已经被这个API折磨的不行了，每天都脑壳疼。。。。。怎么才能创建一个自定义的特征提取类，我现在还没头绪。。。。。
如果你身边有大神熟悉这个API,能否推荐我的微信（yanghe_hehe）给ta，谢啦。
。。。。。。。。。。。。本人太菜了。。。。。。。。。。。哎。
有好几天没看代码，发现又忘了很多东西。
串一下检测流程。
这里发现一个比较简单的特征提取器，就是faster_rcnn_inception_v2_feature_extractor.py这里的特征提取器。faster rcnn在这里仅仅提取inception_v2_base的['Mixed_4e']层。
重新整理一下思路，发现做了很多无用功，浪费了很多时间。
model_builder.py总体上导入两个功能函数
- 特征提取模型
- 目标检测模型
特征提取函数到哪里去了？？
def _build_faster_rcnn_feature_extractor(
feature_extractor_config, is_training, reuse_weights=None):
feature_type = feature_extractor_config.type
first_stage_features_stride = (
feature_extractor_config.first_stage_features_stride)
batch_norm_trainable = feature_extractor_config.batch_norm_trainable
if feature_type not in FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP:
raise ValueError('Unknown Faster R-CNN feature_extractor: {}'.format(
feature_type))
feature_extractor_class = FASTER_RCNN_FEATURE_EXTRACTOR_CLASS_MAP[
feature_type]
return feature_extractor_class(
is_training, first_stage_features_stride,
batch_norm_trainable, reuse_weights)
直接看返回值 feature_extractor_class
feature_extractor_class(
is_training, first_stage_features_stride,
batch_norm_trainable, reuse_weights)
这里可以到特征提取的模型看看，看这里
class FasterRCNNInceptionV2FeatureExtractor(
faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
def __init__(self,
is_training,
first_stage_features_stride,
batch_norm_trainable=False,
reuse_weights=None,
weight_decay=0.0,
depth_multiplier=1.0,
min_depth=16)
回到model_builder.py, 看看检测模型去了哪。
def _build_faster_rcnn_model(frcnn_config, is_training):
"""Builds a Faster R-CNN or R-FCN detection model based on the model config.
Builds R-FCN model if the second_stage_box_predictor in the config is of type
`rfcn_box_predictor` else builds a Faster R-CNN model.
Args:
frcnn_config: A faster_rcnn.proto object containing the config for the
desired FasterRCNNMetaArch or RFCNMetaArch.
is_training: True if this model is being built for training purposes.
Returns:
FasterRCNNMetaArch based on the config.
Raises:
ValueError: If frcnn_config.type is not recognized (i.e. not registered in
model_class_map).
"""
num_classes = frcnn_config.num_classes
image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
feature_extractor = _build_faster_rcnn_feature_extractor(
frcnn_config.feature_extractor, is_training)
first_stage_only = frcnn_config.first_stage_only
first_stage_anchor_generator = anchor_generator_builder.build(
frcnn_config.first_stage_anchor_generator)
first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
first_stage_box_predictor_arg_scope = hyperparams_builder.build(
frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
first_stage_box_predictor_kernel_size = (
frcnn_config.first_stage_box_predictor_kernel_size)
first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
first_stage_positive_balance_fraction = (
frcnn_config.first_stage_positive_balance_fraction)
first_stage_nms_score_threshold = frcnn_config.first_stage_nms_score_threshold
first_stage_nms_iou_threshold = frcnn_config.first_stage_nms_iou_threshold
first_stage_max_proposals = frcnn_config.first_stage_max_proposals
first_stage_loc_loss_weight = (
frcnn_config.first_stage_localization_loss_weight)
first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
initial_crop_size = frcnn_config.initial_crop_size
maxpool_kernel_size = frcnn_config.maxpool_kernel_size
maxpool_stride = frcnn_config.maxpool_stride
second_stage_box_predictor = box_predictor_builder.build(
hyperparams_builder.build,
frcnn_config.second_stage_box_predictor,
is_training=is_training,
num_classes=num_classes)
second_stage_batch_size = frcnn_config.second_stage_batch_size
