Faster R-CNN框架
bilibili
faster_rcnn_framework.py
class FasterRCNNBase(nn.Module):
"""
Main class for Generalized R-CNN.
Arguments:
backbone (nn.Module):
rpn (nn.Module):
roi_heads (nn.Module): takes the features + the proposals from the RPN and computes
detections / masks from it.
transform (nn.Module): performs the data transformation from the inputs to feed into
the model
"""
def __init__(self, backbone, rpn, roi_heads, transform):
super(FasterRCNNBase, self).__init__()
self.transform = transform
self.backbone = backbone
self.rpn = rpn
self.roi_heads = roi_heads
# used only on torchscript mode
self._has_warned = False
@torch.jit.unused
def eager_outputs(self, losses, detections):
# type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Union[Dict[str, Tensor], List[Dict[str, Tensor]]]
if self.training:
return losses
return detections
def forward(self, images, targets=None):
# type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]
"""
Arguments:
images (list[Tensor]): images to be processed
targets (list[Dict[Tensor]]): ground-truth boxes present in the image (optional)
Returns:
result (list[BoxList] or dict[Tensor]): the output from the model.
During training, it returns a dict[Tensor] which contains the losses.
During testing, it returns list[BoxList] contains additional fields
like `scores`, `labels` and `mask` (for Mask R-CNN models).
"""
if self.training and targets is None:
raise ValueError("In training mode, targets should be passed")
if self.training:
assert targets is not None
for target in targets: # 进一步判断传入的target的boxes参数是否符合规定
boxes = target["boxes"]
if isinstance(boxes, torch.Tensor):
if len(boxes.shape) != 2 or boxes.shape[-1] != 4:
raise ValueError("Expected target boxes to be a tensor"
"of shape [N, 4], got {:}.".format(
boxes.shape))
else:
raise ValueError("Expected target boxes to be of type "
"Tensor, got {:}.".format(type(boxes)))
original_image_sizes = torch.jit.annotate(List[Tuple[int, int]], [])
for img in images:
val = img.shape[-2:]
assert len(val) == 2 # 防止输入的是个一维向量
original_image_sizes.append((val[0], val[1]))
# original_image_sizes = [img.shape[-2:] for img in images]
images, targets = self.transform(images, targets) # 对图像进行预处理
# print(images.tensors.shape)
features = self.backbone(images.tensors) # 将图像输入backbone得到特征图
if isinstance(features, torch.Tensor): # 若只在一层特征层上预测，将feature放入有序字典中，并编号为‘0’
features = OrderedDict([('0', features)]) # 若在多层特征层上预测，传入的就是一个有序字典
# 将特征层以及标注target信息传入rpn中
# proposals: List[Tensor], Tensor_shape: [num_proposals, 4],
# 每个proposals是绝对坐标，且为(x1, y1, x2, y2)格式
proposals, proposal_losses = self.rpn(images, features, targets)
# 将rpn生成的数据以及标注target信息传入fast rcnn后半部分
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
# 对网络的预测结果进行后处理（主要将bboxes还原到原图像尺度上）
detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)
losses = {
}
losses.update(detector_losses)
losses.update(proposal_losses)
if torch.jit.is_scripting():
if not self._has_warned:
warnings.warn("RCNN always returns a (Losses, Detections) tuple in scripting")
self._has_warned = True
return losses, detections
else:
return self.eager_outputs(losses, detections)
# if self.training:
# return losses
#
# return detections
class FasterRCNN(FasterRCNNBase):
"""
Implements Faster R-CNN.
The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
image, and should be in 0-1 range. Different images can have different sizes.
The behavior of the model changes depending if it is in training or evaluation mode.
During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:
- boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values
between 0 and H and 0 and W
- labels (Int64Tensor[N]): the class label for each ground-truth box
The model returns a Dict[Tensor] during training, containing the classification and regression
losses for both the RPN and the R-CNN.
During inference, the model requires only the input tensors, and returns the post-processed
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
follows:
- boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values between
0 and H and 0 and W
- labels (Int64Tensor[N]): the predicted labels for each image
- scores (Tensor[N]): the scores or each prediction
Arguments:
backbone (nn.Module): the network used to compute the features for the model.
It should contain a out_channels attribute, which indicates the number of output
channels that each feature map has (and it should be the same for all feature maps).
The backbone should return a single Tensor or and OrderedDict[Tensor].
num_classes (int): number of output classes of the model (including the background).
If box_predictor is specified, num_classes should be None.
min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
image_mean (Tuple[float, float, float]): mean values used for input normalization.
They are generally the mean values of the dataset on which the backbone has been trained
on
image_std (Tuple[float, float, float]): std values used for input normalization.
