wehub-resource-sync
192 lines
7.6 KiB
Python
192 lines
7.6 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import pytest
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from PIL import Image
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from paddlenlp.transformers import CLIPSegProcessor, CLIPTokenizer, ViTImageProcessor
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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IMAGE_PROCESSOR_NAME = "preprocessor_config.json"
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class CLIPSegProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# fmt: off
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vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
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# fmt: on
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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image_processor_map = {
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"do_resize": True,
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"size": 20,
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"do_center_crop": True,
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"crop_size": 18,
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"do_normalize": True,
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"image_mean": [0.48145466, 0.4578275, 0.40821073],
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"image_std": [0.26862954, 0.26130258, 0.27577711],
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}
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self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
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with open(self.image_processor_file, "w", encoding="utf-8") as fp:
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json.dump(image_processor_map, fp)
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def get_tokenizer(self, **kwargs):
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return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_image_processor(self, **kwargs):
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return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True."""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def test_save_load_pretrained_default(self):
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tokenizer_slow = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor_slow = CLIPSegProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
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processor_slow.save_pretrained(self.tmpdirname)
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processor_slow = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=False)
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self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
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self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
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# self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
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self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
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self.assertIsInstance(processor_slow.image_processor, ViTImageProcessor)
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def test_save_load_pretrained_additional_features(self):
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processor = CLIPSegProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = CLIPSegProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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# self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, ViTImageProcessor)
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def test_image_processor(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = image_processor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor)
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input_str = "lower newer"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_processor_text(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input)
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self.assertListEqual(list(inputs.keys()), ["input_ids", "pixel_values"])
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_processor_visual_prompt(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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visual_prompt_input = self.prepare_image_inputs()
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inputs = processor(images=image_input, visual_prompt=visual_prompt_input)
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self.assertListEqual(list(inputs.keys()), ["pixel_values", "conditional_pixel_values"])
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_tokenizer_decode(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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