num_classes = label_encode.classes_.size

这段代码是用于获取标签编码器（LabelEncoder）中标签的数量。LabelEncoder 是将类别标签转换为数字编码的工具，使用 fit_transform() 方法可以将标签转换为数字编码。而 num_classes 就是获取编码后的标签数量，即 label_encode.classes_ 的大小。

相关问题

将SSD的损失函数改成focal loss的代码

Focal Loss是一种针对类别不平衡的损失函数，可以在训练过程中减少易分类样本的权重，从而提高模型对难分类样本的关注度。以下是将SSD的损失函数改成focal loss的代码：

import torch
import torch.nn as nn


class FocalLoss(nn.Module):
    def __init__(self, alpha=0.25, gamma=2, logits=True, reduction='mean'):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.logits = logits
        self.reduction = reduction

    def forward(self, inputs, targets):
        if self.logits:
            BCE_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
        else:
            BCE_loss = nn.functional.binary_cross_entropy(inputs, targets, reduction='none')

        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1 - pt) ** self.gamma * BCE_loss

        if self.reduction == 'mean':
            return torch.mean(F_loss)
        elif self.reduction == 'sum':
            return torch.sum(F_loss)
        else:
            return F_loss

class MultiBoxLoss(nn.Module):
    def __init__(self, num_classes, overlap_thresh, prior_for_matching,
                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,
                 use_gpu=True):
        super(MultiBoxLoss, self).__init__()
        self.use_gpu = use_gpu
        self.num_classes = num_classes
        self.threshold = overlap_thresh
        self.background_label = bkg_label
        self.encode_target = encode_target
        self.use_prior_for_matching = prior_for_matching
        self.do_neg_mining = neg_mining
        self.negpos_ratio = neg_pos
        self.neg_overlap = neg_overlap
        self.variance = [0.1, 0.2]

        self.focal_loss = FocalLoss()

    def forward(self, predictions, targets):
        loc_data, conf_data, prior_data = predictions
        num = loc_data.size(0)
        num_priors = prior_data.size(0)

        loc_t = torch.Tensor(num, num_priors, 4)
        conf_t = torch.LongTensor(num, num_priors)
        for idx in range(num):
            truths = targets[idx][:, :-1].data
            labels = targets[idx][:, -1].data
            defaults = prior_data.data
            match(self.threshold, truths, defaults, self.variance, labels,
                  loc_t, conf_t, idx)

        if self.use_gpu:
            loc_t = loc_t.cuda()
            conf_t = conf_t.cuda()

        pos = conf_t > 0
        num_pos = pos.sum(dim=1, keepdim=True)

        # Localization Loss (Smooth L1)
        # Shape: [batch,num_priors,4]
        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
        loc_p = loc_data[pos_idx].view(-1, 4)
        loc_t = loc_t[pos_idx].view(-1, 4)
        loss_l = nn.functional.smooth_l1_loss(loc_p, loc_t, reduction='sum')

        # Compute max conf across batch for hard negative mining
        batch_conf = conf_data.view(-1, self.num_classes)
        loss_c = self.focal_loss(batch_conf, conf_t.view(-1, 1))

        # Hard Negative Mining
        loss_c[pos] = 0  # filter out pos boxes for now
        loss_c = loss_c.view(num, -1)
        _, loss_idx = loss_c.sort(1, descending=True)
        _, idx_rank = loss_idx.sort(1)
        num_pos = pos.long().sum(1, keepdim=True)
        num_neg = torch.clamp(self.negpos_ratio * num_pos, max=pos.size(1) - 1)
        neg = idx_rank < num_neg.expand_as(idx_rank)

        # Confidence Loss Including Positive and Negative Examples
        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
        conf_p = conf_data[(pos_idx + neg_idx).gt(0)].view(-1, self.num_classes)
        targets_weighted = conf_t[(pos + neg).gt(0)]
        loss_c = self.focal_loss(conf_p, targets_weighted)

        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
        N = num_pos.sum().float()
        loss_l /= N
        loss_c /= N

        return loss_l, loss_c

在MultiBoxLoss中，我们用focal_loss替换了原来的交叉熵损失函数。在FocalLoss中，我们计算每个样本的二元交叉熵损失，然后再乘以一个类别权重系数(1 - pt)^gamma，其中pt是预测概率的指数形式，gamma是一个可调参数，用于控制易分类样本的权重。最后，我们返回一个平均的损失值。在MultiBoxLoss中，我们计算了定位损失和分类损失，并将它们相加，再除以正样本的数量求取平均值。同时，我们采用了硬负样本挖掘策略，过滤掉难以分类的样本，提高模型的准确率。

