mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
713eab45d3
* [Breaking change] Deformable DETR intermediate representations - Fixes naturally the `object-detection` pipeline. - Moves from `[n_decoders, batch_size, ...]` to `[batch_size, n_decoders, ...]` instead. * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
246 lines
11 KiB
Python
246 lines
11 KiB
Python
# Copyright 2021 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 unittest
|
|
|
|
from transformers import (
|
|
MODEL_FOR_OBJECT_DETECTION_MAPPING,
|
|
AutoFeatureExtractor,
|
|
AutoModelForObjectDetection,
|
|
ObjectDetectionPipeline,
|
|
is_vision_available,
|
|
pipeline,
|
|
)
|
|
from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
|
|
|
|
from .test_pipelines_common import ANY, PipelineTestCaseMeta
|
|
|
|
|
|
if is_vision_available():
|
|
from PIL import Image
|
|
else:
|
|
|
|
class Image:
|
|
@staticmethod
|
|
def open(*args, **kwargs):
|
|
pass
|
|
|
|
|
|
@require_vision
|
|
@require_timm
|
|
@require_torch
|
|
class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
|
|
model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
|
|
|
|
def get_test_pipeline(self, model, tokenizer, feature_extractor):
|
|
object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
|
|
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
|
|
|
|
def run_pipeline_test(self, object_detector, examples):
|
|
outputs = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
|
|
|
|
self.assertGreater(len(outputs), 0)
|
|
for detected_object in outputs:
|
|
self.assertEqual(
|
|
detected_object,
|
|
{
|
|
"score": ANY(float),
|
|
"label": ANY(str),
|
|
"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
|
|
},
|
|
)
|
|
|
|
import datasets
|
|
|
|
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
|
|
|
|
batch = [
|
|
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
# RGBA
|
|
dataset[0]["file"],
|
|
# LA
|
|
dataset[1]["file"],
|
|
# L
|
|
dataset[2]["file"],
|
|
]
|
|
batch_outputs = object_detector(batch, threshold=0.0)
|
|
|
|
self.assertEqual(len(batch), len(batch_outputs))
|
|
for outputs in batch_outputs:
|
|
self.assertGreater(len(outputs), 0)
|
|
for detected_object in outputs:
|
|
self.assertEqual(
|
|
detected_object,
|
|
{
|
|
"score": ANY(float),
|
|
"label": ANY(str),
|
|
"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
|
|
},
|
|
)
|
|
|
|
@require_tf
|
|
@unittest.skip("Object detection not implemented in TF")
|
|
def test_small_model_tf(self):
|
|
pass
|
|
|
|
@require_torch
|
|
def test_small_model_pt(self):
|
|
model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
|
|
|
|
model = AutoModelForObjectDetection.from_pretrained(model_id)
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
|
|
object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
|
|
|
|
outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
],
|
|
)
|
|
|
|
outputs = object_detector(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
threshold=0.0,
|
|
)
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
],
|
|
[
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_large_model_pt(self):
|
|
model_id = "facebook/detr-resnet-50"
|
|
|
|
model = AutoModelForObjectDetection.from_pretrained(model_id)
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
|
|
object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
|
|
|
|
outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
)
|
|
|
|
outputs = object_detector(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
]
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_integration_torch_object_detection(self):
|
|
model_id = "facebook/detr-resnet-50"
|
|
|
|
object_detector = pipeline("object-detection", model=model_id)
|
|
|
|
outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
)
|
|
|
|
outputs = object_detector(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
]
|
|
)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
[
|
|
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
|
|
{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
|
|
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_threshold(self):
|
|
threshold = 0.9985
|
|
model_id = "facebook/detr-resnet-50"
|
|
|
|
object_detector = pipeline("object-detection", model=model_id)
|
|
|
|
outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
|
|
{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
|
|
],
|
|
)
|