Qwen2-VL: clean-up and add more tests (#33354)
* clean-up on qwen2-vl and add generation tests * add video tests * Update tests/models/qwen2_vl/test_processing_qwen2_vl.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * fix and add better tests * Update src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * update docs and address comments * Update docs/source/en/model_doc/qwen2_vl.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update docs/source/en/model_doc/qwen2_vl.md Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * update * remove size at all --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Raushan Turganbay
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GitHub
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commit
2f611d30d9
@@ -23,15 +23,12 @@ try:
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from typing import Unpack
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except ImportError:
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from typing_extensions import Unpack
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import unittest
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import numpy as np
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from transformers import CLIPTokenizerFast, ProcessorMixin
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from transformers.models.auto.processing_auto import processor_class_from_name
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from transformers.testing_utils import (
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check_json_file_has_correct_format,
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require_tokenizers,
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require_torch,
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require_vision,
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)
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@@ -41,8 +38,6 @@ from transformers.utils import is_vision_available
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if is_vision_available():
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from PIL import Image
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from transformers import CLIPImageProcessor
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def prepare_image_inputs():
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"""This function prepares a list of PIL images"""
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@@ -53,7 +48,6 @@ def prepare_image_inputs():
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@require_torch
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@require_vision
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@require_torch
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class ProcessorTesterMixin:
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processor_class = None
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@@ -91,6 +85,13 @@ class ProcessorTesterMixin:
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"""This function prepares a list of PIL images for testing"""
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return prepare_image_inputs()
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@require_vision
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def prepare_video_inputs(self):
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"""This function prepares a list of numpy videos."""
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video_input = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] * 8
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image_inputs = [video_input] * 3 # batch-size=3
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return image_inputs
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def test_processor_to_json_string(self):
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processor = self.get_processor()
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obj = json.loads(processor.to_json_string())
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@@ -125,8 +126,6 @@ class ProcessorTesterMixin:
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if not is_kwargs_typed_dict:
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self.skipTest(f"{self.processor_class} doesn't have typed kwargs.")
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@require_vision
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@require_torch
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def test_tokenizer_defaults_preserved_by_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -141,8 +140,6 @@ class ProcessorTesterMixin:
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inputs = processor(text=input_str, images=image_input, return_tensors="pt")
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self.assertEqual(len(inputs["input_ids"][0]), 117)
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@require_torch
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@require_vision
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def test_image_processor_defaults_preserved_by_image_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -158,8 +155,6 @@ class ProcessorTesterMixin:
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inputs = processor(text=input_str, images=image_input)
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self.assertEqual(len(inputs["pixel_values"][0][0]), 234)
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@require_vision
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@require_torch
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def test_kwargs_overrides_default_tokenizer_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -176,8 +171,6 @@ class ProcessorTesterMixin:
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)
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self.assertEqual(len(inputs["input_ids"][0]), 112)
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@require_torch
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@require_vision
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def test_kwargs_overrides_default_image_processor_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -193,8 +186,6 @@ class ProcessorTesterMixin:
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inputs = processor(text=input_str, images=image_input, size=[224, 224])
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self.assertEqual(len(inputs["pixel_values"][0][0]), 224)
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@require_torch
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@require_vision
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def test_unstructured_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -218,8 +209,6 @@ class ProcessorTesterMixin:
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self.assertEqual(inputs["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_unstructured_kwargs_batched(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -244,8 +233,6 @@ class ProcessorTesterMixin:
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self.assertEqual(len(inputs["input_ids"][0]), 6)
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@require_torch
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@require_vision
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def test_doubly_passed_kwargs(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -265,8 +252,6 @@ class ProcessorTesterMixin:
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size={"height": 214, "width": 214},
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)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -293,8 +278,6 @@ class ProcessorTesterMixin:
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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@require_torch
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@require_vision
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def test_structured_kwargs_nested_from_dict(self):
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if "image_processor" not in self.processor_class.attributes:
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self.skipTest(f"image_processor attribute not present in {self.processor_class}")
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@@ -318,48 +301,3 @@ class ProcessorTesterMixin:
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self.assertEqual(inputs["pixel_values"].shape[2], 214)
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self.assertEqual(len(inputs["input_ids"][0]), 76)
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class MyProcessor(ProcessorMixin):
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "CLIPImageProcessor"
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tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
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def __init__(self, image_processor=None, tokenizer=None, processor_attr_1=1, processor_attr_2=True):
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super().__init__(image_processor, tokenizer)
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self.processor_attr_1 = processor_attr_1
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self.processor_attr_2 = processor_attr_2
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@require_tokenizers
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@require_vision
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class ProcessorTest(unittest.TestCase):
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processor_class = MyProcessor
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def prepare_processor_dict(self):
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return {"processor_attr_1": 1, "processor_attr_2": False}
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def get_processor(self):
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image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
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tokenizer = CLIPTokenizerFast.from_pretrained("openai/clip-vit-large-patch14")
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processor = MyProcessor(image_processor, tokenizer, **self.prepare_processor_dict())
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return processor
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def test_processor_to_json_string(self):
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processor = self.get_processor()
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obj = json.loads(processor.to_json_string())
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for key, value in self.prepare_processor_dict().items():
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self.assertEqual(obj[key], value)
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self.assertEqual(getattr(processor, key, None), value)
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def test_processor_from_and_save_pretrained(self):
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processor_first = self.get_processor()
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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processor_second = self.processor_class.from_pretrained(tmpdirname)
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self.assertEqual(processor_second.to_dict(), processor_first.to_dict())
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