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103 lines
4.4 KiB
Python
103 lines
4.4 KiB
Python
# Copyright 2026 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import unittest
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import numpy as np
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from transformers.testing_utils import require_vision
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from transformers.utils import is_vision_available
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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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from transformers import Phi4MultimodalProcessor
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@require_vision
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class Phi4MultimodalProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Phi4MultimodalProcessor
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# Tiny processor created with make_tiny_processor.py from "microsoft/Phi-4-multimodal-instruct"
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# revision "refs/pr/70" (main branch adds auto_map requiring trust_remote_code).
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tiny_model_id = "hf-internal-testing/tiny-processor-phi4_multimodal"
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checkpoint_path = "microsoft/Phi-4-multimodal-instruct"
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revision = "refs/pr/70"
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text_input_name = "input_ids"
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images_input_name = "image_pixel_values"
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audio_input_name = "audio_input_features"
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# Max-length values used in image-text kwargs tests. Override as phi4 needs lots of tokens for images.
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image_text_kwargs_max_length = 400
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image_text_kwargs_override_max_length = 396
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image_unstructured_max_length = 407
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# Max-length values used in audio-text kwargs tests. Override as phi4 needs lots of tokens for audio.
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audio_text_kwargs_max_length = 300
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audio_processor_tester_max_length = 117
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audio_unstructured_max_length = 76
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# Max-length values used in video-text kwargs tests. Override in subclasses if needed.
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video_text_kwargs_max_length = 167
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video_text_kwargs_override_max_length = 162
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video_unstructured_max_length = 176
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@classmethod
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def _setup_test_attributes(cls, processor):
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cls.image_token = processor.image_token
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cls.image_token_id = processor.image_token_id
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cls.audio_token = processor.audio_token
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cls.audio_token_id = processor.audio_token_id
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# override: audio_attention_mask is returned conditionally, and not expected in the input names in this case
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def test_model_input_names(self):
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processor = self.get_processor()
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text = self.prepare_text_inputs(modalities=["image", "video", "audio"])
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image_input = self.prepare_image_inputs()
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video_inputs = self.prepare_video_inputs()
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audio_inputs = self.prepare_audio_inputs()
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inputs_dict = {"text": text, "images": image_input, "videos": video_inputs, "audio": audio_inputs}
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call_signature = inspect.signature(processor.__call__)
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input_args = [param.name for param in call_signature.parameters.values()]
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inputs_dict = {k: v for k, v in inputs_dict.items() if k in input_args}
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inputs = processor(**inputs_dict, return_tensors="pt")
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# audio_attention_mask is returned conditionally, and not expected in the input names in this case
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input_names_expected = set(processor.model_input_names) - {"audio_attention_mask"}
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self.assertSetEqual(set(inputs.keys()), input_names_expected)
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def test_dynamic_hd_kwarg_passed_to_image_processor(self):
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processor = self.get_processor()
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# 1000x1000 image: with size=448, w_crop_num=3, h_crop_num=3 -> 9 HD crops (1 global + 9 = 10 total)
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# With dynamic_hd=4: limits to 2x2 grid -> 4 HD crops (1 global + 4 = 5 total)
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arr = np.random.randint(255, size=(3, 1000, 1000), dtype=np.uint8)
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image_input = Image.fromarray(np.moveaxis(arr, 0, -1))
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input_str = self.prepare_text_inputs(modalities="image")
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inputs_default = processor(text=input_str, images=image_input, return_tensors="pt")
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inputs_limited = processor(
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text=input_str,
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images=image_input,
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dynamic_hd=4,
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return_tensors="pt",
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)
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self.assertEqual(inputs_limited[self.images_input_name].shape[1], 5)
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self.assertEqual(inputs_default[self.images_input_name].shape[1], 10)
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