Fuyu: improve image processing (#27007)
* Fix Fuyu image scaling bug It could produce negative padding and hence inference errors for certain image sizes. * initial rework commit * add batching capabilities, refactor image processing * add functional batching for a list of images and texts * make args explicit * Fuyu processing update (#27133) * Add file headers * Add file headers * First pass - preprocess method with standard args * First pass image processor rework * Small tweaks * More args and docstrings * Tidying iterating over batch * Tidying up * Modify to have quick tests (for now) * Fix up * BatchFeature * Passing tests * Add tests for processor * Sense check when patchifying * Add some tests * FuyuBatchFeature * Post-process box coordinates * Update to `size` in processor * Remove unused and duplicate constants * Store unpadded dims after resize * Fix up * Return FuyuBatchFeature * Get unpadded sizes after resize * Update exception * Fix return * Convert input `<box>` coordinates to model format. * Post-process point coords, support multiple boxes/points in a single sequence * Replace constants * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Preprocess List[List[image]] * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update to Amy's latest state. * post-processing returns a list of tensors * Fix error when target_sizes is None Co-authored-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com> * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Review comments * Update src/transformers/models/fuyu/image_processing_fuyu.py Co-authored-by: Pedro Cuenca <pedro@huggingface.co> * Fix up * Fix up --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-72-126.ec2.internal> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com> * Fix conflicts in fuyu_follow_up_image_processing (#27228) fixing conflicts and updating on main * Revert "Fix conflicts in fuyu_follow_up_image_processing" (#27232) Revert "Fix conflicts in fuyu_follow_up_image_processing (#27228)" This reverts commit acce10b6c653dc7041fb9d18cfed55775afd6207. --------- Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-72-126.ec2.internal>
This commit is contained in:
Pablo Montalvo
@@ -24,7 +24,8 @@ if is_vision_available():
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@require_torchvision
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class TestFuyuImageProcessor(unittest.TestCase):
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def setUp(self):
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self.processor = FuyuImageProcessor(target_height=160, target_width=320, padding_value=1.0)
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self.size = {"height": 160, "width": 320}
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self.processor = FuyuImageProcessor(size=self.size, padding_value=1.0)
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self.batch_size = 3
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self.channels = 3
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self.height = 300
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@@ -38,29 +39,25 @@ class TestFuyuImageProcessor(unittest.TestCase):
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self.sample_image_pil = Image.fromarray(self.sample_image)
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def test_patches(self):
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expected_num_patches = self.processor.get_num_patches(
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img_h=self.height, img_w=self.width, patch_dim_h=self.image_patch_dim_h, patch_dim_w=self.image_patch_dim_w
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)
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expected_num_patches = self.processor.get_num_patches(image_height=self.height, image_width=self.width)
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patches_final = self.processor.patchify_image(
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image=self.image_input, patch_dim_h=self.image_patch_dim_h, patch_dim_w=self.image_patch_dim_w
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)
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patches_final = self.processor.patchify_image(image=self.image_input)
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assert (
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patches_final.shape[1] == expected_num_patches
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), f"Expected {expected_num_patches} patches, got {patches_final.shape[1]}."
