{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": "### 前面的13.1 13.2 因为不可抗力事件消失了（保存的时候乱码了）", "id": "b23af7657da5adfb" }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.242189006Z", "start_time": "2026-07-26T11:57:12.026651705Z" } }, "cell_type": "code", "source": [ "\n", "import torch\n", "from d2l import torch as d2l" ], "id": "b39ef644f6c80c7c", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/yukun/.conda/envs/nn/lib/python3.11/site-packages/torch/cuda/__init__.py:1007: UserWarning: Can't initialize NVML\n", " raw_cnt = _raw_device_count_nvml()\n" ] } ], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.585413376Z", "start_time": "2026-07-26T11:57:15.270700840Z" } }, "cell_type": "code", "source": [ "d2l.set_figsize()\n", "img = d2l.plt.imread('../data/catdog.jpg')\n", "d2l.plt.imshow(img);" ], "id": "4284e525ebcc7e87", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.692690288Z", "start_time": "2026-07-26T11:57:15.588725031Z" } }, "cell_type": "code", "source": [ "def box_corner_to_center(boxes):\n", " \"\"\"从（左上，右下）转换到（中间，宽度，高度）\"\"\"\n", " x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]\n", " cx = (x1 + x2) / 2\n", " cy = (y1 + y2) / 2\n", " w = x2 - x1\n", " h = y2 - y1\n", " boxes = torch.stack((cx, cy, w, h), axis=-1)\n", " return boxes\n", "def box_center_to_corner(boxes):\n", " \"\"\"从（中间，宽度，高度）转换到（左上，右下）\"\"\"\n", " cx, cy, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]\n", " x1 = cx - 0.5 * w\n", " y1 = cy - 0.5 * h\n", " x2 = cx + 0.5 * w\n", " y2 = cy + 0.5 * h\n", " boxes = torch.stack((x1, y1, x2, y2), axis=-1)\n", " return boxes" ], "id": "ee53061c33ad70d7", "outputs": [], "execution_count": 3 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.705559306Z", "start_time": "2026-07-26T11:57:15.694772245Z" } }, "cell_type": "code", "source": "dog_bbox, cat_bbox = [60.0, 45.0, 378.0, 516.0], [400.0, 112.0, 655.0, 493.0]", "id": "2e45aa7592a4d7b1", "outputs": [], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.794704197Z", "start_time": "2026-07-26T11:57:15.707000004Z" } }, "cell_type": "code", "source": [ "boxes = torch.tensor((dog_bbox, cat_bbox))\n", "box_center_to_corner(box_corner_to_center(boxes)) == boxes" ], "id": "22f2ce93eb46eca2", "outputs": [ { "data": { "text/plain": [ "tensor([[True, True, True, True],\n", " [True, True, True, True]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 5 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:15.815124864Z", "start_time": "2026-07-26T11:57:15.799009123Z" } }, "cell_type": "code", "source": [ "def bbox_to_rect(bbox, color):\n", " # 将边界框(左上x,左上y,右下x,右下y)格式转换成matplotlib格式：\n", " # ((左上x,左上y),宽,高)\n", " return d2l.plt.Rectangle(\n", " xy=(bbox[0], bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1],\n", " fill=False, edgecolor=color, linewidth=2)" ], "id": "cc21e1e13103291", "outputs": [], "execution_count": 6 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.101228908Z", "start_time": "2026-07-26T11:57:18.498541915Z" } }, "cell_type": "code", "source": [ "fig = d2l.plt.imshow(img)\n", "fig.axes.add_patch(bbox_to_rect(dog_bbox, 'blue'))\n", "fig.axes.add_patch(bbox_to_rect(cat_bbox, 'red'))" ], "id": "a4f3af406f8b5b1c", "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 7 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.221032646Z", "start_time": "2026-07-26T11:57:19.137271191Z" } }, "cell_type": "code", "source": [ "#@save\n", "def multibox_prior(data, sizes, ratios):\n", " \"\"\"生成以每个像素为中心具有不同形状的锚框\"\"\"\n", " in_height, in_width = data.shape[-2:]\n", " device, num_sizes, num_ratios = data.device, len(sizes), len(ratios)\n", " boxes_per_pixel = (num_sizes + num_ratios - 1)\n", " size_tensor = torch.tensor(sizes, device=device)\n", " ratio_tensor = torch.tensor(ratios, device=device)\n", "\n", " # 为了将锚点移动到像素的中心，需要设置偏移量。