mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +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
345 lines
13 KiB
Python
345 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 numpy as np
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from transformers.testing_utils import (
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require_tf,
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require_torch,
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require_torchvision,
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require_vision,
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)
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from transformers.utils import is_tf_available, is_torch_available, is_vision_available
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from ...test_processing_common import ProcessorTesterMixin, prepare_image_inputs
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if is_vision_available():
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from PIL import Image
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from transformers import AutoProcessor, SamImageProcessor, SamProcessor
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if is_torch_available():
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import torch
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from transformers.models.sam.image_processing_sam import _mask_to_rle_pytorch
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if is_tf_available():
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import tensorflow as tf
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from transformers.models.sam.image_processing_sam import _mask_to_rle_tf
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@require_vision
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@require_torchvision
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class SamProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = SamProcessor
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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 = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(cls.tmpdirname)
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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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def prepare_mask_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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mask_inputs = [np.random.randint(255, size=(30, 400), dtype=np.uint8)]
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mask_inputs = [Image.fromarray(x) for x in mask_inputs]
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return mask_inputs
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def test_chat_template_save_loading(self):
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self.skipTest("SamProcessor does not have a tokenizer")
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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self.skipTest("SamProcessor does not have a tokenizer")
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def test_kwargs_overrides_default_image_processor_kwargs(self):
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self.skipTest("SamProcessor does not have a tokenizer")
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def test_kwargs_overrides_default_tokenizer_kwargs(self):
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self.skipTest("SamProcessor does not have a tokenizer")
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def test_tokenizer_defaults_preserved_by_kwargs(self):
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self.skipTest("SamProcessor does not have a tokenizer")
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def test_save_load_pretrained_additional_features(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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processor = SamProcessor(image_processor=self.get_image_processor())
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processor.save_pretrained(tmpdir)
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = SamProcessor.from_pretrained(tmpdir, do_normalize=False, padding_value=1.0)
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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, SamImageProcessor)
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def test_image_processor_no_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(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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for image in input_feat_extract.pixel_values:
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self.assertEqual(image.shape, (3, 1024, 1024))
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for original_size in input_feat_extract.original_sizes:
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np.testing.assert_array_equal(original_size, np.array([30, 400]))
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for reshaped_input_size in input_feat_extract.reshaped_input_sizes:
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np.testing.assert_array_equal(
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reshaped_input_size, np.array([77, 1024])
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) # reshaped_input_size value is before padding
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def test_image_processor_with_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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mask_input = self.prepare_mask_inputs()
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input_feat_extract = image_processor(images=image_input, segmentation_maps=mask_input, return_tensors="np")
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input_processor = processor(images=image_input, segmentation_maps=mask_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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for label in input_feat_extract.labels:
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self.assertEqual(label.shape, (256, 256))
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@require_torch
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def test_post_process_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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dummy_masks = [torch.ones((1, 3, 5, 5))]
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original_sizes = [[1764, 2646]]
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reshaped_input_size = [[683, 1024]]
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masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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masks = processor.post_process_masks(
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dummy_masks, torch.tensor(original_sizes), torch.tensor(reshaped_input_size)
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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# should also work with np
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dummy_masks = [np.ones((1, 3, 5, 5))]
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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dummy_masks = [[1, 0], [0, 1]]
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with self.assertRaises(ValueError):
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
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def test_rle_encoding(self):
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"""
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Test the run-length encoding function.
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"""
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# Test that a mask of all zeros returns a single run [height * width].
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input_mask = torch.zeros((1, 2, 2), dtype=torch.long) # shape: 1 x 2 x 2
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rle = _mask_to_rle_pytorch(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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# For a 2x2 all-zero mask, we expect a single run of length 4:
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self.assertEqual(rle[0]["counts"], [4])
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# Test that a mask of all ones returns [0, height * width].
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input_mask = torch.ones((1, 2, 2), dtype=torch.long) # shape: 1 x 2 x 2
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rle = _mask_to_rle_pytorch(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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# For a 2x2 all-one mask, we expect two runs: [0, 4].
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self.assertEqual(rle[0]["counts"], [0, 4])
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# Test a mask with mixed 0s and 1s to ensure the run-length encoding is correct.
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# Example mask:
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# Row 0: [0, 1]
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# Row 1: [1, 1]
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# This is shape (1, 2, 2).
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# Flattened in Fortran order -> [0, 1, 1, 1].
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# The RLE for [0,1,1,1] is [1, 3].
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input_mask = torch.tensor([[[0, 1], [1, 1]]], dtype=torch.long)
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rle = _mask_to_rle_pytorch(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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self.assertEqual(rle[0]["counts"], [1, 3]) # 1 zero, followed by 3 ones
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@require_vision
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@require_tf
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class TFSamProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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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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# This is to avoid repeating the skipping of the common tests
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images."""
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return prepare_image_inputs()
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def test_save_load_pretrained_additional_features(self):
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processor = SamProcessor(image_processor=self.get_image_processor())
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processor.save_pretrained(self.tmpdirname)
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = SamProcessor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)
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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, SamImageProcessor)
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def test_image_processor(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(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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input_feat_extract.pop("original_sizes") # pop original_sizes as it is popped in the processor
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input_feat_extract.pop("reshaped_input_sizes") # pop reshaped_input_sizes as it is popped in the processor
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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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@require_tf
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def test_post_process_masks(self):
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image_processor = self.get_image_processor()
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processor = SamProcessor(image_processor=image_processor)
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dummy_masks = [tf.ones((1, 3, 5, 5))]
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original_sizes = [[1764, 2646]]
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reshaped_input_size = [[683, 1024]]
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masks = processor.post_process_masks(dummy_masks, original_sizes, reshaped_input_size, return_tensors="tf")
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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masks = processor.post_process_masks(
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dummy_masks,
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tf.convert_to_tensor(original_sizes),
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tf.convert_to_tensor(reshaped_input_size),
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return_tensors="tf",
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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# should also work with np
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dummy_masks = [np.ones((1, 3, 5, 5))]
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masks = processor.post_process_masks(
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dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
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)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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dummy_masks = [[1, 0], [0, 1]]
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with self.assertRaises(tf.errors.InvalidArgumentError):
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masks = processor.post_process_masks(
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dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
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)
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def test_rle_encoding(self):
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"""
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Test the run-length encoding function.
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"""
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# Test that a mask of all zeros returns a single run [height * width].
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input_mask = tf.zeros((1, 2, 2), dtype=tf.int64) # shape: 1 x 2 x 2
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rle = _mask_to_rle_tf(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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# For a 2x2 all-zero mask, we expect a single run of length 4:
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self.assertEqual(rle[0]["counts"], [4])
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# Test that a mask of all ones returns [0, height * width].
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input_mask = tf.ones((1, 2, 2), dtype=tf.int64) # shape: 1 x 2 x 2
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rle = _mask_to_rle_tf(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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# For a 2x2 all-one mask, we expect two runs: [0, 4].
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self.assertEqual(rle[0]["counts"], [0, 4])
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# Test a mask with mixed 0s and 1s to ensure the run-length encoding is correct.
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# Example mask:
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# Row 0: [0, 1]
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# Row 1: [1, 1]
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# This is shape (1, 2, 2).
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# Flattened in Fortran order -> [0, 1, 1, 1].
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# The RLE for [0,1,1,1] is [1, 3].
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input_mask = tf.constant([[[0, 1], [1, 1]]], dtype=tf.int64)
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rle = _mask_to_rle_tf(input_mask)
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self.assertEqual(len(rle), 1)
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self.assertEqual(rle[0]["size"], [2, 2])
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self.assertEqual(rle[0]["counts"], [1, 3]) # 1 zero, followed by 3 ones
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@require_vision
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@require_torchvision
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class SamProcessorEquivalenceTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SamImageProcessor()
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processor = SamProcessor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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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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# This is to avoid repeating the skipping of the common tests
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images."""
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return prepare_image_inputs()
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