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transformers/tests/models/grounding_dino/__init__.py Normal file
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transformers/tests/models/grounding_dino/test_image_processing_grounding_dino.py Normal file
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# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput
if is_vision_available():
from PIL import Image
from transformers import GroundingDinoImageProcessor
if is_torchvision_available():
from transformers import GroundingDinoImageProcessorFast
class GroundingDinoImageProcessingTester:
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
do_rescale=True,
rescale_factor=1 / 255,
do_pad=True,
):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
self.num_queries = 5
self.embed_dim = 5
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_processor_dict with DeformableDetr->GroundingDino
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.get_expected_values with DeformableDetr->GroundingDino
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to GroundingDinoImageProcessor,
assuming do_resize is set to True with a scalar size.
"""
if not batched:
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:
expected_height = int(self.size["shortest_edge"] * h / w)
expected_width = self.size["shortest_edge"]
elif w > h:
expected_height = self.size["shortest_edge"]
expected_width = int(self.size["shortest_edge"] * w / h)
else:
expected_height = self.size["shortest_edge"]
expected_width = self.size["shortest_edge"]
else:
expected_values = []
for image in image_inputs:
expected_height, expected_width = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
expected_height = max(expected_values, key=lambda item: item[0])[0]
expected_width = max(expected_values, key=lambda item: item[1])[1]
return expected_height, expected_width
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.expected_output_image_shape with DeformableDetr->GroundingDino
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
def get_fake_grounding_dino_output(self):
torch.manual_seed(42)
return GroundingDinoObjectDetectionOutput(
pred_boxes=torch.rand(self.batch_size, self.num_queries, 4),
logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim),
)
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_inputs with DeformableDetr->GroundingDino
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
batch_size=self.batch_size,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = GroundingDinoImageProcessor if is_vision_available() else None
fast_image_processing_class = GroundingDinoImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()
self.image_processor_tester = GroundingDinoImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_properties with DeformableDetr->GroundingDino
def test_image_processor_properties(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "do_pad"))
self.assertTrue(hasattr(image_processing, "size"))
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_from_dict_with_kwargs with DeformableDetr->GroundingDino
def test_image_processor_from_dict_with_kwargs(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad, True)
image_processor = image_processing_class.from_dict(
self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
)
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, False)
def test_post_process_object_detection(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class(**self.image_processor_dict)
outputs = self.image_processor_tester.get_fake_grounding_dino_output()
results = image_processor.post_process_object_detection(outputs, threshold=0.0)
self.assertEqual(len(results), self.image_processor_tester.batch_size)
self.assertEqual(list(results[0].keys()), ["scores", "labels", "boxes"])
self.assertEqual(results[0]["boxes"].shape, (self.image_processor_tester.num_queries, 4))
self.assertEqual(results[0]["scores"].shape, (self.image_processor_tester.num_queries,))
expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220])
torch.testing.assert_close(results[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4)
expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947])
torch.testing.assert_close(results[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4)
@slow
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_detection_annotations with DeformableDetr->GroundingDino
def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
target = {"image_id": 39769, "annotations": target}
for image_processing_class in self.image_processor_list:
# encode them
image_processing = image_processing_class()
encoding = image_processing(images=image, annotations=target, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->GroundingDino
def test_batched_coco_detection_annotations(self):
image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt") as f:
target = json.loads(f.read())
annotations_0 = {"image_id": 39769, "annotations": target}
annotations_1 = {"image_id": 39769, "annotations": target}
# Adjust the bounding boxes for the resized image
w_0, h_0 = image_0.size
w_1, h_1 = image_1.size
for i in range(len(annotations_1["annotations"])):
coords = annotations_1["annotations"][i]["bbox"]
new_bbox = [
coords[0] * w_1 / w_0,
coords[1] * h_1 / h_0,
coords[2] * w_1 / w_0,
coords[3] * h_1 / h_0,
]
annotations_1["annotations"][i]["bbox"] = new_bbox
images = [image_0, image_1]
annotations = [annotations_0, annotations_1]
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class()