\n", " # 因为一个像素的高为1且宽为1，我们选择偏移我们的中心0.5\n", " offset_h, offset_w = 0.5, 0.5\n", " steps_h = 1.0 / in_height # 在y轴上缩放步长\n", " steps_w = 1.0 / in_width # 在x轴上缩放步长\n", "\n", " # 生成锚框的所有中心点\n", " center_h = (torch.arange(in_height, device=device) + offset_h) * steps_h\n", " center_w = (torch.arange(in_width, device=device) + offset_w) * steps_w\n", " shift_y, shift_x = torch.meshgrid(center_h, center_w, indexing='ij')\n", " shift_y, shift_x = shift_y.reshape(-1), shift_x.reshape(-1)\n", "\n", " # 生成 “boxes_per_pixel” 个高和宽，\n", " # 之后用于创建锚框的四角坐标(xmin,xmax,ymin,ymax)\n", " w = torch.cat((size_tensor * torch.sqrt(ratio_tensor[0]),\n", " sizes[0] * torch.sqrt(ratio_tensor[1:])))\\\n", " * in_height / in_width # 处理矩形输入\n", "\n", " h = torch.cat((size_tensor / torch.sqrt(ratio_tensor[0]),\n", " sizes[0] / torch.sqrt(ratio_tensor[1:])))\n", "\n", " # 除以2来获得半高和半宽\n", " anchor_manipulations = torch.stack((-w, -h, w, h)).T.repeat(\n", " in_height * in_width, 1) / 2\n", "\n", " # 每个中心点都将有 “boxes_per_pixel” 个锚框，\n", " # 所以生成含所有锚框中心的网格，重复了 “boxes_per_pixel” 次\n", " out_grid = torch.stack([shift_x, shift_y, shift_x, shift_y],\n", " dim=1).repeat_interleave(boxes_per_pixel, dim=0)\n", " output = out_grid + anchor_manipulations\n", " return output.unsqueeze(0)" ], "id": "874f386629d410fb", "outputs": [], "execution_count": 8 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.258339667Z", "start_time": "2026-07-26T11:57:19.246797120Z" } }, "cell_type": "code", "source": [ "img = d2l.plt.imread('../data/catdog.jpg')\n", "h, w = img.shape[:2]" ], "id": "df3d1ba4aeb6c2f7", "outputs": [], "execution_count": 9 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.382932850Z", "start_time": "2026-07-26T11:57:19.281193532Z" } }, "cell_type": "code", "source": [ "print(h, w)\n", "X = torch.rand(size=(1, 3, h, w))\n", "Y = multibox_prior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])\n", "Y.shape" ], "id": "e8737b3210446831", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "561 728\n" ] }, { "data": { "text/plain": [ "torch.Size([1, 2042040, 4])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.433949316Z", "start_time": "2026-07-26T11:57:19.384210708Z" } }, "cell_type": "code", "source": [ "def show_bboxes(axes, bboxes, labels=None, colors=None):\n", " \"\"\"显示所有边界框\"\"\"\n", " def _make_list(obj, default_values=None):\n", " if obj is None:\n", " obj = default_values\n", " elif not isinstance(obj, (list, tuple)):\n", " obj = [obj]\n", " return obj\n", "\n", " labels = _make_list(labels)\n", " colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])\n", " for i, bbox in enumerate(bboxes):\n", " color = colors[i % len(colors)]\n", " rect = d2l.bbox_to_rect(bbox.detach().numpy(), color)\n", " axes.add_patch(rect)\n", " if labels and len(labels) > i:\n", " text_color = 'k' if color == 'w' else 