我正在尝试构建定义为here的Faster-rcnn体系结构。主要包括三个部分：特征提取器，区域建议网络（rpn）和检测/分类网络。我能够使用Resnet架构来构建特征提取器，但是我在区域提案网络方面苦苦挣扎。在Faster-rcnn论文中，他们描述了在图像上放置锚点，并计算每个锚点与最近的带注释对象的联合相交（iou）。
“ RPN进行两种不同类型的预测：二进制>分类和边界框回归调整。
对于培训，我们将所有锚点放入两个不同的类别中。 > Union Intersection（IoU）大于0.5的与地面真实物体重叠的物体被视为>“前景”，与地面真实物体不重叠或> IoU小于0.1 IoU的物体被视为>“前景”。被认为是“背景”。
然后，我们对那些锚点进行随机采样，以形成一个大小大于256的微型批处理—试图在前景和>背景锚点之间保持平衡的比例。
RPN使用为小型批处理选择的所有锚点，使用二进制交叉熵计算>分类损失。然后，它仅使用标记为前景的那些微型批处理锚来计算回归损失。为了计算回归的目标，我们>使用前景锚点和最接近的地面真实对象，并>计算将锚点转换为>对象所需的正确\DeltaΔ。“ source
在尝试中，我能够为每个锚点计算iou，但是我无法弄清楚如何对锚点进行“小批量”处理（保持前景和背景的平衡）并反向传播这些损失个别锚点。我正在使用tensorflow（版本2与tf.keras.Model）。我无法致电rpn_model.fit(x,y)，因为我们并不适合所有锚。我也无法致电rpn_model.minimize(loss)，因为我们再次只想将损失应用于256 /（x锚点数）。
那么，如何将Tensorflow / keras模型的分类和回归损失反向传播到特定输出（锚的mini_batch）？谢谢。
更新 我整天花费我的代码来使用Tensorflow操作，但仍然无法正常工作！
ValueError：可变形状=（1、1、3、1）dtype = float32>具有
None用于渐变。请确保您所有的操作都定义了渐变（即可区分）。没有渐变的常见操作：K.argmax，K.round，K.eval。
def __init__(self, tf, resnet_model, image_height, image_width, in_channels, mid_channels, base_anchor_size, anchor_ratios, anchor_scales, subsample_rate):
"""
in_channels for resnet: 512
mid_channels: 512
base_anchor_size: 128
anchor_ratios: [[1,1],[1,2],[2,1]]
anchor_scales: [1,2,3,4]
subsample_rate: (image_size//feature_map_size), ex. (1000//32) --> 31
"""
self.tf = tf
self.resnet_model = resnet_model
self.resnet_model_inputs = self.resnet_model.input
self.resnet_model_feature_map = resnet_model.output
self.image_height = image_height
self.image_width = image_width
self.in_channels = in_channels
self.mid_channels = mid_channels
self.base_anchor_size = base_anchor_size
self.anchor_ratios = anchor_ratios
self.anchor_scales = anchor_scales
self.subsample_rate = subsample_rate
self.anchor_boxes = self.generate_anchor_boxes(self.base_anchor_size, self.anchor_ratios, self.anchor_scales)
self.anchor_boxes_over_image = self.tf.convert_to_tensor(self.generate_anchor_boxes_over_image(self.anchor_boxes, self.image_height, self.image_width, self.subsample_rate), dtype=self.tf.float64)
self.total_number_of_anchor_boxes = self.tf.size(self.anchor_boxes_over_image)
self.model = self.create_model()
def get_model(self):
return self.model
def create_model(self):
self.tf.keras.backend.set_floatx('float64')
rpn_conv = self.tf.keras.layers.SeparableConv2D(filters=512, kernel_size=[3, 3], strides=[1, 1], padding="same", data_format="channels_last", activation="relu")(self.resnet_model.output)
rpn_class_score = self.tf.keras.layers.SeparableConv2D(filters=24, kernel_size=[1, 1], strides=[1, 1], padding="valid", data_format="channels_last", activation="softmax")(rpn_conv)
rpn_class_score_shape = rpn_class_score.get_shape().as_list()
rpn_class_score = self.tf.keras.layers.Reshape(rpn_class_score_shape[1:3] + [rpn_class_score_shape[3] // 2, 2])(rpn_class_score)
rpn_class_score = self.tf.keras.backend.cast(rpn_class_score, dtype='float64')
rpn_bbox_pred = self.tf.keras.layers.SeparableConv2D(filters=48, kernel_size=[1, 1], strides=[1, 1], padding="valid", data_format="channels_last")(rpn_conv)
rpn_bbox_pred_shape = rpn_bbox_pred.get_shape().as_list()
rpn_bbox_pred = self.tf.keras.layers.Reshape(rpn_bbox_pred_shape[1:3] + [rpn_bbox_pred_shape[3] // 4, 4])(rpn_bbox_pred)
rpn_bbox_pred = self.tf.keras.backend.cast(rpn_bbox_pred, dtype='float64')
rpn_model = self.tf.keras.Model(inputs=self.resnet_model.input, outputs=[rpn_class_score, rpn_bbox_pred])
self.optimizer = self.tf.keras.optimizers.Nadam(0.001)
return rpn_model
