mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 12:50:06 +06:00
222 lines
8.5 KiB
Python
222 lines
8.5 KiB
Python
# 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 shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from transformers import (
|
|
AutoProcessor,
|
|
IdeficsImageProcessor,
|
|
IdeficsProcessor,
|
|
LlamaTokenizerFast,
|
|
PreTrainedTokenizerFast,
|
|
)
|
|
from transformers.testing_utils import require_torch, require_vision
|
|
from transformers.utils import is_torch_available, is_vision_available
|
|
|
|
from ...test_processing_common import ProcessorTesterMixin
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class IdeficsProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
|
processor_class = IdeficsProcessor
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.tmpdirname = tempfile.mkdtemp()
|
|
|
|
image_processor = IdeficsImageProcessor(return_tensors="pt")
|
|
tokenizer = LlamaTokenizerFast.from_pretrained("HuggingFaceM4/tiny-random-idefics")
|
|
|
|
processor = IdeficsProcessor(image_processor, tokenizer)
|
|
|
|
processor.save_pretrained(cls.tmpdirname)
|
|
|
|
cls.input_keys = ["pixel_values", "input_ids", "attention_mask", "image_attention_mask"]
|
|
|
|
def get_tokenizer(self, **kwargs):
|
|
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
|
|
|
|
def get_image_processor(self, **kwargs):
|
|
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
|
|
|
|
def prepare_prompts(self):
|
|
"""This function prepares a list of PIL images"""
|
|
|
|
num_images = 2
|
|
images = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8) for x in range(num_images)]
|
|
images = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in images]
|
|
|
|
# print([type(x) for x in images])
|
|
# die
|
|
|
|
prompts = [
|
|
# text and 1 image
|
|
[
|
|
"User:",
|
|
images[0],
|
|
"Describe this image.\nAssistant:",
|
|
],
|
|
# text and images
|
|
[
|
|
"User:",
|
|
images[0],
|
|
"Describe this image.\nAssistant: An image of two dogs.\n",
|
|
"User:",
|
|
images[1],
|
|
"Describe this image.\nAssistant:",
|
|
],
|
|
# only text
|
|
[
|
|
"User:",
|
|
"Describe this image.\nAssistant: An image of two kittens.\n",
|
|
"User:",
|
|
"Describe this image.\nAssistant:",
|
|
],
|
|
# only images
|
|
[
|
|
images[0],
|
|
images[1],
|
|
],
|
|
]
|
|
|
|
return prompts
|
|
|
|
def test_save_load_pretrained_additional_features(self):
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
processor = IdeficsProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
|
|
processor.save_pretrained(tmpdir)
|
|
|
|
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 = IdeficsProcessor.from_pretrained(
|
|
tmpdir, 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, PreTrainedTokenizerFast)
|
|
|
|
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
|
|
self.assertIsInstance(processor.image_processor, IdeficsImageProcessor)
|
|
|
|
def test_processor(self):
|
|
image_processor = self.get_image_processor()
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
|
|
|
|
prompts = self.prepare_prompts()
|
|
|
|
# test that all prompts succeeded
|
|
input_processor = processor(text=prompts, return_tensors="pt", padding="longest")
|
|
for key in self.input_keys:
|
|
assert torch.is_tensor(input_processor[key])
|
|
|
|
def test_tokenizer_decode(self):
|
|
image_processor = self.get_image_processor()
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor, return_tensors="pt")
|
|
|
|
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_tokenizer_padding(self):
|
|
image_processor = self.get_image_processor()
|
|
tokenizer = self.get_tokenizer(padding_side="right")
|
|
|
|
processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor, return_tensors="pt")
|
|
|
|
predicted_tokens = [
|
|
"<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk>",
|
|
"<s> Describe this image.\nAssistant:<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>",
|
|
]
|
|
predicted_attention_masks = [
|
|
([1] * 10) + ([0] * 9),
|
|
([1] * 10) + ([0] * 10),
|
|
]
|
|
prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
|
|
|
|
max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20, return_tensors="pt")
|
|
longest = processor(text=prompts, padding="longest", truncation=True, max_length=30, return_tensors="pt")
|
|
|
|
decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
|
|
decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
|
|
|
|
self.assertEqual(decoded_max_length, predicted_tokens[1])
|
|
self.assertEqual(decoded_longest, predicted_tokens[0])
|
|
|
|
self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
|
|
self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
|
|
|
|
def test_tokenizer_left_padding(self):
|
|
"""Identical to test_tokenizer_padding, but with padding_side not explicitly set."""
|
|
image_processor = self.get_image_processor()
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
|
|
|
|
predicted_tokens = [
|
|
"<unk><unk><unk><unk><unk><unk><unk><unk><unk><s> Describe this image.\nAssistant:",
|
|
"<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><s> Describe this image.\nAssistant:",
|
|
]
|
|
predicted_attention_masks = [
|
|
([0] * 9) + ([1] * 10),
|
|
([0] * 10) + ([1] * 10),
|
|
]
|
|
prompts = [[prompt] for prompt in self.prepare_prompts()[2]]
|
|
max_length = processor(text=prompts, padding="max_length", truncation=True, max_length=20)
|
|
longest = processor(text=prompts, padding="longest", truncation=True, max_length=30)
|
|
|
|
decoded_max_length = processor.tokenizer.decode(max_length["input_ids"][-1])
|
|
decoded_longest = processor.tokenizer.decode(longest["input_ids"][-1])
|
|
|
|
self.assertEqual(decoded_max_length, predicted_tokens[1])
|
|
self.assertEqual(decoded_longest, predicted_tokens[0])
|
|
|
|
self.assertListEqual(max_length["attention_mask"][-1].tolist(), predicted_attention_masks[1])
|
|
self.assertListEqual(longest["attention_mask"][-1].tolist(), predicted_attention_masks[0])
|
|
|
|
def test_model_input_names(self):
|
|
image_processor = self.get_image_processor()
|
|
tokenizer = self.get_tokenizer()
|
|
|
|
processor = IdeficsProcessor(tokenizer=tokenizer, image_processor=image_processor)
|
|
prompts = self.prepare_prompts()
|
|
|
|
inputs = processor(text=prompts, padding="longest", return_tensors="pt")
|
|
|
|
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
|
|
self.assertSetEqual(set(inputs.keys()), set(self.input_keys))
|