深度梦境
作者： fchollet
创建时间 2016/01/13
上次修改时间 2020/05/02
描述：使用 Keras 生成深度梦境。
引言
“深度梦境”是一种图像滤波技术，它通过获取图像分类模型，并在输入图像上运行梯度上升来尝试最大化特定层（有时是特定层中的特定单元）的激活。它会产生类似幻觉的视觉效果。
它于 2015 年 7 月由 Google 的 Alexander Mordvintsev 首次提出。
过程
- 加载原始图像。
- 定义多个处理尺度（“倍频程”），从小到大。
- 将原始图像调整为最小尺度。
- 对于每个尺度，从最小尺度（即当前尺度）开始：- 运行梯度上升 - 将图像放大到下一个尺度 - 重新注入在放大时丢失的细节
- 当我们回到原始尺寸时停止。为了获取放大过程中丢失的细节，我们只需获取原始图像，将其缩小，放大，并将结果与（调整大小的）原始图像进行比较。
设置
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
import tensorflow as tf
import keras
from keras.applications import inception_v3
base_image_path = keras.utils.get_file("sky.jpg", "https://i.imgur.com/aGBdQyK.jpg")
result_prefix = "sky_dream"
# These are the names of the layers
# for which we try to maximize activation,
# as well as their weight in the final loss
# we try to maximize.
# You can tweak these setting to obtain new visual effects.
layer_settings = {
"mixed4": 1.0,
"mixed5": 1.5,
"mixed6": 2.0,
"mixed7": 2.5,
}
# Playing with these hyperparameters will also allow you to achieve new effects
step = 0.01 # Gradient ascent step size
num_octave = 3 # Number of scales at which to run gradient ascent
octave_scale = 1.4 # Size ratio between scales
iterations = 20 # Number of ascent steps per scale
max_loss = 15.0
这是我们的基础图像
from IPython.display import Image, display
display(Image(base_image_path))
让我们设置一些图像预处理/后处理工具
def preprocess_image(image_path):
# Util function to open, resize and format pictures
# into appropriate arrays.
img = keras.utils.load_img(image_path)
img = keras.utils.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = inception_v3.preprocess_input(img)
return img
def deprocess_image(x):
# Util function to convert a NumPy array into a valid image.
x = x.reshape((x.shape[1], x.shape[2], 3))
# Undo inception v3 preprocessing
x /= 2.0
x += 0.5
x *= 255.0
# Convert to uint8 and clip to the valid range [0, 255]
x = np.clip(x, 0, 255).astype("uint8")
return x
计算深度梦境损失
首先，构建一个特征提取模型，以便在给定输入图像的情况下检索目标层的激活。
# Build an InceptionV3 model loaded with pre-trained ImageNet weights
model = inception_v3.InceptionV3(weights="imagenet", include_top=False)
# Get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict(
[
(layer.name, layer.output)
for layer in [model.get_layer(name) for name in layer_settings.keys()]
]
)
# Set up a model that returns the activation values for every target layer
# (as a dict)
feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)
实际的损失计算非常简单
def compute_loss(input_image):
features = feature_extractor(input_image)
# Initialize the loss
loss = tf.zeros(shape=())
for name in features.keys():
coeff = layer_settings[name]
activation = features[name]
# We avoid border artifacts by only involving non-border pixels in the loss.
scaling = tf.reduce_prod(tf.cast(tf.shape(activation), "float32"))
loss += coeff * tf.reduce_sum(tf.square(activation[:, 2:-2, 2:-2, :])) / scaling
return loss
为一个倍频程设置梯度上升循环
@tf.function
def gradient_ascent_step(img, learning_rate):
with tf.GradientTape() as tape:
tape.watch(img)
loss = compute_loss(img)
# Compute gradients.
grads = tape.gradient(loss, img)