def get_iou(self, anchor_box_predictions, ground_truth_bounding_box, giou=False):
ground_truth_bounding_box = self.tf.convert_to_tensor(ground_truth_bounding_box, dtype=self.tf.float64)
""" Predicted and ground truth bounding box coordinates """
anchor_box_predictions_center_x, anchor_box_predictions_center_y, anchor_box_predictions_width, anchor_box_predictions_height = self.tf.slice(anchor_box_predictions, [0, 0, 0, 0, 0], [-1, -1, -1, -1, 1]), self.tf.slice(anchor_box_predictions, [0, 0, 0, 0, 1], [-1, -1, -1, -1, 1]), self.tf.slice(anchor_box_predictions, [0, 0, 0, 0, 2], [-1, -1, -1, -1, 1]), self.tf.slice(anchor_box_predictions, [0, 0, 0, 0, 3], [-1, -1, -1, -1, 1])
anchor_box_predictions_x1 = anchor_box_predictions_center_x - (anchor_box_predictions_width / 2)
anchor_box_predictions_x2 = anchor_box_predictions_center_x + (anchor_box_predictions_width / 2)
anchor_box_predictions_y1 = anchor_box_predictions_center_y - (anchor_box_predictions_height / 2)
anchor_box_predictions_y2 = anchor_box_predictions_center_y + (anchor_box_predictions_height / 2)
""" For the predicted box ensure x2>x1 and y2>y1: """
anchor_box_predictions_x1, anchor_box_predictions_x2 = self.tf.minimum(anchor_box_predictions_x1, anchor_box_predictions_x2), self.tf.maximum(anchor_box_predictions_x1, anchor_box_predictions_x2)
anchor_box_predictions_y1, anchor_box_predictions_y2 = self.tf.minimum(anchor_box_predictions_y1, anchor_box_predictions_y2), self.tf.maximum(anchor_box_predictions_y1, anchor_box_predictions_y2)
""" ground truth bounding box coordinates """
ground_truth_bounding_boxes_center_x, ground_truth_bounding_boxes_center_y, ground_truth_bounding_boxes_width, ground_truth_bounding_boxes_height = ground_truth_bounding_box[:,0:1], ground_truth_bounding_box[:,1:2], ground_truth_bounding_box[:,2:3], ground_truth_bounding_box[:,3:4]
ground_truth_bounding_boxes_x1 = ground_truth_bounding_boxes_center_x - (ground_truth_bounding_boxes_width / 2)
ground_truth_bounding_boxes_x2 = ground_truth_bounding_boxes_center_x + (ground_truth_bounding_boxes_width / 2)
ground_truth_bounding_boxes_y1 = ground_truth_bounding_boxes_center_y - (ground_truth_bounding_boxes_height / 2)
ground_truth_bounding_boxes_y2 = ground_truth_bounding_boxes_center_y + (ground_truth_bounding_boxes_height / 2)
ground_truth_bounding_boxes_x1, ground_truth_bounding_boxes_x2, ground_truth_bounding_boxes_y1, ground_truth_bounding_boxes_y2 = self.tf.reshape(ground_truth_bounding_boxes_x1, [1,1,1,1,-1]), self.tf.reshape(ground_truth_bounding_boxes_x2, [1,1,1,1,-1]), self.tf.reshape(ground_truth_bounding_boxes_y1, [1,1,1,1,-1]), self.tf.reshape(ground_truth_bounding_boxes_y2, [1,1,1,1,-1])
""" Get areas of boxes """
anchor_box_predictions_area = (anchor_box_predictions_x2 - anchor_box_predictions_x1) * (anchor_box_predictions_y2 - anchor_box_predictions_y1)
ground_truth_bounding_boxes_area = (ground_truth_bounding_boxes_x2 - ground_truth_bounding_boxes_x1) * (ground_truth_bounding_boxes_y2 - ground_truth_bounding_boxes_y1)
""" Calculate intersection between prediction boxes and ground truth boxes """
x1_intersection, x2_intersection = self.tf.maximum(anchor_box_predictions_x1, ground_truth_bounding_boxes_x1), self.tf.minimum(anchor_box_predictions_x2, ground_truth_bounding_boxes_x2)
y1_intersection, y2_intersection = self.tf.maximum(anchor_box_predictions_y1, ground_truth_bounding_boxes_y1), self.tf.minimum(anchor_box_predictions_y2, ground_truth_bounding_boxes_y2)
""" Calculate intersection area """
