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115 lines
4.6 KiB
Python
115 lines
4.6 KiB
Python
# Copyright 2025 HuggingFace Inc.
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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 unittest
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from transformers import AutoTokenizer
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from transformers.models.sam3.processing_sam3 import Sam3Processor
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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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@require_torch
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@require_vision
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class Sam3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Sam3Processor
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@classmethod
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def _setup_tokenizer(cls):
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return AutoTokenizer.from_pretrained(
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"hf-internal-testing/tiny-processor-clip", max_length=32, model_max_length=32
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)
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@classmethod
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def _setup_image_processor(cls):
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from transformers.models.sam3.image_processing_sam3 import Sam3ImageProcessor
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# Default size=1008×1008 allocates large tensors; use tiny sizes for tests
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return Sam3ImageProcessor(size={"height": 64, "width": 64}, mask_size={"height": 16, "width": 16})
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# Sam3Processor has a custom non-standard __call__ signature (no chat template, extra
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# prompting args like input_boxes). Skip mixin tests that assume a standard VLM interface.
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def test_chat_template_save_loading(self):
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self.skipTest("Sam3Processor does not use a chat template")
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def test_model_input_names(self):
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self.skipTest("Sam3Processor outputs extra keys (e.g. original_sizes) beyond model_input_names")
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def test_tokenizer_defaults(self):
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self.skipTest("Sam3Processor always pads tokenizer output to max_length=32")
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def test_processor_text_has_no_visual(self):
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self.skipTest("Sam3Processor has a custom interface, not a standard VLM text+image interface")
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def test_processor_with_multiple_inputs(self):
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self.skipTest("Sam3Processor has a custom interface, not a standard VLM text+image interface")
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# --- Sam3-specific tests ---
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def test_input_boxes_default_labels_mixed_batch(self):
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# Regression test for https://github.com/huggingface/transformers/issues/45059:
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# None entries should get pad label (-10), real entries should get positive label (1).
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processor = self.get_processor()
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images = self.prepare_image_inputs(batch_size=2)
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inputs = processor(
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images=images,
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text=["cat", None],
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input_boxes=[None, [[100, 100, 200, 200]]],
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return_tensors="pt",
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)
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self.assertIn("input_boxes", inputs)
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self.assertIn("input_boxes_labels", inputs)
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# The None entry (index 0) should have label -10 (pad value)
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self.assertEqual(inputs["input_boxes_labels"][0, 0].item(), -10)
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# The real entry (index 1) should have label 1 (positive)
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self.assertEqual(inputs["input_boxes_labels"][1, 0].item(), 1)
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def test_input_boxes_default_labels_all_real(self):
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processor = self.get_processor()
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images = self.prepare_image_inputs(batch_size=2)
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inputs = processor(
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images=images,
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text=["cat", "dog"],
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input_boxes=[[[50, 50, 150, 150]], [[200, 200, 300, 300]]],
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return_tensors="pt",
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)
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self.assertIn("input_boxes_labels", inputs)
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self.assertTrue((inputs["input_boxes_labels"] == 1).all())
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def test_no_input_boxes_omits_labels(self):
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processor = self.get_processor()
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images = self.prepare_image_inputs(batch_size=1)
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inputs = processor(
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images=images,
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text=["cat"],
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return_tensors="pt",
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)
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self.assertNotIn("input_boxes", inputs)
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self.assertNotIn("input_boxes_labels", inputs)
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def test_user_provided_labels_preserved(self):
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processor = self.get_processor()
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images = self.prepare_image_inputs(batch_size=2)
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inputs = processor(
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images=images,
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text=["cat", "dog"],
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input_boxes=[[[50, 50, 150, 150]], [[200, 200, 300, 300]]],
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input_boxes_labels=[[1], [0]],
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return_tensors="pt",
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)
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self.assertEqual(inputs["input_boxes_labels"][0, 0].item(), 1)
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self.assertEqual(inputs["input_boxes_labels"][1, 0].item(), 0)
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