Image Feature Extraction pipeline (#28216)
* Draft pipeline * Fixup * Fix docstrings * Update doctest * Update pipeline_model_mapping * Update docstring * Update tests * Update src/transformers/pipelines/image_feature_extraction.py Co-authored-by: Omar Sanseviero <osanseviero@gmail.com> * Fix docstrings - review comments * Remove pipeline mapping for composite vision models * Add to pipeline tests * Remove for flava (multimodal) * safe pil import * Add requirements for pipeline run * Account for super slow efficientnet * Review comments * Fix tests * Swap order of kwargs * Use build_pipeline_init_args * Add back FE pipeline for Vilt * Include image_processor_kwargs in docstring * Mark test as flaky * Update TODO * Update tests/pipelines/test_pipelines_image_feature_extraction.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Add license header --------- Co-authored-by: Omar Sanseviero <osanseviero@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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amyeroberts
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tests/pipelines/test_pipelines_image_feature_extraction.py
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tests/pipelines/test_pipelines_image_feature_extraction.py
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import pytest
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from transformers import (
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MODEL_MAPPING,
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TF_MODEL_MAPPING,
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TOKENIZER_MAPPING,
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ImageFeatureExtractionPipeline,
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is_tf_available,
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is_torch_available,
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is_vision_available,
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pipeline,
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)
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from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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if is_vision_available():
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from PIL import Image
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@is_pipeline_test
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class ImageFeatureExtractionPipelineTests(unittest.TestCase):
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model_mapping = MODEL_MAPPING
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tf_model_mapping = TF_MODEL_MAPPING
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@require_torch
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def test_small_model_pt(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
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)
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img = prepare_img()
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outputs = feature_extractor(img)
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self.assertEqual(
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nested_simplify(outputs[0][0]),
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[-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
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@require_tf
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def test_small_model_tf(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
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)
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img = prepare_img()
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outputs = feature_extractor(img)
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self.assertEqual(
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nested_simplify(outputs[0][0]),
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[-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
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@require_torch
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def test_image_processing_small_model_pt(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
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)
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# test with image processor parameters
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image_processor_kwargs = {"size": {"height": 300, "width": 300}}
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img = prepare_img()
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with pytest.raises(ValueError):
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# Image doesn't match model input size
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feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
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image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
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img = prepare_img()
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outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
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self.assertEqual(np.squeeze(outputs).shape, (226, 32))
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@require_tf
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def test_image_processing_small_model_tf(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
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)
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# test with image processor parameters
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image_processor_kwargs = {"size": {"height": 300, "width": 300}}
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img = prepare_img()
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with pytest.raises(ValueError):
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# Image doesn't match model input size
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feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
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image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
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img = prepare_img()
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outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
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self.assertEqual(np.squeeze(outputs).shape, (226, 32))
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@require_torch
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def test_return_tensors_pt(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
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)
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img = prepare_img()
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outputs = feature_extractor(img, return_tensors=True)
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self.assertTrue(torch.is_tensor(outputs))
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@require_tf
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def test_return_tensors_tf(self):
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feature_extractor = pipeline(
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task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
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)
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img = prepare_img()
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outputs = feature_extractor(img, return_tensors=True)
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self.assertTrue(tf.is_tensor(outputs))
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def get_test_pipeline(self, model, tokenizer, processor):
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if processor is None:
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self.skipTest("No image processor")
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elif type(model.config) in TOKENIZER_MAPPING:
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self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
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elif model.config.is_encoder_decoder:
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self.skipTest(
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"""encoder_decoder models are trickier for this pipeline.
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Do we want encoder + decoder inputs to get some featues?
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Do we want encoder only features ?
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For now ignore those.
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"""
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)
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feature_extractor = ImageFeatureExtractionPipeline(model=model, image_processor=processor)
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img = prepare_img()
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return feature_extractor, [img, img]
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def run_pipeline_test(self, feature_extractor, examples):
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imgs = examples
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outputs = feature_extractor(imgs[0])
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self.assertEqual(len(outputs), 1)
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outputs = feature_extractor(imgs)
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self.assertEqual(len(outputs), 2)
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