intersection = self.tf.where(self.tf.logical_and(x2_intersection > x1_intersection, y2_intersection > y1_intersection), (x2_intersection - x1_intersection) * (y2_intersection - y1_intersection), 0)
""" Find the coordinates of smallest enclosing box (union) """
x1_coord, x2_coord = self.tf.minimum(anchor_box_predictions_x1, ground_truth_bounding_boxes_x1), self.tf.maximum(anchor_box_predictions_x2, ground_truth_bounding_boxes_x2)
y1_coord, y2_coord = self.tf.minimum(anchor_box_predictions_y1, ground_truth_bounding_boxes_y1), self.tf.maximum(anchor_box_predictions_y2, ground_truth_bounding_boxes_y2)
""" Get area of union box """
union_area = (x2_coord - x1_coord) * (y2_coord - y1_coord)
""" Intersection over union """
iou = intersection / (anchor_box_predictions_area + ground_truth_bounding_boxes_area - intersection)
Giou = iou - ((union_area - (anchor_box_predictions_area + ground_truth_bounding_boxes_area - intersection)) / union_area)
""" get max iou and giou and bounding box for max iou/Giou"""
if giou is True:
bounding_box_true = self.tf.slice(self.tf.gather(ground_truth_bounding_box,self.tf.argmax(Giou, axis=4)),[0,0,0,0,0],[-1,-1,-1,-1,4])
else:
bounding_box_true = self.tf.slice(self.tf.gather(ground_truth_bounding_box,self.tf.argmax(iou, axis=4)),[0,0,0,0,0],[-1,-1,-1,-1,4])
iou = self.tf.reduce_max(iou, axis=4)
Giou = self.tf.reduce_max(Giou, axis=4)
return (Giou,bounding_box_true) if giou else (iou, bounding_box_true)
def model_loss(self, y_true, y_pred):
mini_batch_sample_size = 256
object_predictions = y_pred[0]
anchor_box_change_predictions = y_pred[1]
anchor_box_predictions = self.anchor_boxes_over_image + anchor_box_change_predictions
anchor_boxes_giou, anchor_boxes_nearest_bounding_box = self.get_iou(anchor_box_predictions, y_true, giou=True)
object_prediction_labels = self.tf.one_hot(self.tf.where(anchor_boxes_giou > 0.5, 1, 0),depth=2, on_value=1, off_value=0)
object_predictions_flat = self.tf.reshape(object_predictions,[-1,2])
object_prediction_labels_flat = self.tf.reshape(object_prediction_labels, [-1, 2])
anchor_boxes_flat = self.tf.reshape(self.anchor_boxes_over_image,[-1, 4])
anchor_box_predictions_flat = self.tf.reshape(anchor_box_predictions,[-1, 4])
anchor_boxes_nearest_bounding_box_flat = self.tf.reshape(anchor_boxes_nearest_bounding_box, [-1, 4])
"""Shuffle flattened y_pred and y_true all in the same order"""
random_indices = self.tf.random.shuffle(self.tf.range(self.tf.gather(self.tf.shape(object_predictions_flat),0)))
object_predictions_flat = self.tf.gather(object_predictions_flat, random_indices)
object_prediction_labels_flat = self.tf.gather(object_prediction_labels_flat, random_indices)
anchor_boxes_flat = self.tf.gather(anchor_boxes_flat, random_indices)
anchor_box_predictions_flat = self.tf.gather(anchor_box_predictions_flat, random_indices)
anchor_boxes_nearest_bounding_box_flat = self.tf.gather(anchor_boxes_nearest_bounding_box_flat, random_indices)
""" Sort in ascending order from background to foreground """
ind_sorted = self.tf.argsort(self.tf.argmax(object_prediction_labels_flat, axis=1),axis=0)
object_predictions_flat = self.tf.gather(object_predictions_flat, ind_sorted)
object_prediction_labels_flat = self.tf.gather(object_prediction_labels_flat, ind_sorted)
anchor_boxes_flat = self.tf.gather(anchor_boxes_flat, ind_sorted)
anchor_box_predictions_flat = self.tf.gather(anchor_box_predictions_flat, ind_sorted)
anchor_boxes_nearest_bounding_box_flat = self.tf.gather(anchor_boxes_nearest_bounding_box_flat, ind_sorted)
