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[Proposal] Breaking change zero-shot-object-detection for improved consistency. (#20280)

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* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.

This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.

The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.

The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.

The pipeline is changed to `ChunkPipeline` to support batching cleanly.

This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.

**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.

**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.

**Breaking change**: The types is now simplified and flattened:

`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.

It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.

* Fixing the doctests.

* Behind is_torch_available.
This commit is contained in:
Nicolas Patry
2022-11-18 15:57:28 +01:00
committed by GitHub
parent 84c9cc6d15
commit 8e777b3ba4
2 changed files with 133 additions and 255 deletions
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240
src/transformers/pipelines/zero_shot_object_detection.py
View File

@@ -1,17 +1,7 @@
from typing import Dict, List, Union
from typing import Any, Dict, List, Union
import numpy as np
from ..tokenization_utils_base import BatchEncoding
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
@@ -22,13 +12,15 @@ if is_vision_available():
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
logger = logging.get_logger(__name__)
@add_end_docstrings(PIPELINE_INIT_ARGS)
class ZeroShotObjectDetectionPipeline(Pipeline):
class ZeroShotObjectDetectionPipeline(ChunkPipeline):
"""
Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of
objects when you provide an image and a set of `candidate_labels`.
@@ -43,13 +35,13 @@ class ZeroShotObjectDetectionPipeline(Pipeline):
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... candidate_labels=["cat", "couch"],
... )
[[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]]
[{'score': 0.287, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.254, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.121, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]
>>> detector(
... "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png",
... candidate_labels=["head", "bird"],
... )
[[{'score': 0.119, 'label': 'bird', 'box': {'xmin': 71, 'ymin': 170, 'xmax': 410, 'ymax': 508}}]]
[{'score': 0.119, 'label': 'bird', 'box': {'xmin': 71, 'ymin': 170, 'xmax': 410, 'ymax': 508}}]
```
[Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
@@ -72,24 +64,45 @@ class ZeroShotObjectDetectionPipeline(Pipeline):
def __call__(
self,
images: Union[str, List[str], "Image.Image", List["Image.Image"]],
text_queries: Union[str, List[str], List[List[str]]] = None,
image: Union[str, "Image.Image", List[Dict[str, Any]]],
candidate_labels: Union[str, List[str]] = None,
**kwargs
):
"""
Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
Args:
images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
image (`str`, `PIL.Image` or `List[Dict[str, Any]]`):
The pipeline handles three types of images:
- A string containing an http url pointing to an image
- A string containing a local path to an image
- An image loaded in PIL directly
text_queries (`str` or `List[str]` or `List[List[str]]`): Text queries to query the target image with.
If given multiple images, `text_queries` should be provided as a list of lists, where each nested list
contains the text queries for the corresponding image.
You can use this parameter to send directly a list of images, or a dataset or a generator like so:
```python
>>> from transformers import pipeline
>>> detector = pipeline(model="google/owlvit-base-patch32", task="zero-shot-object-detection")
>>> detector(
... [
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... {
... "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
... "candidate_labels": ["cat", "couch"],
... },
... ]
... )
[[{'score': 0.286811888217926, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.2537279725074768, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.12082888185977936, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}], [{'score': 0.286811888217926, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}}, {'score': 0.2537279725074768, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}}, {'score': 0.12082888185977936, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}}]]
```
candidate_labels (`str` or `List[str]` or `List[List[str]]`):
What the model should recognize in the image.
threshold (`float`, *optional*, defaults to 0.1):
The probability necessary to make a prediction.
@@ -108,28 +121,13 @@ class ZeroShotObjectDetectionPipeline(Pipeline):
- **box** (`Dict[str,int]`) -- Bounding box of the detected object in image's original size. It is a
dictionary with `x_min`, `x_max`, `y_min`, `y_max` keys.
"""
if "candidate_labels" in kwargs:
text_queries = kwargs.pop("candidate_labels")
