作者: Sayak Paul
创建日期 2021/04/30
最后修改 2023/12/18
描述: 如何针对给定分辨率最佳地学习图像表示。
人们普遍认为,如果我们将视觉模型限制为像人类一样感知事物,其性能可以得到提升。例如,在这项工作中,Geirhos 等人表明,在 ImageNet-1k 数据集上预训练的视觉模型偏向于纹理,而人类主要使用形状描述符来形成共同感知。但是,这种观点是否总是适用,特别是在提升视觉模型性能方面?
事实证明并非总是如此。在训练视觉模型时,通常会将图像大小调整到较低维度(例如 (224 x 224), (299 x 299) 等),以便进行小批量学习并适应计算限制。我们通常使用诸如双线性插值之类的图像缩放方法进行此步骤,并且缩放后的图像对人眼而言不会损失太多感知特性。在《学习为计算机视觉任务调整图像大小》一文中,Talebi 等人表明,如果我们尝试优化图像对视觉模型而非人眼的感知质量,它们的性能可以进一步提升。他们研究了以下问题:
对于给定的图像分辨率和模型,如何最佳地调整给定图像的大小?
如论文所示,这个想法有助于持续提升常见视觉模型(在 ImageNet-1k 上预训练)的性能,例如 DenseNet-121、ResNet-50、MobileNetV2 和 EfficientNets。在本示例中,我们将实现论文中提出的可学习图像缩放模块,并使用 DenseNet-121 架构在 Cats and Dogs 数据集上进行演示。
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
import keras
from keras import ops
from keras import layers
import tensorflow as tf
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
import matplotlib.pyplot as plt
import numpy as np
为了方便小批量学习,我们需要在给定批次中为图像设置固定的形状。这就是为什么需要进行初始缩放。我们首先将所有图像缩放为 (300 x 300) 形状,然后学习它们在 (150 x 150) 分辨率下的最佳表示。
INP_SIZE = (300, 300)
TARGET_SIZE = (150, 150)
INTERPOLATION = "bilinear"
AUTO = tf.data.AUTOTUNE
BATCH_SIZE = 64
EPOCHS = 5
在本示例中,我们将使用双线性插值,但可学习图像缩放模块不依赖于任何特定的插值方法。我们也可以使用其他方法,例如双三次插值。
在本示例中,我们将只使用总训练数据集的 40%。
train_ds, validation_ds = tfds.load(
"cats_vs_dogs",
# Reserve 10% for validation
split=["train[:40%]", "train[40%:50%]"],
as_supervised=True,
)
def preprocess_dataset(image, label):
image = ops.image.resize(image, (INP_SIZE[0], INP_SIZE[1]))
label = ops.one_hot(label, num_classes=2)
return (image, label)
train_ds = (
train_ds.shuffle(BATCH_SIZE * 100)
.map(preprocess_dataset, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
validation_ds = (
validation_ds.map(preprocess_dataset, num_parallel_calls=AUTO)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
下图(来源:《学习为计算机视觉任务调整图像大小》)展示了可学习缩放模块的结构:
def conv_block(x, filters, kernel_size, strides, activation=layers.LeakyReLU(0.2)):
x = layers.Conv2D(filters, kernel_size, strides, padding="same", use_bias=False)(x)
x = layers.BatchNormalization()(x)
if activation:
x = activation(x)
return x
def res_block(x):
inputs = x
x = conv_block(x, 16, 3, 1)
x = conv_block(x, 16, 3, 1, activation=None)
return layers.Add()([inputs, x])
# Note: user can change num_res_blocks to >1 also if needed
def get_learnable_resizer(filters=16, num_res_blocks=1, interpolation=INTERPOLATION):
inputs = layers.Input(shape=[None, None, 3])
# First, perform naive resizing.
naive_resize = layers.Resizing(*TARGET_SIZE, interpolation=interpolation)(inputs)
# First convolution block without batch normalization.
x = layers.Conv2D(filters=filters, kernel_size=7, strides=1, padding="same")(inputs)
x = layers.LeakyReLU(0.2)(x)
# Second convolution block with batch normalization.
x = layers.Conv2D(filters=filters, kernel_size=1, strides=1, padding="same")(x)
x = layers.LeakyReLU(0.2)(x)
x = layers.BatchNormalization()(x)
# Intermediate resizing as a bottleneck.
bottleneck = layers.Resizing(*TARGET_SIZE, interpolation=interpolation)(x)
