自2014年RCNN论文发表之后，机器学习在目标检测领域得到了飞速发展，本系列文章将介绍一些目标检测发展的里程碑著作的代码实现。

FasterRCNN

FasterRCNN框架图如下图所示，实际上就是RPN与Fast RCNN的结合

大致框架可以分为以下几步：

1. 对图像进行预处理
2. 将图像输入backbone得到特征图
3. 将特征层以及标注target信息传入rpn中得到proposals和proposals loss
4. 将rpn生成的数据以及标注target信息通过ROIPooling和MLPHead
5. 所得结果传入fast rcnn后半部分，得到detections和detector loss
6. 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

在本文中，我们只关注FasterRCNN的关键部分结构，包含RPN，ROIHead和Fast RCNN Predictor三个部分：

一、RPN

RPN中完成的任务有：

1. 计算每个预测特征层上的预测目标概率和bboxes回归参数
2. 生成一个batch的图像的所有anchors信息
3. 将预测的bbox regression参数应用到anchors上得到最终预测bbox坐标
4. 筛除小boxes框，nms处理，根据预测概率获取前post_nms_top_n个目标
5. 计算每个anchors最匹配的gt，并将anchors进分类为前景，背景以及废弃的anchors
6. 结合anchors以及对应的gt，计算regression参数
7. 结合anchors以及对应的gt，计算损失

1. 计算预测目标分数和bbox回归参数

首先经过一个3x3的卷积层，然后分别使用一个1x1的卷积层预测目标分数和目标bbox regression参数：

class RPNHead(nn.Module):
    """
    add a RPN head with classification and regression
    通过滑动窗口计算预测目标概率与bbox regression参数
    Arguments:
        in_channels: number of channels of the input feature
        num_anchors: number of anchors to be predicted
    """
    def __init__(self, in_channels, num_anchors):
        super(RPNHead, self).__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
        self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
        self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1)
        
        for layer in self.children():
            if isinstance(layer, nn.Conv2d):
                torch.nn.init.normal_(layer.weight, std=0.01)
                torch.nn.init.constant_(layer.bias, 0)

    def forward(self, x):
        # type: (List[Tensor]) -> Tuple[List[Tensor], List[Tensor]]
        logits = []
        bbox_reg = []
        for i, feature in enumerate(x):
            t = F.relu(self.conv(feature))   	# 首先经过一个3x3的卷积层
            logits.append(self.cls_logits(t))	# 使用一个1x1的卷积层预测目标分数
            bbox_reg.append(self.bbox_pred(t))	# 使用一个1x1的卷积层预测目标bbox regression
        return logits, bbox_reg

2. 生成Anchors

generate_anchors函数用于生成Anchors，该函数会生成大小分别为32, 64, 128, 256, 512，长宽比分别为0.5, 1.0, 2.0的共15个anchors，这些anchors是以(0, 0)为中心的，形状为[N, 4]的tensor，如下图所示。

 	def generate_anchors(self, scales, aspect_ratios, dtype=torch.float32, device=torch.device("cpu")):
        # type: (List[int], List[float], torch.dtype, torch.device) -> Tensor
        """
        compute anchor sizes
        Arguments:
            scales: sqrt(anchor_area)
            aspect_ratios: h/w ratios
            dtype: float32
            device: cpu/gpu
        """
        scales = torch.as_tensor(scales, dtype=dtype, device=device)
        aspect_ratios = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)
        h_ratios = torch.sqrt(aspect_ratios)
        w_ratios = 1.0 / h_ratios

        # [r1, r2, r3]' * [s1, s2, s3, s4, s5]
        # number of elements is len(ratios)*len(scales)
        # 其中sizes=((32, 64, 128, 256, 512),)
        # 	  aspect_ratios=((0.5, 1.0, 2.0),))
        ws = (w_ratios[:, None] * scales[None, :]).view(-1)
        hs = (h_ratios[:, None] * scales[None, :]).view(-1)

        # left-top, right-bottom coordinate relative to anchor center(0, 0)
        # 生成的anchors模板都是以（0, 0）为中心的, shape [len(ratios)*len(scales), 4]
        base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2

        return base_anchors.round()  # round 四舍五入

grid_anchors函数用于计算预测特征图对应原始图像上的所有anchors的坐标，如下图所示

   def grid_anchors(self, grid_sizes, strides):
        # type: (List[List[int]], List[List[Tensor]]) -> List[Tensor]
        """
        anchors position in grid coordinate axis map into origin image
        计算预测特征图对应原始图像上的所有anchors的坐标
        Args:
            grid_sizes: 预测特征矩阵的height和width
            strides: 预测特征矩阵上一步对应原始图像上的步距
        """
        anchors = []
        cell_anchors = self.cell_anchors
        assert cell_anchors is not None