if isinstance(text_queries, str) or (isinstance(text_queries, List) and not isinstance(text_queries[0], List)):
if isinstance(images, (str, Image.Image)):
inputs = {"images": images, "text_queries": text_queries}
elif isinstance(images, List):
assert len(images) == 1, "Input text_queries and images must have correspondance"
inputs = {"images": images[0], "text_queries": text_queries}
else:
raise TypeError(f"Innapropriate type of images: {type(images)}")
if "text_queries" in kwargs:
candidate_labels = kwargs.pop("text_queries")
elif isinstance(text_queries, str) or (isinstance(text_queries, List) and isinstance(text_queries[0], List)):
if isinstance(images, (Image.Image, str)):
images = [images]
assert len(images) == len(text_queries), "Input text_queries and images must have correspondance"
inputs = {"images": images, "text_queries": text_queries}
if isinstance(image, (str, Image.Image)):
inputs = {"image": image, "candidate_labels": candidate_labels}
else:
"""
Supports the following format
- {"images": images, "text_queries": text_queries}
"""
inputs = images
inputs = image
results = super().__call__(inputs, **kwargs)
return results
@@ -142,49 +140,54 @@ class ZeroShotObjectDetectionPipeline(Pipeline):
return {}, {}, postprocess_params
def preprocess(self, inputs):
if not isinstance(inputs["images"], List):
inputs["images"] = [inputs["images"]]
images = [load_image(img) for img in inputs["images"]]
text_queries = inputs["text_queries"]
if isinstance(text_queries, str) or isinstance(text_queries[0], str):
text_queries = [text_queries]
image = load_image(inputs["image"])
candidate_labels = inputs["candidate_labels"]
if isinstance(candidate_labels, str):
candidate_labels = candidate_labels.split(",")
target_sizes = [torch.IntTensor([[img.height, img.width]]) for img in images]
target_sizes = torch.cat(target_sizes)
inputs = self._processor(text=inputs["text_queries"], images=images, return_tensors="pt")
return {"target_sizes": target_sizes, "text_queries": text_queries, **inputs}
target_size = torch.tensor([[image.height, image.width]], dtype=torch.int32)
for i, candidate_label in enumerate(candidate_labels):
text_inputs = self.tokenizer(candidate_label, return_tensors=self.framework)
image_features = self.feature_extractor(image, return_tensors=self.framework)
yield {
"is_last": i == len(candidate_labels) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _forward(self, model_inputs):
target_sizes = model_inputs.pop("target_sizes")
text_queries = model_inputs.pop("text_queries")
target_size = model_inputs.pop("target_size")
candidate_label = model_inputs.pop("candidate_label")
is_last = model_inputs.pop("is_last")
outputs = self.model(**model_inputs)
model_outputs = outputs.__class__({"target_sizes": target_sizes, "text_queries": text_queries, **outputs})
model_outputs = {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
return model_outputs
def postprocess(self, model_outputs, threshold=0.1, top_k=None):
texts = model_outputs["text_queries"]
outputs = self.feature_extractor.post_process(
outputs=model_outputs, target_sizes=model_outputs["target_sizes"]
)
results = []
for i in range(len(outputs)):
keep = outputs[i]["scores"] >= threshold
labels = outputs[i]["labels"][keep].tolist()
scores = outputs[i]["scores"][keep].tolist()
boxes = [self._get_bounding_box(box) for box in outputs[i]["boxes"][keep]]
for model_output in model_outputs:
label = model_output["candidate_label"]
model_output = BaseModelOutput(model_output)
outputs = self.feature_extractor.post_process(
outputs=model_output, target_sizes=model_output["target_size"]
)[0]
keep = outputs["scores"] >= threshold
result = [
{"score": score, "label": texts[i][label], "box": box}
for score, label, box in zip(scores, labels, boxes)
]
for index in keep.nonzero():
score = outputs["scores"][index].item()
box = self._get_bounding_box(outputs["boxes"][index][0])
result = sorted(result, key=lambda x: x["score"], reverse=True)
if top_k:
result = result[:top_k]
results.append(result)
result = {"score": score, "label": label, "box": box}
results.append(result)
results = sorted(results, key=lambda x: x["score"], reverse=True)
if top_k:
results = results[:top_k]
return results
@@ -208,94 +211,3 @@ class ZeroShotObjectDetectionPipeline(Pipeline):
"ymax": ymax,
}
return bbox
# Replication of OwlViTProcessor __call__ method, since pipelines don't auto infer processor's yet!
def _processor(self, text=None, images=None, padding="max_length", return_tensors="np", **kwargs):
"""
Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
`kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
doctsring of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`,
`List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify at least one text or image. Both cannot be none.")
if text is not None:
if isinstance(text, str) or (isinstance(text, List) and not isinstance(text[0], List)):
encodings = [self.tokenizer(text, padding=padding, return_tensors=return_tensors, **kwargs)]
