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Dive-into-DL-PyTorch/code/chapter09_computer-vision/9.4_anchor.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 9.4 锚框"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2.0\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from PIL import Image\n",
"import numpy as np\n",
"import math\n",
"import torch\n",
"\n",
"import sys\n",
"sys.path.append(\"..\") \n",
"import d2lzh_pytorch as d2l\n",
"print(torch.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.1 生成多个锚框"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w = 728, h = 561\n"
]
}
],
"source": [
"d2l.set_figsize()\n",
"img = Image.open('../../img/catdog.jpg')\n",
"w, h = img.size\n",
"print(\"w = %d, h = %d\" % (w, h))\n",
"\n",
"# d2l.plt.imshow(img); # 加分号只显示图"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# 本函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def MultiBoxPrior(feature_map, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5]):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" feature_map: torch tensor, Shape: [N, C, H, W].\n",
" sizes: List of sizes (0~1) of generated MultiBoxPriores. \n",
" ratios: List of aspect ratios (non-negative) of generated MultiBoxPriores. \n",
" Returns:\n",
" anchors of shape (1, num_anchors, 4). 由于batch里每个都一样, 所以第一维为1\n",
" \"\"\"\n",
" pairs = [] # pair of (size, sqrt(ration))\n",
" for r in ratios:\n",
" pairs.append([sizes[0], math.sqrt(r)])\n",
" for s in sizes[1:]:\n",
" pairs.append([s, math.sqrt(ratios[0])])\n",
" \n",
" pairs = np.array(pairs)\n",
" \n",
" ss1 = pairs[:, 0] * pairs[:, 1] # size * sqrt(ration)\n",
" ss2 = pairs[:, 0] / pairs[:, 1] # size / sqrt(ration)\n",
" \n",
" base_anchors = np.stack([-ss1, -ss2, ss1, ss2], axis=1) / 2\n",
" \n",
" h, w = feature_map.shape[-2:]\n",
" shifts_x = np.arange(0, w) / w\n",
" shifts_y = np.arange(0, h) / h\n",
" shift_x, shift_y = np.meshgrid(shifts_x, shifts_y)\n",
" shift_x = shift_x.reshape(-1)\n",
" shift_y = shift_y.reshape(-1)\n",
" shifts = np.stack((shift_x, shift_y, shift_x, shift_y), axis=1)\n",
" \n",
" anchors = shifts.reshape((-1, 1, 4)) + base_anchors.reshape((1, -1, 4))\n",
" \n",
" return torch.tensor(anchors, dtype=torch.float32).view(1, -1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 2042040, 4])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = torch.Tensor(1, 3, h, w) # 构造输入数据\n",
"Y = MultiBoxPrior(X, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5])\n",
"Y.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([-0.0316, 0.0706, 0.7184, 0.8206])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boxes = Y.reshape((h, w, 5, 4))\n",
"boxes[250, 250, 0, :]# * torch.tensor([w, h, w, h], dtype=torch.float32)\n",
"# 第一个size和ratio分别为0.75和1, 则宽高均为0.75 = 0.7184 + 0.0316 = 0.8206 - 0.0706"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# 本函数已保存在dd2lzh_pytorch包中方便以后使用\n",
"def show_bboxes(axes, bboxes, labels=None, colors=None):\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().cpu().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=6, color=text_color,\n",
" bbox=dict(facecolor=color, lw=0))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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],
"source": [
"d2l.set_figsize()\n",
"fig = d2l.plt.imshow(img)\n",
"bbox_scale = torch.tensor([[w, h, w, h]], dtype=torch.float32)\n",
"show_bboxes(fig.axes, boxes[250, 250, :, :] * bbox_scale,\n",
" ['s=0.75, r=1', 's=0.75, r=2', 's=0.55, r=0.5', 's=0.5, r=1', 's=0.25, r=1'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.2 交并比\n",
"代码来自https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/utils.py#L356"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def compute_intersection(set_1, set_2):\n",
" \"\"\"\n",
" 计算anchor之间的交集\n",
" Args:\n",
" set_1: a tensor of dimensions (n1, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" set_2: a tensor of dimensions (n2, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" Returns:\n",
" intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2)\n",
" \"\"\"\n",
" # PyTorch auto-broadcasts singleton dimensions\n",
" lower_bounds = torch.max(set_1[:, :2].unsqueeze(1), set_2[:, :2].unsqueeze(0)) # (n1, n2, 2)\n",
" upper_bounds = torch.min(set_1[:, 2:].unsqueeze(1), set_2[:, 2:].unsqueeze(0)) # (n1, n2, 2)\n",
" intersection_dims = torch.clamp(upper_bounds - lower_bounds, min=0) # (n1, n2, 2)\n",
" return intersection_dims[:, :, 0] * intersection_dims[:, :, 1] # (n1, n2)\n",
"\n",
"\n",
"def compute_jaccard(set_1, set_2):\n",
" \"\"\"\n",
" 计算anchor之间的Jaccard系数(IoU)\n",
" Args:\n",
" set_1: a tensor of dimensions (n1, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" set_2: a tensor of dimensions (n2, 4), anchor表示成(xmin, ymin, xmax, ymax)\n",
" Returns:\n",
" Jaccard Overlap of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2)\n",
" \"\"\"\n",
" # Find intersections\n",
" intersection = compute_intersection(set_1, set_2) # (n1, n2)\n",
"\n",
" # Find areas of each box in both sets\n",
" areas_set_1 = (set_1[:, 2] - set_1[:, 0]) * (set_1[:, 3] - set_1[:, 1]) # (n1)\n",
" areas_set_2 = (set_2[:, 2] - set_2[:, 0]) * (set_2[:, 3] - set_2[:, 1]) # (n2)\n",
"\n",
" # Find the union\n",
" # PyTorch auto-broadcasts singleton dimensions\n",
" union = areas_set_1.unsqueeze(1) + areas_set_2.unsqueeze(0) - intersection # (n1, n2)\n",
"\n",
" return intersection / union # (n1, n2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.3 标注训练集的锚框"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
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"source": [
"bbox_scale = torch.tensor((w, h, w, h), dtype=torch.float32)\n",
"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",
"\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']);"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" [0.1417, 0.0000],\n",
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],
"source": [
"compute_jaccard(anchors, ground_truth[:, 1:]) # 验证一下写的compute_jaccard函数"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"def assign_anchor(bb, anchor, jaccard_threshold=0.5):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」图9.3所讲为每个anchor分配真实的bb, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" bb: 真实边界框(bounding box), shape:（nb, 4）\n",
" anchor: 待分配的anchor, shape:（na, 4）\n",
" jaccard_threshold: 预先设定的阈值\n",
" Returns:\n",
" assigned_idx: shape: (na, ), 每个anchor分配的真实bb对应的索引, 若未分配任何bb则为-1\n",
" \"\"\"\n",
" na = anchor.shape[0]\n",
" nb = bb.shape[0]\n",
" jaccard = compute_jaccard(anchor, bb).detach().cpu().numpy() # shape: (na, nb)\n",
" assigned_idx = np.ones(na) * -1 # 初始全为-1\n",
" \n",
" # 先为每个bb分配一个anchor(不要求满足jaccard_threshold)\n",
" jaccard_cp = jaccard.copy()\n",
" for j in range(nb):\n",
" i = np.argmax(jaccard_cp[:, j])\n",
" assigned_idx[i] = j\n",
" jaccard_cp[i, :] = float(\"-inf\") # 赋值为负无穷, 相当于去掉这一行\n",
" \n",
" # 处理还未被分配的anchor, 要求满足jaccard_threshold\n",
" for i in range(na):\n",
" if assigned_idx[i] == -1:\n",
" j = np.argmax(jaccard[i, :])\n",
" if jaccard[i, j] >= jaccard_threshold:\n",
" assigned_idx[i] = j\n",
" \n",
" return torch.tensor(assigned_idx, dtype=torch.long)\n",
"\n",
"\n",
"def xy_to_cxcy(xy):\n",
" \"\"\"\n",
" 将(x_min, y_min, x_max, y_max)形式的anchor转换成(center_x, center_y, w, h)形式的.\n",
" https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection/blob/master/utils.py\n",
" Args:\n",
" xy: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)\n",
" Returns: \n",
" bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)\n",
" \"\"\"\n",
" return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2, # c_x, c_y\n",
" xy[:, 2:] - xy[:, :2]], 1) # w, h\n",
"\n",
"def MultiBoxTarget(anchor, label):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" anchor: torch tensor, 输入的锚框, 一般是通过MultiBoxPrior生成, shape:（1，锚框总数，4）\n",