        # 遍历每个预测特征层的grid_size，strides和cell_anchors
        for size, stride, base_anchors in zip(grid_sizes, strides, cell_anchors):
            grid_height, grid_width = size
            stride_height, stride_width = stride
            device = base_anchors.device

            # For output anchor, compute [x_center, y_center, x_center, y_center]
            # shape: [grid_width] 对应原图上的x坐标(列)
            shifts_x = torch.arange(0, grid_width, dtype=torch.float32, device=device) * stride_width
            # shape: [grid_height] 对应原图上的y坐标(行)
            shifts_y = torch.arange(0, grid_height, dtype=torch.float32, device=device) * stride_height

            # 计算预测特征矩阵上每个点对应原图上的坐标(anchors模板的坐标偏移量)
            # torch.meshgrid函数分别传入行坐标和列坐标，生成网格行坐标矩阵和网格列坐标矩阵
            # shape: [grid_height, grid_width]
            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
            shift_x = shift_x.reshape(-1)
            shift_y = shift_y.reshape(-1)

            # 计算anchors坐标(xmin, ymin, xmax, ymax)在原图上的坐标偏移量
            # shape: [grid_width*grid_height, 4]
            shifts = torch.stack([shift_x, shift_y, shift_x, shift_y], dim=1)

            # 将anchors模板与原图上的坐标偏移量相加得到原图上所有anchors的坐标信息(shape不同时会使用广播机制)
            shifts_anchor = shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)
            anchors.append(shifts_anchor.reshape(-1, 4))

        return anchors  # List[Tensor(all_num_anchors, 4)]

3. 得到预测bbox的坐标

这一步骤是对（1）和（2）所得结果的结合，将bbox回归参数应用到anchors上得到预测bbox的坐标。利用如下公式，利用预测得到的bbox回归参数，得到预测bbox的坐标

    def decode_single(self, rel_codes, boxes):
        """
        From a set of original boxes and encoded relative box offsets,
        get the decoded boxes.
        Arguments:
            rel_codes (Tensor): encoded boxes (bbox regression parameters)
            boxes (Tensor): reference boxes (anchors/proposals)
        """
        boxes = boxes.to(rel_codes.dtype)

        # xyxy -> xywh
        widths = boxes[:, 2] - boxes[:, 0]   # anchor/proposal宽度
        heights = boxes[:, 3] - boxes[:, 1]  # anchor/proposal高度
        ctr_x = boxes[:, 0] + 0.5 * widths   # anchor/proposal中心x坐标
        ctr_y = boxes[:, 1] + 0.5 * heights  # anchor/proposal中心y坐标

        wx, wy, ww, wh = self.weights  # RPN中为[1,1,1,1], fastrcnn中为[10,10,5,5]
        dx = rel_codes[:, 0::4] / wx   # 预测anchors/proposals的中心坐标x回归参数
        dy = rel_codes[:, 1::4] / wy   # 预测anchors/proposals的中心坐标y回归参数
        dw = rel_codes[:, 2::4] / ww   # 预测anchors/proposals的宽度回归参数
        dh = rel_codes[:, 3::4] / wh   # 预测anchors/proposals的高度回归参数

        # limit max value, prevent sending too large values into torch.exp()
        # self.bbox_xform_clip=math.log(1000. / 16)   4.135
        dw = torch.clamp(dw, max=self.bbox_xform_clip)
        dh = torch.clamp(dh, max=self.bbox_xform_clip)

        pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
        pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
        pred_w = torch.exp(dw) * widths[:, None]
        pred_h = torch.exp(dh) * heights[:, None]

      	# xywh -> xyxy
        pred_boxes1 = pred_ctr_x - torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w
        pred_boxes2 = pred_ctr_y - torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h
        pred_boxes3 = pred_ctr_x + torch.tensor(0.5, dtype=pred_ctr_x.dtype, device=pred_w.device) * pred_w
        pred_boxes4 = pred_ctr_y + torch.tensor(0.5, dtype=pred_ctr_y.dtype, device=pred_h.device) * pred_h
        pred_boxes = torch.stack((pred_boxes1, pred_boxes2, pred_boxes3, pred_boxes4), dim=2).flatten(1)
        return pred_boxes

