236 lines
11 KiB
Python
236 lines
11 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 HuggingFace Inc.
|
|
#
|
|
# 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 unittest
|
|
from io import BytesIO
|
|
|
|
import requests
|
|
|
|
from transformers import Idefics2Processor
|
|
from transformers.testing_utils import require_torch, require_vision
|
|
from transformers.utils import is_vision_available
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class Idefics2ProcessorTest(unittest.TestCase):
|
|
def setUp(self):
|
|
self.processor = Idefics2Processor.from_pretrained("HuggingFaceM4/idefics2-8b", image_seq_len=2)
|
|
self.image1 = Image.open(
|
|
BytesIO(
|
|
requests.get(
|
|
"https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
|
|
).content
|
|
)
|
|
)
|
|
self.image2 = Image.open(
|
|
BytesIO(requests.get("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg").content)
|
|
)
|
|
self.image3 = Image.open(
|
|
BytesIO(
|
|
requests.get(
|
|
"https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
|
|
).content
|
|
)
|
|
)
|
|
self.bos_token = self.processor.tokenizer.bos_token
|
|
self.image_token = self.processor.image_token.content
|
|
self.fake_image_token = self.processor.fake_image_token.content
|
|
|
|
self.bos_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.bos_token)
|
|
self.image_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.image_token)
|
|
self.fake_image_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.fake_image_token)
|
|
self.image_seq_len = self.processor.image_seq_len
|
|
|
|
def test_process_interleaved_images_prompts_no_image_splitting(self):
|
|
old_image_splitting = self.processor.image_processor.do_image_splitting
|
|
|
|
self.processor.image_processor.do_image_splitting = False
|
|
|
|
# Test that a single image is processed correctly
|
|
inputs = self.processor(images=self.image1)
|
|
self.assertEqual(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
|
|
self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
|
|
# fmt: on
|
|
|
|
# Test a single sample with image and text
|
|
image_str = "<image>"
|
|
text_str = "In this image, we see"
|
|
text = image_str + text_str
|
|
inputs = self.processor(text=text, images=self.image1)
|
|
|
|
# fmt: off
|
|
tokenized_sentence = self.processor.tokenizer(text_str, add_special_tokens=False)
|
|
expected_input_ids = [[self.bos_token_id] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
|
|
self.assertEqual(inputs["input_ids"], expected_input_ids)
|
|
self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
|
|
self.assertEqual(inputs["pixel_values"].shape, (1, 1, 3, 653, 980))
|
|
self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 1, 653, 980))
|
|
# fmt: on
|
|
|
|
# Test that batch is correctly processed
|
|
image_str = "<image>"
|
|
text_str_1 = "In this image, we see"
|
|
text_str_2 = "bla, bla"
|
|
|
|
text = [
|
|
image_str + text_str_1,
|
|
text_str_2 + image_str + image_str,
|
|
]
|
|
images = [[self.image1], [self.image2, self.image3]]
|
|
|
|
inputs = self.processor(text=text, images=images, padding=True)
|
|
|
|
# fmt: off
|
|
tokenized_sentence_1 = self.processor.tokenizer(text_str_1, add_special_tokens=False)
|
|
tokenized_sentence_2 = self.processor.tokenizer(text_str_2, add_special_tokens=False)
|
|
expected_input_ids_1 = [self.bos_token_id] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
|
|
expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len + [self.fake_image_token_id]
|
|
# Pad the first input to match the second input
|
|
pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
|
|
padded_expected_input_ids_1 = [0] * pad_len + expected_input_ids_1
|
|
|
|
self.assertEqual(
|
|
inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
|
|
)
|
|
self.assertEqual(
|
|
inputs["attention_mask"],
|
|
[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
|
|
)
|
|
self.assertEqual(inputs['pixel_values'].shape, (2, 2, 3, 767, 980))
|
|
self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 2, 767, 980))
|
|
# fmt: on
|
|
|
|
self.processor.image_processor.do_image_splitting = old_image_splitting
|
|
|
|
def test_process_interleaved_images_prompts_image_splitting(self):
|
|
old_image_splitting = self.processor.image_processor.do_image_splitting
|
|
|
|
self.processor.image_processor.do_image_splitting = True
|
|
|
|
# Test that a single image is processed correctly
|
|
inputs = self.processor(images=self.image1)
|
|
self.assertEqual(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