# Residual passes.
# First res_block will get bottleneck output as input
x = res_block(bottleneck)
# Remaining res_blocks will get previous res_block output as input
for _ in range(num_res_blocks - 1):
x = res_block(x)
# Projection.
x = layers.Conv2D(
filters=filters, kernel_size=3, strides=1, padding="same", use_bias=False
)(x)
x = layers.BatchNormalization()(x)
# Skip connection.
x = layers.Add()([bottleneck, x])
# Final resized image.
x = layers.Conv2D(filters=3, kernel_size=7, strides=1, padding="same")(x)
final_resize = layers.Add()([naive_resize, x])
return keras.Model(inputs, final_resize, name="learnable_resizer")
learnable_resizer = get_learnable_resizer()
在这里,我们可视化图像经过缩放器随机权重处理后的样子。
sample_images, _ = next(iter(train_ds))
plt.figure(figsize=(16, 10))
for i, image in enumerate(sample_images[:6]):
image = image / 255
ax = plt.subplot(3, 4, 2 * i + 1)
plt.title("Input Image")
plt.imshow(image.numpy().squeeze())
plt.axis("off")
ax = plt.subplot(3, 4, 2 * i + 2)
resized_image = learnable_resizer(image[None, ...])
plt.title("Resized Image")
plt.imshow(resized_image.numpy().squeeze())
plt.axis("off")
Corrupt JPEG data: 65 extraneous bytes before marker 0xd9
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
def get_model():
backbone = keras.applications.DenseNet121(
weights=None,
include_top=True,
classes=2,
input_shape=((TARGET_SIZE[0], TARGET_SIZE[1], 3)),
)
backbone.trainable = True
inputs = layers.Input((INP_SIZE[0], INP_SIZE[1], 3))
x = layers.Rescaling(scale=1.0 / 255)(inputs)
x = learnable_resizer(x)
outputs = backbone(x)
return keras.Model(inputs, outputs)
可学习图像缩放模块的结构可以灵活地与不同的视觉模型集成。
model = get_model()
model.compile(
loss=keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
optimizer="sgd",
metrics=["accuracy"],
)
model.fit(train_ds, validation_data=validation_ds, epochs=EPOCHS)
Epoch 1/5
146/146 ━━━━━━━━━━━━━━━━━━━━ 1790s 12s/step - accuracy: 0.5783 - loss: 0.6877 - val_accuracy: 0.4953 - val_loss: 0.7173
Epoch 2/5
146/146 ━━━━━━━━━━━━━━━━━━━━ 1738s 12s/step - accuracy: 0.6516 - loss: 0.6436 - val_accuracy: 0.6148 - val_loss: 0.6605
Epoch 3/5
146/146 ━━━━━━━━━━━━━━━━━━━━ 1730s 12s/step - accuracy: 0.6881 - loss: 0.6185 - val_accuracy: 0.5529 - val_loss: 0.8655
Epoch 4/5
146/146 ━━━━━━━━━━━━━━━━━━━━ 1725s 12s/step - accuracy: 0.6985 - loss: 0.5980 - val_accuracy: 0.6862 - val_loss: 0.6070
Epoch 5/5
146/146 ━━━━━━━━━━━━━━━━━━━━ 1722s 12s/step - accuracy: 0.7499 - loss: 0.5595 - val_accuracy: 0.6737 - val_loss: 0.6321
<keras.src.callbacks.history.History at 0x7f126c5440a0>
plt.figure(figsize=(16, 10))
for i, image in enumerate(sample_images[:6]):
image = image / 255
ax = plt.subplot(3, 4, 2 * i + 1)
plt.title("Input Image")
plt.imshow(image.numpy().squeeze())
plt.axis("off")
ax = plt.subplot(3, 4, 2 * i + 2)
resized_image = learnable_resizer(image[None, ...])
plt.title("Resized Image")
plt.imshow(resized_image.numpy().squeeze() / 10)
plt.axis("off")
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
图表显示,经过训练后,图像的视觉效果得到了改善。下表展示了使用缩放模块与使用双线性插值相比的优势:
模型 | 参数数量(百万) | Top-1 准确率 |
---|---|---|
使用可学习缩放器 | 7.051717 | 67.67% |
不使用可学习缩放器 | 7.039554 | 60.19% |
更多详细信息,请查看此仓库。请注意,与本示例不同的是,上面报告的模型在 Cats and Dogs 数据集 90% 的训练集上训练了 10 个 epoch。此外,请注意,由于缩放模块导致的参数数量增加非常微小。为了确保性能提升不是由于随机性造成的,模型是使用相同的初始随机权重进行训练的。
现在,一个值得问的问题是——与基线相比,准确率的提高难道不是仅仅因为给模型添加了更多层(毕竟缩放器是一个迷你网络)的结果吗?
为了表明事实并非如此,作者们进行了以下实验:
作者们认为,使用第二种选择更好,因为它有助于模型学习如何针对给定分辨率更好地调整表示。由于结果纯粹是经验性的,进行更多实验,例如分析跨通道交互,会更好。值得注意的是,Squeeze and Excitation (SE) 块、Global Context (GC) 块等元素也会为现有网络增加少量参数,但已知它们有助于网络以系统化的方式处理信息,从而提高整体性能。