4. 对预测的bbox进行筛选

筛选bboxes的过程包含

1. 只取预测概率位于前post_nms_top_n个目标
2. 调整预测的boxes坐标，将越界的坐标调整到图片边界上
3. 移除宽或高小于min_size的bbox
4. nms处理

① 只取预测概率位于前post_nms_top_n个目标

主要利用ob.topk函数，作用：取最大的前k个目标

    def _get_top_n_idx(self, objectness, num_anchors_per_level):
        # type: (Tensor, List[int]) -> Tensor
        """
        获取每张预测特征图上预测概率排前pre_nms_top_n的anchors索引值
        Args:
            objectness: Tensor(每张图像的预测目标概率信息 )
            num_anchors_per_level: List（每个预测特征层上的预测的anchors个数）
        Returns:

        """
        r = []  # 记录每个预测特征层上预测目标概率前pre_nms_top_n的索引信息
        offset = 0
        # 遍历每个预测特征层上的预测目标概率信息
        for ob in objectness.split(num_anchors_per_level, 1):
            if torchvision._is_tracing():
                num_anchors, pre_nms_top_n = _onnx_get_num_anchors_and_pre_nms_top_n(ob, self.pre_nms_top_n())
            else:
                num_anchors = ob.shape[1]  # 预测特征层上的预测的anchors个数
                pre_nms_top_n = min(self.pre_nms_top_n(), num_anchors)

            # 取最大的前k个目标
            _, top_n_idx = ob.topk(pre_nms_top_n, dim=1)
            r.append(top_n_idx + offset)
            offset += num_anchors
        return torch.cat(r, dim=1)

② 调整预测的boxes坐标，将越界的坐标调整到图片边界上

主要利用最大最小值函数，把范围限制在 [0，图片尺寸] 之间

def clip_boxes_to_image(boxes, size):
    # type: (Tensor, Tuple[int, int]) -> Tensor
    """
    Clip boxes so that they lie inside an image of size `size`.
    裁剪预测的boxes信息，将越界的坐标调整到图片边界上
    Arguments:
        boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format
        size (Tuple[height, width]): size of the image
    Returns:
        clipped_boxes (Tensor[N, 4])
    """
    dim = boxes.dim()
    boxes_x = boxes[..., 0::2]  # x1, x2
    boxes_y = boxes[..., 1::2]  # y1, y2
    height, width = size

    if torchvision._is_tracing():
        boxes_x = torch.max(boxes_x, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
        boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
        boxes_y = torch.max(boxes_y, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
        boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
    else:
        boxes_x = boxes_x.clamp(min=0, max=width)   # 限制x坐标范围在[0,width]之间
        boxes_y = boxes_y.clamp(min=0, max=height)  # 限制y坐标范围在[0,height]之间

    clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim)
    return clipped_boxes.reshape(boxes.shape)

③ 移除宽或高小于min_size的bbox

主要利用最大最小值函数，把范围限制在 [0，图片尺寸] 之间

def remove_small_boxes(boxes, min_size):
    # type: (Tensor, float) -> Tensor
    """
    Remove boxes which contains at least one side smaller than min_size.
    移除宽高小于指定阈值的索引
    Arguments:
        boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format
        min_size (float): minimum size
    Returns:
        keep (Tensor[K]): indices of the boxes that have both sides
            larger than min_size
    """
    ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]  # 预测boxes的宽和高
    # keep = (ws >= min_size) & (hs >= min_size)  # 当满足宽，高都大于给定阈值时为True
    keep = torch.logical_and(torch.ge(ws, min_size), torch.ge(hs, min_size))
    # nonzero(): Returns a tensor containing the indices of all non-zero elements of input
    # keep = keep.nonzero().squeeze(1)
    keep = torch.where(keep)[0]
    return keep

④ nms处理

如果直接利用官方nms函数的话，需要对每一个类别进行nms处理，在这里采取了一个巧妙的方法，就是将不同类别的bbox的坐标位置加上一个较大的偏移量，使得不同类别的bbox没有重合，就可以对所有类别的bbox只进行一次nms处理。例如：