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
return_tensors="pt", # do_convert_annotations=True
)
# Check the pixel values have been padded
postprocessed_height, postprocessed_width = 800, 1066
expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# Check the bounding boxes have been adjusted for padded images
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
expected_boxes_0 = torch.tensor(
[
[0.6879, 0.4609, 0.0755, 0.3691],
[0.2118, 0.3359, 0.2601, 0.1566],
[0.5011, 0.5000, 0.9979, 1.0000],
[0.5010, 0.5020, 0.9979, 0.9959],
[0.3284, 0.5944, 0.5884, 0.8112],
[0.8394, 0.5445, 0.3213, 0.9110],
]
)
expected_boxes_1 = torch.tensor(
[
[0.4130, 0.2765, 0.0453, 0.2215],
[0.1272, 0.2016, 0.1561, 0.0940],
[0.3757, 0.4933, 0.7488, 0.9865],
[0.3759, 0.5002, 0.7492, 0.9955],
[0.1971, 0.5456, 0.3532, 0.8646],
[0.5790, 0.4115, 0.3430, 0.7161],
]
)
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
encoding = image_processing(
images=images,
annotations=annotations,
return_segmentation_masks=True,
do_convert_annotations=False,
return_tensors="pt",
)
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
# Convert to absolute coordinates
unnormalized_boxes_0 = torch.vstack(
[
expected_boxes_0[:, 0] * postprocessed_width,
expected_boxes_0[:, 1] * postprocessed_height,
expected_boxes_0[:, 2] * postprocessed_width,
expected_boxes_0[:, 3] * postprocessed_height,
]
).T
unnormalized_boxes_1 = torch.vstack(
[
expected_boxes_1[:, 0] * postprocessed_width,
expected_boxes_1[:, 1] * postprocessed_height,
expected_boxes_1[:, 2] * postprocessed_width,
expected_boxes_1[:, 3] * postprocessed_height,
]
).T
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
expected_boxes_0 = torch.vstack(
[
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
]
).T
expected_boxes_1 = torch.vstack(
[
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
@slow
# Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_panoptic_annotations with DeformableDetr->GroundingDino
def test_call_pytorch_with_coco_panoptic_annotations(self):
# prepare image, target and masks_path
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt") as f:
target = json.loads(f.read())
target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
for image_processing_class in self.image_processor_list:
# encode them
image_processing = image_processing_class(format="coco_panoptic")
encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
torch.testing.assert_close(encoding["pixel_values"][0, 0, 0, :3], expected_slice, rtol=1e-4, atol=1e-4)
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
torch.testing.assert_close(encoding["labels"][0]["area"], expected_area)
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
torch.testing.assert_close(encoding["labels"][0]["boxes"][0], expected_boxes_slice, rtol=1e-3, atol=1e-3)
# verify image_id
expected_image_id = torch.tensor([39769])
torch.testing.assert_close(encoding["labels"][0]["image_id"], expected_image_id)
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
torch.testing.assert_close(encoding["labels"][0]["iscrowd"], expected_is_crowd)
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
torch.testing.assert_close(encoding["labels"][0]["class_labels"], expected_class_labels)
# verify masks
expected_masks_sum = 822873
relative_error = torch.abs(encoding["labels"][0]["masks"].sum() - expected_masks_sum) / expected_masks_sum
self.assertTrue(relative_error < 1e-3)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
torch.testing.assert_close(encoding["labels"][0]["orig_size"], expected_orig_size)
# verify size
expected_size = torch.tensor([800, 1066])
torch.testing.assert_close(encoding["labels"][0]["size"], expected_size)
@slow
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->GroundingDino
def test_batched_coco_panoptic_annotations(self):
# prepare image, target and masks_path
image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt") as f:
target = json.loads(f.read())
annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
w_0, h_0 = image_0.size
w_1, h_1 = image_1.size
for i in range(len(annotation_1["segments_info"])):
coords = annotation_1["segments_info"][i]["bbox"]
new_bbox = [
coords[0] * w_1 / w_0,
coords[1] * h_1 / h_0,
coords[2] * w_1 / w_0,
coords[3] * h_1 / h_0,
]
annotation_1["segments_info"][i]["bbox"] = new_bbox
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
images = [image_0, image_1]
annotations = [annotation_0, annotation_1]
for image_processing_class in self.image_processor_list:
# encode them
image_processing = image_processing_class(format="coco_panoptic")
encoding = image_processing(
images=images,
annotations=annotations,
masks_path=masks_path,
return_tensors="pt",
return_segmentation_masks=True,
)
# Check the pixel values have been padded
postprocessed_height, postprocessed_width = 800, 1066
expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
# Check the bounding boxes have been adjusted for padded images
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
expected_boxes_0 = torch.tensor(
[
[0.2625, 0.5437, 0.4688, 0.8625],
[0.7719, 0.4104, 0.4531, 0.7125],
[0.5000, 0.4927, 0.9969, 0.9854],
[0.1688, 0.2000, 0.2063, 0.0917],
[0.5492, 0.2760, 0.0578, 0.2187],
[0.4992, 0.4990, 0.9984, 0.9979],
]
)
expected_boxes_1 = torch.tensor(
[
[0.1576, 0.3262, 0.2814, 0.5175],
[0.4634, 0.2463, 0.2720, 0.4275],
[0.3002, 0.2956, 0.5985, 0.5913],
[0.1013, 0.1200, 0.1238, 0.0550],
[0.3297, 0.1656, 0.0347, 0.1312],
[0.2997, 0.2994, 0.5994, 0.5987],