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def test_scale_to_target_aspect_ratio(self):
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# (h:450, w:210) fitting (160, 320) -> (160, 210*160/450)
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scaled_image = self.processor._scale_to_target_aspect_ratio(self.sample_image)
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scaled_image = self.processor.resize(self.sample_image, size=self.size)
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self.assertEqual(scaled_image.shape[0], 160)
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self.assertEqual(scaled_image.shape[1], 74)
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def test_apply_transformation_numpy(self):
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transformed_image = self.processor.apply_transformation(self.sample_image)
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self.assertEqual(transformed_image.shape[0], 160)
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self.assertEqual(transformed_image.shape[1], 320)
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transformed_image = self.processor.preprocess(self.sample_image).images[0][0]
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self.assertEqual(transformed_image.shape[1], 160)
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self.assertEqual(transformed_image.shape[2], 320)
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def test_apply_transformation_pil(self):
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transformed_image = self.processor.apply_transformation(self.sample_image_pil)
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self.assertEqual(transformed_image.shape[0], 160)
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self.assertEqual(transformed_image.shape[1], 320)
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transformed_image = self.processor.preprocess(self.sample_image_pil).images[0][0]
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self.assertEqual(transformed_image.shape[1], 160)
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self.assertEqual(transformed_image.shape[2], 320)
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@@ -3,7 +3,7 @@ import unittest
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import requests
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from transformers import AutoTokenizer, FuyuConfig, is_torch_available, is_vision_available
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from transformers import FuyuConfig, is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
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from ...test_modeling_common import ids_tensor, random_attention_mask
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@@ -14,7 +14,7 @@ if is_vision_available():
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if is_torch_available() and is_vision_available():
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from transformers import FuyuImageProcessor, FuyuProcessor
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from transformers import FuyuProcessor
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if is_torch_available():
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@@ -267,11 +267,8 @@ class FuyuIntegrationTest(unittest.TestCase): # , ModelTesterMixin)
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all_model_classes = ("FuyuForCausalLM") if is_torch_available() else ()
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def setUp(self):
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self.pretrained_model_name = "huggingface/new_model_release_weights"
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tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model_name)
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image_processor = FuyuImageProcessor()
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self.processor = FuyuProcessor(image_processor=image_processor, tokenizer=tokenizer)
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self.pretrained_model_name = "adept/fuyu-8b"
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self.processor = FuyuProcessor.from_pretrained(self.pretrained_model_name)
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self.model = FuyuForCausalLM.from_pretrained(self.pretrained_model_name)
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self.bus_image_url = (
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"https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png"
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@@ -280,9 +277,8 @@ class FuyuIntegrationTest(unittest.TestCase): # , ModelTesterMixin)
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@slow
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def test_model_8b_chat_greedy_generation_bus_captioning(self):
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EXPECTED_TEXT_COMPLETION = """A bus parked on the side of a road.|ENDOFTEXT|"""
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EXPECTED_TEXT_COMPLETION = """A blue bus parked on the side of a road.|ENDOFTEXT|"""
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text_prompt_coco_captioning = "Generate a coco-style caption.\n"
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model_inputs_bus_captioning = self.processor(text=text_prompt_coco_captioning, images=self.bus_image_pil)
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generated_tokens = self.model.generate(**model_inputs_bus_captioning, max_new_tokens=10)
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text = self.processor.tokenizer.batch_decode(generated_tokens)
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@@ -297,7 +293,7 @@ class FuyuIntegrationTest(unittest.TestCase): # , ModelTesterMixin)
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"""
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@slow
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@require_torch_gpu
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@require_torch_accelerator
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def test_model_8b_chat_greedy_generation_bus_color(self):
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EXPECTED_TEXT_COMPLETION = "The bus is blue.\n|ENDOFTEXT|"
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text_prompt_bus_color = "What color is the bus?\n"
