# Copyright 2022 Meta Platforms authors and 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 json import os import random import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import FlavaImageProcessor, FlavaProcessor from transformers.models.flava.image_processing_flava import ( FLAVA_CODEBOOK_MEAN, FLAVA_CODEBOOK_STD, FLAVA_IMAGE_MEAN, FLAVA_IMAGE_STD, ) @require_vision class FlavaProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() # fmt: off vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: on self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write("".join([x + "\n" for x in vocab_tokens])) image_processor_map = { "image_mean": FLAVA_IMAGE_MEAN, "image_std": FLAVA_IMAGE_STD, "do_normalize": True, "do_resize": True, "size": 224, "do_center_crop": True, "crop_size": 224, "input_size_patches": 14, "total_mask_patches": 75, "mask_group_max_patches": None, "mask_group_min_patches": 16, "mask_group_min_aspect_ratio": 0.3, "mask_group_max_aspect_ratio": None, "codebook_do_resize": True, "codebook_size": 112, "codebook_do_center_crop": True, "codebook_crop_size": 112, "codebook_do_map_pixels": True, "codebook_do_normalize": True, "codebook_image_mean": FLAVA_CODEBOOK_MEAN, "codebook_image_std": FLAVA_CODEBOOK_STD, } self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return FlavaImageProcessor.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() tokenizer_fast = self.get_rust_tokenizer() image_processor = self.get_image_processor() processor_slow = FlavaProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = FlavaProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = FlavaProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = FlavaProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, BertTokenizer) self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, FlavaImageProcessor) self.assertIsInstance(processor_fast.image_processor, FlavaImageProcessor) def test_save_load_pretrained_additional_features(self): processor = FlavaProcessor(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 = FlavaProcessor.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, BertTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, FlavaImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = FlavaProcessor(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) # With rest of the args random.seed(1234) input_feat_extract = image_processor( image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np" ) random.seed(1234) input_processor = processor( images=image_input, return_image_mask=True, return_codebook_pixels=True, 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 = FlavaProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" 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 = FlavaProcessor(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()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]) # add extra args inputs = processor(text=input_str, images=image_input, return_codebook_pixels=True, return_image_mask=True) self.assertListEqual( list(inputs.keys()), [ "input_ids", "token_type_ids", "attention_mask", "pixel_values", "codebook_pixel_values", "bool_masked_pos", ], ) # 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 = FlavaProcessor(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 = FlavaProcessor(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()), processor.model_input_names)