# Copyright 2022 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 shutil import tempfile import unittest import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor @require_vision class CLIPSegProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() # fmt: off vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l", "w", "r", "t", "low", "er", "lowest", "newer", "wider", "", "<|startoftext|>", "<|endoftext|>"] # fmt: on vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w", "e r", ""] self.special_tokens_map = {"unk_token": ""} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) image_processor_map = { "do_resize": True, "size": 20, "do_center_crop": True, "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], } 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 CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return ViTImageProcessor.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 = CLIPSegProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = CLIPSegProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = CLIPSegProcessor.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, CLIPTokenizer) self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast) 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, ViTImageProcessor) self.assertIsInstance(processor_fast.image_processor, ViTImageProcessor) def test_save_load_pretrained_additional_features(self): processor = CLIPSegProcessor(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 = CLIPSegProcessor.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, CLIPTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, ViTImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(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 = CLIPSegProcessor(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_text(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(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", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_visual_prompt(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() visual_prompt_input = self.prepare_image_inputs() inputs = processor(images=image_input, visual_prompt=visual_prompt_input) self.assertListEqual(list(inputs.keys()), ["pixel_values", "conditional_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 = CLIPSegProcessor(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)