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# Copyright 2024 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for retrieval."""
from unittest import mock
from big_vision.evaluators.proj.image_text import retrieval
from big_vision.pp import ops_general # pylint: disable=unused-import
from big_vision.pp import ops_image # pylint: disable=unused-import
from big_vision.pp import registry
import chex
import flax.linen as nn
import jax
import jax.numpy as jnp
import tensorflow as tf
import tensorflow_datasets as tfds
def _get_test_texts2labels():
def pp(features):
features["labels"] = tf.strings.to_number(features["texts"])
return features
return pp
def _get_copy_from(**key_map):
def copy_from(d):
d = dict(d)
for k1, k2 in key_map.items():
d[k1] = d[k2]
return d
return copy_from
class _Model(nn.Module):
@nn.compact
def __call__(self, image, texts):
self.param("x", lambda _: 0.)
def z(x):
if x is not None:
batch_size = len(x)
# Note that the returned vector is most similar with other vectors
# generated from the same underlying `x[:]`.
x = jnp.concatenate([100 * jnp.ones([batch_size, 1]), x[:, None]],
axis=1)
return x / jnp.linalg.norm(x, axis=1)[:, None]
return z(image), z(texts), None
def setUpModule():
chex.set_n_cpu_devices(8)
class RetrievalTest(tf.test.TestCase):
def test_prepare_datasets(self):
def generator():
yield {
"image": tf.ones([5, 5, 3], tf.float32),
"captions": {
"text": tf.constant(["11", "12"])
}
}
yield {
"image": tf.ones([4, 4, 3], tf.float32),
"captions": {
"text": tf.constant(["21", "22", "23"])
}
}
ds = tf.data.Dataset.from_generator(
generator,
output_signature={
"image": tf.TensorSpec(shape=[None, None, 3], dtype=tf.float32),
"captions": {
"text": tf.TensorSpec(shape=[None], dtype=tf.string),
},
})
with registry.temporary_ops(test_texts2labels=_get_test_texts2labels):
ds_img, ds_txt = retrieval.prepare_datasets(
ds,
pp_img="resize(2)",
pp_txt="test_texts2labels()",
txt_name=("captions", "text"),
)
it_img = iter(ds_img)
it_txt = iter(ds_txt)
batch = next(it_img)
self.assertAllEqual(batch["id"], 0)
self.assertAllEqual(batch["image"], tf.ones([2, 2, 3]))
batch = next(it_img)
self.assertAllEqual(batch["id"], 1)
self.assertAllEqual(batch["image"], tf.ones([2, 2, 3]))
batch = next(it_txt)
self.assertAllEqual(batch["id"], 0)
self.assertAllEqual(batch["caption_i"], 0)
self.assertAllEqual(batch["labels"], 11.0)
batch = next(it_txt)
self.assertAllEqual(batch["id"], 0)
self.assertAllEqual(batch["caption_i"], 1)
self.assertAllEqual(batch["labels"], 12.0)
batch = next(it_txt)
self.assertAllEqual(batch["id"], 1)
self.assertAllEqual(batch["caption_i"], 0)
self.assertAllEqual(batch["labels"], 21.0)
batch = next(it_txt)
self.assertAllEqual(batch["id"], 1)
self.assertAllEqual(batch["caption_i"], 1)
self.assertAllEqual(batch["labels"], 22.0)
batch = next(it_txt)
self.assertAllEqual(batch["id"], 1)
self.assertAllEqual(batch["caption_i"], 2)
self.assertAllEqual(batch["labels"], 23.0)
def test_evaluate(self):
per_device_batch_size = 2
batch_size = per_device_batch_size * jax.device_count()
num_examples = 1 * batch_size + 1
splits = {
"test":
tfds.core.SplitInfo(
name="test", shard_lengths=[num_examples], num_bytes=0)
}
model = _Model()
params = model.init(jax.random.PRNGKey(0), None, None)["params"]
with tfds.testing.mock_data(num_examples=num_examples):
info_mock = mock.Mock()
info_mock.splits = splits
with mock.patch.object(retrieval, "_get_dataset_info",
lambda _: info_mock):
with registry.temporary_ops(copy_from=_get_copy_from):
evaluator = retrieval.Evaluator(
lambda p, b: model.apply({"params": p},
b.get("image", None),
b.get("labels", None)),
dataset="coco_captions",
batch_size=batch_size,
devices=jax.devices(),
txt_name=("captions", "text"),
pp_img="copy_from(image='id')",
pp_txt="copy_from(labels='id')",
)
results = evaluator.evaluate(params)
# Assert all examples were processed.
self.assertLen(results["images"]["embeddings"], num_examples)
self.assertLen(results["images"]["id"], num_examples)
# Assert no padding was processed (expects exactly one (=first) image.id=0
self.assertEqual((results["images"]["id"] == 0).sum(), 1)
# Expect perfect ITR with above _Model()...
self.assertEqual(results["img2txt"]["Recall@1"], 1.0)
self.assertEqual(results["txt2img"]["Recall@5"], 1.0)
if __name__ == "__main__":
tf.test.main()
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