]
)
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1e-3, rtol=1e-3)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1e-3, rtol=1e-3)
# Check the masks have also been padded
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
# format and not in the range [0, 1]
encoding = image_processing(
images=images,
annotations=annotations,
masks_path=masks_path,
return_segmentation_masks=True,
do_convert_annotations=False,
return_tensors="pt",
)
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
# Convert to absolute coordinates
unnormalized_boxes_0 = torch.vstack(
[
expected_boxes_0[:, 0] * postprocessed_width,
expected_boxes_0[:, 1] * postprocessed_height,
expected_boxes_0[:, 2] * postprocessed_width,
expected_boxes_0[:, 3] * postprocessed_height,
]
).T
unnormalized_boxes_1 = torch.vstack(
[
expected_boxes_1[:, 0] * postprocessed_width,
expected_boxes_1[:, 1] * postprocessed_height,
expected_boxes_1[:, 2] * postprocessed_width,
expected_boxes_1[:, 3] * postprocessed_height,
]
).T
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
expected_boxes_0 = torch.vstack(
[
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
]
).T
expected_boxes_1 = torch.vstack(
[
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
]
).T
torch.testing.assert_close(encoding["labels"][0]["boxes"], expected_boxes_0, atol=1, rtol=1)
torch.testing.assert_close(encoding["labels"][1]["boxes"], expected_boxes_1, atol=1, rtol=1)
# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_max_width_max_height_resizing_and_pad_strategy with Detr->GroundingDino
def test_max_width_max_height_resizing_and_pad_strategy(self):
for image_processing_class in self.image_processor_list:
image_1 = torch.ones([200, 100, 3], dtype=torch.uint8)
# do_pad=False, max_height=100, max_width=100, image=200x100 -> 100x50
image_processor = image_processing_class(
size={"max_height": 100, "max_width": 100},
do_pad=False,
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 50]))
# do_pad=False, max_height=300, max_width=100, image=200x100 -> 200x100
image_processor = image_processing_class(
size={"max_height": 300, "max_width": 100},
do_pad=False,
)
inputs = image_processor(images=[image_1], return_tensors="pt")
# do_pad=True, max_height=100, max_width=100, image=200x100 -> 100x100
image_processor = image_processing_class(
size={"max_height": 100, "max_width": 100}, do_pad=True, pad_size={"height": 100, "width": 100}
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 100, 100]))
# do_pad=True, max_height=300, max_width=100, image=200x100 -> 300x100
image_processor = image_processing_class(
size={"max_height": 300, "max_width": 100},
do_pad=True,
pad_size={"height": 301, "width": 101},
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 301, 101]))
### Check for batch
image_2 = torch.ones([100, 150, 3], dtype=torch.uint8)
# do_pad=True, max_height=150, max_width=100, images=[200x100, 100x150] -> 150x100
image_processor = image_processing_class(
size={"max_height": 150, "max_width": 100},
do_pad=True,
pad_size={"height": 150, "width": 100},
)
inputs = image_processor(images=[image_1, image_2], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([2, 3, 150, 100]))
def test_longest_edge_shortest_edge_resizing_strategy(self):
image_1 = torch.ones([958, 653, 3], dtype=torch.uint8)
# max size is set; width < height;
# do_pad=False, longest_edge=640, shortest_edge=640, image=958x653 -> 640x436
image_processor = GroundingDinoImageProcessor(
size={"longest_edge": 640, "shortest_edge": 640},
do_pad=False,
)
inputs = image_processor(images=[image_1], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 640, 436]))
image_2 = torch.ones([653, 958, 3], dtype=torch.uint8)
# max size is set; height < width;
# do_pad=False, longest_edge=640, shortest_edge=640, image=653x958 -> 436x640
image_processor = GroundingDinoImageProcessor(
size={"longest_edge": 640, "shortest_edge": 640},
do_pad=False,
)
inputs = image_processor(images=[image_2], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 436, 640]))
image_3 = torch.ones([100, 120, 3], dtype=torch.uint8)
# max size is set; width == size; height > max_size;
# do_pad=False, longest_edge=118, shortest_edge=100, image=120x100 -> 118x98
image_processor = GroundingDinoImageProcessor(
size={"longest_edge": 118, "shortest_edge": 100},
do_pad=False,
)
inputs = image_processor(images=[image_3], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 98, 118]))
image_4 = torch.ones([128, 50, 3], dtype=torch.uint8)
# max size is set; height == size; width < max_size;
# do_pad=False, longest_edge=256, shortest_edge=50, image=50x128 -> 50x128
image_processor = GroundingDinoImageProcessor(
size={"longest_edge": 256, "shortest_edge": 50},
do_pad=False,
)
inputs = image_processor(images=[image_4], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 128, 50]))
image_5 = torch.ones([50, 50, 3], dtype=torch.uint8)
# max size is set; height == width; width < max_size;
# do_pad=False, longest_edge=117, shortest_edge=50, image=50x50 -> 50x50
image_processor = GroundingDinoImageProcessor(
size={"longest_edge": 117, "shortest_edge": 50},
do_pad=False,
)
inputs = image_processor(images=[image_5], return_tensors="pt")
self.assertEqual(inputs["pixel_values"].shape, torch.Size([1, 3, 50, 50]))

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transformers/tests/models/grounding_dino/test_modeling_grounding_dino.py Normal file
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Grounding DINO model."""
