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Add function for creating mock images #3803

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Merged
Samoed merged 2 commits into from
Dec 29, 2025
Merged

Add function for creating mock images #3803

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dbbf47d
create function for creating mock tasks
Samoed Dec 28, 2025
995b5bc
add annotations
Samoed Dec 28, 2025
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149 changes: 33 additions & 116 deletions tests/mock_tasks.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,11 @@
"""This implements minimal viable mock tasks for testing the benchmarking framework."""

from __future__ import annotations

from typing import TYPE_CHECKING

import datasets
import numpy as np
from datasets import Dataset, DatasetDict
from sklearn.linear_model import LogisticRegression

Expand All @@ -26,6 +31,9 @@
AbsTaskZeroShotClassification,
)

if TYPE_CHECKING:
from PIL.Image import Image

general_args = {
"description": "a mock task for testing",
"reference": "https://github.com/embeddings-benchmark/mteb",
Expand Down Expand Up @@ -111,6 +119,13 @@ def instruction_retrieval_datasplit() -> RetrievalSplitData:
return base_ds


def create_mock_images(np_rng: np.random.Generator, n: int = 2) -> list[Image]:
from PIL import Image

images = [np_rng.integers(0, 255, (100, 100, 3)) for _ in range(n)]
return [Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images]


class MockClassificationTask(AbsTaskClassification):
classifier = LogisticRegression(n_jobs=1, max_iter=10)

Expand Down Expand Up @@ -1133,17 +1148,8 @@ class MockPairImageClassificationTask(AbsTaskPairClassification):
input2_column_name = "image2"

def load_data(self) -> None:
from PIL import Image

images1 = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images1 = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images1
]

images2 = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images2 = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images2
]
images1 = create_mock_images(self.np_rng)
images2 = create_mock_images(self.np_rng)

labels = [1, 0]

Expand Down Expand Up @@ -2701,12 +2707,7 @@ class MockMultiChoiceTask(AbsTaskRetrieval):
metadata.category = "it2i"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
retrieval_split_data = RetrievalSplitData(
queries=Dataset.from_dict(
{
Expand All @@ -2716,14 +2717,12 @@ def load_data(self) -> None:
"This is a positive sentence",
"This is another positive sentence",
],
"modality": ["image,text" for _ in range(2)],
}
),
corpus=Dataset.from_dict(
{
"id": ["d1", "d2"],
"image": [images[i] for i in range(2)],
"modality": ["image" for _ in range(2)],
}
),
relevant_docs={
Expand Down Expand Up @@ -2885,12 +2884,7 @@ class MockMultilingualMultiChoiceTask(AbsTaskRetrieval):
metadata.category = "it2i"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)

split_data = RetrievalSplitData(
queries=Dataset.from_dict(
Expand All @@ -2901,14 +2895,12 @@ def load_data(self) -> None:
"This is a positive sentence",
"This is another positive sentence",
],
"modality": ["image,text" for _ in range(2)],
}
),
corpus=Dataset.from_dict(
{
"id": ["d1", "d2"],
"image": [images[i] for i in range(2)],
"modality": ["image" for _ in range(2)],
}
),
relevant_docs={
Expand Down Expand Up @@ -2976,19 +2968,13 @@ class MockAny2AnyRetrievalI2TTask(AbsTaskRetrieval):
metadata.category = "i2t"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)

retrieval_split_data = RetrievalSplitData(
queries=Dataset.from_dict(
{
"id": [f"q{i}" for i in range(2)],
"image": [images[i] for i in range(2)],
"modality": ["image" for _ in range(2)],
}
),
corpus=Dataset.from_dict(
Expand All @@ -2998,7 +2984,6 @@ def load_data(self) -> None:
"This is a positive sentence",
"This is another positive sentence",
],
"modality": ["text" for _ in range(2)],
}
),
relevant_docs={
Expand Down Expand Up @@ -3055,12 +3040,7 @@ class MockAny2AnyRetrievalT2ITask(AbsTaskRetrieval):
metadata.category = "t2i"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)

retrieval_split_data = RetrievalSplitData(
queries=Dataset.from_dict(
Expand All @@ -3070,14 +3050,12 @@ def load_data(self) -> None:
"This is a positive sentence",
"This is another positive sentence",
],
"modality": ["text" for _ in range(2)],
}
),
corpus=Dataset.from_dict(
{
"id": ["d1", "d2"],
"image": [images[i] for i in range(2)],
"modality": ["image" for _ in range(2)],
}
),
relevant_docs={
Expand Down Expand Up @@ -3149,12 +3127,7 @@ class MockImageClassificationTask(AbsTaskClassification):
input_column_name = "image"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [1, 0]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -3324,12 +3297,7 @@ class MockMultilingualImageClassificationTask(AbsTaskClassification):
input_column_name = "image"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [1, 0]
data = {
"test": Dataset.from_dict(
Expand Down Expand Up @@ -3390,12 +3358,7 @@ class MockImageClusteringTask(AbsTaskClusteringLegacy):
label_column_name = "label"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [1, 0]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -3448,12 +3411,7 @@ class MockImageClusteringFastTask(AbsTaskClustering):
max_document_to_embed = 2

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [1, 0]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -3538,12 +3496,7 @@ class MockImageMultilabelClassificationTask(AbsTaskMultilabelClassification):
input_column_name = "image"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [["0", "3"], ["1", "2"]]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -3743,12 +3696,7 @@ class MockMultilingualImageMultilabelClassificationTask(
input_column_name = "image"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = [["0", "3"], ["1", "2"]]

data = {
Expand Down Expand Up @@ -3808,12 +3756,7 @@ class MockImageTextPairClassificationTask(AbsTaskImageTextPairClassification):
metadata.category = "i2t"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
texts = ["This is a test sentence", "This is another test sentence"]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -3906,12 +3849,7 @@ class MockMultilingualImageTextPairClassificationTask(
metadata.eval_langs = multilingual_eval_langs

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
texts = ["This is a test sentence", "This is another test sentence"]
data = {
"test": Dataset.from_dict(
Expand Down Expand Up @@ -3971,12 +3909,7 @@ class MockVisualSTSTask(AbsTaskSTS):
metadata.category = "i2i"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
scores = [0.5, 0.5]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -4037,12 +3970,7 @@ class MockZeroShotClassificationTask(AbsTaskZeroShotClassification):
metadata.category = "i2t"

def load_data(self) -> None:
from PIL import Image

images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
images = [
Image.fromarray(image.astype("uint8")).convert("RGBA") for image in images
]
images = create_mock_images(self.np_rng)
labels = ["label1", "label2"]

self.dataset = DatasetDict(
Expand Down Expand Up @@ -4228,19 +4156,8 @@ class MockImageRegressionTask(AbsTaskRegression):
input_column_name = "image"

def load_data(self, **kwargs):
from PIL import Image

train_images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
train_images = [
Image.fromarray(image.astype("uint8")).convert("RGBA")
for image in train_images
]

test_images = [self.np_rng.integers(0, 255, (100, 100, 3)) for _ in range(2)]
test_images = [
Image.fromarray(image.astype("uint8")).convert("RGBA")
for image in test_images
]
train_images = create_mock_images(self.np_rng)
test_images = create_mock_images(self.np_rng)

train_values = [1.0, 0.0]
test_values = [1.0, 0.0]
Expand Down