是否可以按批次裁剪/调整图像大小？
我使用Tensorflow数据集API如下：
dataset = dataset.shuffle().repeat().batch(batch_size, drop_remainder=True)
我想，在这个批次中，所有的图像都应该有相同的大小。然而，在不同的批次中，它可能有不同的尺寸。
例如，第一批具有形状(batch_size，3003003(的所有图像。下一批可以具有形状的图像(batch_size，224224，3(。另一个批次可以具有形状的图像(batch_size，400400，3(。
基本上，我想要动态成形的批，但是批中的所有图像都有静态形状。
如果我们这样做：
dataset = dataset.shuffle().repeat().batch(batch_size, drop_remainder=True).map(lambda x, y: map_fn(x, y))
上面的.map((是单独应用于每个批次，还是应用于整个数据集？
如果上面的.map((没有分别应用于每个批次，我们该怎么做？我们可以在dataset.batch((之后定义任何迭代器吗？在每个批次的每个图像上应用tf.image.crop_and_resize((，然后使用dataset.contenate((组合所有转换的批次？
我正在创建如下数据集：
# Dataset creation (read image data from files of COCO dataset)
dataset = tf.data.Dataset.list_files(self._file_pattern, shuffle=False)
dataset = dataset.shard(dataset_num_shards, dataset_shard_index)
dataset = dataset.shuffle(tf.cast(256 / dataset_num_shards, tf.int64))
dataset = dataset.interleave(map_func=tf.data.TFRecordDataset(filename).prefetch(1), cycle_length=32, block_length=1, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.map(tf_example_decoder.TfExampleDecoder().decode, num_parallel_calls=64)
dataset = dataset.shuffle(64).repeat()
# Parse each image for preprocessing
dataset = dataset.map(lambda data, _: _parse_example(data), num_parallel_calls=64)
dataset = dataset.batch(batch_size=batch_size, drop_remainder=True)
# Below code suggested by you to resize images to fixed shape in each batch
def resize_data(images, labels):
tf.print('Original shape -->', tf.shape(images))
SIZE = (300, 300)
return tf.image.resize(images, SIZE), labels
dataset = dataset.map(resize_data)
dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
tf.estimator.Estimator(...).train(
input_fn=dataset,
steps=steps,
hooks=train_hooks)
通常，您可以尝试以下操作：
import tensorflow as tf
import numpy as np
dataset1 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 300, 300, 3)))
dataset2 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 224, 224, 3)))
dataset3 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 400, 400, 3)))
dataset = dataset1.concatenate(dataset2.concatenate(dataset3))
dataset = dataset.shuffle(1).repeat().batch(32, drop_remainder=True)
def resize_data(images):
tf.print('Original shape -->', tf.shape(images))
SIZE = (180, 180)
return tf.image.resize(images, SIZE)
dataset = dataset.map(resize_data)
for images in dataset.take(3):
tf.print('New shape -->', tf.shape(images))
Original shape --> [32 300 300 3]
New shape --> [32 180 180 3]
Original shape --> [32 224 224 3]
New shape --> [32 180 180 3]
Original shape --> [32 400 400 3]
New shape --> [32 180 180 3]
如果需要，也可以使用tf.image.resize_with_crop_or_pad：
def resize_data(images):
tf.print('Original shape -->', tf.shape(images))
SIZE = (180, 180)
return tf.image.resize_with_crop_or_pad(images, SIZE[0], SIZE[1])
dataset = dataset.map(resize_data)
for images in dataset.take(3):
tf.print('New shape -->', tf.shape(images))
请注意，使用repeat()将创建一个无限的数据集。
更新1
如果你想要每个批次的随机大小，可以试试这样的方法：
import tensorflow as tf
import numpy as np
dataset1 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 300, 300, 3)))
dataset2 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 224, 224, 3)))
dataset3 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 400, 400, 3)))
dataset = dataset1.concatenate(dataset2.concatenate(dataset3))
dataset = dataset.batch(32, drop_remainder=True).shuffle(96)
def resize_data(images):
batch_size = tf.shape(images)[0]
images_resized = tf.TensorArray(dtype=tf.float32, size = 0, dynamic_size=True)
SIZE = tf.random.uniform((2,), minval=300, maxval=500, dtype=tf.int32)
for i in range(batch_size):
images_resized = images_resized.write(images_resized.size(), tf.image.resize(images[i], SIZE))
return images_resized.stack()
dataset = dataset.map(resize_data)
for images in dataset:
tf.print('New shape -->', tf.shape(images))
New shape --> [32 392 385 3]
New shape --> [32 468 459 3]
New shape --> [32 466 461 3]
更新2
适用于任何批量大小的非常灵活的选项如下所示：
import tensorflow as tf
import numpy as np
dataset1 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 300, 300, 3)))
dataset2 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 224, 224, 3)))
dataset3 = tf.data.Dataset.from_tensor_slices(np.random.random((32, 400, 400, 3)))
dataset = dataset1.concatenate(dataset2.concatenate(dataset3))
def resize_and_batch(dataset, batch_size):
final_dataset = None
duration = len(dataset)//batch_size
random_sizes = [tf.random.uniform((2,), minval=300, maxval=500, dtype=tf.int32) for _ in range(duration)]
for i, size in zip(range(duration), random_sizes):
idx = i * batch_size
if i == 0:
final_dataset = tf.data.Dataset.from_tensor_slices([tf.image.resize(x, size) for x in dataset.take(batch_size)])
else:
final_dataset = final_dataset.concatenate(tf.data.Dataset.from_tensor_slices([tf.image.resize(x, size) for x in dataset.skip(idx).take(batch_size)]))
return final_dataset
batch_size = 10
ds = resize_and_batch(dataset, batch_size)
ds = ds.batch(batch_size).shuffle(len(ds))
for images in ds:
tf.print('New shape -->', images.shape)
New shape --> TensorShape([10, 399, 348, 3])
New shape --> TensorShape([10, 356, 329, 3])
New shape --> TensorShape([10, 473, 373, 3])
New shape --> TensorShape([10, 489, 489, 3])
New shape --> TensorShape([10, 421, 335, 3])
New shape --> TensorShape([10, 447, 455, 3])
New shape --> TensorShape([10, 355, 382, 3])
New shape --> TensorShape([10, 310, 396, 3])
New shape --> TensorShape([10, 345, 356, 3])