import collections
import inspect
import math
import re
import unittest
from functools import cached_property
from datasets import load_dataset
from transformers import (
GroundingDinoConfig,
SwinConfig,
is_torch_available,
is_vision_available,
)
from transformers.testing_utils import (
Expectations,
is_flaky,
require_timm,
require_torch,
require_torch_accelerator,
require_vision,
slow,
torch_device,
)
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GroundingDinoConfig, GroundingDinoForObjectDetection, GroundingDinoModel
from transformers.pytorch_utils import id_tensor_storage
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor
def generate_fake_bounding_boxes(n_boxes):
"""Generate bounding boxes in the format (center_x, center_y, width, height)"""
# Validate the input
if not isinstance(n_boxes, int):
raise TypeError("n_boxes must be an integer")
if n_boxes <= 0:
raise ValueError("n_boxes must be a positive integer")
# Generate random bounding boxes in the format (center_x, center_y, width, height)
bounding_boxes = torch.rand((n_boxes, 4))
# Extract the components
center_x = bounding_boxes[:, 0]
center_y = bounding_boxes[:, 1]
width = bounding_boxes[:, 2]
height = bounding_boxes[:, 3]
# Ensure width and height do not exceed bounds
width = torch.min(width, torch.tensor(1.0))
height = torch.min(height, torch.tensor(1.0))
# Ensure the bounding box stays within the normalized space
center_x = torch.where(center_x - width / 2 < 0, width / 2, center_x)
center_x = torch.where(center_x + width / 2 > 1, 1 - width / 2, center_x)
center_y = torch.where(center_y - height / 2 < 0, height / 2, center_y)
center_y = torch.where(center_y + height / 2 > 1, 1 - height / 2, center_y)
# Combine back into bounding boxes
bounding_boxes = torch.stack([center_x, center_y, width, height], dim=1)
return bounding_boxes
class GroundingDinoModelTester:
def __init__(
self,
parent,
batch_size=4,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
num_queries=2,
num_channels=3,
image_size=98,
n_targets=8,
num_labels=2,
num_feature_levels=4,
encoder_n_points=2,
decoder_n_points=6,
max_text_len=7,
):
self.parent = parent
self.batch_size = batch_size
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_queries = num_queries
self.num_channels = num_channels
self.image_size = image_size
self.n_targets = n_targets
self.num_labels = num_labels
self.num_feature_levels = num_feature_levels
self.encoder_n_points = encoder_n_points
self.decoder_n_points = decoder_n_points
self.max_text_len = max_text_len
# we also set the expected seq length for both encoder and decoder
self.encoder_seq_length_vision = (
math.ceil(self.image_size / 8) ** 2
+ math.ceil(self.image_size / 16) ** 2
+ math.ceil(self.image_size / 32) ** 2
+ math.ceil(self.image_size / 64) ** 2
)
self.encoder_seq_length_text = self.max_text_len
self.decoder_seq_length = self.num_queries
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
# When using `GroundingDino` the text input template is '{label1}. {label2}. {label3. ... {labelN}.'
# Therefore to avoid errors when running tests with `labels` `input_ids` have to follow this structure.
# Otherwise when running `build_label_maps` it will throw an error when trying to split the input_ids into segments.
input_ids = torch.tensor([101, 3869, 1012, 11420, 3869, 1012, 102], device=torch_device)
input_ids = input_ids.unsqueeze(0).expand(self.batch_size, -1)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = generate_fake_bounding_boxes(self.n_targets).to(torch_device)
target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device)
labels.append(target)
config = self.get_config()
return config, pixel_values, pixel_mask, input_ids, labels
def get_config(self):
swin_config = SwinConfig(
window_size=7,
embed_dim=8,
depths=[1, 1, 1, 1],
num_heads=[1, 1, 1, 1],
image_size=self.image_size,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
)
text_backbone = {
"hidden_size": 8,
"num_hidden_layers": 2,
"num_attention_heads": 2,
"intermediate_size": 8,
"max_position_embeddings": 8,
"model_type": "bert",
}
return GroundingDinoConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
num_queries=self.num_queries,
num_labels=self.num_labels,
num_feature_levels=self.num_feature_levels,
encoder_n_points=self.encoder_n_points,
decoder_n_points=self.decoder_n_points,
use_timm_backbone=False,
backbone_config=swin_config,
max_text_len=self.max_text_len,
text_config=text_backbone,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, input_ids, labels = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask, "input_ids": input_ids}
return config, inputs_dict
def create_and_check_model(self, config, pixel_values, pixel_mask, input_ids, labels):
model = GroundingDinoModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size))
def create_and_check_object_detection_head_model(self, config, pixel_values, pixel_mask, input_ids, labels):
model = GroundingDinoForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
@require_torch
class GroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GroundingDinoModel, GroundingDinoForObjectDetection) if is_torch_available() else ()
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
pipeline_model_mapping = (
{"image-feature-extraction": GroundingDinoModel, "zero-shot-object-detection": GroundingDinoForObjectDetection}
if is_torch_available()
else {}
)
# special case for head models
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "GroundingDinoForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
target["masks"] = torch.ones(
self.model_tester.n_targets,
self.model_tester.image_size,
self.model_tester.image_size,
device=torch_device,
dtype=torch.float,