原本的bbox位置如下：

坐标加上偏移量之后的bbox位置如下：

这样，不同类别的bbox没有重合，可以对所有类别的bbox只进行一次nms处理。

def batched_nms(boxes, scores, idxs, iou_threshold):
    # type: (Tensor, Tensor, Tensor, float) -> Tensor
    """
    Performs non-maximum suppression in a batched fashion.
    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.
    Parameters：
	    boxes : Tensor[N, 4], boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format
	    scores : Tensor[N], scores for each one of the boxes
	    idxs : Tensor[N] indices of the categories for each one of the boxes.
	    iou_threshold : float, discards all overlapping boxes with IoU < iou_threshold
    Returns：
	    keep : Tensor, int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores
    """
    if boxes.numel() == 0:
        return torch.empty((0,), dtype=torch.int64, device=boxes.device)
    # 获取所有boxes中最大的坐标值（xmin, ymin, xmax, ymax）
    max_coordinate = boxes.max()
    # 为每一个类别/每一层生成一个很大的偏移量
    # 这里的to只是让生成tensor的dytpe和device与boxes保持一致
    offsets = idxs.to(boxes) * (max_coordinate + 1)
    # boxes加上对应层的偏移量后，保证不同类别/层之间boxes不会有重合的现象
    boxes_for_nms = boxes + offsets[:, None]
    keep = nms(boxes_for_nms, scores, iou_threshold)
    return keep

5. anchors分类

交并比iou的计算方法如下：

实现该功能的类为Matcher，传入参数match_quality_matrix的例子如下：

match_quality_matrix矩阵其中的元素为对应行gtbox与对应列anchor的交并比，算法步骤如下：

1. matched_vals和matches取得每一列最大元素的值和最大元素的位置
2. iou<low_threshold的matches索引置为-1
3. iou在[low_threshold, high_threshold]之间的matches索引置为-2

class Matcher(object):
    BELOW_LOW_THRESHOLD = -1
    BETWEEN_THRESHOLDS = -2
    def __call__(self, match_quality_matrix):
        """
        计算anchors与每个gtboxes匹配的iou最大值，并记录索引，
        iou<low_threshold索引值为-1， low_threshold<=iou<high_threshold索引值为-2
        Args:
            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
            pairwise quality between M ground-truth elements and N predicted elements.
        Returns:
            matches (Tensor[int64]): an N tensor where N[i] is a matched gt in
            [0, M - 1] or a negative value indicating that prediction i could not
            be matched.
        """
        # M x N 的每一列代表一个anchors与所有gt的匹配iou值
        # matched_vals代表每列的最大值，即每个anchors与所有gt匹配的最大iou值
        # matches对应最大值所在的索引
        matched_vals, matches = match_quality_matrix.max(dim=0)  # the dimension to reduce.
        if self.allow_low_quality_matches:
            all_matches = matches.clone()
        else:
            all_matches = None

        # 计算iou小于low_threshold的索引
        below_low_threshold = matched_vals < self.low_threshold
        # 计算iou在low_threshold与high_threshold之间的索引值
        between_thresholds = (matched_vals >= self.low_threshold) & (
            matched_vals < self.high_threshold
        )
        # iou小于low_threshold的matches索引置为-1
        matches[below_low_threshold] = self.BELOW_LOW_THRESHOLD  # -1

        # iou在[low_threshold, high_threshold]之间的matches索引置为-2
        matches[between_thresholds] = self.BETWEEN_THRESHOLDS    # -2

		# 是否将gtbox对应所有anchors中iou最大的保留（仅对于maxiou<low_threshold时有效）
        if self.allow_low_quality_matches:
            assert all_matches is not None
            self.set_low_quality_matches_(matches, all_matches, match_quality_matrix)

        return matches

4. 将每个gtbox对应所有anchors中iou最大的matches保留（可选）

还是以之前的match_quality_matrix为例， highest_quality_foreach_gt为每个gt boxes对应iou的最大值，

gt_pred_pairs_of_highest_quality为每个gt boxes对应最大iou的坐标，

pre_inds_to_update代表是对应的anchor index

    def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
        """
        将gtbox对应所有anchors中iou最大的保留（仅对于maxiou<low_threshold时有效）
        """
        # 对于每个gt boxes寻找与其iou最大的anchor，
        # highest_quality_foreach_gt为匹配到的最大iou值
        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)  # the dimension to reduce.

