# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2022 The HuggingFace Inc. 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 json import os import shutil import tempfile import unittest import numpy as np import pytest from PIL import Image from paddlenlp.transformers import ( ErnieViLImageProcessor, ErnieViLProcessor, ErnieViLTokenizer, ) class ErnieViLProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "的", "价", "格", "是", "15", "便", "alex", "##andra", "，", "。", "-", "t", "shirt", ] self.vocab_file = os.path.join(self.tmpdirname, ErnieViLTokenizer.resource_files_names["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) image_processor_map = { "do_resize": True, "size": 224, "do_center_crop": True, "crop_size": {"height": 18, "width": 18}, "do_normalize": True, "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225], "do_convert_rgb": True, } self.image_processor_file = os.path.join(self.tmpdirname, "preprocessor_config.json") with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return ErnieViLTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return ErnieViLImageProcessor.from_pretrained(self.tmpdirname, **kwargs) 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_default(self): tokenizer_slow = self.get_tokenizer() image_processor = self.get_image_processor() processor_slow = ErnieViLProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = ErnieViLProcessor.from_pretrained(self.tmpdirname, use_fast=False) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, ErnieViLTokenizer) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, ErnieViLImageProcessor) def test_save_load_pretrained_additional_features(self): processor = ErnieViLProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(cls_token="(CLS)", sep_token="(SEP)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False) processor = ErnieViLProcessor.from_pretrained( self.tmpdirname, cls_token="(CLS)", sep_token="(SEP)", do_normalize=False ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, ErnieViLImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ErnieViLProcessor(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 = ErnieViLProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra，T-shirt的价格是15便士。" encoded_processor = processor(text=input_str) encoded_tok = tokenizer(input_str) 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 = ErnieViLProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra，T-shirt的价格是15便士。" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "pixel_values"]) # 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 = ErnieViLProcessor(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 = ErnieViLProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra，T-shirt的价格是15便士。" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)