I'd like to train a classifier on one ImageNet dataset (1000 classes each with around 1300 images). For some reason, I need each batch to contain 64 images from a specific class (provided as int or placeholder). How to do it efficiently with the latest TensorFlow?
This is a follow-up question to How to sample batch from only one class at each iteration.
My current thought is to use tf.data.Dataset.filter:
specific_class = 2 # as an example
dataset = tf.data.TFRecordDataset(filenames)
# __parser_fun__ produces datum tuple (x, y)
dataset = dataset.map(__parser_fun__, num_parallel_calls=num_threads)
dataset = dataset.shuffle(20000)
# print(dataset) gives <ShuffleDataset shapes: ((3, 128, 128), (1,)),
# types: (tf.float32, tf.int64)>
dataset = dataset.filter(lambda x, y: tf.equal(y[0], specific_class))
dataset = dataset.batch(64)
dataset = dataset.repeat()
iterator = dataset.make_one_shot_iterator()
x_batch, y_batch = iterator.get_next()
A minor problem with filter is that I need to construct an iterator every time I want to sample from a new class.
Another idea is to use tf.contrib.data.rejection_resample but it seems prohibitive computationally (or is it?).
I wonder if there is other efficient way to sample batches from a particular class?
Conceptually your Dataset is parameterized by a variable (the label to sample). This is totally doable!
Executing eagerly:
import numpy as np
import tensorflow as tf
tf.enable_eager_execution()
data = dict(
x=tf.constant([1., 2., 3., 4.]),
y=tf.constant([1, 2, 1, 2])
)
requested_label = tf.Variable(1)
dataset = (
tf.data.Dataset.from_tensor_slices(data)
.repeat()
.filter(lambda d: tf.equal(d["y"], requested_label)))
it = dataset.make_one_shot_iterator()
for i, datum in enumerate(it):
assert int(datum["y"]) == 1
assert float(datum["x"]) in [1., 3.]
if i > 5:
break
requested_label.assign(2)
for i, datum in enumerate(it):
assert int(datum["y"]) == 2
assert float(datum["x"]) in [2., 4.]
if i > 5:
break
Graph building:
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
data = dict(
x=tf.constant([1., 2., 3., 4.]),
y=tf.constant([1, 2, 1, 2])
)
requested_label = tf.Variable(1)
dataset = (
tf.data.Dataset.from_tensor_slices(data)
.repeat()
.filter(lambda d: tf.equal(d["y"], requested_label)))
it = dataset.make_initializable_iterator()
datum_tensors = it.get_next()
switch_label_op = requested_label.assign(2)
graph.finalize()
with tf.Session() as session:
session.run(requested_label.initializer) # label=1
session.run(it.initializer)
for _ in range(5):
datum = session.run(datum_tensors)
assert int(datum["y"]) == 1
assert float(datum["x"]) in [1., 3.]
session.run(switch_label_op) # label=2
for _ in range(5):
datum = session.run(datum_tensors)
assert int(datum["y"]) == 2
assert float(datum["x"]) in [2., 4.]
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