        # 寻找每个gt boxes与其iou最大的anchor索引，一个gt匹配到的最大iou可能有多个anchor
        # gt_pred_pairs_of_highest_quality = torch.nonzero(
        #     match_quality_matrix == highest_quality_foreach_gt[:, None]
        # )
        gt_pred_pairs_of_highest_quality = torch.where(
            torch.eq(match_quality_matrix, highest_quality_foreach_gt[:, None])
        )
        # gt_pred_pairs_of_highest_quality[:, 0]代表是对应的gt index(不需要)
        # pre_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
        pre_inds_to_update = gt_pred_pairs_of_highest_quality[1]
        # 保留该anchor匹配gt最大iou的索引，即使iou低于设定的阈值
        matches[pre_inds_to_update] = all_matches[pre_inds_to_update]

综上所述，我们计算了每个anchors最匹配的gt，并将anchors分类，将负样本的索引置为-1，丢弃样本的索引置为-2，所以通过获取索引为正数的样本就可以得到正样本。

6. 计算regression参数

利用如下公式，利用预测的anchors坐标以及对应的gt，得到预测的bbox回归参数

def encode_boxes(reference_boxes, proposals, weights):
    # type: (torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
    """
    Encode a set of proposals with respect to some
    reference boxes
    Arguments:
        reference_boxes (Tensor): reference boxes(gt)
        proposals (Tensor): boxes to be encoded(anchors)
        weights:
    """
    # perform some unpacking to make it JIT-fusion friendly
    wx = weights[0]
    wy = weights[1]
    ww = weights[2]
    wh = weights[3]

    # unsqueeze()
    # Returns a new tensor with a dimension of size one inserted at the specified position.
    proposals_x1 = proposals[:, 0].unsqueeze(1)
    proposals_y1 = proposals[:, 1].unsqueeze(1)
    proposals_x2 = proposals[:, 2].unsqueeze(1)
    proposals_y2 = proposals[:, 3].unsqueeze(1)

    reference_boxes_x1 = reference_boxes[:, 0].unsqueeze(1)
    reference_boxes_y1 = reference_boxes[:, 1].unsqueeze(1)
    reference_boxes_x2 = reference_boxes[:, 2].unsqueeze(1)
    reference_boxes_y2 = reference_boxes[:, 3].unsqueeze(1)

    # implementation starts here
    # parse widths and heights
    ex_widths = proposals_x2 - proposals_x1
    ex_heights = proposals_y2 - proposals_y1
    # parse coordinate of center point
    ex_ctr_x = proposals_x1 + 0.5 * ex_widths
    ex_ctr_y = proposals_y1 + 0.5 * ex_heights

    gt_widths = reference_boxes_x2 - reference_boxes_x1
    gt_heights = reference_boxes_y2 - reference_boxes_y1
    gt_ctr_x = reference_boxes_x1 + 0.5 * gt_widths
    gt_ctr_y = reference_boxes_y1 + 0.5 * gt_heights

    targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
    targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
    targets_dw = ww * torch.log(gt_widths / ex_widths)
    targets_dh = wh * torch.log(gt_heights / ex_heights)

    targets = torch.cat((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
    return targets

7. 计算损失

损失的计算包含：

1. 边界框回归损失，使用torch.smooth_l1_loss
   
2. 目标预测概率损失，使用F.binary_cross_entropy_with_logits
   

    def compute_loss(self, objectness, pred_bbox_deltas, labels, regression_targets):
        # type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor]
        """
        计算RPN损失，包括类别损失（前景与背景），bbox regression损失
        Arguments:
            objectness (Tensor)：预测的前景概率
            pred_bbox_deltas (Tensor)：预测的bbox regression
            labels (List[Tensor])：真实的标签 1, 0, -1（batch中每一张图片的labels对应List的一个元素中）
            regression_targets (List[Tensor])：真实的bbox regression
        Returns:
            objectness_loss (Tensor) : 类别损失
            box_loss (Tensor)：边界框回归损失
        """
        # 按照给定的batch_size_per_image, positive_fraction选择正负样本
        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
        # 将一个batch中的所有正负样本List(Tensor)分别拼接在一起，并获取非零位置的索引
        # sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1)
        sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
        # sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1)
        sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]

        # 将所有正负样本索引拼接在一起
        sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
        objectness = objectness.flatten()

        labels = torch.cat(labels, dim=0)
        regression_targets = torch.cat(regression_targets, dim=0)