They are generally the std values of the dataset on which the backbone has been trained on
rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
maps.
rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN
rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training
rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing
rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training
rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing
rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
considered as positive during training of the RPN.
rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
considered as negative during training of the RPN.
rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN
for computing the loss
rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training
of the RPN
box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
the locations indicated by the bounding boxes
box_head (nn.Module): module that takes the cropped feature maps as input
box_predictor (nn.Module): module that takes the output of box_head and returns the
classification logits and box regression deltas.
box_score_thresh (float): during inference, only return proposals with a classification score
greater than box_score_thresh
box_nms_thresh (float): NMS threshold for the prediction head. Used during inference
box_detections_per_img (int): maximum number of detections per image, for all classes.
box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be
considered as positive during training of the classification head
box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be
considered as negative during training of the classification head
box_batch_size_per_image (int): number of proposals that are sampled during training of the
classification head
box_positive_fraction (float): proportion of positive proposals in a mini-batch during training
of the classification head
bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the
bounding boxes
"""
def __init__(self, backbone, num_classes=None,
# transform parameter
min_size=800, max_size=1000, # 预处理resize时限制的最小尺寸与最大尺寸
image_mean=None, image_std=None, # 预处理normalize时使用的均值和方差
# RPN parameters
rpn_anchor_generator=None, rpn_head=None,
rpn_pre_nms_top_n_train=2000, rpn_pre_nms_top_n_test=1000, # rpn中在nms处理前保留的proposal数(根据score)
rpn_post_nms_top_n_train=2000, rpn_post_nms_top_n_test=1000, # rpn中在nms处理后保留的proposal数
rpn_nms_thresh=0.7, # rpn中进行nms处理时使用的iou阈值
rpn_fg_iou_thresh=0.7, rpn_bg_iou_thresh=0.3, # rpn计算损失时，采集正负样本设置的阈值
rpn_batch_size_per_image=256, rpn_positive_fraction=0.5, # rpn计算损失时采样的样本数，以及正样本占总样本的比例
# Box parameters
box_roi_pool=None, box_head=None, box_predictor=None,
# 移除低目标概率 fast rcnn中进行nms处理的阈值 对预测结果根据score排序取前100个目标
box_score_thresh=0.05, box_nms_thresh=0.5, box_detections_per_img=100,
box_fg_iou_thresh=0.5, box_bg_iou_thresh=0.5, # fast rcnn计算误差时，采集正负样本设置的阈值
box_batch_size_per_image=512, box_positive_fraction=0.25, # fast rcnn计算误差时采样的样本数，以及正样本占所有样本的比例
bbox_reg_weights=None):
if not hasattr(backbone, "out_channels"):
raise ValueError(
"backbone should contain an attribute out_channels"
"specifying the number of output channels (assumed to be the"
"same for all the levels"
)
assert isinstance(rpn_anchor_generator, (AnchorsGenerator, type(None)))
assert isinstance(box_roi_pool, (MultiScaleRoIAlign, type(None)))
if num_classes is not None:
if box_predictor is not None:
raise ValueError("num_classes should be None when box_predictor "
"is specified")
else:
if box_predictor is None:
raise ValueError("num_classes should not be None when box_predictor "
"is not specified")
# 预测特征层的channels
out_channels = backbone.out_channels
# 若anchor生成器为空，则自动生成针对resnet50_fpn的anchor生成器
if rpn_anchor_generator is None:
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
rpn_anchor_generator = AnchorsGenerator(
anchor_sizes, aspect_ratios
)
# 生成RPN通过滑动窗口预测网络部分
if rpn_head is None:
rpn_head = RPNHead(
out_channels, rpn_anchor_generator.num_anchors_per_location()[0]
)
# 默认rpn_pre_nms_top_n_train = 2000, rpn_pre_nms_top_n_test = 1000,
# 默认rpn_post_nms_top_n_train = 2000, rpn_post_nms_top_n_test = 1000,
rpn_pre_nms_top_n = dict(training=rpn_pre_nms_top_n_train, testing=rpn_pre_nms_top_n_test)
rpn_post_nms_top_n = dict(training=rpn_post_nms_top_n_train, testing=rpn_post_nms_top_n_test)
# 定义整个RPN框架
rpn = RegionProposalNetwork(
rpn_anchor_generator, rpn_head,
rpn_fg_iou_thresh, rpn_bg_iou_thresh,
rpn_batch_size_per_image, rpn_positive_fraction,
rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_nms_thresh)
# Multi-scale RoIAlign pooling
if box_roi_pool is None:
box_roi_pool = MultiScaleRoIAlign(
featmap_names=['0', '1', '2', '3'], # 在哪些特征层进行roi pooling
output_size=[7, 7],
sampling_ratio=2)
# fast RCNN中roi pooling后的展平处理两个全连接层部分
if box_head is None:
resolution = box_roi_pool.output_size[0] # 默认等于7
representation_size = 1024
box_head = TwoMLPHead(
out_channels * resolution ** 2,
representation_size
)
# 在box_head的输出上预测部分
if box_predictor is None:
representation_size = 1024
box_predictor = FastRCNNPredictor(
representation_size,
num_classes)
# 将roi pooling, box_head以及box_predictor结合在一起
roi_heads = RoIHeads(
# box
box_roi_pool, box_head, box_predictor,
box_fg_iou_thresh, box_bg_iou_thresh, # 0.5 0.5
box_batch_size_per_image, box_positive_fraction, # 512 0.25
bbox_reg_weights,
box_score_thresh, box_nms_thresh, box_detections_per_img) # 0.05 0.5 100
if image_mean is None:
image_mean = [0.485, 0.456, 0.406]
if image_std is None:
image_std = [0.229, 0.224, 0.225]
# 对数据进行标准化，缩放，打包成batch等处理部分
transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std)
super(FasterRCNN, self).__init__(backbone, rpn, roi_heads, transform)