'w'\n", " axes.text(rect.xy[0], rect.xy[1], labels[i],\n", " va='center', ha='center', fontsize=9, color=text_color,\n", " bbox=dict(facecolor=color, lw=0))" ], "id": "e7070cbdb1d68616", "outputs": [], "execution_count": 11 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.483140764Z", "start_time": "2026-07-26T11:57:19.435383512Z" } }, "cell_type": "code", "source": "boxes = Y.reshape(h, w, 5, 4)", "id": "4cc20a550931aef7", "outputs": [], "execution_count": 12 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.621504492Z", "start_time": "2026-07-26T11:57:19.484527807Z" } }, "cell_type": "code", "source": [ "bbox_scale = torch.tensor((w, h, w, h))\n", "fig = d2l.plt.imshow(img)\n", "show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,\n", " ['s=0.75, r=1', 's=0.5, r=1', 's=0.25, r=1', 's=0.75, r=2',\n", " 's=0.75, r=0.5'])" ], "id": "2daf2903aa5b1516", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 13 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.688402612Z", "start_time": "2026-07-26T11:57:19.637442252Z" } }, "cell_type": "code", "source": [ "def box_iou(boxes1, boxes2):\n", " \"\"\"计算两个锚框或边界框列表中成对的交并比\"\"\"\n", " box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) *\n", " (boxes[:, 3] - boxes[:, 1]))\n", " # boxes1,boxes2,areas1,areas2的形状:\n", " # boxes1：(boxes1的数量,4),\n", " # boxes2：(boxes2的数量,4),\n", " # areas1：(boxes1的数量,),\n", " # areas2：(boxes2的数量,)\n", " areas1 = box_area(boxes1)\n", " areas2 = box_area(boxes2)\n", " # inter_upperlefts,inter_lowerrights,inters的形状:\n", " # (boxes1的数量,boxes2的数量,2)\n", " inter_upperlefts = torch.max(boxes1[:, None, :2], boxes2[:, :2])\n", " inter_lowerrights = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])\n", " inters = (inter_lowerrights - inter_upperlefts).clamp(min=0)\n", " # inter_areasandunion_areas的形状:(boxes1的数量,boxes2的数量)\n", " inter_areas = inters[:, :, 0] * inters[:, :, 1]\n", " union_areas = areas1[:, None] + areas2 - inter_areas\n", " return inter_areas / union_areas" ], "id": "ffe3178858e5a9b1", "outputs": [], "execution_count": 14 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.741523163Z", "start_time": "2026-07-26T11:57:19.690408939Z" } }, "cell_type": "code", "source": [ "#@save\n", "def assign_anchor_to_bbox(ground_truth, anchors, device, iou_threshold=0.5):\n", " \"\"\"将最接近的真实边界框分配给锚框\"\"\"\n", " num_anchors, num_gt_boxes = anchors.shape[0], ground_truth.shape[0]\n", " # 位于第i行和第j列的元素x_ij是锚框i和真实边界框j的IoU\n", " jaccard = box_iou(anchors, ground_truth)\n", " # 对于每个锚框，分配的真实边界框的张量\n", " anchors_bbox_map = torch.full((num_anchors,), -1, dtype=torch.long,\n", " device=device)\n", " # 根据阈值，决定是否分配真实边界框\n", " max_ious, indices = torch.max(jaccard, dim=1)\n", " anc_i = torch.nonzero(max_ious >= iou_threshold).reshape(-1)\n", " box_j = indices[max_ious >= iou_threshold]\n", " anchors_bbox_map[anc_i] = box_j\n", " col_discard = torch.full((num_anchors,), -1)\n", " row_discard = torch.full((num_gt_boxes,), -1)\n", " for _ in range(num_gt_boxes):\n", " max_idx = torch.argmax(jaccard)\n", " box_idx = (max_idx % num_gt_boxes).long()\n", " anc_idx = (max_idx / num_gt_boxes).long()\n", " anchors_bbox_map[anc_idx] = box_idx\n", " jaccard[:, box_idx] = col_discard\n", " jaccard[anc_idx, :] = row_discard\n", " return anchors_bbox_map\n" ], "id": "2ebf82c1768e24c6", "outputs": [], "execution_count": 15 