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = GroundingDinoModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=GroundingDinoConfig,
has_text_modality=False,
common_properties=["d_model", "encoder_attention_heads", "decoder_attention_heads"],
)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_object_detection_head_model(*config_and_inputs)
@unittest.skip(reason="Grounding DINO does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Grounding DINO does not have a get_input_embeddings method")
def test_model_get_set_embeddings(self):
pass
@unittest.skip(reason="Grounding DINO does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class._from_config(config, attn_implementation="eager")
config = model.config
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.encoder_n_points,
],
)
out_len = len(outputs)
correct_outlen = 12
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes and input_ids
if model_class.__name__ == "GroundingDinoForObjectDetection":
correct_outlen += 3
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions[0]
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, self.model_tester.num_queries, self.model_tester.num_queries],
)
# cross attentions
cross_attentions = outputs.decoder_attentions[-1]
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.decoder_n_points,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 3, len(outputs))
self_attentions = outputs.encoder_attentions[-1]
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
self.model_tester.num_feature_levels,
self.model_tester.encoder_n_points,
],
)
# overwrite since hidden_states are called encoder_text_hidden_states
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_vision_hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = self.model_tester.encoder_seq_length_vision
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_len, self.model_tester.hidden_size],
)
hidden_states = outputs.encoder_text_hidden_states
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = self.model_tester.encoder_seq_length_text
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_len, self.model_tester.hidden_size],
)
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_vision_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0][0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
cross_attentions = outputs.decoder_attentions[-1][0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values", "input_ids"]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_different_timm_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# let's pick a random timm backbone
config.backbone = "tf_mobilenetv3_small_075"
config.use_timm_backbone = True
config.backbone_config = None
config.backbone_kwargs = {"in_chans": 3, "out_indices": (2, 3, 4)}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "GroundingDinoForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
config.max_text_len,
)
self.assertEqual(outputs.logits.shape, expected_shape)
self.assertTrue(outputs)
@require_timm
def test_hf_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Load a pretrained HF checkpoint as backbone
config.backbone = "microsoft/resnet-18"
config.backbone_config = None
config.use_timm_backbone = False
config.use_pretrained_backbone = True
config.backbone_kwargs = {"out_indices": [2, 3, 4]}
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "GroundingDinoForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
config.max_text_len,
)
self.assertEqual(outputs.logits.shape, expected_shape)
self.assertTrue(outputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if (
"level_embed" in name
or "sampling_offsets.bias" in name
or "text_param" in name
or "vision_param" in name
or "value_proj" in name
or "output_proj" in name
or "reference_points" in name
or "vision_proj" in name
or "text_proj" in name
):
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# Copied from tests.models.deformable_detr.test_modeling_deformable_detr.DeformableDetrModelTest.test_two_stage_training with DeformableDetr->GroundingDino
def test_two_stage_training(self):
model_class = GroundingDinoForObjectDetection
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
config.two_stage = True
config.auxiliary_loss = True
config.with_box_refine = True
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_tied_weights_keys(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
config.tie_word_embeddings = True
for model_class in self.all_model_classes:
model_tied = model_class(config)
ptrs = collections.defaultdict(list)
for name, tensor in model_tied.state_dict().items():
ptrs[id_tensor_storage(tensor)].append(name)
# These are all the pointers of shared tensors.
tied_params = [names for _, names in ptrs.items() if len(names) > 1]
tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else []
# Detect we get a hit for each key
for key in tied_weight_keys:
if not any(re.search(key, p) for group in tied_params for p in group):
raise ValueError(f"{key} is not a tied weight key for {model_class}.")
# Removed tied weights found from tied params -> there should only be one left after
for key in tied_weight_keys:
for i in range(len(tied_params)):
tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None]
# GroundingDino when sharing weights also uses the shared ones in GroundingDinoDecoder
# Therefore, differently from DeformableDetr, we expect the group lens to be 2
# one for self.bbox_embed in GroundingDinoForObjectDetection and another one
# in the decoder
tied_params = [group for group in tied_params if len(group) > 2]
self.assertListEqual(
tied_params,
[],
f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.",
)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
def prepare_text():
text = "a cat."
