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Modify ProcessorTesterMixin for better generalization (#32637)
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* Add padding="max_length" to tokenizer kwargs and change crop_size to size for image_processor kwargs

* remove crop_size argument in align processor tests to be coherent with base tests

* Add pad_token when loading tokenizer if needed, change test override tokenizer kwargs, remove unnecessary test overwrites in grounding dino
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@yonigozlan
yonigozlan authored Aug 13, 2024
1 parent c3cd9d8 commit 5bcbdff
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Showing 3 changed files with 18 additions and 190 deletions.
2 changes: 0 additions & 2 deletions tests/models/align/test_processor_align.py
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Original file line number Diff line number Diff line change
Expand Up @@ -66,8 +66,6 @@ def setUp(self):
image_processor_map = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_std": [0.26862954, 0.26130258, 0.27577711],
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174 changes: 0 additions & 174 deletions tests/models/grounding_dino/test_processor_grounding_dino.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,177 +263,3 @@ def test_model_input_names(self):
inputs = processor(text=input_str, images=image_input)

self.assertListEqual(list(inputs.keys()), processor.model_input_names)

@require_torch
@require_vision
def test_image_processor_defaults_preserved_by_image_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", size={"height": 234, "width": 234})
tokenizer = self.get_component("tokenizer", max_length=117)

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input)
self.assertEqual(len(inputs["pixel_values"][0][0]), 234)

@require_vision
@require_torch
def test_kwargs_overrides_default_tokenizer_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(
text=input_str, images=image_input, return_tensors="pt", padding="max_length", max_length=112
)
self.assertEqual(len(inputs["input_ids"][0]), 112)

@require_vision
@require_torch
def test_tokenizer_defaults_preserved_by_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, return_tensors="pt", padding="max_length")
self.assertEqual(len(inputs["input_ids"][0]), 117)

@require_torch
@require_vision
def test_kwargs_overrides_default_image_processor_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117)

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, size=[224, 224])
self.assertEqual(len(inputs["pixel_values"][0][0]), 224)

@require_torch
@require_vision
def test_structured_kwargs_nested(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)

self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
@require_vision
def test_structured_kwargs_nested_from_dict(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")

image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
@require_vision
def test_unstructured_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
size={"height": 214, "width": 214},
padding="max_length",
max_length=76,
)

self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
@require_vision
def test_unstructured_kwargs_batched(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
if not tokenizer.pad_token:
tokenizer.pad_token = "[TEST_PAD]"
processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = ["lower newer", "upper older longer string"]
image_input = self.prepare_image_inputs() * 2
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"height": 214, "width": 214},
size={"height": 214, "width": 214},
padding="longest",
max_length=76,
)
self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 6)
32 changes: 18 additions & 14 deletions tests/test_processing_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,8 @@ def get_component(self, attribute, **kwargs):

component_class = processor_class_from_name(component_class_name)
component = component_class.from_pretrained(self.tmpdirname, **kwargs) # noqa
if attribute == "tokenizer" and not component.pad_token:
component.pad_token = "[TEST_PAD]"

return component

Expand Down Expand Up @@ -126,7 +128,7 @@ def test_tokenizer_defaults_preserved_by_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)
tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
Expand All @@ -141,8 +143,8 @@ def test_tokenizer_defaults_preserved_by_kwargs(self):
def test_image_processor_defaults_preserved_by_image_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", crop_size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117)
image_processor = self.get_component("image_processor", size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
Expand All @@ -159,31 +161,33 @@ def test_kwargs_overrides_default_tokenizer_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)
tokenizer = self.get_component("tokenizer", padding="longest")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, return_tensors="pt", max_length=112)
inputs = processor(
text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
)
self.assertEqual(len(inputs["input_ids"][0]), 112)

@require_torch
@require_vision
def test_kwargs_overrides_default_image_processor_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", crop_size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117)
image_processor = self.get_component("image_processor", size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length")

processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, crop_size=[224, 224])
inputs = processor(text=input_str, images=image_input, size=[224, 224])
self.assertEqual(len(inputs["pixel_values"][0][0]), 224)

@require_torch
Expand All @@ -203,7 +207,7 @@ def test_unstructured_kwargs(self):
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"height": 214, "width": 214},
size={"height": 214, "width": 214},
padding="max_length",
max_length=76,
)
Expand All @@ -228,7 +232,7 @@ def test_unstructured_kwargs_batched(self):
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"height": 214, "width": 214},
size={"height": 214, "width": 214},
padding="longest",
max_length=76,
)
Expand All @@ -254,8 +258,8 @@ def test_doubly_passed_kwargs(self):
_ = processor(
text=input_str,
images=image_input,
images_kwargs={"crop_size": {"height": 222, "width": 222}},
crop_size={"height": 214, "width": 214},
images_kwargs={"size": {"height": 222, "width": 222}},
size={"height": 214, "width": 214},
)

@require_torch
Expand All @@ -275,7 +279,7 @@ def test_structured_kwargs_nested(self):
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"crop_size": {"height": 214, "width": 214}},
"images_kwargs": {"size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

Expand Down Expand Up @@ -303,7 +307,7 @@ def test_structured_kwargs_nested_from_dict(self):
# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"crop_size": {"height": 214, "width": 214}},
"images_kwargs": {"size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

Expand Down

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