Question

我正在尝试自己实现YOLOv2对象检测算法，只是为了了解算法的工作原理。当然，我会使用预先训练过的砝码来加快速度。我使用keras-yolo2存储库中的代码作为我自己的代码的基础，但我有一个关于代码如何与基础YOLO算法相关的问题。

据我所知 - 从高层来看 - YOLO（你只看一次）会：

将图片分成SxS网格。
对于网格中的每个单元格，进行分类并为每个潜在标签分配概率。
根据框/类置信度是否超过某个阈值来修剪分类框。

此后还会发生多个其他事情，包括非最大抑制等。

我正在查看上述存储库中的一些代码，试图找出作者如何将图像实际分解为SxS网格，以便在单元格内执行对象分类。任何人都可以在下面的代码中看到该算法的位置。可能是我对tensorflow的了解不足，但我无法分辨下面代码中的实现方式。似乎对cell_x = tf.to_float(tf.reshape(tf.tile(tf.range(GRID_W), [GRID_H]), (1, GRID_H, GRID_W, 1, 1)))的初始调用会将图像分解为单元格，但我不理解如何在不循环每个网格单元的情况下如何工作？我也不明白tf.reshape和tf.tile和tf.range如何协同工作以将图片分解为单元格。

任何帮助都将不胜感激。

IMAGE_H, IMAGE_W = 416, 416
GRID_H,  GRID_W  = 13 , 13
BOX              = 5
CLASS            = len(LABELS)
CLASS_WEIGHTS    = np.ones(CLASS, dtype='float32')
OBJ_THRESHOLD    = 0.3#0.5
NMS_THRESHOLD    = 0.3#0.45
ANCHORS          = [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828]

NO_OBJECT_SCALE  = 1.0
OBJECT_SCALE     = 5.0
COORD_SCALE      = 1.0
CLASS_SCALE      = 1.0

BATCH_SIZE       = 16
WARM_UP_BATCHES  = 0
TRUE_BOX_BUFFER  = 50

def custom_loss(y_true, y_pred):
    mask_shape = tf.shape(y_true)[:4]

    cell_x = tf.to_float(tf.reshape(tf.tile(tf.range(GRID_W), [GRID_H]), (1, GRID_H, GRID_W, 1, 1)))
    cell_y = tf.transpose(cell_x, (0,2,1,3,4))

    cell_grid = tf.tile(tf.concat([cell_x,cell_y], -1), [BATCH_SIZE, 1, 1, 5, 1])

    coord_mask = tf.zeros(mask_shape)
    conf_mask  = tf.zeros(mask_shape)
    class_mask = tf.zeros(mask_shape)

    seen = tf.Variable(0.)
    total_recall = tf.Variable(0.)

    """
    Adjust prediction
    """
    ### adjust x and y      
    pred_box_xy = tf.sigmoid(y_pred[..., :2]) + cell_grid

    ### adjust w and h
    pred_box_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(ANCHORS, [1,1,1,BOX,2])

    ### adjust confidence
    pred_box_conf = tf.sigmoid(y_pred[..., 4])

    ### adjust class probabilities
    pred_box_class = y_pred[..., 5:]

    """
    Adjust ground truth
    """
    ### adjust x and y
    true_box_xy = y_true[..., 0:2] # relative position to the containing cell

    ### adjust w and h
    true_box_wh = y_true[..., 2:4] # number of cells accross, horizontally and vertically

    ### adjust confidence
    true_wh_half = true_box_wh / 2.
    true_mins    = true_box_xy - true_wh_half
    true_maxes   = true_box_xy + true_wh_half

    pred_wh_half = pred_box_wh / 2.
    pred_mins    = pred_box_xy - pred_wh_half
    pred_maxes   = pred_box_xy + pred_wh_half       

    intersect_mins  = tf.maximum(pred_mins,  true_mins)
    intersect_maxes = tf.minimum(pred_maxes, true_maxes)
    intersect_wh    = tf.maximum(intersect_maxes - intersect_mins, 0.)
    intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]

    true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]
    pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]

    union_areas = pred_areas + true_areas - intersect_areas
    iou_scores  = tf.truediv(intersect_areas, union_areas)

    true_box_conf = iou_scores * y_true[..., 4]

    ### adjust class probabilities
    true_box_class = tf.argmax(y_true[..., 5:], -1)

    """
    Determine the masks
    """
    ### coordinate mask: simply the position of the ground truth boxes (the predictors)
    coord_mask = tf.expand_dims(y_true[..., 4], axis=-1) * COORD_SCALE