second_stage_balance_fraction = frcnn_config.second_stage_balance_fraction
(second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
second_stage_localization_loss_weight = (
frcnn_config.second_stage_localization_loss_weight)
second_stage_classification_loss = (
losses_builder.build_faster_rcnn_classification_loss(
frcnn_config.second_stage_classification_loss))
second_stage_classification_loss_weight = (
frcnn_config.second_stage_classification_loss_weight)
second_stage_mask_prediction_loss_weight = (
frcnn_config.second_stage_mask_prediction_loss_weight)
hard_example_miner = None
if frcnn_config.HasField('hard_example_miner'):
hard_example_miner = losses_builder.build_hard_example_miner(
frcnn_config.hard_example_miner,
second_stage_classification_loss_weight,
second_stage_localization_loss_weight)
common_kwargs = {
'is_training': is_training,
'num_classes': num_classes,
'image_resizer_fn': image_resizer_fn,
'feature_extractor': feature_extractor,
'first_stage_only': first_stage_only,
'first_stage_anchor_generator': first_stage_anchor_generator,
'first_stage_atrous_rate': first_stage_atrous_rate,
'first_stage_box_predictor_arg_scope':
first_stage_box_predictor_arg_scope,
'first_stage_box_predictor_kernel_size':
first_stage_box_predictor_kernel_size,
'first_stage_box_predictor_depth': first_stage_box_predictor_depth,
'first_stage_minibatch_size': first_stage_minibatch_size,
'first_stage_positive_balance_fraction':
first_stage_positive_balance_fraction,
'first_stage_nms_score_threshold': first_stage_nms_score_threshold,
'first_stage_nms_iou_threshold': first_stage_nms_iou_threshold,
'first_stage_max_proposals': first_stage_max_proposals,
'first_stage_localization_loss_weight': first_stage_loc_loss_weight,
'first_stage_objectness_loss_weight': first_stage_obj_loss_weight,
'second_stage_batch_size': second_stage_batch_size,
'second_stage_balance_fraction': second_stage_balance_fraction,
'second_stage_non_max_suppression_fn':
second_stage_non_max_suppression_fn,
'second_stage_score_conversion_fn': second_stage_score_conversion_fn,
'second_stage_localization_loss_weight':
second_stage_localization_loss_weight,
'second_stage_classification_loss':
second_stage_classification_loss,
'second_stage_classification_loss_weight':
second_stage_classification_loss_weight,
'hard_example_miner': hard_example_miner}
if isinstance(second_stage_box_predictor, box_predictor.RfcnBoxPredictor):
return rfcn_meta_arch.RFCNMetaArch(
second_stage_rfcn_box_predictor=second_stage_box_predictor,
**common_kwargs)
else:
return faster_rcnn_meta_arch.FasterRCNNMetaArch(
initial_crop_size=initial_crop_size,
maxpool_kernel_size=maxpool_kernel_size,
maxpool_stride=maxpool_stride,
second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
second_stage_mask_prediction_loss_weight=(
second_stage_mask_prediction_loss_weight),
**common_kwargs)
这里的_build_faster_rcnn_model就是整个检测模型的构建过程。
num_classes = frcnn_config.num_classes
image_resizer_fn = image_resizer_builder.build(frcnn_config.image_resizer)
这里不学解释，就是获取一个图片变换函数，
feature_extractor = _build_faster_rcnn_feature_extractor(
frcnn_config.feature_extractor, is_training)
这里就是拿到特征提取模型
first_stage_only = frcnn_config.first_stage_only
first_stage_anchor_generator = anchor_generator_builder.build(
frcnn_config.first_stage_anchor_generator)
是否仅仅是训练RPN，同时拿到box产生模型
first_stage_atrous_rate = frcnn_config.first_stage_atrous_rate
first_stage_box_predictor_arg_scope = hyperparams_builder.build(
frcnn_config.first_stage_box_predictor_conv_hyperparams, is_training)
first_stage_box_predictor_kernel_size = (
frcnn_config.first_stage_box_predictor_kernel_size)
first_stage_box_predictor_depth = frcnn_config.first_stage_box_predictor_depth
