西凤酒网站建设的基本情况,自己做的网站主页被人篡改,新媒体营销案例,食品网站开发这里是对那一节代码的通俗注释#xff0c;希望对各位学习有帮助。 值得注意的是#xff0c;multibox_prior函数的宽高计算网络上有争议#xff0c;此处我仍认为作者的写法是正确的#xff0c;如果读者有想法#xff0c;可以在评论区留言#xff0c;我们进行讨论。 import…这里是对那一节代码的通俗注释希望对各位学习有帮助。 值得注意的是multibox_prior函数的宽高计算网络上有争议此处我仍认为作者的写法是正确的如果读者有想法可以在评论区留言我们进行讨论。 import torch
from d2l import torch as d2ltorch.set_printoptions(2) # 设置张量输出精度# 定义一个函数用于生成以每个像素为中心具有不同形状的锚框
def multibox_prior(data, sizes, ratios):in_height, in_width data.shape[-2:]device, num_sizes, num_ratios data.device, len(sizes), len(ratios)boxes_per_pixel (num_sizes num_ratios - 1) # 计算以每一个像素为中心要生成多少个锚框size_tensor torch.tensor(sizes, devicedevice) # 将这些锚框的大小缩放比转换为张量ratio_tensor torch.tensor(ratios, devicedevice) # 将这些锚框的宽高比转换为张量offset_h, offset_w 0.5, 0.5 # 设置偏移量将中心点移动到每一个像素的中心steps_h 1.0 / in_heightsteps_w 1.0 / in_widthcenter_h (torch.arange(in_height, devicedevice) offset_h) * steps_hcenter_w (torch.arange(in_width, devicedevice) offset_w) * steps_wshift_y, shift_x torch.meshgrid(center_h, center_w, indexingij)shift_y, shift_x shift_y.reshape(-1), shift_x.reshape(-1)w torch.cat((size_tensor * torch.sqrt(ratio_tensor[0]), # 保持宽高比不变遍历所有缩放比下一行是保持缩放比不变遍历所有宽高比sizes[0] * torch.sqrt(ratio_tensor[1:]))) \* in_height / in_width # 最终计算得到宽度h torch.cat((size_tensor / torch.sqrt(ratio_tensor[0]),sizes[0] / torch.sqrt(ratio_tensor[1:])))anchor_manipulations torch.stack((-w, -h, w, h)).T.repeat(in_height * in_width, 1) / 2 # 重复这些锚框为wxh次因为有这么多像素除以2是因为将锚框的上下和左右度量均方以放置中点上out_grid torch.stack([shift_x, shift_y, shift_x, shift_y],dim1).repeat_interleave(boxes_per_pixel, dim0) # 得到中心点位置output out_grid anchor_manipulations # 两者相加得到正确的锚框坐标return output.unsqueeze(0)# 显示所有边界框
def show_bboxes(axes, bboxes, labelsNone, colorsNone):def _make_list(obj, default_valuesNone):if obj is None:obj default_valueselif not isinstance(obj, (list, tuple)):obj [obj]return objlabels _make_list(labels)colors _make_list(colors, [b, g, r, m, c])for i, bbox in enumerate(bboxes):color colors[i % len(colors)]rect d2l.bbox_to_rect(bbox.detach().numpy(), color)axes.add_patch(rect)if labels and len(labels) i:text_color k if color w else waxes.text(rect.xy[0], rect.xy[1], labels[i],vacenter, hacenter, fontsize9, colortext_color,bboxdict(facecolorcolor, lw0))# 计算两个锚框或边界框列表中成对的交并比
def box_iou(boxes1, boxes2):box_area lambda boxes: ((boxes[:, 2] - boxes[:, 0]) *(boxes[:, 3] - boxes[:, 1]))# 计算给定框的面积areas1 box_area(boxes1)areas2 box_area(boxes2)# 计算交集的左上角和右下角的坐标inter_upperlefts torch.max(boxes1[:, None, :2], boxes2[:, :2])inter_lowerrights torch.min(boxes1[:, None, 2:], boxes2[:, 2:])# 计算交集的宽高以及面积inters (inter_lowerrights - inter_upperlefts).clamp(min0)inter_areas inters[:, :, 0] * inters[:, :, 1]# 计算并集的面积union_areas areas1[:, None] areas2 - inter_areas# 返回交并比return inter_areas / union_areas# 将最接近的真实边界框分配给锚框
