使用 TensorFlow Lite 进行艺术风格迁移

在 TensorFlow.org 上查看 在 Google Colab 运行 在 GitHub 上查看源代码 下载笔记本 查看 TF Hub 模型

最近,深度学习领域最令人激动的发展之一是艺术风格迁移(或者说是创建新图像的能力),这种技术称为混仿,它基于两张输入图像:一张代表艺术风格,另一张代表内容。

Style transfer example

利用这种技术,我们可以使用一系列风格生成漂亮的新作品。

Style transfer example

如果您是 TensorFlow Lite 新用户,并且使用的是 Android 平台,我们建议您研究以下可以帮助您入门的示例应用。

Android 示例 iOS 示例

如果您使用的不是 Android 或 iOS 平台,或者您已经熟悉 TensorFlow Lite API,则可以按照本教程学习如何使用预训练的 TensorFlow Lite 模型为任何一对内容与风格图像应用风格迁移。利用该模型,您可以将风格迁移添加到自己的移动应用。

该模型在 GitHub 上开源。您可以使用不同的参数重新训练它(例如,提高内容层权重,使输出图像看起来更像内容图像)。

了解模型架构

Model Architecture

该艺术风格迁移模型包括两个子模型:

  1. 风格预测模型:一个基于 MobilenetV2 的神经网络,它将输入风格图像转换为 100 维风格瓶颈向量。
  2. 风格迁移模型:将风格瓶颈向量应用到内容图像并创建风格化图像的神经网络。

如果您的应用只需要支持一组固定的风格图像,则可以提前计算其风格瓶颈向量,并从应用的二进制文件中排除风格预测模型。

设置

导入依赖项。

import tensorflow as tf
print(tf.__version__)
2022-08-11 16:31:06.691660: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-08-11 16:31:07.524194: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-08-11 16:31:07.524478: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-08-11 16:31:07.524493: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2.10.0-rc0
import IPython.display as display

import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,12)
mpl.rcParams['axes.grid'] = False

import numpy as np
import time
import functools

下载内容和风格图像以及预训练的 TensorFlow Lite 模型。

content_path = tf.keras.utils.get_file('belfry.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/belfry-2611573_1280.jpg')
style_path = tf.keras.utils.get_file('style23.jpg','https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/style23.jpg')

style_predict_path = tf.keras.utils.get_file('style_predict.tflite', 'https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite')
style_transform_path = tf.keras.utils.get_file('style_transform.tflite', 'https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite')
Downloading data from https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/belfry-2611573_1280.jpg
458481/458481 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/khanhlvg-public.appspot.com/arbitrary-style-transfer/style23.jpg
108525/108525 [==============================] - 0s 0us/step
Downloading data from https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite
2828838/2828838 [==============================] - 0s 0us/step
Downloading data from https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite
284398/284398 [==============================] - 0s 0us/step

预处理输入

  • 内容图像和风格图像必须是 RGB 格式,且像素值为 [0..1] 范围内的 float32 数字。
  • 风格图像大小必须为 (1, 256, 256, 3)。我们从中间裁剪该图像并调整其大小。
  • 内容图像必须为 (1, 384, 384, 3)。我们从中间裁剪该图像并调整其大小。
# Function to load an image from a file, and add a batch dimension.
def load_img(path_to_img):
  img = tf.io.read_file(path_to_img)
  img = tf.io.decode_image(img, channels=3)
  img = tf.image.convert_image_dtype(img, tf.float32)
  img = img[tf.newaxis, :]

  return img

# Function to pre-process by resizing an central cropping it.
def preprocess_image(image, target_dim):
  # Resize the image so that the shorter dimension becomes 256px.
  shape = tf.cast(tf.shape(image)[1:-1], tf.float32)
  short_dim = min(shape)
  scale = target_dim / short_dim
  new_shape = tf.cast(shape * scale, tf.int32)
  image = tf.image.resize(image, new_shape)

  # Central crop the image.
  image = tf.image.resize_with_crop_or_pad(image, target_dim, target_dim)

  return image

# Load the input images.
content_image = load_img(content_path)
style_image = load_img(style_path)

# Preprocess the input images.
preprocessed_content_image = preprocess_image(content_image, 384)
preprocessed_style_image = preprocess_image(style_image, 256)

print('Style Image Shape:', preprocessed_style_image.shape)
print('Content Image Shape:', preprocessed_content_image.shape)
Style Image Shape: (1, 256, 256, 3)
Content Image Shape: (1, 384, 384, 3)

