.path.join(params['experiment'], 'op_ims_test', '') | |
| #params['notes'] = '' # can save notes about an experiment here | |
| # spec parameters | |
| params['target_samp_rate'] = 256000 # resamples all audio so that it is at this rate | |
| params['fft_win_length'] = 512 / 256000.0 # in milliseconds, amount of time per stft time step | |
| params['fft_overlap'] = 0.75 # stft window overlap | |
| params['max_freq'] = 120000 # in Hz, everything above this will be discarded | |
| params['min_freq'] = 10000 # in Hz, everything below this will be discarded | |
| params['resize_factor'] = 0.5 # resize so the spectrogram at the input of the network | |
| params['spec_height'] = 256 # units are number of frequency bins (before resizing is performed) | |
| params['spec_train_width'] = 512 # units are number of time steps (before resizing is performed) | |
| params['spec_divide_factor'] = 32 # spectrogram should be divisible by this amount in width and height | |
| # spec processing params | |
| params['denoise_spec_avg'] = True # removes the mean for each frequency band | |
| params['scale_raw_audio'] = False # scales the raw audio to [-1, 1] | |
| params['max_scale_spec'] = False # scales the spectrogram so that it is max 1 | |
| params['spec_scale'] = 'pcen' # 'log', 'pcen', 'none' | |
| # detection params | |
| params['detection_overlap'] = 0.01 # has to be within this number of ms to count as detection | |
| params['ignore_start_end'] = 0.01 # if start of GT calls are within this time from the start/end of file ignore | |
| params['detection_threshold'] = 0.01 # the smaller this is the better the recall will be | |
| params['nms_kernel_size'] = 9 | |
| params['nms_top_k_per_sec'] = 200 # keep top K highest predictions per second of audio | |
| params['target_sigma'] = 2.0 | |
| # augmentation params | |
| params['aug_prob'] = 0.20 # augmentations will be performed with this probability | |
| params['augment_at_train'] = True | |
| params['augment_at_train_combine'] = True | |
| params['echo_max_delay'] = 0.005 # simulate echo by adding copy of raw audio | |
| params['stretch_squeeze_delta'] = 0.04 # stretch or squeeze spec | |
| params['mask_max_time_perc'] = 0.05 # max mask size - here percentage, not ideal | |
| params['mask_max_freq_perc'] = 0.10 # max mask size - here percentage, not ideal | |
| params['spec_amp_scaling'] = 2.0 # multiply the "volume" by 0:X times current amount | |
| params['aug_sampling_rates'] = [220500, 256000, 300000, 312500, 384000, 441000, 500000] | |
| # loss params | |
| params['train_loss'] = 'focal' # mse or focal | |
| params['det_loss_weight'] = 1.0 # weight for the detection part of the loss | |
| params['size_loss_weight'] = 0.1 # weight for the bbox size loss | |
| params['class_loss_weight'] = 2.0 # weight for the classification loss | |
| params['individual_loss_weight'] = 0.0 # not used | |
| if params['individual_loss_weight'] == 0.0: | |
| params['emb_dim'] = 0 # number of dimensions used for individual id embedding | |
| else: | |
| params['emb_dim'] = 3 | |
| # train params | |
| params['lr'] = 0.001 | |
| params['batch_size'] = 8 | |
| params['num_workers'] = 4 | |
| params['num_epochs'] = 200 | |
| params['num_eval_epochs'] = 5 # run evaluation every X epochs | |
| params['device'] = 'cuda' | |
| params['save_test_image_during_train'] = False | |
| params['save_test_image_after_train'] = True | |
| params['convert_to_genus'] = False | |
| params['genus_mapping'] = [] | |
| params['class_names'] = [] | |
| params['classes_to_ignore'] = ['', ' ', 'Unknown', 'Not Bat'] | |
| params['generic_class'] = ['Bat'] | |
| params['events_of_interest'] = ['Echolocation'] # will ignore all other types of events e.g. social calls | |
| # the classes in this list are standardized during training so that the same low and high freq are used | |
| params['standardize_classs_names'] = [] | |
| # create directories | |
| if make_dirs: | |
| print('Model name : ' + params['model_name']) | |
| print('Model file : ' + params['model_file_name']) | |
| print('Experiment : ' + params['experiment']) | |
| mk_dir(params['experiment']) | |
| if params['save_test_image_during_train']: | |
| mk_dir(params['op_im_dir']) | |
| if params['save_test_image_after_train']: | |
| mk_dir(params['op_im_dir_test']) | |
| mk_dir(os.path.dirname(params['model_file_name'])) | |
| return params | |