def assign_anchor_to_bbox(ground_truth, anchors, device, iou_threshold0.5):num_anchors, num_gt_boxes anchors.shape[0], ground_truth.shape[0] # 获取锚框的数量和真实边界框的数量jaccard box_iou(anchors, ground_truth) # 得到交并比矩阵anchors_bbox_map torch.full((num_anchors,), -1, dtypetorch.long,devicedevice) # 创建真实边界框分配列表初始用-1填充表示不分配max_ious, indices torch.max(jaccard, dim1) # 求得每一个锚框与所有真实边界框的最大交并比和其索引anc_i torch.nonzero(max_ious iou_threshold).reshape(-1) # 得到满足阈值要求交并比box_j indices[max_ious iou_threshold] # 得到满足阈值要求交并比的索引anchors_bbox_map[anc_i] box_j # 如果交并比满足阈值要求将真实边界框索引分配到对应的锚框col_discard torch.full((num_anchors,), -1) # 列丢弃索引用来标记交并比矩阵已经丢弃的列row_discard torch.full((num_gt_boxes,), -1) # 行丢弃索引用来标记交并比矩阵已经丢弃的行for _ in range(num_gt_boxes):max_idx torch.argmax(jaccard) # 获取整个交并比矩阵中值最大的索引矩阵扁平化后的索引box_idx (max_idx % num_gt_boxes).long() # 得到该交并比对应的真实边界框的索引anc_idx (max_idx / num_gt_boxes).long() # 得到该交并比对应的锚框的索引anchors_bbox_map[anc_idx] box_idx # 分配真实边界框jaccard[:, box_idx] col_discard # 丢弃对应的列jaccard[anc_idx, :] row_discard # 丢弃对应的行return anchors_bbox_mapdef offset_boxes(anchors, assigned_bb, eps1e-6):c_anc d2l.box_corner_to_center(anchors) # 获取所有锚框的中心坐标c_assigned_bb d2l.box_corner_to_center(assigned_bb) # 获取真实边界框的中心坐标offset_xy 10 * (c_assigned_bb[:, :2] - c_anc[:, :2]) / c_anc[:, 2:] # 计算锚框和真实边界框的中心坐标偏移量offset_wh 5 * torch.log(eps c_assigned_bb[:, 2:] / c_anc[:, 2:]) # 计算宽高缩放的偏移量offset torch.cat([offset_xy, offset_wh], axis1) # 将两种偏移量进行连接排成一行然后返回return offsetdef multibox_target(anchors, labels): # labels的形状batchsize,边界框数量,5后面的5中第一个元素是真实标签后面是坐标信息batch_size, anchors labels.shape[0], anchors.squeeze(0)batch_offset, batch_mask, batch_class_labels [], [], []device, num_anchors anchors.device, anchors.shape[0]for i in range(batch_size):label labels[i, :, :] # 获取每一个样本的所有真实边界框的信息标签和坐标anchors_bbox_map assign_anchor_to_bbox( # 获取真实标签对锚框的分配表label[:, 1:], anchors, device)bbox_mask ((anchors_bbox_map 0).float().unsqueeze(-1)).repeat( # 生成偏移量掩码为了屏蔽掉未分配的锚框的偏移量1, 4)class_labels torch.zeros(num_anchors, dtypetorch.long,devicedevice)assigned_bb torch.zeros((num_anchors, 4), dtypetorch.float32,devicedevice)indices_true torch.nonzero(anchors_bbox_map 0) # 获取已分配真实边界框的锚框的索引bb_idx anchors_bbox_map[indices_true] # 获取真实边界框的索引class_labels[indices_true] label[bb_idx, 0].long() 1 # 获取真实标签的同时将标签索引改为从1开始assigned_bb[indices_true] label[bb_idx, 1:] # 获取真实边界框坐标offset offset_boxes(anchors, assigned_bb) * bbox_mask # 获取锚框与真实边界框的偏移量已屏蔽未分配真实标签的锚框batch_offset.append(offset.reshape(-1)) # 扁平化batch_mask.append(bbox_mask.reshape(-1))batch_class_labels.append(class_labels)bbox_offset torch.stack(batch_offset) # bbox_offset 的形状是 (batch_size, num_anchors * 4)bbox_mask torch.stack(batch_mask) # bbox_mask 的形状也是 (batch_size, num_anchors * 4)class_labels torch.stack(batch_class_labels) # class_labels 的形状是 (batch_size, num_anchors)return (bbox_offset, bbox_mask, class_labels)def offset_inverse(anchors, offset_preds):根据带有预测偏移量的锚框来预测边界框anc d2l.box_corner_to_center(anchors)pred_bbox_xy (offset_preds[:, :2] * anc[:, 2:] / 10) anc[:, :2]pred_bbox_wh torch.exp(offset_preds[:, 2:] / 5) * anc[:, 2:]pred_bbox torch.cat((pred_bbox_xy, pred_bbox_wh), axis1)predicted_bbox d2l.box_center_to_corner(pred_bbox)return predicted_bboxdef nms(boxes, scores, iou_threshold):对预测边界框的置信度进行排序B torch.argsort(scores, dim-1, descendingTrue)keep [] # 保留预测边界框的指标while B.numel() 0:i B[0]keep.append(i)if B.numel() 1: breakiou box_iou(boxes[i, :].reshape(-1, 4), # 将当前边界框与其他所有边界框进行IoU计算boxes[B[1:], :].reshape(-1, 4)).reshape(-1)inds torch.nonzero(iou iou_threshold).reshape(-1) # 获取低于阈值的所有交并比索引B B[inds 1] # 