return text
@require_timm
@require_vision
@slow
class GroundingDinoModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny") if is_vision_available() else None
def test_inference_object_detection_head(self):
model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device)
processor = self.default_processor
image = prepare_img()
text = prepare_text()
encoding = processor(images=image, text=text, return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**encoding)
expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.d_model))
self.assertEqual(outputs.logits.shape, expected_shape_logits)
expectations = Expectations(
{
(None, None): [[0.7674, 0.4136, 0.4572], [0.2566, 0.5463, 0.4760], [0.2585, 0.5442, 0.4641]],
("cuda", 8): [[0.7674, 0.4135, 0.4571], [0.2566, 0.5463, 0.4760], [0.2585, 0.5442, 0.4640]],
}
)
expected_boxes = torch.tensor(expectations.get_expectation()).to(torch_device)
expectations = Expectations(
{
(None, None): [[-4.8913, -0.1900, -0.2161], [-4.9653, -0.3719, -0.3950], [-5.9599, -3.3765, -3.3104]],
("cuda", 8): [[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]],
}
)
expected_logits = torch.tensor(expectations.get_expectation()).to(torch_device)
torch.testing.assert_close(outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
expected_shape_boxes = torch.Size((1, model.config.num_queries, 4))
self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_boxes, rtol=2e-4, atol=2e-4)
# verify postprocessing
results = processor.image_processor.post_process_object_detection(
outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
)[0]
expectations = Expectations(
{
(None, None): [[0.4526, 0.4082]],
("cuda", 8): [0.4524, 0.4074],
}
)
expected_scores = torch.tensor(expectations.get_expectation()).to(torch_device)
expectations = Expectations(
{
(None, None): [344.8143, 23.1796, 637.4004, 373.8295],
("cuda", 8): [344.8210, 23.1831, 637.3943, 373.8227],
}
)
expected_slice_boxes = torch.tensor(expectations.get_expectation()).to(torch_device)
self.assertEqual(len(results["scores"]), 2)
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
# verify grounded postprocessing
expected_labels = ["a cat", "a cat"]
results = processor.post_process_grounded_object_detection(
outputs=outputs,
input_ids=encoding.input_ids,
threshold=0.35,
text_threshold=0.3,
target_sizes=[(image.height, image.width)],
)[0]
torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes, rtol=1e-2, atol=1e-2)
self.assertListEqual(results["text_labels"], expected_labels)
@require_torch_accelerator
@is_flaky()
def test_inference_object_detection_head_equivalence_cpu_accelerator(self):
processor = self.default_processor
image = prepare_img()
text = prepare_text()
encoding = processor(images=image, text=text, return_tensors="pt")
# 1. run model on CPU
model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny")
with torch.no_grad():
cpu_outputs = model(**encoding)
# 2. run model on accelerator
model.to(torch_device)
encoding = encoding.to(torch_device)
with torch.no_grad():
gpu_outputs = model(**encoding)
# 3. assert equivalence
for key in cpu_outputs:
torch.testing.assert_close(cpu_outputs[key], gpu_outputs[key].cpu(), rtol=1e-3, atol=1e-3)
expected_logits = torch.tensor(
[[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]]
)
torch.testing.assert_close(cpu_outputs.logits[0, :3, :3], expected_logits, rtol=1e-3, atol=1e-3)
# assert postprocessing
results_cpu = processor.image_processor.post_process_object_detection(
cpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
)[0]
result_gpu = processor.image_processor.post_process_object_detection(
gpu_outputs, threshold=0.35, target_sizes=[(image.height, image.width)]
)[0]
torch.testing.assert_close(results_cpu["scores"], result_gpu["scores"].cpu(), rtol=1e-3, atol=1e-3)
torch.testing.assert_close(results_cpu["boxes"], result_gpu["boxes"].cpu(), rtol=1e-3, atol=1e-3)
@is_flaky()
def test_cross_attention_mask(self):
model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device)
processor = self.default_processor
image = prepare_img()
text1 = "a cat."
text2 = "a remote control."
text_batched = [text1, text2]
encoding1 = processor(images=image, text=text1, return_tensors="pt").to(torch_device)
encoding2 = processor(images=image, text=text2, return_tensors="pt").to(torch_device)
# If we batch the text and cross attention masking is working the batched result should be equal to
# The single text result
encoding_batched = processor(
images=[image] * len(text_batched), text=text_batched, padding="longest", return_tensors="pt"
).to(torch_device)
with torch.no_grad():
outputs1 = model(**encoding1)
outputs2 = model(**encoding2)
outputs_batched = model(**encoding_batched)
torch.testing.assert_close(outputs1.logits, outputs_batched.logits[:1], rtol=1e-3, atol=1e-3)
# For some reason 12 elements are > 1e-3, but the rest are fine
self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3))
def test_grounding_dino_loss(self):
ds = load_dataset("EduardoPacheco/aquarium-sample", split="train")
image_processor = self.default_processor.image_processor
tokenizer = self.default_processor.tokenizer
id2label = {0: "fish", 1: "jellyfish", 2: "penguins", 3: "sharks", 4: "puffins", 5: "stingrays", 6: "starfish"}
prompt = ". ".join(id2label.values()) + "."
text_inputs = tokenizer([prompt, prompt], return_tensors="pt")
image_inputs = image_processor(
images=list(ds["image"]), annotations=list(ds["annotations"]), return_tensors="pt"
)
# Passing auxiliary_loss=True to compare with the expected loss
model = GroundingDinoForObjectDetection.from_pretrained(
"IDEA-Research/grounding-dino-tiny",
auxiliary_loss=True,
)
# Interested in the loss only
model.eval()
with torch.no_grad():
outputs = model(**text_inputs, **image_inputs)