""" Get 128 background anchors and 128 foreground anchors and merge them into a single batch """
split_amount = mini_batch_sample_size // 2
#Background
background_object_predictions_flat = self.tf.slice(object_predictions_flat, [0,0],[split_amount,-1])
background_object_prediction_labels_flat = self.tf.slice(object_prediction_labels_flat, [0,0], [split_amount,-1])
background_anchor_boxes_flat = self.tf.slice(anchor_boxes_flat, [0, 0], [split_amount, -1])
background_anchor_box_predictions_flat = self.tf.slice(anchor_box_predictions_flat, [0, 0], [split_amount, -1])
background_anchor_boxes_nearest_bounding_box_flat = self.tf.slice(anchor_boxes_nearest_bounding_box_flat, [0,0], [split_amount,-1])
#Foreground
foreground_object_predictions_flat = self.tf.slice(object_predictions_flat, [split_amount,0], [-1,-1])
foreground_object_prediction_labels_flat = self.tf.slice(object_prediction_labels_flat, [split_amount,0], [-1,-1])
foreground_anchor_boxes_flat = self.tf.slice(anchor_boxes_flat, [split_amount,0], [-1,-1])
foreground_anchor_box_predictions_flat = self.tf.slice(anchor_box_predictions_flat, [split_amount,0], [-1,-1])
foreground_anchor_boxes_nearest_bounding_box_flat = self.tf.slice(anchor_boxes_nearest_bounding_box_flat, [split_amount,0], [-1,-1])
#Merge
final_object_predictions_flat = self.tf.concat((background_object_predictions_flat,foreground_object_predictions_flat), axis=0)
final_object_prediction_labels_flat = self.tf.concat((background_object_prediction_labels_flat, foreground_object_prediction_labels_flat), axis=0)
all_anchor_boxes_flat = self.tf.concat((background_anchor_boxes_flat, foreground_anchor_boxes_flat), axis=0)
all_anchor_box_predictions_flat = self.tf.concat((background_anchor_box_predictions_flat, foreground_anchor_box_predictions_flat), axis=0)
all_anchor_boxes_nearest_bounding_box_flat = self.tf.concat((background_anchor_boxes_nearest_bounding_box_flat, foreground_anchor_boxes_nearest_bounding_box_flat), axis=0)
#Take only foreground anchor and the closest ground truth boxes from the mini batch for bounding box regression
final_anchor_boxes_flat = self.tf.gather(foreground_anchor_boxes_flat, self.tf.squeeze(self.tf.where(self.tf.squeeze(self.tf.equal(self.tf.slice(foreground_object_prediction_labels_flat,[0,1],[-1,1]),1),-1)),-1))
final_anchor_box_predictions_flat = self.tf.gather(foreground_anchor_box_predictions_flat, self.tf.squeeze(self.tf.where(self.tf.squeeze(self.tf.equal(self.tf.slice(foreground_object_prediction_labels_flat,[0,1],[-1,1]),1),-1)),-1))
final_anchor_boxes_nearest_bounding_box_flat = self.tf.gather(foreground_anchor_boxes_nearest_bounding_box_flat, self.tf.squeeze(self.tf.where(self.tf.squeeze(self.tf.equal(self.tf.slice(foreground_object_prediction_labels_flat,[0,1],[-1,1]),1),-1)),-1))
#losses
anchor_boxes_loss = self.anchors_loss(final_object_prediction_labels_flat, final_object_predictions_flat, all_anchor_boxes_nearest_bounding_box_flat, all_anchor_box_predictions_flat)
regression_loss = self.bounding_box_regression_loss(final_anchor_boxes_nearest_bounding_box_flat, final_anchor_box_predictions_flat, final_anchor_boxes_flat)
sum_anchor_boxes_loss = self.tf.reduce_sum(anchor_boxes_loss)
sum_regression_loss = self.tf.reduce_sum(regression_loss)
return sum_anchor_boxes_loss + sum_regression_loss
def train_model(self, inputs, ground_truth_bounding_boxes):
mini_batch_sample_size = 256
for image, bb_true in zip(list(inputs), ground_truth_bounding_boxes):
