各位前輩,由於問題描述的太過簡略,容小弟詳細說明 其程式碼來自 YOLO 的 predict result decoder,source code 如下 : netout #為Numpy NDarray anchors #為 Numpy DNarray obj_thresh #為 Float32 其數值為 0.6 net_h ,netw #為 int 其數值為 416 def decode_netout(netout, anchors, obj_thresh, net_h, net_w): grid_h, grid_w = netout.shape[:2] nb_box = 3 netout = netout.reshape((grid_h, grid_w, nb_box, -1)) nb_class = netout.shape[-1] - 5 boxes = [] netout[..., :2] = _sigmoid(netout[..., :2]) netout[..., 4:] = _sigmoid(netout[..., 4:]) netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:] netout[..., 5:] *= netout[..., 5:] > obj_thresh for i in range(grid_h*grid_w): row = i / grid_w col = i % grid_w for b in range(nb_box): # 4th element is objectness score objectness = netout[int(row)][int(col)][b][4] if(objectness.all() <= obj_thresh): continue # first 4 elements are x, y, w, and h x, y, w, h = netout[int(row)][int(col)][b][:4] x = (col + x) / grid_w # center position, unit: image width y = (row + y) / grid_h # center position, unit: image height w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height # last elements are class probabilities classes = netout[int(row)][col][b][5:] box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes) boxes.append(box) return boxes 而小弟追蹤該 function 時發現該 objectness 為 float32 (請看截圖) 而無法理解 該ojbectness為何會有 .all() 這個方法, 並且用來判斷 bool ? 一直無法理解這個問題 故上來請教前輩們這個疑惑 謝謝大家幫忙 圖片如下 : https://photos.app.goo.gl/rbsPt91WBi1FcuvN6 --

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