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caa0ff0bf1
* initial commit * add processor, add fuyu naming * add draft processor * fix processor * remove dropout to fix loading of weights * add image processing fixes from Pedro * fix * fix processor * add basic processing fuyu test * add documentation and TODO * address comments, add tests, add doc * replace assert with torch asserts * add Mixins and fix tests * clean imports * add model tester, clean imports * fix embedding test * add updated tests from pre-release model * Processor: return input_ids used for inference * separate processing and model tests * relax test tolerance for embeddings * add test for logit comparison * make sure fuyu image processor is imported in the init * fix formattingh * more formatting issues * and more * fixups * remove some stuff * nits * update init * remove the fuyu file * Update integration test with release model * Update conversion script. The projection is not used, as confirmed by the authors. * improve geenration * Remove duplicate function * Trickle down patches to model call * processing fuyu updates * remove things * fix prepare_inputs_for_generation to fix generate() * remove model_input * update * add generation tests * nits * draft leverage automodel and autoconfig * nits * fix dtype patch * address comments, update READMEs and doc, include tests * add working processing test, remove refs to subsequences * add tests, remove Sequence classification * processing * update * update the conversion script * more processing cleanup * safe import * take out ModelTesterMixin for early release * more cl;eanup * more cleanup * more cleanup * and more * register a buffer * nits * add postprocessing of generate output * nits * updates * add one working test * fix test * make fixup works * fixup * Arthur's updates * nits * update * update * fix processor * update tests * passe more fixups * fix * nits * don't import torch * skip fuyu config for now * fixup done * fixup * update * oups * nits * Use input embeddings * no buffer * update * styling processing fuyu * fix test * update licence * protect torch import * fixup and update not doctested * kwargs should be passed * udpates * update the impofixuprts in the test * protect import * protecting imports * protect imports in type checking * add testing decorators * protect top level import structure * fix typo * fix check init * move requires_backend to functions * Imports * Protect types --------- Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: ArthurZucker <arthur.zucker@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Lysandre <lysandre@huggingface.co>
127 lines
8.7 KiB
Python
127 lines
8.7 KiB
Python
import io
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import unittest
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import requests
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from transformers import AutoTokenizer, is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_torch_gpu, slow
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if is_vision_available():
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from PIL import Image
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if is_vision_available() and is_torch_available():
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from transformers import FuyuImageProcessor, FuyuProcessor
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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_torch_gpu
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@slow
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class FuyuProcessingTest(unittest.TestCase): # TODO Which mixins do we add here?
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""" """
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def setUp(self):
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pretrained_model_name = "huggingface/pre_release_model"
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name)
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image_processor = FuyuImageProcessor()
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processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
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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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bus_image_pil = Image.open(io.BytesIO(requests.get(bus_image_url).content))
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self.one_image_bus_model_inputs = processor(text=text_prompt, images=bus_image_pil)
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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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# fmt: on
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torch.testing.assert_close(
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self.one_image_bus_model_inputs["image_patches_indices"], EXPECTED_IMAGE_PATCH_INPUTS
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)
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torch.testing.assert_close(self.one_image_bus_model_inputs["input_ids"], EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS)
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@require_torch
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class TestImageTextProcessingUtils(unittest.TestCase):
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def setUp(self):
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self.batch_size = 2
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self.new_seq_len = 8
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self.num_sub_sequences = 1
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self.all_bi_tokens_to_place = [4, 6]
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self.full_unpacked_stream = [torch.tensor([1, 2, 3, 4]), torch.tensor([5, 6, 7, 8, 9, 10])]
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self.fill_value = 0
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self.num_real_text_tokens = [[3, 2], [2, 4]]
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# Here the input stream is padded to avoid inconsistencies (current model release matches)
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self.input_stream = torch.tensor([[[1, 2, 3], [4, 5, 0]], [[6, 7, 0], [8, 9, 10]]])
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self.image_tokens = [
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[torch.tensor([1, 2]), torch.tensor([3])],
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[torch.tensor([4, 5, 6]), torch.tensor([7, 8])],
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]
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def test_full_unpacked_stream_to_tensor(self):
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result = full_unpacked_stream_to_tensor(
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self.all_bi_tokens_to_place,
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self.full_unpacked_stream,
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self.fill_value,
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self.batch_size,
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self.new_seq_len,
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offset=0,
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)
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EXPECTED_TENSOR = torch.tensor([[1, 2, 3, 4, 0, 0, 0, 0], [5, 6, 7, 8, 9, 10, 0, 0]])
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self.assertTrue(torch.equal(result, EXPECTED_TENSOR))
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def test_construct_full_unpacked_stream(self):
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result = construct_full_unpacked_stream(
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self.num_real_text_tokens, self.input_stream, self.image_tokens, self.batch_size, self.num_sub_sequences
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)
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EXPECTED_UNPACKED_STREAM = [torch.tensor([1, 2, 1, 2, 3]), torch.tensor([4, 5, 6, 6, 7])]
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for i in range(len(result)):
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self.assertTrue(torch.equal(result[i], EXPECTED_UNPACKED_STREAM[i]))
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@require_torch
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class TestProcessImagesForModelInput(unittest.TestCase):
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def setUp(self):
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"""
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Adding a mix of present and absent images.
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"""
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self.image_processor = FuyuImageProcessor()
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self.image_input = torch.randn([1, 1, 3, 64, 64])
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self.image_present = torch.tensor([[1]])
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self.image_unpadded_h = torch.tensor([[45]]) # Adjusted for subsequence of 1
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self.image_unpadded_w = torch.tensor([[50]]) # Adjusted for subsequence of 1
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self.image_patch_dim_h = 16
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self.image_patch_dim_w = 16
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self.image_placeholder_id = 999
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self.image_newline_id = 888
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self.variable_sized = True
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def test_process_images_for_model_input_fixed_sized(self):
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self.variable_sized = False
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result = self.image_processor.process_images_for_model_input(
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image_input=self.image_input,
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image_present=self.image_present,
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image_unpadded_h=self.image_unpadded_h,
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image_unpadded_w=self.image_unpadded_w,
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image_patch_dim_h=self.image_patch_dim_h,
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image_patch_dim_w=self.image_patch_dim_w,
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image_placeholder_id=self.image_placeholder_id,
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image_newline_id=self.image_newline_id,
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variable_sized=self.variable_sized,
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)
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print(result["images"][0][0])
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self.assertEqual(result["images"][0][0].shape, torch.Size([3, 64, 64]))
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