获取低于阈值的所有边界框进行下一轮抑制return torch.tensor(keep, deviceboxes.device)def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold0.5,pos_threshold0.009999999):使用非极大值抑制来预测边界框device, batch_size cls_probs.device, cls_probs.shape[0]anchors anchors.squeeze(0) # 压缩后的形状(num_anchors,4)num_classes, num_anchors cls_probs.shape[1], cls_probs.shape[2] # 获得每个样本的类别数量和锚框数量out [] # 存储预测结果for i in range(batch_size):cls_prob, offset_pred cls_probs[i], offset_preds[i].reshape(-1, 4) # 每次取出一个样本conf, class_id torch.max(cls_prob[1:], 0) # 获取样本的锚框对于所有类别的置信度predicted_bb offset_inverse(anchors, offset_pred) # 逆转偏移量计算操作得到预测边界框的真实坐标keep nms(predicted_bb, conf, nms_threshold) # 获取通过非最大值抑制操作后保留的预测框的索引# 找到所有的non_keep索引并将类设置为背景all_idx torch.arange(num_anchors, dtypetorch.long, devicedevice)combined torch.cat((keep, all_idx)) # 该混合操作会使最终combined张量有重复元素便于后边将非重复的设置为背景uniques, counts combined.unique(return_countsTrue)non_keep uniques[counts 1] # 未重复的就是不保留的all_id_sorted torch.cat((keep, non_keep)) # 将要保留的和不保留的连接在一起class_id[non_keep] -1 # 将不保留预测框的类别索引设置为-1表示没有class_id class_id[all_id_sorted] # 重新排列类别索引conf, predicted_bb conf[all_id_sorted], predicted_bb[all_id_sorted] # 重新排列置信度和预测框# pos_threshold是一个用于非背景预测的阈值below_min_idx (conf pos_threshold) # 获取置信度小于阈值的预测框的索引class_id[below_min_idx] -1 # 将对应位置类别索引设置为-1conf[below_min_idx] 1 - conf[below_min_idx] # 将低于阈值的置信度与背景置信度互换pred_info torch.cat((class_id.unsqueeze(1), # 重排成列一行表示一个类别索引conf.unsqueeze(1), # 重拍成列一行表示一个类别的置信度predicted_bb), dim1)out.append(pred_info) # 完成一个样本的处理return torch.stack(out) # 将分开处理样本合并为一个批量# 读取图片
img d2l.plt.imread(../img/catdog.jpg)
h, w img.shape[:2]# 生成锚框
X torch.rand(size(1, 3, h, w))
Y multibox_prior(X, sizes[0.75, 0.5, 0.25], ratios[1, 2, 0.5])# 显示部分锚框及其对应的标签
boxes Y.reshape(h, w, 5, 4)
d2l.set_figsize()
bbox_scale torch.tensor((w, h, w, h))
fig d2l.plt.imshow(img)
show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,[s0.75, r1, s0.5, r1, s0.25, r1, s0.75, r2,s0.75, r0.5])
d2l.plt.show()ground_truth torch.tensor([[0, 0.1, 0.08, 0.52, 0.92],[1, 0.55, 0.2, 0.9, 0.88]])
anchors torch.tensor([[0, 0.1, 0.2, 0.3], [0.15, 0.2, 0.4, 0.4],[0.63, 0.05, 0.88, 0.98], [0.66, 0.45, 0.8, 0.8],[0.57, 0.3, 0.92, 0.9]])fig d2l.plt.imshow(img)
show_bboxes(fig.axes, ground_truth[:, 1:] * bbox_scale, [dog, cat], k)
show_bboxes(fig.axes, anchors * bbox_scale, [0, 1, 2, 3, 4]);
d2l.plt.show()labels multibox_target(anchors.unsqueeze(dim0),ground_truth.unsqueeze(dim0))
print(labels[2])
print(labels[1])
print(labels[0])anchors torch.tensor([[0.1, 0.08, 0.52, 0.92], [0.08, 0.2, 0.56, 0.95],[0.15, 0.3, 0.62, 0.91], [0.55, 0.2, 0.9, 0.88]])
offset_preds torch.tensor([0] * anchors.numel())
cls_probs torch.tensor([[0] * 4, # 背景的预测概率[0.9, 0.8, 0.7, 0.1], # 狗的预测概率[0.1, 0.2, 0.3, 0.9]]) # 猫的预测概率fig d2l.plt.imshow(img)
show_bboxes(fig.axes, anchors * bbox_scale,[dog0.9, dog0.8, dog0.7, cat0.9])
d2l.plt.show()output multibox_detection(cls_probs.unsqueeze(dim0),offset_preds.unsqueeze(dim0),anchors.unsqueeze(dim0),nms_threshold0.5)
print(output)fig d2l.plt.imshow(img)
for i in output[0].detach().numpy():if i[0] -1:continuelabel (dog, cat)[int(i[0])] str(i[1])show_bboxes(fig.axes, [torch.tensor(i[2:]) * bbox_scale], label)
d2l.plt.show()