elif isinstance(text, List) and isinstance(text[0], List):
encodings = []
# Maximum number of queries across batch
max_num_queries = max([len(t) for t in text])
# Pad all batch samples to max number of text queries
for t in text:
if len(t) != max_num_queries:
t = t + [" "] * (max_num_queries - len(t))
encoding = self.tokenizer(t, padding=padding, return_tensors=return_tensors, **kwargs)
encodings.append(encoding)
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings")
if return_tensors == "np":
input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
elif return_tensors == "pt" and is_torch_available():
import torch
input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0)
attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0)
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
input_ids = tf.stack([encoding["input_ids"] for encoding in encodings], axis=0)
attention_mask = tf.stack([encoding["attention_mask"] for encoding in encodings], axis=0)
else:
raise ValueError("Target return tensor type could not be returned")
encoding = BatchEncoding()
encoding["input_ids"] = input_ids
encoding["attention_mask"] = attention_mask
if images is not None:
image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)

148
tests/pipelines/test_pipelines_zero_shot_object_detection.py
View File

@@ -43,28 +43,28 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
examples = [
{
"images": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"text_queries": ["cat", "remote", "couch"],
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
]
return object_detector, examples
def run_pipeline_test(self, object_detector, examples):
batch_outputs = object_detector(examples, threshold=0.0)
outputs = object_detector(examples[0], threshold=0.0)
self.assertEqual(len(examples), len(batch_outputs))
for outputs in batch_outputs:
for output_per_image in outputs:
self.assertGreater(len(output_per_image), 0)
for detected_object in output_per_image:
self.assertEqual(
detected_object,
{
"score": ANY(float),
"label": ANY(str),
"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
},
)
n = len(outputs)
self.assertGreater(n, 0)
self.assertEqual(
outputs,
[
{
"score": ANY(float),
"label": ANY(str),
"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
}
for i in range(n)
],
)
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF")
@@ -79,64 +79,32 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
outputs = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png",
text_queries=["cat", "remote", "couch"],
candidate_labels=["cat", "remote", "couch"],
threshold=0.64,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
]
],
)
outputs = object_detector(
["./tests/fixtures/tests_samples/COCO/000000039769.png"],
text_queries=["cat", "remote", "couch"],
threshold=0.64,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
]
],
)
outputs = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png",
text_queries=[["cat", "remote", "couch"]],
threshold=0.64,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
]
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
],
)
outputs = object_detector(
[
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"http://images.cocodataset.org/val2017/000000039769.jpg",
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
],
text_queries=[["cat", "remote", "couch"], ["cat", "remote", "couch"]],
threshold=0.64,
)
@@ -145,16 +113,15 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
[
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
],
[
{"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
],
{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
]
],
)
@@ -164,27 +131,30 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
object_detector = pipeline("zero-shot-object-detection")
outputs = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg", text_queries=["cat", "remote", "couch"]
"http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=["cat", "remote", "couch"]
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
]
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
)
outputs = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
],
text_queries=[["cat", "remote", "couch"], ["cat", "remote", "couch"]],
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
@@ -219,17 +189,15 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
outputs = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg",
text_queries=["cat", "remote", "couch"],
candidate_labels=["cat", "remote", "couch"],
threshold=threshold,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
]
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
],
)
@@ -241,15 +209,13 @@ class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=Pipeline
outputs = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg",
text_queries=["cat", "remote", "couch"],
candidate_labels=["cat", "remote", "couch"],
top_k=top_k,
)
self.assertEqual(
nested_simplify(outputs, decimals=4),
[
[
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
]
{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
],
)