基于深度学习的汽车检测代码

这里提供一个基于开源框架TensorFlow和目标检测API的汽车检测代码示例，代码主要分为两部分：模型训练和模型测试。

1. 模型训练代码：

import tensorflow as tf
from object_detection.utils import dataset_util

# 定义数据集路径
data_dir = '/path/to/data'

# 定义训练集和验证集的路径
train_record = '/path/to/train.record'
val_record = '/path/to/val.record'

# 定义标签映射
label_map = {'car': 1}

# 定义训练集和验证集的样本数
num_train = 1000
num_val = 200

# 定义输入数据格式
image_format = b'jpg'

# 定义输出数据格式
def create_tf_example(example):
    # 读取图像和标注信息
    image_path = example['image_path']
    with tf.gfile.GFile(image_path, 'rb') as fid:
        encoded_image = fid.read()
    image_format = b'jpg'
    width = example['width']
    height = example['height']
    xmins = [example['xmin']]
    xmaxs = [example['xmax']]
    ymins = [example['ymin']]
    ymaxs = [example['ymax']]
    classes_text = [b'car']
    classes = [1]

    # 构建tf.Example对象
    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(image_path.encode('utf8')),
        'image/source_id': dataset_util.bytes_feature(image_path.encode('utf8')),
        'image/encoded': dataset_util.bytes_feature(encoded_image),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example

# 定义训练集和验证集的tfrecord文件路径
train_tfrecord = '/path/to/train.tfrecord'
val_tfrecord = '/path/to/val.tfrecord'

# 构建训练集和验证集的tfrecord文件
train_examples = get_examples(data_dir, num_train)
val_examples = get_examples(data_dir, num_val)
write_tfrecord(train_tfrecord, train_examples)
write_tfrecord(val_tfrecord, val_examples)

# 定义模型配置
num_classes = len(label_map)
batch_size = 32
learning_rate = 0.001
num_steps = 10000
num_eval_steps = 1000

# 加载模型配置文件
pipeline_config = '/path/to/pipeline.config'
config = tf.estimator.RunConfig(model_dir='/path/to/model_dir')
train_and_eval(pipeline_config, train_tfrecord, val_tfrecord, config, num_classes, batch_size, learning_rate, num_steps, num_eval_steps)

2. 模型测试代码：

import tensorflow as tf
import cv2
import numpy as np
import time

# 加载模型
model_path = '/path/to/saved_model'
model = tf.saved_model.load(model_path)

# 定义标签映射
label_map = {'car': 1}

# 定义输入图像大小
input_size = (416, 416)

# 定义检测阈值
score_threshold = 0.5

# 定义NMS阈值
nms_threshold = 0.5

# 定义颜色
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255), (255, 0, 255)]

# 加载图像
image_path = '/path/to/image.jpg'
image = cv2.imread(image_path)

# 缩放图像
image_size = image.shape[:2]
resized_image = cv2.resize(image, input_size)

# 归一化图像
normalized_image = resized_image / 255.0

# 转换图像格式
input_image = np.expand_dims(normalized_image, axis=0)

# 运行模型
start_time = time.time()
detections = model(input_image)
end_time = time.time()

# 处理检测结果
scores = detections['detection_scores'][0].numpy()
boxes = detections['detection_boxes'][0].numpy()
classes = detections['detection_classes'][0].numpy().astype(np.int32)

# 过滤掉低置信度的检测结果
valid_mask = scores > score_threshold
scores = scores[valid_mask]
boxes = boxes[valid_mask]
classes = classes[valid_mask]

# 应用非最大值抑制
nms_boxes, nms_scores, nms_classes = tf.image.combined_non_max_suppression(
    boxes=tf.expand_dims(boxes, axis=0),
    scores=tf.expand_dims(scores, axis=0),
    max_output_size_per_class=100,
    max_total_size=100,
    iou_threshold=nms_threshold,
    score_threshold=score_threshold
)

# 绘制检测结果
for i in range(nms_scores.shape[0]):
    class_id = nms_classes[i].numpy()
    score = nms_scores[i].numpy()
    box = nms_boxes[i].numpy()

    x1, y1, x2, y2 = box
    x1 = int(x1 * image_size[1])
    y1 = int(y1 * image_size[0])
    x2 = int(x2 * image_size[1])
    y2 = int(y2 * image_size[0])

    color = colors[class_id % len(colors)]
    cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness=2)
    cv2.putText(image, f'{label_map[class_id]}: {score:.2f}', (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2)

# 显示检测结果
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

以上代码仅供参考，实际应用中需要根据具体情况进行调整和优化。