" label: 真实标签, shape为(bn, 每张图片最多的真实锚框数, 5)\n",
" 第二维中，如果给定图片没有这么多锚框, 可以先用-1填充空白, 最后一维中的元素为[类别标签, 四个坐标值]\n",
" Returns:\n",
" 列表, [bbox_offset, bbox_mask, cls_labels]\n",
" bbox_offset: 每个锚框的标注偏移量，形状为(bn，锚框总数*4)\n",
" bbox_mask: 形状同bbox_offset, 每个锚框的掩码, 一一对应上面的偏移量, 负类锚框(背景)对应的掩码均为0, 正类锚框的掩码均为1\n",
" cls_labels: 每个锚框的标注类别, 其中0表示为背景, 形状为(bn，锚框总数)\n",
" \"\"\"\n",
" assert len(anchor.shape) == 3 and len(label.shape) == 3\n",
" bn = label.shape[0]\n",
" \n",
" def MultiBoxTarget_one(anc, lab, eps=1e-6):\n",
" \"\"\"\n",
" MultiBoxTarget函数的辅助函数, 处理batch中的一个\n",
" Args:\n",
" anc: shape of (锚框总数, 4)\n",
" lab: shape of (真实锚框数, 5), 5代表[类别标签, 四个坐标值]\n",
" eps: 一个极小值, 防止log0\n",
" Returns:\n",
" offset: (锚框总数*4, )\n",
" bbox_mask: (锚框总数*4, ), 0代表背景, 1代表非背景\n",
" cls_labels: (锚框总数, 4), 0代表背景\n",
" \"\"\"\n",
" an = anc.shape[0]\n",
" assigned_idx = assign_anchor(lab[:, 1:], anc) # (锚框总数, )\n",
" bbox_mask = ((assigned_idx >= 0).float().unsqueeze(-1)).repeat(1, 4) # (锚框总数, 4)\n",
"\n",
" cls_labels = torch.zeros(an, dtype=torch.long) # 0表示背景\n",
" assigned_bb = torch.zeros((an, 4), dtype=torch.float32) # 所有anchor对应的bb坐标\n",
" for i in range(an):\n",
" bb_idx = assigned_idx[i]\n",
" if bb_idx >= 0: # 即非背景\n",
" cls_labels[i] = lab[bb_idx, 0].long().item() + 1 # 注意要加一\n",
" assigned_bb[i, :] = lab[bb_idx, 1:]\n",
"\n",
" center_anc = xy_to_cxcy(anc) # (center_x, center_y, w, h)\n",
" center_assigned_bb = xy_to_cxcy(assigned_bb)\n",
"\n",
" offset_xy = 10.0 * (center_assigned_bb[:, :2] - center_anc[:, :2]) / center_anc[:, 2:]\n",
" offset_wh = 5.0 * torch.log(eps + center_assigned_bb[:, 2:] / center_anc[:, 2:])\n",
" offset = torch.cat([offset_xy, offset_wh], dim = 1) * bbox_mask # (锚框总数, 4)\n",
"\n",
" return offset.view(-1), bbox_mask.view(-1), cls_labels\n",
" \n",
" batch_offset = []\n",
" batch_mask = []\n",
" batch_cls_labels = []\n",
" for b in range(bn):\n",
" offset, bbox_mask, cls_labels = MultiBoxTarget_one(anchor[0, :, :], label[b, :, :])\n",
" \n",
" batch_offset.append(offset)\n",
" batch_mask.append(bbox_mask)\n",
" batch_cls_labels.append(cls_labels)\n",
" \n",
" bbox_offset = torch.stack(batch_offset)\n",
" bbox_mask = torch.stack(batch_mask)\n",
" cls_labels = torch.stack(batch_cls_labels)\n",
" \n",
" return [bbox_offset, bbox_mask, cls_labels]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"labels = MultiBoxTarget(anchors.unsqueeze(dim=0),\n",
" ground_truth.unsqueeze(dim=0))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0, 1, 2, 0, 2]])"
]
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"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"labels[2]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1.,\n",
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"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
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"source": [
"labels[1]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, 1.4000e+00,\n",
" 1.0000e+01, 2.5940e+00, 7.1754e+00, -1.2000e+00, 2.6882e-01,\n",
" 1.6824e+00, -1.5655e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,\n",
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"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"labels[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9.4.4. 输出预测边界框"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"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([0.0] * (4 * len(anchors)))\n",
"cls_probs = torch.tensor([[0., 0., 0., 0.,], # 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"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
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},
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"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'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# 以下函数已保存在d2lzh_pytorch包中方便以后使用\n",
"from collections import namedtuple\n",
"Pred_BB_Info = namedtuple(\"Pred_BB_Info\", [\"index\", \"class_id\", \"confidence\", \"xyxy\"])\n",
"\n",
"def non_max_suppression(bb_info_list, nms_threshold = 0.5):\n",
" \"\"\"\n",
" 非极大抑制处理预测的边界框\n",
" Args:\n",
" bb_info_list: Pred_BB_Info的列表, 包含预测类别、置信度等信息\n",
" nms_threshold: 阈值\n",
" Returns:\n",
" output: Pred_BB_Info的列表, 只保留过滤后的边界框信息\n",
" \"\"\"\n",
" output = []\n",
" # 先根据置信度从高到低排序\n",
" sorted_bb_info_list = sorted(bb_info_list, key = lambda x: x.confidence, reverse=True)\n",
"\n",
" while len(sorted_bb_info_list) != 0:\n",
" best = sorted_bb_info_list.pop(0)\n",
" output.append(best)\n",
" \n",
" if len(sorted_bb_info_list) == 0:\n",
" break\n",
"\n",
" bb_xyxy = []\n",
" for bb in sorted_bb_info_list:\n",
" bb_xyxy.append(bb.xyxy)\n",
" \n",
" iou = compute_jaccard(torch.tensor([best.xyxy]), \n",
" torch.tensor(bb_xyxy))[0] # shape: (len(sorted_bb_info_list), )\n",
" \n",
" n = len(sorted_bb_info_list)\n",
" sorted_bb_info_list = [sorted_bb_info_list[i] for i in range(n) if iou[i] <= nms_threshold]\n",
" return output\n",
"\n",
"def MultiBoxDetection(cls_prob, loc_pred, anchor, nms_threshold = 0.5):\n",
" \"\"\"\n",
" # 按照「9.4.1. 生成多个锚框」所讲的实现, anchor表示成归一化(xmin, ymin, xmax, ymax).\n",
" https://zh.d2l.ai/chapter_computer-vision/anchor.html\n",
" Args:\n",
" cls_prob: 经过softmax后得到的各个锚框的预测概率, shape:(bn, 预测总类别数+1, 锚框个数)\n",
" loc_pred: 预测的各个锚框的偏移量, shape:(bn, 锚框个数*4)\n",
" anchor: MultiBoxPrior输出的默认锚框, shape: (1, 锚框个数, 4)\n",
" nms_threshold: 非极大抑制中的阈值\n",
" Returns:\n",
" 所有锚框的信息, shape: (bn, 锚框个数, 6)\n",
" 每个锚框信息由[class_id, confidence, xmin, ymin, xmax, ymax]表示\n",
" class_id=-1 表示背景或在非极大值抑制中被移除了\n",
" \"\"\"\n",
" assert len(cls_prob.shape) == 3 and len(loc_pred.shape) == 2 and len(anchor.shape) == 3\n",
" bn = cls_prob.shape[0]\n",
" \n",
" def MultiBoxDetection_one(c_p, l_p, anc, nms_threshold = 0.5):\n",
" \"\"\"\n",
" MultiBoxDetection的辅助函数, 处理batch中的一个\n",
" Args:\n",
" c_p: (预测总类别数+1, 锚框个数)\n",
" l_p: (锚框个数*4, )\n",
" anc: (锚框个数, 4)\n",
" nms_threshold: 非极大抑制中的阈值\n",
" Return:\n",
" output: (锚框个数, 6)\n",
" \"\"\"\n",
" pred_bb_num = c_p.shape[1]\n",
" anc = (anc + l_p.view(pred_bb_num, 4)).detach().cpu().numpy() # 加上偏移量\n",
" \n",
" confidence, class_id = torch.max(c_p, 0)\n",
" confidence = confidence.detach().cpu().numpy()\n",
" class_id = class_id.detach().cpu().numpy()\n",
" \n",
" pred_bb_info = [Pred_BB_Info(\n",
" index = i,\n",
" class_id = class_id[i] - 1, # 正类label从0开始\n",
" confidence = confidence[i],\n",
" xyxy=[*anc[i]]) # xyxy是个列表\n",
" for i in range(pred_bb_num)]\n",
" \n",
" # 正类的index\n",
" obj_bb_idx = [bb.index for bb in non_max_suppression(pred_bb_info, nms_threshold)]\n",
" \n",
" output = []\n",
" for bb in pred_bb_info:\n",
" output.append([\n",
" (bb.class_id if bb.index in obj_bb_idx else -1.0),\n",
" bb.confidence,\n",
" *bb.xyxy\n",
" ])\n",
" \n",
" return torch.tensor(output) # shape: (锚框个数, 6)\n",
" \n",
" batch_output = []\n",
" for b in range(bn):\n",
" batch_output.append(MultiBoxDetection_one(cls_prob[b], loc_pred[b], anchor[0], nms_threshold))\n",
" \n",
" return torch.stack(batch_output)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
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{
"data": {
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" [-1.0000, 0.8000, 0.0800, 0.2000, 0.5600, 0.9500],\n",
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]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output = MultiBoxDetection(\n",
" cls_probs.unsqueeze(dim=0), offset_preds.unsqueeze(dim=0),\n",
" anchors.unsqueeze(dim=0), nms_threshold=0.5)\n",
"output"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
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"for i in output[0].detach().cpu().numpy():\n",
" if i[0] == -1:\n",
" continue\n",
" label = ('dog=', 'cat=')[int(i[0])] + str(i[1])\n",
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},
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