|
|
self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
|
|
# fmt: on
|
|
|
|
# Test a single sample with image and text
|
|
image_str = "<image>"
|
|
text_str = "In this image, we see"
|
|
text = image_str + text_str
|
|
inputs = self.processor(text=text, images=self.image1)
|
|
|
|
# fmt: off
|
|
tokenized_sentence = self.processor.tokenizer(text_str, add_special_tokens=False)
|
|
expected_input_ids = [[self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id] + tokenized_sentence["input_ids"]]
|
|
self.assertEqual(inputs["input_ids"], expected_input_ids)
|
|
self.assertEqual(inputs["attention_mask"], [[1] * len(expected_input_ids[0])])
|
|
self.assertEqual(inputs["pixel_values"].shape, (1, 5, 3, 653, 980))
|
|
self.assertEqual(inputs["pixel_attention_mask"].shape, (1, 5, 653, 980))
|
|
# fmt: on
|
|
|
|
# Test that batch is correctly processed
|
|
image_str = "<image>"
|
|
text_str_1 = "In this image, we see"
|
|
text_str_2 = "bla, bla"
|
|
|
|
text = [
|
|
image_str + text_str_1,
|
|
text_str_2 + image_str + image_str,
|
|
]
|
|
images = [[self.image1], [self.image2, self.image3]]
|
|
|
|
inputs = self.processor(text=text, images=images, padding=True)
|
|
|
|
# fmt: off
|
|
tokenized_sentence_1 = self.processor.tokenizer(text_str_1, add_special_tokens=False)
|
|
tokenized_sentence_2 = self.processor.tokenizer(text_str_2, add_special_tokens=False)
|
|
expected_input_ids_1 = [self.bos_token_id] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id] + tokenized_sentence_1["input_ids"]
|
|
expected_input_ids_2 = [self.bos_token_id] + tokenized_sentence_2["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * 5 + [self.fake_image_token_id]
|
|
# Pad the first input to match the second input
|
|
pad_len = len(expected_input_ids_2) - len(expected_input_ids_1)
|
|
padded_expected_input_ids_1 = [0] * pad_len + expected_input_ids_1
|
|
|
|
self.assertEqual(
|
|
inputs["input_ids"], [padded_expected_input_ids_1, expected_input_ids_2]
|
|
)
|
|
self.assertEqual(
|
|
inputs["attention_mask"],
|
|
[[0] * pad_len + [1] * len(expected_input_ids_1), [1] * len(expected_input_ids_2)]
|
|
)
|
|
self.assertEqual(inputs['pixel_values'].shape, (2, 10, 3, 767, 980))
|
|
self.assertEqual(inputs['pixel_attention_mask'].shape, (2, 10, 767, 980))
|
|
# fmt: on
|
|
|
|
self.processor.image_processor.do_image_splitting = old_image_splitting
|
|
|
|
def test_add_special_tokens_processor(self):
|
|
image_str = "<image>"
|
|
text_str = "In this image, we see"
|
|
text = text_str + image_str
|
|
|
|
n_image_repeat = 5 if self.processor.image_processor.do_image_splitting else 1
|
|
|
|
# fmt: off
|
|
inputs = self.processor(text=text, images=self.image1, add_special_tokens=False)
|
|
tokenized_sentence = self.processor.tokenizer(text_str, add_special_tokens=False)
|
|
expected_input_ids = [tokenized_sentence["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
|
|
self.assertEqual(inputs["input_ids"], expected_input_ids)
|
|
|
|
inputs = self.processor(text=text, images=self.image1)
|
|
expected_input_ids = [[self.bos_token_id] + tokenized_sentence["input_ids"] + ([self.fake_image_token_id] + [self.image_token_id] * self.image_seq_len) * n_image_repeat + [self.fake_image_token_id]]
|
|
self.assertEqual(inputs["input_ids"], expected_input_ids)
|
|
# fmt: on
|
|
|
|
def test_apply_chat_template(self):
|
|
# Message contains content which a mix of lists with images and image urls and string
|
|
messages = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "What do these images show?"},
|
|
{"type": "image"},
|
|
{"type": "image"},
|
|
"What do these images show?",
|
|
],
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": [
|
|
{
|
|
"type": "text",
|
|
"text": "The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.",
|
|
}
|
|
],
|
|
},
|
|
{"role": "user", "content": [{"type": "text", "text": "And who is that?"}]},
|
|
]
|
|
|
|
processor = self.processor
|
|
# Make short sequence length to test that the fake tokens are added correctly
|
|
rendered = processor.apply_chat_template(messages, add_generation_prompt=True)
|
|
|
|
expected_rendered = (
|
|
"User: What do these images show?<image><image><end_of_utterance>\n"
|
|
"Assistant: The first image shows the statue of Liberty in New York. The second image picture depicts Idefix, the dog of Obelix in Asterix and Obelix.<end_of_utterance>\n"
|
|
"User: And who is that?<end_of_utterance>\n"
|
|
"Assistant:"
|
|
)
|
|
self.assertEqual(rendered, expected_rendered)
|