        # 计算边界框回归损失
        box_loss = det_utils.smooth_l1_loss(
            pred_bbox_deltas[sampled_pos_inds],
            regression_targets[sampled_pos_inds],
            beta=1 / 9,
            size_average=False,
        ) / (sampled_inds.numel())

        # 计算目标预测概率损失
        objectness_loss = F.binary_cross_entropy_with_logits(
            objectness[sampled_inds], labels[sampled_inds]
        )

        return objectness_loss, box_loss

二、ROIHead

包含ROIpooling和Two MLPHead两个部分

1. ROIpooling

使用pytorch自带的函数MultiScaleRoIAlign

    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)

2. Two MLPHead

是一个Flatten层和两个FC层组成的网络

class TwoMLPHead(nn.Module):
    """
    Standard heads for FPN-based models
    Arguments:
        in_channels (int): number of input channels
        representation_size (int): size of the intermediate representation
    """

    def __init__(self, in_channels, representation_size):
        super(TwoMLPHead, self).__init__()
        self.fc6 = nn.Linear(in_channels, representation_size)
        self.fc7 = nn.Linear(representation_size, representation_size)

    def forward(self, x):
        x = x.flatten(start_dim=1)
        x = F.relu(self.fc6(x))
        x = F.relu(self.fc7(x))
        return x

三、FastRCNNPredictor

1. 预测目标分数和目标bbox回归参数

分别使用一个全连接层预测目标分数和目标bbox回归参数

class FastRCNNPredictor(nn.Module):
    """
    Standard classification + bounding box regression layers
    for Fast R-CNN.
    Arguments:
        in_channels (int): number of input channels
        num_classes (int): number of output classes (including background)
    """
    def __init__(self, in_channels, num_classes):
        super(FastRCNNPredictor, self).__init__()
        self.cls_score = nn.Linear(in_channels, num_classes)
        self.bbox_pred = nn.Linear(in_channels, num_classes * 4)
        
    def forward(self, x):
        x = x.flatten(start_dim=1)
        scores = self.cls_score(x)
        bbox_deltas = self.bbox_pred(x)

        return scores, bbox_deltas

2. 损失计算

训练时需要计算损失函数，在计算损失之前也需要进行和之前RPN部分类似的步骤，如下：

1. 划分正负样本，统计对应gt的标签以及边界框回归信息
2. 按给定数量和比例采样正负样本
3. 根据gt和proposal计算边框回归参数
4. 计算损失函数

这里的损失函数同样也是包含了两个部分：

1. 边界框回归损失，使用torch.smooth_l1_loss
   
2. 类别损失函数。使用F.cross_entropy
   

def fastrcnn_loss(class_logits, box_regression, labels, regression_targets):
    # type: (Tensor, Tensor, List[Tensor], List[Tensor]) -> Tuple[Tensor, Tensor]
    """
    Computes the loss for Faster R-CNN.

    Arguments:
        class_logits : 预测类别概率信息，shape=[num_anchors, num_classes]
        box_regression : 预测边目标界框回归信息
        labels : 真实类别信息
        regression_targets : 真实目标边界框信息

    Returns:
        classification_loss (Tensor)
        box_loss (Tensor)
    """

    labels = torch.cat(labels, dim=0)
    regression_targets = torch.cat(regression_targets, dim=0)

    # 计算类别损失信息
    classification_loss = F.cross_entropy(class_logits, labels)

    # get indices that correspond to the regression targets for
    # the corresponding ground truth labels, to be used with
    # advanced indexing
    # 返回标签类别大于0的索引
    # sampled_pos_inds_subset = torch.nonzero(torch.gt(labels, 0)).squeeze(1)
    sampled_pos_inds_subset = torch.where(torch.gt(labels, 0))[0]

    # 返回标签类别大于0位置的类别信息
    labels_pos = labels[sampled_pos_inds_subset]

    # shape=[num_proposal, num_classes]
    N, num_classes = class_logits.shape
    box_regression = box_regression.reshape(N, -1, 4)

    # 计算边界框损失信息
    box_loss = det_utils.smooth_l1_loss(
        # 获取指定索引proposal的指定类别box信息
        box_regression[sampled_pos_inds_subset, labels_pos],
        regression_targets[sampled_pos_inds_subset],
        beta=1 / 9,
        size_average=False,
    ) / labels.numel()

    return classification_loss, box_loss

源代码

最后

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