有tensorflow1.12可以用的计算感知损失的代码（生成图片，风格图片，内容图片）吗

当然可以，以下是计算感知损失的代码示例：

import tensorflow as tf
import numpy as np
import cv2

def gram_matrix(input_tensor):
    # 将特征图展平为2D矩阵
    input_shape = tf.shape(input_tensor)
    h = input_shape[1]
    w = input_shape[2]
    c = input_shape[3]
    features = tf.reshape(input_tensor, [-1, h * w, c])
    # 计算Gram矩阵
    gram = tf.matmul(features, features, transpose_a=True)
    return gram

def compute_style_loss(style_features, generated_features):
    # 计算风格损失
    style_loss = 0.
    for style_feature, generated_feature in zip(style_features, generated_features):
        style_gram = gram_matrix(style_feature)
        generated_gram = gram_matrix(generated_feature)
        style_loss += tf.reduce_mean(tf.square(style_gram - generated_gram)) / (4. * tf.cast(tf.size(generated_feature), tf.float32) ** 2)
    return style_loss

def compute_content_loss(content_features, generated_features):
    # 计算内容损失
    content_loss = tf.reduce_mean(tf.square(content_features - generated_features))
    return content_loss

def compute_perceptual_loss(style_image_path, content_image_path, generated_image_path):
    # 加载风格图片、内容图片和生成图片
    style_image = tf.keras.preprocessing.image.load_img(style_image_path, target_size=(256, 256))
    content_image = tf.keras.preprocessing.image.load_img(content_image_path, target_size=(256, 256))
    generated_image = tf.keras.preprocessing.image.load_img(generated_image_path, target_size=(256, 256))

    # 将图片转换为numpy数组，并扩展维度以匹配模型的输入形状
    style_image_array = np.expand_dims(tf.keras.preprocessing.image.img_to_array(style_image), axis=0)
    content_image_array = np.expand_dims(tf.keras.preprocessing.image.img_to_array(content_image), axis=0)
    generated_image_array = np.expand_dims(tf.keras.preprocessing.image.img_to_array(generated_image), axis=0)

    # 对图片进行归一化处理
    style_image_array = tf.keras.applications.vgg19.preprocess_input(style_image_array)
    content_image_array = tf.keras.applications.vgg19.preprocess_input(content_image_array)
    generated_image_array = tf.keras.applications.vgg19.preprocess_input(generated_image_array)

    # 加载VGG19模型，并指定需要输出的层
    vgg19 = tf.keras.applications.VGG19(include_top=False, weights='imagenet')
    style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1']
    content_layers = ['block5_conv2']

    # 构建模型，用于获取风格图片、内容图片和生成图片在指定层的特征
    outputs = [vgg19.get_layer(name).output for name in style_layers + content_layers]
    model = tf.keras.models.Model(inputs=vgg19.inputs, outputs=outputs)

    # 计算风格图片、内容图片和生成图片在指定层的特征
    style_features = model(style_image_array)[:len(style_layers)]
    content_features = model(content_image_array)[len(style_layers):]
    generated_features = model(generated_image_array)[len(style_layers):]

    # 计算损失
    style_loss = compute_style_loss(style_features, generated_features[:len(style_layers)])
    content_loss = compute_content_loss(content_features, generated_features[len(style_layers):])
    perceptual_loss = style_loss + content_loss

    return perceptual_loss.numpy()

其中，`style_image_path`、`content_image_path`和`generated_image_path`分别表示风格图片、内容图片和生成图片的路径。需要注意的是，该代码使用VGG19模型来计算特征，并计算风格损失和内容损失，最终得到感知损失（即风格损失和内容损失之和）。