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def find_class_threshold(f, dataset, iters, test_funcs, network, splits=10, beg=0.0, end=0.3):
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for li_idx in range(iters):
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avg_errors = []
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threshold = list(np.arange(beg, end, (end - beg) / splits))
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log(f, 'threshold:'+str(threshold))
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for class_threshold in threshold:
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avg_error = test_lsccnn(test_funcs, dataset, 'test_valid', network, True, thresh=class_threshold)
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avg_errors.append(avg_error[0]['new_mae'])
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log(f, "class threshold: %f, avg_error: %f" % (class_threshold, avg_error[0]['new_mae']))
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mid = np.asarray(avg_errors).argmin()
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beg = threshold[max(mid - 2, 0)]
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end = threshold[min(mid + 2, splits - 1)]
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log(f, "Best threshold: %f" % threshold[mid])
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optimal_threshold = threshold[mid]
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return optimal_threshold
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'''
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This function performs box NMS on the predictions of the net.
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Parameters
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----------
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predictions: multiscale predictions - list of numpy maps
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each map is of size 4 x H x W
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Returns
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----------
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nms_out: Binary map of where the prediction person is
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box_out: Size of the box at the predicted dot
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NOTE: count(nms_out) == count(box_out)
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'''
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def box_NMS(predictions, thresh):
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Scores = []
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Boxes = []
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for k in range(len(BOXES)):
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scores = np.max(predictions[k], axis=0)
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boxes = np.argmax(predictions[k], axis=0)
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# index the boxes with BOXES to get h_map and w_map (both are the same for us)
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mask = (boxes<3) # removing Z
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boxes = (boxes+1) * mask
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scores = (scores * mask) # + 100 # added 100 since we take logsoftmax and it's negative!!
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boxes = (boxes==1)*BOXES[k][0] + (boxes==2)*BOXES[k][1] + (boxes==3)*BOXES[k][2]
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Scores.append(scores)
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Boxes.append(boxes)
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x, y, h, w, scores = apply_nms.apply_nms(Scores, Boxes, Boxes, 0.5, thresh=thresh)
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nms_out = np.zeros((predictions[0].shape[1], predictions[0].shape[2])) # since predictions[0] is of size 4 x H x W
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box_out = np.zeros((predictions[0].shape[1], predictions[0].shape[2])) # since predictions[0] is of size 4 x H x W
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for (xx, yy, hh) in zip(x, y, h):
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nms_out[yy, xx] = 1
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box_out[yy, xx] = hh
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assert(np.count_nonzero(nms_out) == len(x))
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return nms_out, box_out
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"""
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A function to return dotmaps and box maps of either gt
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or predictions. In case of predictions, it would be NMSed
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output and in case of gt maps, it would be would be from each
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individual scale.
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Parameters
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----------
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pred: list of ndarray (currently MUST be of length 3
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- each for one scale)
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Returns
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----------
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nms_out: dot map of NMSed output of the given predictions.
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h: box map of NMSed output
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"""
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def get_box_and_dot_maps(pred, thresh):
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assert(len(pred) == 4)
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all_dot_maps = []
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all_box_maps = []
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# NMS on the multi-scale outputs
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nms_out, h = box_NMS(pred, thresh)
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return nms_out, h
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'''
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Main training code for LSC-CNN.
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Parameters
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-----------
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network : (torch model) network. In this case len(network) == 1
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dataset: (class object) data_reader class object
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network_function: (class) network_functions() class object to get test and train
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functions.
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log_path: (str) path to log losses and stats.
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Returns
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----------
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This method does not return anything. It directly logs all the losses,
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metrics and statistics of training/validation/testing stages.
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'''
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def train_networks(network, dataset, network_functions, log_path):
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