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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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.
import unittest
import numpy as np
import pytest
from transformers import (
MODEL_MAPPING,
TF_MODEL_MAPPING,
TOKENIZER_MAPPING,
ImageFeatureExtractionPipeline,
is_tf_available,
is_torch_available,
is_vision_available,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_vision_available():
from PIL import Image
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@is_pipeline_test
class ImageFeatureExtractionPipelineTests(unittest.TestCase):
model_mapping = MODEL_MAPPING
tf_model_mapping = TF_MODEL_MAPPING
@require_torch
def test_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_torch
def test_small_model_w_pooler_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img, pool=True)
self.assertEqual(
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_tf
def test_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_tf
def test_small_model_w_pooler_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, pool=True)
self.assertEqual(
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_torch
def test_image_processing_small_model_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
# Test pooling option
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_tf
def test_image_processing_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
# Test pooling option
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_torch
def test_return_tensors_pt(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(torch.is_tensor(outputs))
@require_tf
def test_return_tensors_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(tf.is_tensor(outputs))
def get_test_pipeline(
self,
model,
tokenizer=None,
image_processor=None,
feature_extractor=None,
processor=None,
torch_dtype="float32",
):
if image_processor is None:
self.skipTest(reason="No image processor")
elif type(model.config) in TOKENIZER_MAPPING:
self.skipTest(
reason="This is a bimodal model, we need to find a more consistent way to switch on those models."
)
elif model.config.is_encoder_decoder:
self.skipTest(
"""encoder_decoder models are trickier for this pipeline.
Do we want encoder + decoder inputs to get some features?
Do we want encoder only features ?
For now ignore those.
"""
)
feature_extractor_pipeline = ImageFeatureExtractionPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_dtype,
)
img = prepare_img()
return feature_extractor_pipeline, [img, img]
def run_pipeline_test(self, feature_extractor, examples):
imgs = examples
outputs = feature_extractor(imgs[0])
self.assertEqual(len(outputs), 1)
outputs = feature_extractor(imgs)
self.assertEqual(len(outputs), 2)
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