# Loss differs by CPU and accelerator, also this can be changed in future.
expected_loss_dicts = Expectations(
{
("xpu", 3): {
"loss_ce": torch.tensor(1.1147),
"loss_bbox": torch.tensor(0.2031),
"loss_giou": torch.tensor(0.5819),
"loss_ce_0": torch.tensor(1.1941),
"loss_bbox_0": torch.tensor(0.1978),
"loss_giou_0": torch.tensor(0.5524),
"loss_ce_1": torch.tensor(1.1621),
"loss_bbox_1": torch.tensor(0.1909),
"loss_giou_1": torch.tensor(0.5892),
"loss_ce_2": torch.tensor(1.1641),
"loss_bbox_2": torch.tensor(0.1892),
"loss_giou_2": torch.tensor(0.5626),
"loss_ce_3": torch.tensor(1.1943),
"loss_bbox_3": torch.tensor(0.1941),
"loss_giou_3": torch.tensor(0.5592),
"loss_ce_4": torch.tensor(1.0956),
"loss_bbox_4": torch.tensor(0.2037),
"loss_giou_4": torch.tensor(0.5813),
"loss_ce_enc": torch.tensor(16226.3164),
"loss_bbox_enc": torch.tensor(0.3063),
"loss_giou_enc": torch.tensor(0.7380),
},
("cuda", None): {
"loss_ce": torch.tensor(1.1147),
"loss_bbox": torch.tensor(0.2031),
"loss_giou": torch.tensor(0.5819),
"loss_ce_0": torch.tensor(1.1941),
"loss_bbox_0": torch.tensor(0.1978),
"loss_giou_0": torch.tensor(0.5524),
"loss_ce_1": torch.tensor(1.1621),
"loss_bbox_1": torch.tensor(0.1909),
"loss_giou_1": torch.tensor(0.5892),
"loss_ce_2": torch.tensor(1.1641),
"loss_bbox_2": torch.tensor(0.1892),
"loss_giou_2": torch.tensor(0.5626),
"loss_ce_3": torch.tensor(1.1943),
"loss_bbox_3": torch.tensor(0.1941),
"loss_giou_3": torch.tensor(0.5607),
"loss_ce_4": torch.tensor(1.0956),
"loss_bbox_4": torch.tensor(0.2008),
"loss_giou_4": torch.tensor(0.5836),
"loss_ce_enc": torch.tensor(16226.3164),
"loss_bbox_enc": torch.tensor(0.3063),
"loss_giou_enc": torch.tensor(0.7380),
},
}
) # fmt: skip
expected_loss_dict = expected_loss_dicts.get_expectation()
expected_loss = torch.tensor(32482.2305)
for key in expected_loss_dict:
torch.testing.assert_close(outputs.loss_dict[key], expected_loss_dict[key], rtol=1e-5, atol=1e-3)
self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=1e-3))

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transformers/tests/models/grounding_dino/test_processing_grounding_dino.py Normal file
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@@ -0,0 +1,309 @@
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
from typing import Optional
import pytest
from transformers import BertTokenizer, BertTokenizerFast, GroundingDinoProcessor
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
from ...test_processing_common import ProcessorTesterMixin
if is_torch_available():
import torch
from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput
if is_vision_available():
from transformers import GroundingDinoImageProcessor
@require_torch
@require_vision
class GroundingDinoProcessorTest(ProcessorTesterMixin, unittest.TestCase):
from_pretrained_id = "IDEA-Research/grounding-dino-base"
processor_class = GroundingDinoProcessor
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
vocab_tokens = ["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]","want","##want","##ed","wa","un","runn","##ing",",","low","lowest"] # fmt: skip
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
image_processor_map = {
"do_resize": True,
"size": None,
"do_normalize": True,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
"do_rescale": True,
"rescale_factor": 1 / 255,
"do_pad": True,
}
cls.image_processor_file = os.path.join(cls.tmpdirname, IMAGE_PROCESSOR_NAME)
with open(cls.image_processor_file, "w", encoding="utf-8") as fp:
json.dump(image_processor_map, fp)
image_processor = GroundingDinoImageProcessor()
tokenizer = BertTokenizer.from_pretrained(cls.from_pretrained_id)
processor = GroundingDinoProcessor(image_processor, tokenizer)
processor.save_pretrained(cls.tmpdirname)
cls.batch_size = 7
cls.num_queries = 5
cls.embed_dim = 5
cls.seq_length = 5
def prepare_text_inputs(self, batch_size: Optional[int] = None, **kwargs):
labels = ["a cat", "remote control"]
labels_longer = ["a person", "a car", "a dog", "a cat"]
if batch_size is None:
return labels
if batch_size < 1:
raise ValueError("batch_size must be greater than 0")
if batch_size == 1:
return [labels]
return [labels, labels_longer] + [labels] * (batch_size - 2)
@classmethod
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.get_tokenizer with CLIP->Bert
def get_tokenizer(cls, **kwargs):
return BertTokenizer.from_pretrained(cls.tmpdirname, **kwargs)
@classmethod
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.get_rust_tokenizer with CLIP->Bert
def get_rust_tokenizer(cls, **kwargs):
return BertTokenizerFast.from_pretrained(cls.tmpdirname, **kwargs)
@classmethod
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.get_image_processor with CLIP->GroundingDino
def get_image_processor(cls, **kwargs):
return GroundingDinoImageProcessor.from_pretrained(cls.tmpdirname, **kwargs)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
def get_fake_grounding_dino_output(self):
torch.manual_seed(42)
return GroundingDinoObjectDetectionOutput(
pred_boxes=torch.rand(self.batch_size, self.num_queries, 4),
logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim),
input_ids=self.get_fake_grounding_dino_input_ids(),
)
def get_fake_grounding_dino_input_ids(self):
input_ids = torch.tensor([101, 1037, 4937, 1012, 102])
return torch.stack([input_ids] * self.batch_size, dim=0)
def test_post_process_grounded_object_detection(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
grounding_dino_output = self.get_fake_grounding_dino_output()
