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# Copyright 2023 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 shutil | |
import tempfile | |
import unittest | |
import numpy as np | |
import pytest | |
from transformers.testing_utils import require_vision | |
from transformers.utils import is_vision_available | |
if is_vision_available(): | |
from PIL import Image | |
from transformers import AutoProcessor, Blip2Processor, BlipImageProcessor, GPT2Tokenizer, PreTrainedTokenizerFast | |
class Blip2ProcessorTest(unittest.TestCase): | |
def setUp(self): | |
self.tmpdirname = tempfile.mkdtemp() | |
image_processor = BlipImageProcessor() | |
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model") | |
processor = Blip2Processor(image_processor, tokenizer) | |
processor.save_pretrained(self.tmpdirname) | |
def get_tokenizer(self, **kwargs): | |
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer | |
def get_image_processor(self, **kwargs): | |
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor | |
def tearDown(self): | |
shutil.rmtree(self.tmpdirname) | |
def prepare_image_inputs(self): | |
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, | |
or a list of PyTorch tensors if one specifies torchify=True. | |
""" | |
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] | |
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] | |
return image_inputs | |
def test_save_load_pretrained_additional_features(self): | |
processor = Blip2Processor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) | |
processor.save_pretrained(self.tmpdirname) | |
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") | |
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) | |
processor = Blip2Processor.from_pretrained( | |
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 | |
) | |
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) | |
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast) | |
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) | |
self.assertIsInstance(processor.image_processor, BlipImageProcessor) | |
def test_image_processor(self): | |
image_processor = self.get_image_processor() | |
tokenizer = self.get_tokenizer() | |
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor) | |
image_input = self.prepare_image_inputs() | |
input_feat_extract = image_processor(image_input, return_tensors="np") | |
input_processor = processor(images=image_input, return_tensors="np") | |
for key in input_feat_extract.keys(): | |
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) | |
def test_tokenizer(self): | |
image_processor = self.get_image_processor() | |
tokenizer = self.get_tokenizer() | |
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor) | |
input_str = "lower newer" | |
encoded_processor = processor(text=input_str) | |
encoded_tok = tokenizer(input_str, return_token_type_ids=False) | |
for key in encoded_tok.keys(): | |
self.assertListEqual(encoded_tok[key], encoded_processor[key]) | |
def test_processor(self): | |
image_processor = self.get_image_processor() | |
tokenizer = self.get_tokenizer() | |
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor) | |
input_str = "lower newer" | |
image_input = self.prepare_image_inputs() | |
inputs = processor(text=input_str, images=image_input) | |
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"]) | |
# test if it raises when no input is passed | |
with pytest.raises(ValueError): | |
processor() | |
def test_tokenizer_decode(self): | |
image_processor = self.get_image_processor() | |
tokenizer = self.get_tokenizer() | |
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor) | |
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] | |
decoded_processor = processor.batch_decode(predicted_ids) | |
decoded_tok = tokenizer.batch_decode(predicted_ids) | |
self.assertListEqual(decoded_tok, decoded_processor) | |
def test_model_input_names(self): | |
image_processor = self.get_image_processor() | |
tokenizer = self.get_tokenizer() | |
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor) | |
input_str = "lower newer" | |
image_input = self.prepare_image_inputs() | |
inputs = processor(text=input_str, images=image_input) | |
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask'] | |
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"]) | |