# Normalize gradients.
grads /= tf.maximum(tf.reduce_mean(tf.abs(grads)), 1e-6)
img += learning_rate * grads
return loss, img
def gradient_ascent_loop(img, iterations, learning_rate, max_loss=None):
for i in range(iterations):
loss, img = gradient_ascent_step(img, learning_rate)
if max_loss is not None and loss > max_loss:
break
print("... Loss value at step %d: %.2f" % (i, loss))
return img
运行训练循环，迭代不同的倍频程
original_img = preprocess_image(base_image_path)
original_shape = original_img.shape[1:3]
successive_shapes = [original_shape]
for i in range(1, num_octave):
shape = tuple([int(dim / (octave_scale**i)) for dim in original_shape])
successive_shapes.append(shape)
successive_shapes = successive_shapes[::-1]
shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])
img = tf.identity(original_img) # Make a copy
for i, shape in enumerate(successive_shapes):
print("Processing octave %d with shape %s" % (i, shape))
img = tf.image.resize(img, shape)
img = gradient_ascent_loop(
img, iterations=iterations, learning_rate=step, max_loss=max_loss
)
upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)
same_size_original = tf.image.resize(original_img, shape)
lost_detail = same_size_original - upscaled_shrunk_original_img
img += lost_detail
shrunk_original_img = tf.image.resize(original_img, shape)
keras.utils.save_img(result_prefix + ".png", deprocess_image(img.numpy()))
Processing octave 0 with shape (326, 489)
... Loss value at step 0: 0.45
... Loss value at step 1: 0.63
... Loss value at step 2: 0.91
... Loss value at step 3: 1.24
... Loss value at step 4: 1.57
... Loss value at step 5: 1.91
... Loss value at step 6: 2.20
... Loss value at step 7: 2.50
... Loss value at step 8: 2.82
... Loss value at step 9: 3.11
... Loss value at step 10: 3.40
... Loss value at step 11: 3.70
... Loss value at step 12: 3.95
... Loss value at step 13: 4.20
... Loss value at step 14: 4.48
... Loss value at step 15: 4.72
... Loss value at step 16: 4.99
... Loss value at step 17: 5.23
... Loss value at step 18: 5.47
... Loss value at step 19: 5.69
Processing octave 1 with shape (457, 685)
... Loss value at step 0: 1.11
... Loss value at step 1: 1.77
... Loss value at step 2: 2.35
... Loss value at step 3: 2.82
... Loss value at step 4: 3.25
... Loss value at step 5: 3.67
... Loss value at step 6: 4.05
... Loss value at step 7: 4.44
... Loss value at step 8: 4.79
... Loss value at step 9: 5.15
... Loss value at step 10: 5.50
... Loss value at step 11: 5.84
... Loss value at step 12: 6.18
... Loss value at step 13: 6.49
... Loss value at step 14: 6.82
... Loss value at step 15: 7.12
... Loss value at step 16: 7.42
... Loss value at step 17: 7.71
... Loss value at step 18: 8.01
... Loss value at step 19: 8.30
Processing octave 2 with shape (640, 960)
... Loss value at step 0: 1.27
... Loss value at step 1: 2.02
... Loss value at step 2: 2.63
... Loss value at step 3: 3.15
... Loss value at step 4: 3.66
... Loss value at step 5: 4.12
... Loss value at step 6: 4.58
... Loss value at step 7: 5.01
... Loss value at step 8: 5.42
... Loss value at step 9: 5.80
... Loss value at step 10: 6.19
... Loss value at step 11: 6.54
... Loss value at step 12: 6.89
... Loss value at step 13: 7.22
... Loss value at step 14: 7.57
... Loss value at step 15: 7.88
... Loss value at step 16: 8.21
... Loss value at step 17: 8.53
... Loss value at step 18: 8.80
... Loss value at step 19: 9.10
显示结果。
您可以使用托管在 Hugging Face Hub 上的训练模型，并在 Hugging Face Spaces 上尝试演示。
display(Image(result_prefix + ".png"))
