[video processors] support frame sampling within processors (#38105)
* apply updates smolVLM (still needs workaround for chat template) * add other models * dump qwen omni for now, come back later * port qwen omni from their impl * wait, all qwens sample videos in same way! * clean up * make smolvlm backwards compatible and fix padding * dix some tests * fox smolvlm tests * more clean up and test fixing * delete unused arg * fix * address comments * style * fix test
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Raushan Turganbay
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27459025b8
@@ -507,7 +507,7 @@ class ProcessorTesterMixin:
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if "video_processor" not in self.processor_class.attributes:
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self.skipTest(f"video_processor attribute not present in {self.processor_class}")
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processor_components = self.prepare_components()
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=167, padding="max_length")
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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@@ -515,7 +515,7 @@ class ProcessorTesterMixin:
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input_str = self.prepare_text_inputs(modality="video")
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video_input = self.prepare_video_inputs()
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inputs = processor(text=input_str, videos=video_input, return_tensors="pt")
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self.assertEqual(inputs[self.text_input_name].shape[-1], 117)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 167)
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def test_video_processor_defaults_preserved_by_video_kwargs(self):
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"""
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@@ -529,7 +529,7 @@ class ProcessorTesterMixin:
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processor_components["video_processor"] = self.get_component(
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"video_processor", do_rescale=True, rescale_factor=-1
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)
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=167, padding="max_length")
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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@@ -553,9 +553,9 @@ class ProcessorTesterMixin:
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input_str = self.prepare_text_inputs(modality="video")
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video_input = self.prepare_video_inputs()
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inputs = processor(
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text=input_str, videos=video_input, return_tensors="pt", max_length=112, padding="max_length"
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text=input_str, videos=video_input, return_tensors="pt", max_length=162, padding="max_length"
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)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 112)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 162)
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def test_kwargs_overrides_default_video_processor_kwargs(self):
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if "video_processor" not in self.processor_class.attributes:
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@@ -564,7 +564,7 @@ class ProcessorTesterMixin:
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processor_components["video_processor"] = self.get_component(
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"video_processor", do_rescale=True, rescale_factor=1
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)
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length")
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processor_components["tokenizer"] = self.get_component("tokenizer", max_length=167, padding="max_length")
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processor_kwargs = self.prepare_processor_dict()
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processor = self.processor_class(**processor_components, **processor_kwargs)
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@@ -593,11 +593,11 @@ class ProcessorTesterMixin:
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do_rescale=True,
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rescale_factor=-1,
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padding="max_length",
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max_length=76,
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max_length=176,
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)
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self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 176)
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def test_unstructured_kwargs_batched_video(self):
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if "video_processor" not in self.processor_class.attributes:
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@@ -616,13 +616,13 @@ class ProcessorTesterMixin:
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do_rescale=True,
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rescale_factor=-1,
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padding="longest",
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max_length=76,
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max_length=176,
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)
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self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
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self.assertTrue(
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len(inputs[self.text_input_name][0]) == len(inputs[self.text_input_name][1])
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and len(inputs[self.text_input_name][1]) < 76
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and len(inputs[self.text_input_name][1]) < 176
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)
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def test_doubly_passed_kwargs_video(self):
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@@ -659,14 +659,14 @@ class ProcessorTesterMixin:
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"videos_kwargs": {"do_rescale": True, "rescale_factor": -1},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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"text_kwargs": {"padding": "max_length", "max_length": 176},
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}
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inputs = processor(text=input_str, videos=video_input, **all_kwargs)
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self.skip_processor_without_typed_kwargs(processor)
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self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 176)
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def test_structured_kwargs_nested_from_dict_video(self):
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if "video_processor" not in self.processor_class.attributes:
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@@ -682,12 +682,12 @@ class ProcessorTesterMixin:
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all_kwargs = {
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"common_kwargs": {"return_tensors": "pt"},
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"videos_kwargs": {"do_rescale": True, "rescale_factor": -1},
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"text_kwargs": {"padding": "max_length", "max_length": 76},
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"text_kwargs": {"padding": "max_length", "max_length": 176},
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}
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inputs = processor(text=input_str, videos=video_input, **all_kwargs)
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self.assertLessEqual(inputs[self.videos_input_name][0].mean(), 0)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 76)
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self.assertEqual(inputs[self.text_input_name].shape[-1], 176)
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# TODO: the same test, but for audio + text processors that have strong overlap in kwargs
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# TODO (molbap) use the same structure of attribute kwargs for other tests to avoid duplication
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@@ -884,7 +884,7 @@ class ProcessorTesterMixin:
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tokenize=True,
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return_dict=True,
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return_tensors=return_tensors,
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num_frames=4, # by default no more than 4 frames, otherwise too slow
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num_frames=2, # by default no more than 2 frames, otherwise too slow
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)
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input_name = getattr(self, input_name)
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self.assertTrue(input_name in out_dict)
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@@ -983,6 +983,21 @@ class ProcessorTesterMixin:
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), video_fps * 10)
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# Whan `do_sample_frames=False` no sampling is done and whole video is loaded, even if number of frames is passed
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video_fps = 1
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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do_sample_frames=False,
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video_fps=video_fps,
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return_tensors="pt",
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)
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self.assertTrue(self.videos_input_name in out_dict_with_video)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 300)
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# Load with `video_fps` and `num_frames` args, should raise an error
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with self.assertRaises(ValueError):
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out_dict_with_video = processor.apply_chat_template(
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@@ -1024,75 +1039,6 @@ class ProcessorTesterMixin:
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 2)
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@require_av
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@require_torch
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def test_apply_chat_template_video_special_processing(self):
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"""
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Tests that models can use their own preprocessing to preprocess conversations.
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"""
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processor = self.get_processor()
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if processor.chat_template is None:
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self.skipTest("Processor has no chat template")
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signature = inspect.signature(processor.__call__)
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if "videos" not in {*signature.parameters.keys()} or (
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signature.parameters.get("videos") is not None
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and signature.parameters["videos"].annotation == inspect._empty
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):
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self.skipTest("Processor doesn't accept videos at input")
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video_file_path = hf_hub_download(
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repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset"
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)
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "video", "path": video_file_path},
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{"type": "text", "text": "What is shown in this video?"},
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],
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},
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]
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]
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def _process_messages_for_chat_template(
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conversation,
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batch_images,
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batch_videos,
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batch_video_metadata,
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**chat_template_kwargs,
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):
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# Let us just always return a dummy prompt
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new_msg = [
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[
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{
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"role": "user",
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"content": [
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{"type": "video"}, # no need to use path, video is loaded already by this moment
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{"type": "text", "text": "Dummy prompt for preprocess testing"},
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],
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},
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]
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]
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return new_msg
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processor._process_messages_for_chat_template = _process_messages_for_chat_template
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out_dict_with_video = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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)
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self.assertTrue(self.videos_input_name in out_dict_with_video)
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# Check with `in` because we don't know how each template formats the prompt with BOS/EOS/etc
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formatted_text = processor.batch_decode(out_dict_with_video["input_ids"], skip_special_tokens=True)[0]
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self.assertTrue("Dummy prompt for preprocess testing" in formatted_text)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1)
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self.assertEqual(len(out_dict_with_video[self.videos_input_name][0]), 243)
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@require_librosa
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@require_av
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def test_chat_template_audio_from_video(self):
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