first_stage_minibatch_size = frcnn_config.first_stage_minibatch_size
first_stage_positive_balance_fraction = (
frcnn_config.first_stage_positive_balance_fraction)
first_stage_nms_score_threshold = frcnn_config.first_stage_nms_score_threshold
first_stage_nms_iou_threshold = frcnn_config.first_stage_nms_iou_threshold
first_stage_max_proposals = frcnn_config.first_stage_max_proposals
first_stage_loc_loss_weight = (
frcnn_config.first_stage_localization_loss_weight)
first_stage_obj_loss_weight = frcnn_config.first_stage_objectness_loss_weight
initial_crop_size = frcnn_config.initial_crop_size
maxpool_kernel_size = frcnn_config.maxpool_kernel_size
maxpool_stride = frcnn_config.maxpool_stride
这上面就是拿第一阶段到的config设置参数。
second_stage_box_predictor = box_predictor_builder.build(
hyperparams_builder.build,
frcnn_config.second_stage_box_predictor,
is_training=is_training,
num_classes=num_classes)
second_stage_batch_size = frcnn_config.second_stage_batch_size
second_stage_balance_fraction = frcnn_config.second_stage_balance_fraction
(second_stage_non_max_suppression_fn, second_stage_score_conversion_fn
) = post_processing_builder.build(frcnn_config.second_stage_post_processing)
second_stage_localization_loss_weight = (
frcnn_config.second_stage_localization_loss_weight)
second_stage_classification_loss = (
losses_builder.build_faster_rcnn_classification_loss(
frcnn_config.second_stage_classification_loss))
second_stage_classification_loss_weight = (
frcnn_config.second_stage_classification_loss_weight)
second_stage_mask_prediction_loss_weight = (
frcnn_config.second_stage_mask_prediction_loss_weight)
拿到第二阶段预测需要的检测模型和config参数。
if frcnn_config.HasField('hard_example_miner'):
hard_example_miner = losses_builder.build_hard_example_miner(
frcnn_config.hard_example_miner,
second_stage_classification_loss_weight,
second_stage_localization_loss_weight)
hard_example_miner是在进行RPN训练时对正负样本平衡。
faster_rcnn_meta_arch.FasterRCNNMetaArch(
initial_crop_size=initial_crop_size,
maxpool_kernel_size=maxpool_kernel_size,
maxpool_stride=maxpool_stride,
second_stage_mask_rcnn_box_predictor=second_stage_box_predictor,
second_stage_mask_prediction_loss_weight=(
second_stage_mask_prediction_loss_weight),
**common_kwargs)
_build_faster_rcnn_model 返回值就是faster_rcnn_meta_arch.FasterRCNNMetaArch，这就是FasterRCNN的检测模型。
为了把大流程弄出来，这里借用一下evaluator.py中的一个简单流程。在这
def _extract_prediction_tensors(model,create_input_dict_fn, ignore_groundtruth=False)里面，有这么三句话：
model就是上面说的_build_faster_rcnn_model返回值。
preprocessed_image = model.preprocess(tf.to_float(original_image))
prediction_dict = model.predict(preprocessed_image)
detections = model.postprocess(prediction_dict)
- 处理图片
- 进行预测
- 处理预测的结果
这里model.preprocess(preprocessed_image)已经介绍过了。
def preprocess(self, inputs):
if inputs.dtype is not tf.float32:
raise ValueError('`preprocess` expects a tf.float32 tensor')
with tf.name_scope('Preprocessor'):
outputs = shape_utils.static_or_dynamic_map_fn(
self._image_resizer_fn,
elems=inputs,
dtype=[tf.float32, tf.int32],
parallel_iterations=self._parallel_iterations)
resized_inputs = outputs[0]
true_image_shapes = outputs[1]
return (self._feature_extractor.preprocess(resized_inputs),
true_image_shapes)
这里把图片的尺寸进行进行resize，在调用特征提取的preprocessor就是把图片的值缩放到[-1,1]。
再看看predict
prediction_dict = model.predict(preprocessed_image)
def predict(self, preprocessed_inputs, true_image_shapes):
(rpn_box_predictor_features, rpn_features_to_crop, anchors_boxlist,
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
(rpn_box_encodings, rpn_objectness_predictions_with_background
) = self._predict_rpn_proposals(rpn_box_predictor_features)
