mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-31 18:22:34 +06:00
4d0de5f73a
* More limited setup -> setupclass conversion * make fixup * Trigger tests * Fixup UDOP * Missed a spot * tearDown -> tearDownClass where appropriate * Couple more class fixes * Fixups for UDOP and VisionTextDualEncoder * Ignore errors when removing the tmpdir, in case it already got cleaned up somewhere * CLIP fixes * More correct classmethods * Wav2Vec2Bert fixes * More methods become static * More class methods * More class methods * Revert changes for integration tests / modeling files * Use a different tempdir for tests that actually write to it * Remove addClassCleanup and just use teardownclass * Remove changes in modeling files * Cleanup get_processor_dict() for got_ocr2 * Fix regression on Wav2Vec2BERT test that was masked by this before * Rework tests that modify the tmpdir * make fix-copies * revert clvp modeling test changes * Fix CLIP processor test * make fix-copies
206 lines
8.1 KiB
Python
206 lines
8.1 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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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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BridgeTowerImageProcessor,
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BridgeTowerProcessor,
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RobertaTokenizerFast,
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)
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@require_vision
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class BridgeTowerProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = BridgeTowerProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = BridgeTowerImageProcessor()
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tokenizer = RobertaTokenizerFast.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
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processor = BridgeTowerProcessor(image_processor, tokenizer)
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processor.save_pretrained(cls.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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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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# Some kwargs tests are overriden from common tests to handle shortest_edge
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# and size_divisor behaviour
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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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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(
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"image_processor",
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crop_size={"shortest_edge": 234, "longest_edge": 234},
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)
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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["pixel_values"][0][0]), 234)
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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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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": {
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"crop_size": {"shortest_edge": 214},
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},
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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["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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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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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", crop_size={"shortest_edge": 234})
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tokenizer = self.get_component("tokenizer", max_length=117)
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if not tokenizer.pad_token:
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tokenizer.pad_token = "[TEST_PAD]"
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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, crop_size={"shortest_edge": 224})
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self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
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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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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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if not tokenizer.pad_token:
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tokenizer.pad_token = "[TEST_PAD]"
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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(batch_size=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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crop_size={"shortest_edge": 214},
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padding="longest",
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max_length=76,
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)
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self.assertEqual(inputs["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 6)
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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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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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if not tokenizer.pad_token:
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tokenizer.pad_token = "[TEST_PAD]"
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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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crop_size={"shortest_edge": 214},
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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["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["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(self):
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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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if not tokenizer.pad_token:
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tokenizer.pad_token = "[TEST_PAD]"
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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": {"crop_size": {"shortest_edge": 214}},
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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["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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