post_processed = processor.post_process_grounded_object_detection(grounding_dino_output)
self.assertEqual(len(post_processed), self.batch_size)
self.assertEqual(list(post_processed[0].keys()), ["scores", "boxes", "text_labels", "labels"])
self.assertEqual(post_processed[0]["boxes"].shape, (self.num_queries, 4))
self.assertEqual(post_processed[0]["scores"].shape, (self.num_queries,))
expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220])
torch.testing.assert_close(post_processed[0]["scores"], expected_scores, rtol=1e-4, atol=1e-4)
expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947])
torch.testing.assert_close(post_processed[0]["boxes"][0], expected_box_slice, rtol=1e-4, atol=1e-4)
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.test_save_load_pretrained_default with CLIP->GroundingDino,GroundingDinoTokenizer->BertTokenizer
def test_save_load_pretrained_default(self):
tokenizer_slow = self.get_tokenizer()
tokenizer_fast = self.get_rust_tokenizer()
image_processor = self.get_image_processor()
with tempfile.TemporaryDirectory() as tmpdir:
processor_slow = GroundingDinoProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
processor_slow.save_pretrained(tmpdir)
processor_slow = GroundingDinoProcessor.from_pretrained(tmpdir, use_fast=False)
processor_fast = GroundingDinoProcessor(tokenizer=tokenizer_fast, image_processor=image_processor)
processor_fast.save_pretrained(tmpdir)
processor_fast = GroundingDinoProcessor.from_pretrained(tmpdir)
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer, BertTokenizer)
self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast)
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor, GroundingDinoImageProcessor)
self.assertIsInstance(processor_fast.image_processor, GroundingDinoImageProcessor)
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.test_save_load_pretrained_additional_features with CLIP->GroundingDino,GroundingDinoTokenizer->BertTokenizer
def test_save_load_pretrained_additional_features(self):
with tempfile.TemporaryDirectory() as tmpdir:
processor = GroundingDinoProcessor(
tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()
)
processor.save_pretrained(tmpdir)
tokenizer_add_kwargs = BertTokenizer.from_pretrained(tmpdir, bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = GroundingDinoImageProcessor.from_pretrained(
tmpdir, do_normalize=False, padding_value=1.0
)
processor = GroundingDinoProcessor.from_pretrained(
tmpdir, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, BertTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, GroundingDinoImageProcessor)
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.test_image_processor with CLIP->GroundingDino
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_image_proc = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_image_proc:
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.test_tokenizer with CLIP->GroundingDino
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok:
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertSetEqual(
set(inputs.keys()), {"input_ids", "token_type_ids", "attention_mask", "pixel_values", "pixel_mask"}
)
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
# Copied from tests.models.clip.test_processing_clip.CLIPProcessorTest.test_tokenizer_decode with CLIP->GroundingDino
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_text_preprocessing_equivalence(self):
processor = GroundingDinoProcessor.from_pretrained(self.tmpdirname)
# check for single input
formatted_labels = "a cat. a remote control."
labels = ["a cat", "a remote control"]
inputs1 = processor(text=formatted_labels, return_tensors="pt")
inputs2 = processor(text=labels, return_tensors="pt")
self.assertTrue(
torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
f"Input ids are not equal for single input: {inputs1['input_ids']} != {inputs2['input_ids']}",
)
# check for batched input
formatted_labels = ["a cat. a remote control.", "a car. a person."]
labels = [["a cat", "a remote control"], ["a car", "a person"]]
inputs1 = processor(text=formatted_labels, return_tensors="pt", padding=True)
inputs2 = processor(text=labels, return_tensors="pt", padding=True)
self.assertTrue(
torch.allclose(inputs1["input_ids"], inputs2["input_ids"]),
f"Input ids are not equal for batched input: {inputs1['input_ids']} != {inputs2['input_ids']}",
)
def test_processor_text_has_no_visual(self):
# Overwritten: text inputs have to be nested as well
processor = self.get_processor()
text = self.prepare_text_inputs(batch_size=3, modalities="image")
image_inputs = self.prepare_image_inputs(batch_size=3)
processing_kwargs = {"return_tensors": "pt", "padding": True}
# Call with nested list of vision inputs
image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs]
inputs_dict_nested = {"text": text, "images": image_inputs_nested}
inputs = processor(**inputs_dict_nested, **processing_kwargs)
self.assertTrue(self.text_input_name in inputs)
# Call with one of the samples with no associated vision input
plain_text = ["lower newer"]
image_inputs_nested[0] = []
text[0] = plain_text
inputs_dict_no_vision = {"text": text, "images": image_inputs_nested}
inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs)
# Check that text samples are same and are expanded with placeholder tokens correctly. First sample
# has no vision input associated, so we skip it and check it has no vision
self.assertListEqual(
inputs[self.text_input_name][1:].tolist(), inputs_nested[self.text_input_name][1:].tolist()
)