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Add support for custom inputs and batched inputs in ProcessorTesterMixin (#33711)

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* add support for custom inputs and batched inputs in ProcessorTesterMixin

* Fix batch_size behavior ProcessorTesterMixin

* Change format prepare inputs batched

* Remove override test pixtral processor

* Remove unnecessary tests and cleanup after new prepare_inputs functions

* Fix instructBlipVideo image processor
This commit is contained in:
Yoni Gozlan
2024-10-01 23:52:03 +02:00
committed by GitHub
parent 1baa08897d
commit 61ac161a9d
8 changed files with 95 additions and 269 deletions
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166
tests/models/omdet_turbo/test_processor_omdet_turbo.py
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@@ -17,7 +17,6 @@ import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import AutoProcessor, CLIPTokenizerFast, OmDetTurboProcessor
@@ -36,8 +35,6 @@ if is_torch_available():
from transformers.models.omdet_turbo.modeling_omdet_turbo import OmDetTurboObjectDetectionOutput
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
@@ -45,6 +42,7 @@ if is_vision_available():
@require_vision
class OmDetTurboProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = OmDetTurboProcessor
text_input_name = "classes_input_ids"
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
@@ -77,17 +75,6 @@ class OmDetTurboProcessorTest(ProcessorTesterMixin, unittest.TestCase):
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def get_fake_omdet_turbo_output(self):
torch.manual_seed(42)
return OmDetTurboObjectDetectionOutput(
@@ -210,154 +197,3 @@ class OmDetTurboProcessorTest(ProcessorTesterMixin, unittest.TestCase):
inputs = processor(images=image_input, text=input_classes, task=input_tasks, return_tensors="pt")
self.assertListEqual(list(inputs.keys()), self.input_keys)
@require_vision
@require_torch
def test_tokenizer_defaults_preserved_by_kwargs(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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(images=image_input, text=[input_str], task=input_str, return_tensors="pt")
self.assertEqual(len(inputs["tasks_input_ids"][0]), 117)
self.assertEqual(len(inputs["classes_input_ids"][0]), 117)
@require_vision
@require_torch
def test_kwargs_overrides_default_tokenizer_kwargs(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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(images=image_input, text=[input_str], task=input_str, return_tensors="pt", max_length=112)
self.assertEqual(len(inputs["tasks_input_ids"][0]), 112)
self.assertEqual(len(inputs["classes_input_ids"][0]), 112)
@require_torch
@require_vision
def test_unstructured_kwargs(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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(
images=image_input,
text=[input_str],
task=input_str,
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["tasks_input_ids"][0]), 76)
self.assertEqual(len(inputs["classes_input_ids"][0]), 76)
@require_torch
@require_vision
def test_unstructured_kwargs_batched(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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", "upper older longer string"]
image_input = self.prepare_image_inputs() * 2
inputs = processor(
images=image_input,
text=[input_str],
task=input_str,
return_tensors="pt",
size={"height": 214, "width": 214},
padding="longest",
max_length=76,
)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["tasks_input_ids"][0]), 6)
self.assertEqual(len(inputs["classes_input_ids"][0]), 6)
@require_torch
@require_vision
def test_structured_kwargs_nested(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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, "task": input_str},
}
inputs = processor(images=image_input, text=[input_str], **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["tasks_input_ids"][0]), 76)
self.assertEqual(len(inputs["classes_input_ids"][0]), 76)
@require_torch
@require_vision
def test_structured_kwargs_nested_from_dict(self):
# Rewrite as OmDet-Turbo processor outputs "input_ids" for both tasks and classes.
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, "task": input_str},
}
inputs = processor(images=image_input, text=[input_str], **all_kwargs)
self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["tasks_input_ids"][0]), 76)
self.assertEqual(len(inputs["classes_input_ids"][0]), 76)