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Support CLIPTokenizerFast for CLIPProcessor (#15913)

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* Fix to support fast tokenizer with `CLIPProcessor`

* Update CLIPProcessor test for fast tokenizer

* Fix Docstring Style

* Rename into meaningful Variable name in test code
This commit is contained in:
ParkSangJun
2022-03-04 19:57:09 +09:00
committed by GitHub
parent b71474895d
commit 6b104c5bb0
2 changed files with 32 additions and 20 deletions
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18
src/transformers/models/clip/processing_clip.py
View File

@@ -23,17 +23,17 @@ class CLIPProcessor(ProcessorMixin):
r""" r"""
Constructs a CLIP processor which wraps a CLIP feature extractor and a CLIP tokenizer into a single processor. Constructs a CLIP processor which wraps a CLIP feature extractor and a CLIP tokenizer into a single processor.
[`CLIPProcessor`] offers all the functionalities of [`CLIPFeatureExtractor`] and [`CLIPTokenizer`]. See the [`CLIPProcessor`] offers all the functionalities of [`CLIPFeatureExtractor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information. [`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information.
Args: Args:
feature_extractor ([`CLIPFeatureExtractor`]): feature_extractor ([`CLIPFeatureExtractor`]):
The feature extractor is a required input. The feature extractor is a required input.
tokenizer ([`CLIPTokenizer`]): tokenizer ([`CLIPTokenizerFast`]):
The tokenizer is a required input. The tokenizer is a required input.
""" """
feature_extractor_class = "CLIPFeatureExtractor" feature_extractor_class = "CLIPFeatureExtractor"
tokenizer_class = "CLIPTokenizer" tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__(self, feature_extractor, tokenizer): def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer) super().__init__(feature_extractor, tokenizer)
@@ -42,8 +42,8 @@ class CLIPProcessor(ProcessorMixin):
def __call__(self, text=None, images=None, return_tensors=None, **kwargs): def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
""" """
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizer's [`~CLIPTokenizer.__call__`] if `text` is not `None` to encode the and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
doctsring of the above two methods for more information. doctsring of the above two methods for more information.
@@ -94,14 +94,14 @@ class CLIPProcessor(ProcessorMixin):
def batch_decode(self, *args, **kwargs): def batch_decode(self, *args, **kwargs):
""" """
This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
to the docstring of this method for more information. refer to the docstring of this method for more information.
""" """
return self.tokenizer.batch_decode(*args, **kwargs) return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs): def decode(self, *args, **kwargs):
""" """
This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
docstring of this method for more information. the docstring of this method for more information.
""" """
return self.tokenizer.decode(*args, **kwargs) return self.tokenizer.decode(*args, **kwargs)

34
tests/clip/test_processor_clip.py
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@@ -21,7 +21,7 @@ import unittest
import numpy as np import numpy as np
import pytest import pytest
from transformers import CLIPTokenizer from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_vision_available from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_vision_available
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision from transformers.testing_utils import require_vision
@@ -39,7 +39,7 @@ class CLIPProcessorTest(unittest.TestCase):
self.tmpdirname = tempfile.mkdtemp() self.tmpdirname = tempfile.mkdtemp()
# fmt: off # fmt: off
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|endoftext|>"] 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|>"]
# fmt: on # fmt: on
vocab_tokens = dict(zip(vocab, range(len(vocab)))) vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""] merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
@@ -68,6 +68,9 @@ class CLIPProcessorTest(unittest.TestCase):
def get_tokenizer(self, **kwargs): def get_tokenizer(self, **kwargs):
return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs) return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
def get_feature_extractor(self, **kwargs): def get_feature_extractor(self, **kwargs):
return CLIPFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) return CLIPFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
@@ -86,19 +89,28 @@ class CLIPProcessorTest(unittest.TestCase):
return image_inputs return image_inputs
def test_save_load_pretrained_default(self): def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer() tokenizer_slow = self.get_tokenizer()
tokenizer_fast = self.get_rust_tokenizer()
feature_extractor = self.get_feature_extractor() feature_extractor = self.get_feature_extractor()
processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) processor_slow = CLIPProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
processor_slow.save_pretrained(self.tmpdirname)
processor_slow = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=False)
processor.save_pretrained(self.tmpdirname) processor_fast = CLIPProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
processor = CLIPProcessor.from_pretrained(self.tmpdirname) processor_fast.save_pretrained(self.tmpdirname)
processor_fast = CLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
self.assertIsInstance(processor.tokenizer, CLIPTokenizer) 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.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, CLIPFeatureExtractor) self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor_slow.feature_extractor, CLIPFeatureExtractor)
self.assertIsInstance(processor_fast.feature_extractor, CLIPFeatureExtractor)
def test_save_load_pretrained_additional_features(self): def test_save_load_pretrained_additional_features(self):
processor = CLIPProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor = CLIPProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
@@ -112,7 +124,7 @@ class CLIPProcessorTest(unittest.TestCase):
) )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, CLIPTokenizer) self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, CLIPFeatureExtractor) self.assertIsInstance(processor.feature_extractor, CLIPFeatureExtractor)