# The Faster R-CNN paper recommends pruning anchors that venture outside
# the image window at training time and clipping at inference time.
clip_window = tf.to_float(tf.stack([0, 0, image_shape[1], image_shape[2]]))
if self._is_training:
if self.clip_anchors_to_image:
anchors_boxlist = box_list_ops.clip_to_window(
anchors_boxlist, clip_window, filter_nonoverlapping=False)
else:
(rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist) = self._remove_invalid_anchors_and_predictions(
rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist, clip_window)
else:
anchors_boxlist = box_list_ops.clip_to_window(
anchors_boxlist, clip_window)
self._anchors = anchors_boxlist
prediction_dict = {
'rpn_box_predictor_features': rpn_box_predictor_features,
'rpn_features_to_crop': rpn_features_to_crop,
'image_shape': image_shape,
'rpn_box_encodings': rpn_box_encodings,
'rpn_objectness_predictions_with_background':
rpn_objectness_predictions_with_background,
'anchors': self._anchors.get()
}
if self._number_of_stages >= 2:
# If mixed-precision training on TPU is enabled, rpn_box_encodings and
# rpn_objectness_predictions_with_background are bfloat16 tensors.
# Considered prediction results, they need to be casted to float32
# tensors for correct postprocess_rpn computation in predict_second_stage.
prediction_dict.update(self._predict_second_stage(
tf.to_float(rpn_box_encodings),
tf.to_float(rpn_objectness_predictions_with_background),
rpn_features_to_crop,
self._anchors.get(), image_shape, true_image_shapes))
if self._number_of_stages == 3:
prediction_dict = self._predict_third_stage(
prediction_dict, true_image_shapes)
return prediction_dict
开始看看这里的_extract_rpn_feature_maps
(rpn_box_predictor_features, rpn_features_to_crop, anchors_boxlist,
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
这里的_extract_rpn_feature_maps其实没对太多的工作，就是拿到所有的feature map，同时对进入RPN的所有的feature map的维度进一个设置，感觉就像一个普通的卷积层。
FasterRCNN就是在这个函数分为两路的。
(rpn_box_encodings, rpn_objectness_predictions_with_background
) = self._predict_rpn_proposals(rpn_box_predictor_features)
这里就是把上面的rpn_box_predictor_features进行计算，得到box的坐标和box里面的类别。
clip_window = tf.to_float(tf.stack([0, 0, image_shape[1], image_shape[2]]))
if self._is_training:
if self.clip_anchors_to_image:
anchors_boxlist = box_list_ops.clip_to_window(
anchors_boxlist, clip_window, filter_nonoverlapping=False)
else:
(rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist) = self._remove_invalid_anchors_and_predictions(
rpn_box_encodings, rpn_objectness_predictions_with_background,
anchors_boxlist, clip_window)
这里就是把box的坐标进行修建，使用了很多其他的函数。
def _predict_second_stage(self, rpn_box_encodings,
rpn_objectness_predictions_with_background,
rpn_features_to_crop,
anchors,
image_shape,
true_image_shapes):
prediction_dict = {
'refined_box_encodings': refined_box_encodings,
'class_predictions_with_background':
class_predictions_with_background,
'num_proposals': num_proposals,
'proposal_boxes': absolute_proposal_boxes,
'box_classifier_features': box_classifier_features,
'proposal_boxes_normalized': proposal_boxes_normalized,
}
return prediction_dict
对RPN提取的网络进行预测，输出最后的box坐标，同时输出classes的预测。
把这里的box从预测值转码：从预测值转化到图片上尺寸。
detections = model.postprocess(prediction_dict)
就到这里吧 ！！！！！
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