248 lines
9.3 KiB
Python
248 lines
9.3 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 hashlib
|
|
import unittest
|
|
|
|
from transformers import (
|
|
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
|
|
AutoFeatureExtractor,
|
|
AutoModelForImageSegmentation,
|
|
ImageSegmentationPipeline,
|
|
is_vision_available,
|
|
pipeline,
|
|
)
|
|
from transformers.testing_utils import (
|
|
is_pipeline_test,
|
|
nested_simplify,
|
|
require_datasets,
|
|
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
|
|
@is_pipeline_test
|
|
class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
|
|
model_mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING
|
|
|
|
def get_test_pipeline(self, model, tokenizer, feature_extractor):
|
|
image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
|
|
return image_segmenter, [
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
]
|
|
|
|
@require_datasets
|
|
def run_pipeline_test(self, image_segmenter, examples):
|
|
outputs = image_segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
|
|
self.assertEqual(outputs, [{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12)
|
|
|
|
import datasets
|
|
|
|
dataset = datasets.load_dataset("Narsil/image_dummy", "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"],
|
|
]
|
|
outputs = image_segmenter(batch, threshold=0.0)
|
|
|
|
self.assertEqual(len(batch), len(outputs))
|
|
self.assertEqual(
|
|
outputs,
|
|
[
|
|
[{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12,
|
|
[{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12,
|
|
[{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12,
|
|
[{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12,
|
|
[{"score": ANY(float), "label": ANY(str), "mask": ANY(str)}] * 12,
|
|
],
|
|
)
|
|
|
|
@require_tf
|
|
@unittest.skip("Image segmentation not implemented in TF")
|
|
def test_small_model_tf(self):
|
|
pass
|
|
|
|
@require_torch
|
|
def test_small_model_pt(self):
|
|
model_id = "mishig/tiny-detr-mobilenetsv3-panoptic"
|
|
|
|
model = AutoModelForImageSegmentation.from_pretrained(model_id)
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
|
|
image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
|
|
|
|
outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
|
|
for o in outputs:
|
|
# shortening by hashing
|
|
o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
],
|
|
)
|
|
|
|
outputs = image_segmenter(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
threshold=0.0,
|
|
)
|
|
for output in outputs:
|
|
for o in output:
|
|
o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
],
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_0",
|
|
"mask": "4276f7db4ca2983b2666f7e0c102d8186aed20be",
|
|
},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_integration_torch_image_segmentation(self):
|
|
model_id = "facebook/detr-resnet-50-panoptic"
|
|
|
|
image_segmenter = pipeline("image-segmentation", model=model_id)
|
|
|
|
outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
for o in outputs:
|
|
o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
|
|
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
|
|
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
|
|
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
|
|
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
|
|
],
|
|
)
|
|
|
|
outputs = image_segmenter(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
threshold=0.0,
|
|
)
|
|
for output in outputs:
|
|
for o in output:
|
|
o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
|
|
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
|
|
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
|
|
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
|
|
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
|
|
],
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
|
|
{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
|
|
{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
|
|
{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
|
|
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_threshold(self):
|
|
threshold = 0.999
|
|
model_id = "facebook/detr-resnet-50-panoptic"
|
|
|
|
image_segmenter = pipeline("image-segmentation", model=model_id)
|
|
|
|
outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
|
|
|
|
for o in outputs:
|
|
o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
|
|
{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
|
|
],
|
|
)
|