    ### confidence mask: penelize predictors + penalize boxes with low IOU
    # penalize the confidence of the boxes, which have IOU with some ground truth box < 0.6
    true_xy = true_boxes[..., 0:2]
    true_wh = true_boxes[..., 2:4]

    true_wh_half = true_wh / 2.
    true_mins    = true_xy - true_wh_half
    true_maxes   = true_xy + true_wh_half

    pred_xy = tf.expand_dims(pred_box_xy, 4)
    pred_wh = tf.expand_dims(pred_box_wh, 4)

    pred_wh_half = pred_wh / 2.
    pred_mins    = pred_xy - pred_wh_half
    pred_maxes   = pred_xy + pred_wh_half    

    intersect_mins  = tf.maximum(pred_mins,  true_mins)
    intersect_maxes = tf.minimum(pred_maxes, true_maxes)
    intersect_wh    = tf.maximum(intersect_maxes - intersect_mins, 0.)
    intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]

    true_areas = true_wh[..., 0] * true_wh[..., 1]
    pred_areas = pred_wh[..., 0] * pred_wh[..., 1]

    union_areas = pred_areas + true_areas - intersect_areas
    iou_scores  = tf.truediv(intersect_areas, union_areas)

    best_ious = tf.reduce_max(iou_scores, axis=4)
    conf_mask = conf_mask + tf.to_float(best_ious < 0.6) * (1 - y_true[..., 4]) * NO_OBJECT_SCALE

    # penalize the confidence of the boxes, which are reponsible for corresponding ground truth box
    conf_mask = conf_mask + y_true[..., 4] * OBJECT_SCALE

    ### class mask: simply the position of the ground truth boxes (the predictors)
    class_mask = y_true[..., 4] * tf.gather(CLASS_WEIGHTS, true_box_class) * CLASS_SCALE       

    """
    Warm-up training
    """
    no_boxes_mask = tf.to_float(coord_mask < COORD_SCALE/2.)
    seen = tf.assign_add(seen, 1.)

    true_box_xy, true_box_wh, coord_mask = tf.cond(tf.less(seen, WARM_UP_BATCHES), 
                          lambda: [true_box_xy + (0.5 + cell_grid) * no_boxes_mask, 
                                   true_box_wh + tf.ones_like(true_box_wh) * np.reshape(ANCHORS, [1,1,1,BOX,2]) * no_boxes_mask, 
                                   tf.ones_like(coord_mask)],
                          lambda: [true_box_xy, 
                                   true_box_wh,
                                   coord_mask])

    """
    Finalize the loss
    """
    nb_coord_box = tf.reduce_sum(tf.to_float(coord_mask > 0.0))
    nb_conf_box  = tf.reduce_sum(tf.to_float(conf_mask  > 0.0))
    nb_class_box = tf.reduce_sum(tf.to_float(class_mask > 0.0))

    loss_xy    = tf.reduce_sum(tf.square(true_box_xy-pred_box_xy)     * coord_mask) / (nb_coord_box + 1e-6) / 2.
    loss_wh    = tf.reduce_sum(tf.square(true_box_wh-pred_box_wh)     * coord_mask) / (nb_coord_box + 1e-6) / 2.
    loss_conf  = tf.reduce_sum(tf.square(true_box_conf-pred_box_conf) * conf_mask)  / (nb_conf_box  + 1e-6) / 2.
    loss_class = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class)
    loss_class = tf.reduce_sum(loss_class * class_mask) / (nb_class_box + 1e-6)

    loss = loss_xy + loss_wh + loss_conf + loss_class

    nb_true_box = tf.reduce_sum(y_true[..., 4])
    nb_pred_box = tf.reduce_sum(tf.to_float(true_box_conf > 0.5) * tf.to_float(pred_box_conf > 0.3))

    """
    Debugging code
    """    
    current_recall = nb_pred_box/(nb_true_box + 1e-6)
    total_recall = tf.assign_add(total_recall, current_recall) 

    loss = tf.Print(loss, [tf.zeros((1))], message='Dummy Line \t', summarize=1000)
    loss = tf.Print(loss, [loss_xy], message='Loss XY \t', summarize=1000)
    loss = tf.Print(loss, [loss_wh], message='Loss WH \t', summarize=1000)
    loss = tf.Print(loss, [loss_conf], message='Loss Conf \t', summarize=1000)
    loss = tf.Print(loss, [loss_class], message='Loss Class \t', summarize=1000)
    loss = tf.Print(loss, [loss], message='Total Loss \t', summarize=1000)
    loss = tf.Print(loss, [current_recall], message='Current Recall \t', summarize=1000)
    loss = tf.Print(loss, [total_recall/seen], message='Average Recall \t', summarize=1000)

    return loss

Answer 1

每个人说，

Yolo v2不会将图像分解为13x13网格，而是在网格级别而不是像素级别进行预测。

网络获取大小为416x416的输入图像并输出13x13个预测，每个预测都是一个包含类概率和框坐标的数组（425大小向量，实际输出大小为13x13x425）。因此，每个输出pixel被视为输入图像中区域的预测。例如，输出的索引[2,3]对应于输入图像区域(64,96,96,128)的预测（425长度矢量）。

作为425长度向量的一部分的框坐标相对于cell_grid进行编码。

代码中的cell_grid只计算整批的mesh grid大小13x13，用于预测实际坐标，而不是其他任何内容。

cell_grid = tf.tile(tf.concat([cell_x,cell_y], -1), [BATCH_SIZE, 1, 1, 5, 1])

深度学习YOLO对象检测：如何迭代图像上定义的网格中的单元格

1 个答案: