mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 21:30:07 +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
410 lines
26 KiB
Python
410 lines
26 KiB
Python
import io
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import tempfile
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import unittest
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from shutil import rmtree
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import requests
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from transformers import (
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AutoProcessor,
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AutoTokenizer,
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FuyuImageProcessor,
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FuyuProcessor,
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is_torch_available,
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is_vision_available,
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)
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from transformers.testing_utils import require_torch, require_vision
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from PIL import Image
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if is_torch_available():
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import torch
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from transformers.models.fuyu.processing_fuyu import construct_full_unpacked_stream, full_unpacked_stream_to_tensor
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@require_torch
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@require_vision
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class FuyuProcessingTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = FuyuProcessor
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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 = FuyuImageProcessor()
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tokenizer = AutoTokenizer.from_pretrained("adept/fuyu-8b")
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processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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cls.text_prompt = "Generate a coco-style caption.\\n"
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bus_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png"
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cls.bus_image_pil = Image.open(io.BytesIO(requests.get(bus_image_url).content))
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@classmethod
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def tearDownClass(cls):
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rmtree(cls.tmpdirname)
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def get_processor(self):
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image_processor = FuyuImageProcessor()
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tokenizer = AutoTokenizer.from_pretrained("adept/fuyu-8b")
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processor = FuyuProcessor(image_processor, tokenizer, **self.prepare_processor_dict())
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return processor
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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 test_fuyu_processing(self):
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"""
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Test to ensure that the standard processing on a gold example matches adept's code.
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"""
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# fmt: off
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EXPECTED_IMAGE_PATCH_INPUTS = torch.Tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,]]).to(torch.int64)
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EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122,]]).to(torch.int64)
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one_image_bus_model_inputs = self.get_processor()(text=self.text_prompt, images=self.bus_image_pil)
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# fmt: on
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torch.testing.assert_close(one_image_bus_model_inputs["image_patches_indices"], EXPECTED_IMAGE_PATCH_INPUTS)
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torch.testing.assert_close(one_image_bus_model_inputs["input_ids"], EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS)
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def test_fuyu_processing_no_image(self):
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"""
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Test to check processor works with just text input
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"""
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processor_outputs = self.get_processor()(text=self.text_prompt)
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tokenizer_outputs = self.get_tokenizer()(self.text_prompt)
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self.assertEqual(processor_outputs["input_ids"], tokenizer_outputs["input_ids"])
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def test_fuyu_processing_no_text(self):
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"""
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Test to check processor works with just image input
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"""
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# fmt: off
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EXPECTED_IMAGE_PATCH_INPUTS = torch.Tensor([
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[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
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14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26,
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27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
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41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
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54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66,
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67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
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81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93,
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94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
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108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
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121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133,
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134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
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148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160,
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161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,
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175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,
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188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200,
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201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214,
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215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227,
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228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241,
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-1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,
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255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267,
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268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
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282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294,
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295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1,
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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
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]).to(torch.int64)
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# fmt: on
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processor_outputs = self.get_processor()(images=self.bus_image_pil)
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self.assertTrue((processor_outputs["image_patches_indices"] == EXPECTED_IMAGE_PATCH_INPUTS).all())
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def test_fuyu_processing_multiple_image_sample(self):
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"""
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Test to check processor works with multiple image inputs for a single text input
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"""
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# fmt: off
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SINGLE_IMAGE_PATCH_INPUTS = torch.Tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,]]).to(torch.int64)
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SINGLE_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122,]]).to(torch.int64)
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SINGLE_RESIZED_IMAGE_PATCH_INPUTS = torch.Tensor([[ 0, 1, 2, -1, 3, 4, 5, -1, 6, 7, 8, -1, 9, 10, 11, -1, 12, 13, 14, -1, 15, 16, 17, -1, 18, 19, 20, -1, 21, 22, 23, -1, 24, 25, 26, -1, 27, 28, 29, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]])
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SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[ 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122]])
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# fmt: on
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# Batch of two images - equally sized
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images = [self.bus_image_pil, self.bus_image_pil]
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processor_outputs = self.get_processor()(text=[self.text_prompt, self.text_prompt], images=images)
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self.assertTrue(
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(
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processor_outputs["image_patches_indices"]
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== torch.cat([SINGLE_IMAGE_PATCH_INPUTS, SINGLE_IMAGE_PATCH_INPUTS], dim=0)
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).all()
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)
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self.assertTrue(
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(
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processor_outputs["input_ids"]
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== torch.cat([SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, SINGLE_PADDED_UNPACKED_TOKEN_INPUTS], dim=0)
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).all()
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)
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# Processes single images with different sizes as expected
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images = [self.bus_image_pil]
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processor_outputs = self.get_processor()(text=self.text_prompt, images=images)
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self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_IMAGE_PATCH_INPUTS).all())
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self.assertTrue((processor_outputs["input_ids"] == SINGLE_PADDED_UNPACKED_TOKEN_INPUTS).all())
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images = [self.bus_image_pil.resize((64, 300))]
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processor_outputs = self.get_processor()(text=self.text_prompt, images=images)
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self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_RESIZED_IMAGE_PATCH_INPUTS).all())
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self.assertTrue((processor_outputs["input_ids"] == SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS).all())
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# Batch of two images - different sizes. Left-pads the smaller image inputs
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images = [self.bus_image_pil, self.bus_image_pil.resize((64, 300))]
|
|
processor_outputs = self.get_processor()(text=[self.text_prompt, self.text_prompt], images=images)
|
|
|
|
padding_len_patch = SINGLE_IMAGE_PATCH_INPUTS.shape[1] - SINGLE_RESIZED_IMAGE_PATCH_INPUTS.shape[1]
|
|
padded_single_resized_image_patch = torch.cat(
|
|
[torch.ones([1, padding_len_patch]) * -1, SINGLE_RESIZED_IMAGE_PATCH_INPUTS], dim=1
|
|
)
|
|
expected_image_patch_inputs = torch.cat([SINGLE_IMAGE_PATCH_INPUTS, padded_single_resized_image_patch], dim=0)
|
|
|
|
padding_len_token = (
|
|
SINGLE_PADDED_UNPACKED_TOKEN_INPUTS.shape[1] - SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS.shape[1]
|
|
)
|
|
padded_single_resized_padded_unpacked_token_inputs = torch.cat(
|
|
[torch.zeros([1, padding_len_token]), SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS], dim=1
|
|
)
|
|
expected_padded_unpacked_token_inputs = torch.cat(
|
|
[SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, padded_single_resized_padded_unpacked_token_inputs], dim=0
|
|
)
|
|
|
|
self.assertTrue((processor_outputs["image_patches_indices"] == expected_image_patch_inputs).all())
|
|
self.assertTrue((processor_outputs["input_ids"] == expected_padded_unpacked_token_inputs).all())
|
|
|
|
# Rewrite as Fuyu supports tokenizer kwargs only when image is None.
|
|
@require_vision
|
|
@require_torch
|
|
def test_kwargs_overrides_default_tokenizer_kwargs(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer", max_length=117)
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
input_str = self.prepare_text_inputs()
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
|
|
inputs = processor(
|
|
text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
|
|
)
|
|
self.assertEqual(len(inputs["input_ids"][0]), 112)
|
|
|
|
@unittest.skip("Fuyu processor does not support image_processor kwargs")
|
|
def test_image_processor_defaults_preserved_by_image_kwargs(self):
|
|
pass
|
|
|
|
@unittest.skip("Fuyu processor does not support image_processor kwargs")
|
|
def test_kwargs_overrides_default_image_processor_kwargs(self):
|
|
pass
|
|
|
|
# Rewrite as Fuyu supports tokenizer kwargs only when image is None.
|
|
@require_vision
|
|
@require_torch
|
|
def test_tokenizer_defaults_preserved_by_kwargs(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
input_str = self.prepare_text_inputs()
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
|
|
inputs = processor(text=input_str, images=image_input, return_tensors="pt")
|
|
self.assertEqual(len(inputs["input_ids"][0]), 117)
|
|
|
|
# Rewrite as Fuyu image processor does not return pixel values
|
|
@require_torch
|
|
@require_vision
|
|
def test_structured_kwargs_nested(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer")
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
|
|
input_str = self.prepare_text_inputs()
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
|
|
# Define the kwargs for each modality
|
|
all_kwargs = {
|
|
"common_kwargs": {"return_tensors": "pt"},
|
|
"text_kwargs": {"padding": "max_length", "max_length": 76},
|
|
}
|
|
|
|
inputs = processor(text=input_str, images=image_input, **all_kwargs)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
|
|
self.assertEqual(len(inputs["input_ids"][0]), 76)
|
|
|
|
# Rewrite as Fuyu image processor does not return pixel values
|
|
@require_torch
|
|
@require_vision
|
|
def test_structured_kwargs_nested_from_dict(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer")
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
input_str = self.prepare_text_inputs()
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
|
|
# Define the kwargs for each modality
|
|
all_kwargs = {
|
|
"common_kwargs": {"return_tensors": "pt"},
|
|
"text_kwargs": {"padding": "max_length", "max_length": 76},
|
|
}
|
|
|
|
inputs = processor(text=input_str, images=image_input, **all_kwargs)
|
|
|
|
self.assertEqual(len(inputs["input_ids"][0]), 76)
|
|
|
|
# Rewrite as Fuyu supports tokenizer kwargs only when image is None.
|
|
@require_torch
|
|
@require_vision
|
|
def test_unstructured_kwargs(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer")
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
|
|
input_str = self.prepare_text_inputs()
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
inputs = processor(
|
|
text=input_str,
|
|
images=image_input,
|
|
return_tensors="pt",
|
|
padding="max_length",
|
|
max_length=76,
|
|
)
|
|
|
|
self.assertEqual(len(inputs["input_ids"][0]), 76)
|
|
|
|
# Rewrite as Fuyu supports tokenizer kwargs only when image is None.
|
|
@require_torch
|
|
@require_vision
|
|
def test_unstructured_kwargs_batched(self):
|
|
if "image_processor" not in self.processor_class.attributes:
|
|
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
|
|
image_processor = self.get_component("image_processor")
|
|
tokenizer = self.get_component("tokenizer")
|
|
|
|
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
|
|
self.skip_processor_without_typed_kwargs(processor)
|
|
|
|
input_str = self.prepare_text_inputs(batch_size=2)
|
|
# Fuyu uses tokenizer kwargs only when image is None.
|
|
image_input = None
|
|
inputs = processor(
|
|
text=input_str,
|
|
images=image_input,
|
|
return_tensors="pt",
|
|
padding="longest",
|
|
max_length=76,
|
|
)
|
|
|
|
self.assertEqual(len(inputs["input_ids"][0]), 6)
|
|
|
|
|
|
@require_torch
|
|
class TestImageTextProcessingUtils(unittest.TestCase):
|
|
def setUp(self):
|
|
self.batch_size = 2
|
|
self.new_seq_len = 8
|
|
self.num_sub_sequences = 1
|
|
|
|
self.all_bi_tokens_to_place = [4, 6]
|
|
self.full_unpacked_stream = [torch.tensor([1, 2, 3, 4]), torch.tensor([5, 6, 7, 8, 9, 10])]
|
|
self.fill_value = 0
|
|
|
|
self.num_real_text_tokens = [[3, 2], [2, 4]]
|
|
# Here the input stream is padded to avoid inconsistencies (current model release matches)
|
|
self.input_stream = torch.tensor([[[1, 2, 3], [4, 5, 0]], [[6, 7, 0], [8, 9, 10]]])
|
|
self.image_tokens = [
|
|
[torch.tensor([1, 2]), torch.tensor([3])],
|
|
[torch.tensor([4, 5, 6]), torch.tensor([7, 8])],
|
|
]
|
|
|
|
def test_full_unpacked_stream_to_tensor(self):
|
|
result = full_unpacked_stream_to_tensor(
|
|
self.all_bi_tokens_to_place,
|
|
self.full_unpacked_stream,
|
|
self.fill_value,
|
|
self.batch_size,
|
|
self.new_seq_len,
|
|
offset=0,
|
|
)
|
|
EXPECTED_TENSOR = torch.tensor([[1, 2, 3, 4, 0, 0, 0, 0], [5, 6, 7, 8, 9, 10, 0, 0]])
|
|
self.assertTrue(torch.equal(result, EXPECTED_TENSOR))
|
|
|
|
def test_construct_full_unpacked_stream(self):
|
|
result = construct_full_unpacked_stream(
|
|
self.num_real_text_tokens, self.input_stream, self.image_tokens, self.batch_size, self.num_sub_sequences
|
|
)
|
|
EXPECTED_UNPACKED_STREAM = [torch.tensor([1, 2, 1, 2, 3]), torch.tensor([4, 5, 6, 6, 7])]
|
|
for i in range(len(result)):
|
|
self.assertTrue(torch.equal(result[i], EXPECTED_UNPACKED_STREAM[i]))
|
|
|
|
|
|
@require_torch
|
|
class TestProcessImagesForModelInput(unittest.TestCase):
|
|
def setUp(self):
|
|
"""
|
|
Adding a mix of present and absent images.
|
|
"""
|
|
|
|
self.image_input = torch.randn([1, 1, 3, 64, 64])
|
|
self.image_present = torch.tensor([[1]])
|
|
self.image_unpadded_h = torch.tensor([[45]]) # Adjusted for subsequence of 1
|
|
self.image_unpadded_w = torch.tensor([[50]]) # Adjusted for subsequence of 1
|
|
self.image_patch_dim_h = 16
|
|
self.image_patch_dim_w = 16
|
|
self.image_placeholder_id = 999
|
|
self.image_newline_id = 888
|
|
self.variable_sized = True
|
|
self.image_processor = FuyuImageProcessor(
|
|
patch_size={"height": self.image_patch_dim_h, "width": self.image_patch_dim_w}
|
|
)
|
|
|
|
def test_process_images_for_model_input_fixed_sized(self):
|
|
self.variable_sized = False
|
|
result = self.image_processor.preprocess_with_tokenizer_info(
|
|
image_input=self.image_input,
|
|
image_present=self.image_present,
|
|
image_unpadded_h=self.image_unpadded_h,
|
|
image_unpadded_w=self.image_unpadded_w,
|
|
image_placeholder_id=self.image_placeholder_id,
|
|
image_newline_id=self.image_newline_id,
|
|
variable_sized=self.variable_sized,
|
|
)
|
|
self.assertEqual(result["images"][0][0].shape, torch.Size([3, 64, 64]))
|