# 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)