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.791841369Z", "start_time": "2026-07-26T11:57:19.743006544Z" } }, "cell_type": "code", "source": [ "def offset_boxes(anchors, assigned_bb, eps=1e-6):\n", " \"\"\"对锚框偏移量的转换\"\"\"\n", " c_anc = d2l.box_corner_to_center(anchors)\n", " c_assigned_bb = d2l.box_corner_to_center(assigned_bb)\n", " offset_xy = 10 * (c_assigned_bb[:, :2] - c_anc[:, :2]) / c_anc[:, 2:]\n", " offset_wh = 5 * torch.log(eps + c_assigned_bb[:, 2:] / c_anc[:, 2:])\n", " offset = torch.cat([offset_xy, offset_wh], axis=1)\n", " return offset" ], "id": "b83220e939515b39", "outputs": [], "execution_count": 16 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.844930037Z", "start_time": "2026-07-26T11:57:19.792995780Z" } }, "cell_type": "code", "source": [ "def multibox_target(anchors, labels):\n", " \"\"\"使用真实边界框标记锚框\"\"\"\n", " batch_size, anchors = labels.shape[0], anchors.squeeze(0) # 问题：这里赋值给 anchors 会覆盖，但原代码就是这样\n", " batch_offset, batch_mask, batch_class_labels = [], [], []\n", " device, num_anchors = anchors.device, anchors.shape[0]\n", " for i in range(batch_size):\n", " label = labels[i, :, :]\n", " anchors_bbox_map = assign_anchor_to_bbox(\n", " label[:, 1:], anchors, device)\n", " bbox_mask = ((anchors_bbox_map >= 0).float().unsqueeze(-1)).repeat(\n", " 1, 4)\n", " # 将类标签和分配的边界框坐标初始化为零\n", " class_labels = torch.zeros(num_anchors, dtype=torch.long,\n", " device=device)\n", " assigned_bb = torch.zeros((num_anchors, 4), dtype=torch.float32,\n", " device=device)\n", " # 使用真实边界框来标记锚框的类别。\n", " # 如果一个锚框没有被分配，标记其为背景（值为零）\n", " indices_true = torch.nonzero(anchors_bbox_map >= 0)\n", " bb_idx = anchors_bbox_map[indices_true]\n", " class_labels[indices_true] = label[bb_idx, 0].long() + 1\n", " assigned_bb[indices_true] = label[bb_idx, 1:]\n", " # 偏移量转换\n", " offset = offset_boxes(anchors, assigned_bb) * bbox_mask\n", " batch_offset.append(offset.reshape(-1))\n", " batch_mask.append(bbox_mask.reshape(-1))\n", " batch_class_labels.append(class_labels)\n", " bbox_offset = torch.stack(batch_offset)\n", " bbox_mask = torch.stack(batch_mask)\n", " class_labels = torch.stack(batch_class_labels)\n", " return (bbox_offset, bbox_mask, class_labels)" ], "id": "57e69b1186f23243", "outputs": [], "execution_count": 17 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T11:57:19.991066598Z", "start_time": "2026-07-26T11:57:19.846926644Z" } }, "cell_type": "code", "source": [ "ground_truth = torch.tensor([[0, 0.1, 0.08, 0.52, 0.92],\n", "[1, 0.55, 0.2, 0.9, 0.88]])\n", "anchors = torch.tensor([[0, 0.1, 0.2, 0.3], [0.15, 0.2, 0.4, 0.4],\n", "[0.63, 0.05, 0.88, 0.98], [0.66, 0.45, 0.8, 0.8],\n", "[0.57, 0.3, 0.92, 0.9]])\n", "fig = d2l.plt.imshow(img)\n", "show_bboxes(fig.axes, ground_truth[:, 1:] * bbox_scale, ['dog', 'cat'], 'k')\n", "show_bboxes(fig.axes, anchors * bbox_scale, ['0', '1', '2', '3', '4']);" ], "id": "260673166da2aa71", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 18 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T12:01:16.948464226Z", "start_time": "2026-07-26T12:01:16.911551343Z" } }, "cell_type": "code", "source": [ "labels = multibox_target(anchors.unsqueeze(dim=0),\n", " ground_truth.unsqueeze(dim=0))" ], "id": "aafe21dd7b10dde3", "outputs": [], "execution_count": 19 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T12:06:54.735214956Z", "start_time": "2026-07-26T12:06:54.681936998Z" } }, "cell_type": "code", "source": [ "def offset_inverse(anchors, offset_preds):\n", " \"\"\"根据带有预测偏移量的锚框来预测边界框\"\"\"\n", " anc = d2l.box_corner_to_center(anchors)\n", " pred_bbox_xy = (offset_preds[:, :2] * anc[:, 2:] / 10) + anc[:, :2]\n", " pred_bbox_wh = torch.exp(offset_preds[:, 2:] / 5) * anc[:, 2:]\n", " pred_bbox = torch.cat((pred_bbox_xy, pred_bbox_wh), axis=1)\n", " predicted_bbox = d2l.box_center_to_corner(pred_bbox)\n", " return predicted_bbox" ], "id": "f15edcb302b1f95e", "outputs": [], "execution_count": 21 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:27:09.970459950Z", "start_time": "2026-07-26T13:27:09.946476638Z" } }, "cell_type": "code", "source": [ "def nms(boxes, scores, iou_threshold):\n", " \"\"\"对预测边界框的置信度进行排序\"\"\"\n", " B = torch.argsort(scores, dim=-1, descending=True)\n", " keep = [] # 保留预测边界框的指标\n", " while B.numel() > 0:\n", " i = B[0]\n", " keep.append(i)\n", " if B.numel() == 1: break\n", " iou = box_iou(boxes[i, :].reshape(-1, 4),\n", " boxes[B[1:], :].reshape(-1, 4)).reshape(-1)\n", " inds = torch.nonzero(iou <= iou_threshold).reshape(-1)\n", " B = B[inds + 1]\n", " return torch.tensor(keep, device=boxes.device)" ], "id": "7dfcdd54114d8a06", "outputs": [], "execution_count": 30 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:27:10.364513632Z", "start_time": "2026-07-26T13:27:10.338997724Z" } }, "cell_type": "code", "source": [ "#@save\n", "def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold=0.5,\n", " pos_threshold=0.009999999):\n", " \"\"\"使用非极大值抑制来预测边界框\"\"\"\n", " device, batch_size = cls_probs.device, cls_probs.shape[0]\n", " anchors = anchors.squeeze(0)\n", " num_classes, num_anchors = cls_probs.shape[1], cls_probs.shape[2]\n", " out = []\n", " for i in range(batch_size):\n", " cls_prob, offset_pred = cls_probs[i], offset_preds[i].reshape(-1, 4)\n", " conf, class_id = torch.max(cls_prob[1:], 0)\n", " predicted_bb = offset_inverse(anchors, offset_pred)\n", " keep = nms(predicted_bb, conf, nms_threshold)\n", " # 找到所有的non_keep索引，并将类设置为背景\n", " all_idx = torch.arange(num_anchors, dtype=torch.long, device=device)\n", " combined = torch.cat((keep, all_idx))\n", " uniques, counts = combined.unique(return_counts=True)\n", " non_keep = uniques[counts == 1]\n", " all_id_sorted = torch.cat((keep, non_keep))\n", " class_id[non_keep] = -1\n", " class_id = class_id[all_id_sorted]\n", " conf, predicted_bb = conf[all_id_sorted], predicted_bb[all_id_sorted]\n", " # pos_threshold是一个用于非背景预测的阈值\n", " below_min_idx = (conf < pos_threshold)\n", " class_id[below_min_idx] = -1\n", " conf[below_min_idx] = 1 - conf[below_min_idx]\n", " pred_info = torch.cat((class_id.unsqueeze(1),\n", " conf.unsqueeze(1),\n", " predicted_bb), dim=1)\n", "\n", " out.append(pred_info)\n", " return torch.stack(out)" ], "id": "8313f60149168f3d", "outputs": [], "execution_count": 31 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:27:10.685183804Z", "start_time": "2026-07-26T13:27:10.651321744Z" } }, "cell_type": "code", "source": [ "anchors = torch.tensor([[0.1, 0.08, 0.52, 0.92], [0.08, 0.2, 0.56, 0.95],\n", "[0.15, 0.3, 0.62, 0.91], [0.55, 0.2, 0.9, 0.88]])\n", "offset_preds = torch.tensor([0] * anchors.numel())\n", "cls_probs = torch.tensor([[0] * 4, # 背景的预测概率\n", "[0.9, 0.8, 0.7, 0.1], # 狗的预测概率\n", "[0.1, 0.2, 0.3, 0.9]]) # 猫的预测概率" ], "id": "46de8ece74b27875", "outputs": [], "execution_count": 