wehub-resource-sync
196 lines
7.1 KiB
Python
196 lines
7.1 KiB
Python
# 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)
|