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@@ -314,7 +310,7 @@ class FuyuIntegrationTest(unittest.TestCase): # , ModelTesterMixin)
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self.assertEqual(EXPECTED_TEXT_COMPLETION, clean_sequence)
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@slow
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@require_torch_gpu
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@require_torch_accelerator
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def test_model_8b_chat_greedy_generation_chart_vqa(self):
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# fmt: off
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EXPECTED_TEXT_TOKENS = ["The","life expectancy","at","birth","of male","s in","","20","18","is","","80",".","7",".","\n","|ENDOFTEXT|",]
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@@ -340,7 +336,7 @@ class FuyuIntegrationTest(unittest.TestCase): # , ModelTesterMixin)
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self.assertEqual(expected_text_completion, clean_sequence)
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@slow
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@require_torch_gpu
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@require_torch_accelerator
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def test_model_8b_chat_greedy_generation_bounding_box(self):
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EXPECTED_TEXT_COMPLETION = "\x00194213202244\x01|ENDOFTEXT|"
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text_prompt_bbox = "When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\nWilliams" # noqa: E231
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@@ -26,16 +26,14 @@ 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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pretrained_model_name = "adept/fuyu-8b"
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self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name)
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self.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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self.processor = FuyuProcessor(image_processor=self.image_processor, tokenizer=self.tokenizer)
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self.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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self.bus_image_pil = Image.open(io.BytesIO(requests.get(bus_image_url).content))
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def test_fuyu_processing(self):
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"""
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@@ -44,11 +42,119 @@ class FuyuProcessingTest(unittest.TestCase): # TODO Which mixins do we add here
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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.processor(text=self.text_prompt, images=self.bus_image_pil)
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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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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.processor(text=self.text_prompt)
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tokenizer_outputs = self.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.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)
|
||||
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)
|
||||
|
||||
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]])
|
||||
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]])
|
||||
# fmt: on
|
||||
|
||||
# Batch of two images - equally sized
|
||||
images = [self.bus_image_pil, self.bus_image_pil]
|
||||
processor_outputs = self.processor(text=[self.text_prompt, self.text_prompt], images=images)
|
||||
|
||||
self.assertTrue(
|
||||
(
|
||||
processor_outputs["image_patches_indices"]
|
||||
== torch.cat([SINGLE_IMAGE_PATCH_INPUTS, SINGLE_IMAGE_PATCH_INPUTS], dim=0)
|
||||
).all()
|
||||
)
|
||||
torch.testing.assert_close(self.one_image_bus_model_inputs["input_ids"], EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS)
|
||||
self.assertTrue(
|
||||
(
|
||||
processor_outputs["input_ids"]
|
||||
== torch.cat([SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, SINGLE_PADDED_UNPACKED_TOKEN_INPUTS], dim=0)
|
||||
).all()
|
||||
)
|
||||
|
||||
# Processes single images with different sizes as expected
|
||||
images = [self.bus_image_pil]
|
||||
processor_outputs = self.processor(text=self.text_prompt, images=images)
|
||||
self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_IMAGE_PATCH_INPUTS).all())
|
||||
self.assertTrue((processor_outputs["input_ids"] == SINGLE_PADDED_UNPACKED_TOKEN_INPUTS).all())
|
||||
|
||||
images = [self.bus_image_pil.resize((64, 300))]
|
||||
processor_outputs = self.processor(text=self.text_prompt, images=images)
|
||||
self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_RESIZED_IMAGE_PATCH_INPUTS).all())
|
||||
self.assertTrue((processor_outputs["input_ids"] == SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS).all())
|
||||
|
||||
# Batch of two images - different sizes. Left-pads the smaller image inputs
|
||||
images = [self.bus_image_pil, self.bus_image_pil.resize((64, 300))]
|
||||
processor_outputs = self.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())
|
||||
|
||||
|
||||
@require_torch
|
||||
@@ -97,7 +203,6 @@ class TestProcessImagesForModelInput(unittest.TestCase):
|
||||
"""
|
||||
Adding a mix of present and absent images.
|
||||
"""
|
||||
self.image_processor = FuyuImageProcessor()
|
||||
|
||||
self.image_input = torch.randn([1, 1, 3, 64, 64])
|
||||
self.image_present = torch.tensor([[1]])
|
||||
@@ -108,19 +213,19 @@ class TestProcessImagesForModelInput(unittest.TestCase):
|
||||
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.process_images_for_model_input(
|
||||
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_patch_dim_h=self.image_patch_dim_h,
|
||||
image_patch_dim_w=self.image_patch_dim_w,
|
||||
image_placeholder_id=self.image_placeholder_id,
|
||||
image_newline_id=self.image_newline_id,
|
||||
variable_sized=self.variable_sized,
|
||||
)
|
||||
print(result["images"][0][0])
|
||||
self.assertEqual(result["images"][0][0].shape, torch.Size([3, 64, 64]))
|
||||
|
||||
Reference in New Issue
Block a user