mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 22:51:58 +02:00
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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np.seterr(divide="ignore", invalid="ignore")
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def x_coords_to_time(x_pos, sampling_rate, fft_win_length, fft_overlap):
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nfft = int(fft_win_length * sampling_rate)
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noverlap = int(fft_overlap * nfft)
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return ((x_pos * (nfft - noverlap)) + noverlap) / sampling_rate
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# return (1.0 - fft_overlap) * fft_win_length * (x_pos + 0.5) # 0.5 is for center of temporal window
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def overall_class_pred(det_prob, class_prob):
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weighted_pred = (class_prob * det_prob).sum(1)
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return weighted_pred / weighted_pred.sum()
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def run_nms(outputs, params, sampling_rate):
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pred_det = outputs["pred_det"] # probability of box
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pred_size = outputs["pred_size"] # box size
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pred_det_nms = non_max_suppression(pred_det, params["nms_kernel_size"])
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freq_rescale = (params["max_freq"] - params["min_freq"]) / pred_det.shape[
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-2
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]
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# NOTE there will be small differences depending on which sampling rate is chosen
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# as we are choosing the same sampling rate for the entire batch
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duration = x_coords_to_time(
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pred_det.shape[-1],
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sampling_rate[0].item(),
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params["fft_win_length"],
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params["fft_overlap"],
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)
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top_k = int(duration * params["nms_top_k_per_sec"])
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scores, y_pos, x_pos = get_topk_scores(pred_det_nms, top_k)
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# loop over batch to save outputs
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preds = []
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feats = []
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for ii in range(pred_det_nms.shape[0]):
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# get valid indices
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inds_ord = torch.argsort(x_pos[ii, :])
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valid_inds = scores[ii, inds_ord] > params["detection_threshold"]
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valid_inds = inds_ord[valid_inds]
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# create result dictionary
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pred = {}
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pred["det_probs"] = scores[ii, valid_inds]
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pred["x_pos"] = x_pos[ii, valid_inds]
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pred["y_pos"] = y_pos[ii, valid_inds]
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pred["bb_width"] = pred_size[ii, 0, pred["y_pos"], pred["x_pos"]]
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pred["bb_height"] = pred_size[ii, 1, pred["y_pos"], pred["x_pos"]]
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pred["start_times"] = x_coords_to_time(
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pred["x_pos"].float() / params["resize_factor"],
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sampling_rate[ii].item(),
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params["fft_win_length"],
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params["fft_overlap"],
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)
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pred["end_times"] = x_coords_to_time(
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(pred["x_pos"].float() + pred["bb_width"])
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/ params["resize_factor"],
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sampling_rate[ii].item(),
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params["fft_win_length"],
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params["fft_overlap"],
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)
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pred["low_freqs"] = (
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pred_size[ii].shape[1] - pred["y_pos"].float()
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) * freq_rescale + params["min_freq"]
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pred["high_freqs"] = (
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pred["low_freqs"] + pred["bb_height"] * freq_rescale
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)
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# extract the per class votes
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if "pred_class" in outputs:
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pred["class_probs"] = outputs["pred_class"][
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ii, :, y_pos[ii, valid_inds], x_pos[ii, valid_inds]
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]
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# extract the model features
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if "features" in outputs:
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feat = outputs["features"][
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ii, :, y_pos[ii, valid_inds], x_pos[ii, valid_inds]
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].transpose(0, 1)
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feat = feat.cpu().numpy().astype(np.float32)
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feats.append(feat)
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# convert to numpy
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for kk in pred.keys():
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pred[kk] = pred[kk].cpu().numpy().astype(np.float32)
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preds.append(pred)
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return preds, feats
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def non_max_suppression(heat, kernel_size):
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# kernel can be an int or list/tuple
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if type(kernel_size) is int:
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kernel_size_h = kernel_size
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kernel_size_w = kernel_size
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pad_h = (kernel_size_h - 1) // 2
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pad_w = (kernel_size_w - 1) // 2
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hmax = nn.functional.max_pool2d(
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heat, (kernel_size_h, kernel_size_w), stride=1, padding=(pad_h, pad_w)
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)
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keep = (hmax == heat).float()
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return heat * keep
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def get_topk_scores(scores, K):
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# expects input of size: batch x 1 x height x width
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batch, _, height, width = scores.size()
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topk_scores, topk_inds = torch.topk(scores.view(batch, -1), K)
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topk_inds = topk_inds % (height * width)
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topk_ys = torch.div(topk_inds, width, rounding_mode="floor").long()
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topk_xs = (topk_inds % width).long()
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return topk_scores, topk_ys, topk_xs
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