image = image.reshape(1, self.image_height, self.image_width, 3)
predictions = self.model.predict(image)
loss = self.model_loss(bb_true, predictions)
print(loss)
gradients = self.optimizer.get_gradients(loss, self.model.trainable_variables)
def anchors_loss(self, classification_true, classification_pred, anchor_box_true, anchor_box_pred, reg_gamma=10):
mini_batch_size = self.tf.gather(self.tf.shape(classification_pred),0)
classification_loss = self.logloss(classification_true, classification_pred)
true_objects = self.tf.cast(self.tf.argmax(classification_true, -1),dtype=self.tf.float64)
reg_loss = true_objects*self.regloss(anchor_box_true, anchor_box_pred)
anchors_loss = (1 / mini_batch_size) * classification_loss + reg_gamma * (1 / self.total_number_of_anchor_boxes) * reg_loss
return anchors_loss
def regloss(self, box_true, box_predictions, alpha=1):
error = box_predictions - box_true
smooth_l1 = self.tf.reduce_sum(self.tf.where(self.tf.abs(error) < 1, 0.5 * self.tf.square(error), self.tf.abs(error) - 0.5), axis=1)
return smooth_l1
def logloss(self, true_label, predicted, eps=1e-15):
p = self.tf.clip_by_value(predicted, eps, 1 - eps)
log_loss = self.tf.reduce_sum(self.tf.where(self.tf.equal(true_label, 1), -self.tf.math.log(p), -self.tf.math.log(1 - p)), axis=1)
return log_loss
def bounding_box_regression_loss(self, true_box, predicted_box, anchor_box):
p_x, p_y, p_w, p_h = self.tf.slice(predicted_box, [0,0],[-1,1]), self.tf.slice(predicted_box, [0,1],[-1,1]), self.tf.slice(predicted_box, [0,2],[-1,1]), self.tf.slice(predicted_box, [0,3],[-1,1])
t_x, t_y, t_w, t_h = self.tf.slice(true_box, [0,0],[-1,1]), self.tf.slice(true_box, [0,1],[-1,1]), self.tf.slice(true_box, [0,2],[-1,1]), self.tf.slice(true_box, [0,3],[-1,1])
a_x, a_y, a_w, a_h = self.tf.slice(anchor_box, [0, 0], [-1, 1]), self.tf.slice(anchor_box, [0, 1], [-1, 1]), self.tf.slice(anchor_box, [0, 2], [-1, 1]), self.tf.slice(anchor_box, [0, 3], [-1, 1])
p_x = (p_x - a_x) / a_w
p_y = (p_y - a_y) / a_h
p_w = self.tf.math.log(p_w / a_w)
p_h = self.tf.math.log(p_h / a_h)
t_x = (t_x - a_x) / a_w
t_y = (t_y - a_y) / a_h
t_w = self.tf.math.log(t_w / a_w)
t_h = self.tf.math.log(t_h / a_h)
predicted = self.tf.concat((p_x, p_y, p_w, p_h), axis=1)
truth = self.tf.concat((t_x, t_y, t_w, t_h), axis=1)
regression_loss = self.tf.reduce_sum(self.tf.square(predicted-truth),axis=1)
return regression_loss
def generate_anchor_boxes(self, base_anchor_size, anchor_ratios, anchor_scales):
"""
Every anchor box is different
Number of anchor boxes: len(anchor_ratios)*len(anchor_scales)
"""
anchor_boxes = []
for scale in anchor_scales:
for aspect_ratio in anchor_ratios:
height = base_anchor_size*scale*aspect_ratio[0]
width = base_anchor_size*scale*aspect_ratio[1]
anchor_box = [width, height]
anchor_boxes.append(anchor_box)
return anchor_boxes
def generate_anchor_boxes_over_image(self, anchor_boxes, image_height, image_width, subsample_rate):
"""
Returns an array of all the anchor boxes, each with shape [anchor_box_center_x, anchor_box_center_y, width, height]
"""
anchor_boxes_over_image = np.zeros(( image_height//subsample_rate, image_width//subsample_rate, len(anchor_boxes), 4))
for row in range(anchor_boxes_over_image.shape[0]):
for col in range(anchor_boxes_over_image.shape[1]):
for anchor_idx in range(anchor_boxes_over_image.shape[2]):
anchor_boxes_over_image[row,col,anchor_idx] = [(row+1)*subsample_rate, (col+1)*subsample_rate, anchor_boxes[anchor_idx][0], anchor_boxes[anchor_idx][1]]
return anchor_boxes_over_image
请帮助！