可视化输入

def imshow(image, title=None):
  if len(image.shape) > 3:
    image = tf.squeeze(image, axis=0)

  plt.imshow(image)
  if title:
    plt.title(title)

plt.subplot(1, 2, 1)
imshow(preprocessed_content_image, 'Content Image')

plt.subplot(1, 2, 2)
imshow(preprocessed_style_image, 'Style Image')

png

使用 TensorFlow Lite 运行风格迁移

风格预测

# Function to run style prediction on preprocessed style image.
def run_style_predict(preprocessed_style_image):
  # Load the model.
  interpreter = tf.lite.Interpreter(model_path=style_predict_path)

  # Set model input.
  interpreter.allocate_tensors()
  input_details = interpreter.get_input_details()
  interpreter.set_tensor(input_details[0]["index"], preprocessed_style_image)

  # Calculate style bottleneck.
  interpreter.invoke()
  style_bottleneck = interpreter.tensor(
      interpreter.get_output_details()[0]["index"]
      )()

  return style_bottleneck

# Calculate style bottleneck for the preprocessed style image.
style_bottleneck = run_style_predict(preprocessed_style_image)
print('Style Bottleneck Shape:', style_bottleneck.shape)
Style Bottleneck Shape: (1, 1, 1, 100)
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.

风格迁移

# Run style transform on preprocessed style image
def run_style_transform(style_bottleneck, preprocessed_content_image):
  # Load the model.
  interpreter = tf.lite.Interpreter(model_path=style_transform_path)

  # Set model input.
  input_details = interpreter.get_input_details()
  interpreter.allocate_tensors()

  # Set model inputs.
  interpreter.set_tensor(input_details[0]["index"], preprocessed_content_image)
  interpreter.set_tensor(input_details[1]["index"], style_bottleneck)
  interpreter.invoke()

  # Transform content image.
  stylized_image = interpreter.tensor(
      interpreter.get_output_details()[0]["index"]
      )()

  return stylized_image

# Stylize the content image using the style bottleneck.
stylized_image = run_style_transform(style_bottleneck, preprocessed_content_image)

# Visualize the output.
imshow(stylized_image, 'Stylized Image')

png

风格混合

我们可以将内容图像的风格混合到风格化输出中,使输出看起来更像内容图像。

# Calculate style bottleneck of the content image.
style_bottleneck_content = run_style_predict(
    preprocess_image(content_image, 256)
    )
# Define content blending ratio between [0..1].
# 0.0: 0% style extracts from content image.
# 1.0: 100% style extracted from content image.
content_blending_ratio = 0.5

# Blend the style bottleneck of style image and content image
style_bottleneck_blended = content_blending_ratio * style_bottleneck_content \
                           + (1 - content_blending_ratio) * style_bottleneck

# Stylize the content image using the style bottleneck.
stylized_image_blended = run_style_transform(style_bottleneck_blended,
                                             preprocessed_content_image)

# Visualize the output.
imshow(stylized_image_blended, 'Blended Stylized Image')

png

性能基准

性能基准数值通过使用此处所述的工具生成。

模型名称 模型大小 设备 NNAPI CPU GPU
风格预测模型 (int8) 2.8 Mb Pixel 3 (Android 10) 142ms 14ms*
Pixel 4 (Android 10) 5.2ms 6.7ms*
iPhone XS (iOS 12.4.1) 10.7ms**
风格转换模型 (int8) 0.2 Mb Pixel 3 (Android 10) 540ms*
Pixel 4 (Android 10) 405ms*
iPhone XS (iOS 12.4.1) 251ms**
风格预测模型 (float16) 4.7 Mb Pixel 3 (Android 10) 86ms 28ms* 9.1ms
Pixel 4 (Android 10) 32ms 12ms* 10ms
风格转换模型 (float16) 0.4 Mb Pixel 3 (Android 10) 1095ms 545ms* 42ms
Pixel 4 (Android 10) 603ms 377ms* 42ms

** 使用 4 个线程。
* *** 为了获得最佳性能结果,在 iPhone 上使用 2 个线程。*