32 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:27:11.044810335Z", "start_time": "2026-07-26T13:27:10.930620323Z" } }, "cell_type": "code", "source": [ "fig = d2l.plt.imshow(img)\n", "show_bboxes(fig.axes, anchors * bbox_scale,\n", "['dog=0.9', 'dog=0.8', 'dog=0.7', 'cat=0.9'])" ], "id": "ed43634b61e3ec27", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 33 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:27:12.775142785Z", "start_time": "2026-07-26T13:27:12.685045907Z" } }, "cell_type": "code", "source": [ "output = multibox_detection(cls_probs.unsqueeze(dim=0),\n", "offset_preds.unsqueeze(dim=0),\n", "anchors.unsqueeze(dim=0),\n", "nms_threshold=0.5)\n", "output" ], "id": "7eb0e18519f4b55f", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([0, 3])\n", "tensor([0, 1, 2, 3])\n" ] }, { "data": { "text/plain": [ "tensor([[[ 0.0000, 0.9000, 0.1000, 0.0800, 0.5200, 0.9200],\n", " [ 1.0000, 0.9000, 0.5500, 0.2000, 0.9000, 0.8800],\n", " [-1.0000, 0.8000, 0.0800, 0.2000, 0.5600, 0.9500],\n", " [-1.0000, 0.7000, 0.1500, 0.3000, 0.6200, 0.9100]]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 34 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:31:29.890705615Z", "start_time": "2026-07-26T13:31:29.770882766Z" } }, "cell_type": "code", "source": [ "fig = d2l.plt.imshow(img)\n", "for i in output[0].detach().numpy():\n", " if i[0] == -1:\n", " continue\n", " label = ('dog=', 'cat=')[int(i[0])] + str(i[1])\n", " show_bboxes(fig.axes, [torch.tensor(i[2:]) * bbox_scale], label)" ], "id": "fec1c1f8349a44fe", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 35 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:45:58.790473045Z", "start_time": "2026-07-26T13:45:58.732856164Z" } }, "cell_type": "code", "source": [ "img = d2l.plt.imread('../Pictures/1.jpg')\n", "h, w = img.shape[:2]\n", "h, w" ], "id": "ea166e0233574bf3", "outputs": [ { "data": { "text/plain": [ "(640, 640)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 52 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:46:00.668782428Z", "start_time": "2026-07-26T13:46:00.505299012Z" } }, "cell_type": "code", "source": [ "def display_anchors(fmap_w, fmap_h, s):\n", " d2l.set_figsize()\n", " # 前两个维度上的值不影响输出\n", " fmap = torch.zeros((1, 10, fmap_h, fmap_w))\n", " anchors = d2l.multibox_prior(fmap, sizes=s, ratios=[1, 2, 0.5,0.3])\n", " bbox_scale = torch.tensor((w, h, w, h))\n", " d2l.show_bboxes(d2l.plt.imshow(img).axes,\n", " anchors[0] * bbox_scale)\n", "display_anchors(fmap_w=3, fmap_h=3, s=[0.15,0.2,0.3])" ], "id": "41cab7a08738ed58", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 53 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:46:02.600951835Z", "start_time": "2026-07-26T13:46:02.468950170Z" } }, "cell_type": "code", "source": "display_anchors(fmap_w=2, fmap_h=2, s=[0.4])", "id": "6620db5aa275bf52", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 54 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:46:03.940576114Z", "start_time": "2026-07-26T13:46:03.820486087Z" } }, "cell_type": "code", "source": "display_anchors(fmap_w=1, fmap_h=1, s=[0.8])", "id": "8a0c4555b2752c99", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 55 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:48:39.530086370Z", "start_time": "2026-07-26T13:48:39.490640006Z" } }, "cell_type": "code", "source": [ "import torchvision,os\n", "import pandas as pd\n", "def read_data_bananas(is_train=True):\n", " \"\"\"读取香蕉检测数据集中的图像和标签\"\"\"\n", " data_dir = d2l.download_extract('banana-detection')\n", " csv_fname = os.path.join(data_dir, 'bananas_train' if is_train\n", " else 'bananas_val', 'label.csv')\n", " csv_data = pd.read_csv(csv_fname)\n", " csv_data = csv_data.set_index('img_name')\n", " images, targets = [], []\n", " for img_name, target in csv_data.iterrows():\n", " images.append(torchvision.io.read_image(\n", " os.path.join(data_dir, 'bananas_train' if is_train else\n", " 'bananas_val', 'images', f'{img_name}')))\n", " # 这里的target包含（类别，左上角x，左上角y，右下角x，右下角y），\n", " # 其中所有图像都具有相同的香蕉类（索引为0）\n", " targets.append(list(target))\n", " return images, torch.tensor(targets).unsqueeze(1) / 256" ], "id": "70e7bc1342f90cea", "outputs": [], "execution_count": 56 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T13:49:42.340692779Z", "start_time": "2026-07-26T13:49:42.292033898Z" } }, "cell_type": "code", "source": [ "class BananasDataset(torch.utils.data.Dataset):\n", " \"\"\"一个用于加载香蕉检测数据集的自定义数据集\"\"\"\n", " def __init__(self, is_train):\n", " self.features, self.labels = read_data_bananas(is_train)\n", " print('read ' + str(len(self.features)) + (f' training examples' if\n", " is_train else f' validation examples'))\n", " def __getitem__(self, idx):\n", " return (self.features[idx].float(), self.labels[idx])\n", " def __len__(self):\n", " return len(self.features)\n", "def load_data_bananas(batch_size):\n", " \"\"\"加载香蕉检测数据集\"\"\"\n", " train_iter = torch.utils.data.DataLoader(BananasDataset(is_train=True),\n", " batch_size, shuffle=True)\n", " val_iter = torch.utils.data.DataLoader(BananasDataset(is_train=False),\n", " batch_size)\n", " return train_iter, val_iter" ], "id": "1c80340fca8af59a", "outputs": [], "execution_count": 57 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T15:34:46.746160081Z", "start_time": "2026-07-26T15:34:01.758563722Z" } }, "cell_type": "code", "source": [ "batch_size, edge_size = 32, 256\n", "train_iter, _ = load_data_bananas(batch_size)\n", "batch = next(iter(train_iter))\n", "batch[0].shape, batch[1].shape" ], "id": "e6aacbbedaa732f6", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading ../data/banana-detection.zip from http://d2l-data.s3-accelerate.amazonaws.com/banana-detection.zip...\n", "read 1000 training examples\n", "read 100 validation examples\n" ] }, { "data": { "text/plain": [ "(torch.Size([32, 3, 256, 256]), torch.Size([32, 1, 5]))" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 58 }, { "metadata": { "ExecuteTime": { "end_time": "2026-07-26T15:36:52.903923701Z", "start_time": "2026-07-26T15:36:52.509530881Z" } }, "cell_type": "code", "source": [ "imgs = (batch[0][0:10].permute(0, 2, 3, 1)) / 255\n", "axes = d2l.show_images(imgs, 2, 5, scale=2)\n", "for ax, label in zip(axes, batch[1][0:10]):\n", " d2l.show_bboxes(ax, [label[0][1:5] * edge_size], colors=['b'])" ], "id": "528cb4f32e152b5", "outputs": [ { "data": { "text/plain": [ "

" ], "image/svg+xml": "\n\n\n" }, "metadata": {}, "output_type": "display_data", "jetTransient": { "display_id": null } } ], "execution_count": 60 }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "", "id": "eb1131ed49817939" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }