yolov1代码

以下是 YOLOv1 的 Python 代码实现，仅供参考：

import numpy as np
import tensorflow as tf

class YOLOv1:
    def __init__(self, num_classes, S=7, B=2, lambda_coord=5, lambda_noobj=0.5):
        self.num_classes = num_classes
        self.S = S
        self.B = B
        self.lambda_coord = lambda_coord
        self.lambda_noobj = lambda_noobj
        self.cell_size = 1.0 / S
        
        self.images = tf.placeholder(tf.float32, shape=[None, 448, 448, 3])
        self.labels = tf.placeholder(tf.float32, shape=[None, S, S, 5 * B + num_classes])
        
        conv1 = self.conv_layer(self.images, [7, 7, 3, 64], [64])
        pool1 = self.max_pool(conv1, [2, 2])
        
        conv2 = self.conv_layer(pool1, [3, 3, 64, 192], [192])
        pool2 = self.max_pool(conv2, [2, 2])
        
        conv3 = self.conv_layer(pool2, [1, 1, 192, 128], [128])
        conv4 = self.conv_layer(conv3, [3, 3, 128, 256], [256])
        conv5 = self.conv_layer(conv4, [1, 1, 256, 256], [256])
        conv6 = self.conv_layer(conv5, [3, 3, 256, 512], [512])
        pool6 = self.max_pool(conv6, [2, 2])
        
        conv7 = self.conv_layer(pool6, [1, 1, 512, 256], [256])
        conv8 = self.conv_layer(conv7, [3, 3, 256, 512], [512])
        conv9 = self.conv_layer(conv8, [1, 1, 512, 256], [256])
        conv10 = self.conv_layer(conv9, [3, 3, 256, 512], [512])
        conv11 = self.conv_layer(conv10, [1, 1, 512, 256], [256])
        conv12 = self.conv_layer(conv11, [3, 3, 256, 512], [512])
        conv13 = self.conv_layer(conv12, [1, 1, 512, 256], [256])
        conv14 = self.conv_layer(conv13, [3, 3, 256, 512], [512])
        conv15 = self.conv_layer(conv14, [1, 1, 512, 512], [512])
        conv16 = self.conv_layer(conv15, [3, 3, 512, 1024], [1024])
        pool16 = self.max_pool(conv16, [2, 2])
        
        conv17 = self.conv_layer(pool16, [1, 1, 1024, 512], [512])
        conv18 = self.conv_layer(conv17, [3, 3, 512, 1024], [1024])
        conv19 = self.conv_layer(conv18, [1, 1, 1024, 512], [512])
        conv20 = self.conv_layer(conv19, [3, 3, 512, 1024], [1024])
        conv21 = self.conv_layer(conv20, [3, 3, 1024, 1024], [1024])
        conv22 = self.conv_layer(conv21, [3, 3, 1024, 1024], [1024])
        
        flattened = tf.concat([tf.reshape(conv22, [-1, 7 * 7 * 1024]), tf.reshape(self.labels, [-1, 7 * 7 * 30])], axis=1)
        fc1 = self.fc_layer(flattened, 7 * 7 * 1024 + 7 * 7 * 30, 4096)
        fc2 = self.fc_layer(fc1, 4096, S * S * (5 * B + num_classes), relu=False)
        
        self.logits = tf.reshape(fc2, [-1, S, S, 5 * B + num_classes])
        
        self.loss = self.compute_loss()
        
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)
    
    def conv_layer(self, inputs, shape, biases):
        weights = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
        conv = tf.nn.conv2d(inputs, weights, strides=[1, 1, 1, 1], padding='SAME')
        return tf.nn.relu(tf.nn.bias_add(conv, biases))
    
    def max_pool(self, inputs, ksize):
        return tf.nn.max_pool(inputs, ksize=[1, ksize[0], ksize[1], 1], strides=[1, 2, 2, 1], padding='SAME')
    
    def fc_layer(self, inputs, in_size, out_size, relu=True):
        weights = tf.Variable(tf.truncated_normal([in_size, out_size], stddev=0.1))
        biases = tf.Variable(tf.constant(0.1, shape=[out_size]))
        fc = tf.nn.bias_add(tf.matmul(inputs, weights), biases)
        if relu:
            return tf.nn.relu(fc)
        else:
            return fc
    
    def compute_loss(self):
        mask_obj = tf.tile(tf.expand_dims(tf.cast(self.labels[:, :, :, 4] > 0, dtype=tf.float32), axis=-1), [1, 1, 1, self.B])
        mask_noobj = tf.tile(tf.expand_dims(tf.cast(self.labels[:, :, :, 4] == 0, dtype=tf.float32), axis=-1), [1, 1, 1, self.B])
        
        preds_xy = tf.sigmoid(self.logits[:, :, :, :2])
        preds_wh = tf.exp(self.logits[:, :, :, 2:4])
        preds_conf = tf.sigmoid(self.logits[:, :, :, 4:5])
        preds_class = tf.nn.softmax(self.logits[:, :, :, 5:])
        
        targets_xy = self.labels[:, :, :, :2]
        targets_wh = tf.sqrt(self.labels[:, :, :, 2:4])
        targets_conf = self.labels[:, :, :, 4:5]
        targets_class = self.labels[:, :, :, 5:]
        
        loss_xy = tf.reduce_sum(tf.square(mask_obj * (preds_xy - targets_xy)) * self.lambda_coord)
        loss_wh = tf.reduce_sum(tf.square(mask_obj * (preds_wh - targets_wh)) * self.lambda_coord)
        loss_conf_obj = tf.reduce_sum(tf.square(mask_obj * (preds_conf - targets_conf)))
        loss_conf_noobj = tf.reduce_sum(tf.square(mask_noobj * (preds_conf - targets_conf))) * self.lambda_noobj
        loss_class = tf.reduce_sum(tf.square(targets_class - preds_class))
        
        total_loss = loss_xy + loss_wh + loss_conf_obj + loss_conf_noobj + loss_class
        
        return total_loss

注意，这只是一个简单的实现，可能与原论文中的实现略有不同。另外，此代码未包含数据预处理和训练过程，仅提供模型结构和损失函数的实现。