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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
def x_coords_to_time(x_pos, sampling_rate, fft_win_length, fft_overlap):
nfft = int(fft_win_length*sampling_rate)
noverlap = int(fft_overlap*nfft)
return ((x_pos*(nfft - noverlap)) + noverlap) / sampling_rate
#return (1.0 - fft_overlap) * fft_win_length * (x_pos + 0.5) # 0.5 is for center of temporal window
def overall_class_pred(det_prob, class_prob):
weighted_pred = (class_prob*det_prob).sum(1)
return weighted_pred / weighted_pred.sum()
def run_nms(outputs, params, sampling_rate):
pred_det = outputs['pred_det'] # probability of box
pred_size = outputs['pred_size'] # box size
pred_det_nms = non_max_suppression(pred_det, params['nms_kernel_size'])
freq_rescale = (params['max_freq'] - params['min_freq']) /pred_det.shape[-2]
# NOTE there will be small differences depending on which sampling rate is chosen
# as we are choosing the same sampling rate for the entire batch
duration = x_coords_to_time(pred_det.shape[-1], sampling_rate[0].item(),
params['fft_win_length'], params['fft_overlap'])
top_k = int(duration * params['nms_top_k_per_sec'])
scores, y_pos, x_pos = get_topk_scores(pred_det_nms, top_k)
# loop over batch to save outputs
preds = []
feats = []
for ii in range(pred_det_nms.shape[0]):
# get valid indices
inds_ord = torch.argsort(x_pos[ii, :])
valid_inds = scores[ii, inds_ord] > params['detection_threshold']
valid_inds = inds_ord[valid_inds]
# create result dictionary
pred = {}
pred['det_probs'] = scores[ii, valid_inds]
pred['x_pos'] = x_pos[ii, valid_inds]
pred['y_pos'] = y_pos[ii, valid_inds]
pred['bb_width'] = pred_size[ii, 0, pred['y_pos'], pred['x_pos']]
pred['bb_height'] = pred_size[ii, 1, pred['y_pos'], pred['x_pos']]
pred['start_times'] = x_coords_to_time(pred['x_pos'].float() / params['resize_factor'],
sampling_rate[ii].item(), params['fft_win_length'], params['fft_overlap'])
pred['end_times'] = x_coords_to_time((pred['x_pos'].float()+pred['bb_width']) / params['resize_factor'],
sampling_rate[ii].item(), params['fft_win_length'], params['fft_overlap'])
pred['low_freqs'] = (pred_size[ii].shape[1] - pred['y_pos'].float())*freq_rescale + params['min_freq']
pred['high_freqs'] = pred['low_freqs'] + pred['bb_height']*freq_rescale
# extract the per class votes
if 'pred_class' in outputs:
pred['class_probs'] = outputs['pred_class'][ii, :, y_pos[ii, valid_inds], x_pos[ii, valid_inds]]
# extract the model features
if 'features' in outputs:
feat = outputs['features'][ii, :, y_pos[ii, valid_inds], x_pos[ii, valid_inds]].transpose(0, 1)
feat = feat.cpu().numpy().astype(np.float32)
feats.append(feat)
# convert to numpy
for kk in pred.keys():
pred[kk] = pred[kk].cpu().numpy().astype(np.float32)
preds.append(pred)
return preds, feats
def non_max_suppression(heat, kernel_size):
# kernel can be an int or list/tuple
if type(kernel_size) is int:
kernel_size_h = kernel_size
kernel_size_w = kernel_size
pad_h = (kernel_size_h - 1) // 2
pad_w = (kernel_size_w - 1) // 2
hmax = nn.functional.max_pool2d(heat, (kernel_size_h, kernel_size_w), stride=1, padding=(pad_h, pad_w))
keep = (hmax == heat).float()
return heat * keep
def get_topk_scores(scores, K):
# expects input of size: batch x 1 x height x width
batch, _, height, width = scores.size()
topk_scores, topk_inds = torch.topk(scores.view(batch, -1), K)
topk_inds = topk_inds % (height * width)
topk_ys = torch.div(topk_inds, width, rounding_mode='floor').long()
topk_xs = (topk_inds % width).long()
return topk_scores, topk_ys, topk_xs
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