mirror of
https://github.com/huggingface/transformers.git
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5f0c181f4e
* uniformize FUYU processor kwargs * Uniformize instructblip processor kwargs * Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2 * Uniformize llava_next processor * Fix save_load test for processor with chat_template only as extra init args * Fix import Unpack * Fix Fuyu Processor import * Fix FuyuProcessor import * Fix FuyuProcessor * Add defaults for specific kwargs kosmos2 * Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs * Add tests processor Udop * remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature * Fix overwrite tests kwargs processors * Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop * Fix processing test fuyu * remove unnecessary pad_token check in instructblip ProcessorTest * Fix BC tests and cleanup * FIx imports fuyu * Uniformize Pix2Struct * Fix wrong name for FuyuProcessorKwargs * Fix slow tests reversed inputs align fuyu llava-next, change udop warning * Fix wrong logging import udop * Add check images text input order * Fix copies * change text pair handling when positional arg * rebase on main, fix imports in test_processing_common * remove optional args and udop uniformization from this PR * fix failing tests * remove unnecessary test, fix processing utils and test processing common * cleanup Unpack * cleanup * fix conflict grounding dino
330 lines
13 KiB
Python
330 lines
13 KiB
Python
# Copyright 2023 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 shutil
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import tempfile
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import unittest
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import pytest
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import (
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AutoProcessor,
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Pix2StructImageProcessor,
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Pix2StructProcessor,
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PreTrainedTokenizerFast,
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T5Tokenizer,
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)
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@require_vision
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@require_torch
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class Pix2StructProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Pix2StructProcessor
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text_input_name = "decoder_input_ids"
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images_input_name = "flattened_patches"
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = Pix2StructImageProcessor()
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tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
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processor = Pix2StructProcessor(image_processor, tokenizer)
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processor.save_pretrained(self.tmpdirname)
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def get_tokenizer(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_save_load_pretrained_additional_features(self):
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processor = Pix2StructProcessor(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 = Pix2StructProcessor.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, PreTrainedTokenizerFast)
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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, Pix2StructImageProcessor)
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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 = Pix2StructProcessor(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 = Pix2StructProcessor(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, return_token_type_ids=False, add_special_tokens=True)
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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(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = Pix2StructProcessor(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(
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list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
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)
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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_max_patches(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = Pix2StructProcessor(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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max_patches = [512, 1024, 2048, 4096]
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expected_hidden_size = [770, 770, 770, 770]
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# with text
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for i, max_patch in enumerate(max_patches):
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inputs = processor(text=input_str, images=image_input, max_patches=max_patch)
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self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
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self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])
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# without text input
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for i, max_patch in enumerate(max_patches):
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inputs = processor(images=image_input, max_patches=max_patch)
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self.assertEqual(inputs["flattened_patches"][0].shape[0], max_patch)
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self.assertEqual(inputs["flattened_patches"][0].shape[1], expected_hidden_size[i])
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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 = Pix2StructProcessor(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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def test_model_input_names(self):
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image_processor = self.get_image_processor()
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tokenizer = self.get_tokenizer()
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processor = Pix2StructProcessor(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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# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
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self.assertListEqual(
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list(inputs.keys()), ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]
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)
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inputs = processor(text=input_str)
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# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
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@require_torch
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@require_vision
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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# Rewrite as pix2struct processor return "flattened_patches" and not "pixel_values"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", max_patches=1024, patch_size={"height": 8, "width": 8})
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tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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.assertEqual(len(inputs["flattened_patches"][0][0]), 194)
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@require_torch
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@require_vision
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def test_kwargs_overrides_default_image_processor_kwargs(self):
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# Rewrite as pix2struct processor return "flattened_patches" and not "pixel_values"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor", max_patches=4096)
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tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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, max_patches=1024)
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self.assertEqual(len(inputs["flattened_patches"][0]), 1024)
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@require_torch
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@require_vision
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def test_unstructured_kwargs(self):
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# Rewrite as pix2struct processor return "decoder_input_ids" and not "input_ids"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(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(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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max_patches=1024,
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padding="max_length",
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max_length=76,
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)
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self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
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self.assertEqual(len(inputs["decoder_input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_unstructured_kwargs_batched(self):
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# Rewrite as pix2struct processor return "decoder_input_ids" and not "input_ids"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = ["lower newer", "upper older longer string"]
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image_input = self.prepare_image_inputs() * 2
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inputs = processor(
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text=input_str,
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images=image_input,
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return_tensors="pt",
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max_patches=1024,
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padding="longest",
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max_length=76,
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)
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self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
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self.assertEqual(len(inputs["decoder_input_ids"][0]), 5)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested(self):
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# Rewrite as pix2struct processor return "decoder_input_ids" and not "input_ids"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"max_patches": 1024},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
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self.assertEqual(len(inputs["decoder_input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested_from_dict(self):
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# Rewrite as pix2struct processor return "decoder_input_ids" and not "input_ids"
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer")
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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self.skip_processor_without_typed_kwargs(processor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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# Define the kwargs for each modality
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"images_kwargs": {"max_patches": 1024},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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}
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inputs = processor(text=input_str, images=image_input, **all_kwargs)
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self.assertEqual(inputs["flattened_patches"].shape[1], 1024)
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self.assertEqual(len(inputs["decoder_input_ids"][0]), 76)
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