mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
9ac586b3c8
* Rework pipeline tests * Try to fix Flax tests * Try to put it before * Use a new decorator instead * Remove ignore marker since it doesn't work * Filter pipeline tests * Woopsie * Use the fitlered list * Clean up and fake modif * Remove init * Revert fake modif
378 lines
15 KiB
Python
378 lines
15 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
|
|
|
|
import datasets
|
|
from datasets import load_dataset
|
|
|
|
from transformers import (
|
|
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
|
|
MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
|
|
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
|
|
AutoFeatureExtractor,
|
|
AutoModelForImageSegmentation,
|
|
AutoModelForInstanceSegmentation,
|
|
DetrForSegmentation,
|
|
ImageSegmentationPipeline,
|
|
MaskFormerForInstanceSegmentation,
|
|
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
|
|
|
|
|
|
def hashimage(image: Image) -> str:
|
|
m = hashlib.md5(image.tobytes())
|
|
return m.hexdigest()
|
|
|
|
|
|
@require_vision
|
|
@require_timm
|
|
@require_torch
|
|
class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
|
|
model_mapping = {
|
|
k: v
|
|
for k, v in (
|
|
list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
|
|
)
|
|
+ (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
|
|
+ (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
|
|
}
|
|
|
|
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",
|
|
]
|
|
|
|
def run_pipeline_test(self, image_segmenter, examples):
|
|
outputs = image_segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
|
|
self.assertIsInstance(outputs, list)
|
|
n = len(outputs)
|
|
if isinstance(image_segmenter.model, (MaskFormerForInstanceSegmentation)):
|
|
# Instance segmentation (maskformer) have a slot for null class
|
|
# and can output nothing even with a low threshold
|
|
self.assertGreaterEqual(n, 0)
|
|
else:
|
|
self.assertGreaterEqual(n, 1)
|
|
# XXX: PIL.Image implements __eq__ which bypasses ANY, so we inverse the comparison
|
|
# to make it work
|
|
self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n, outputs)
|
|
|
|
dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
|
|
|
|
# RGBA
|
|
outputs = image_segmenter(dataset[0]["file"])
|
|
m = len(outputs)
|
|
self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
|
|
# LA
|
|
outputs = image_segmenter(dataset[1]["file"])
|
|
m = len(outputs)
|
|
self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
|
|
# L
|
|
outputs = image_segmenter(dataset[2]["file"])
|
|
m = len(outputs)
|
|
self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
|
|
|
|
if isinstance(image_segmenter.model, DetrForSegmentation):
|
|
# We need to test batch_size with images with the same size.
|
|
# Detr doesn't normalize the size of the images, meaning we can have
|
|
# 800x800 or 800x1200, meaning we cannot batch simply.
|
|
# We simply bail on this
|
|
batch_size = 1
|
|
else:
|
|
batch_size = 2
|
|
|
|
# 5 times the same image so the output shape is predictable
|
|
batch = [
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
"./tests/fixtures/tests_samples/COCO/000000039769.png",
|
|
]
|
|
outputs = image_segmenter(batch, threshold=0.0, batch_size=batch_size)
|
|
self.assertEqual(len(batch), len(outputs))
|
|
self.assertEqual(len(outputs[0]), n)
|
|
self.assertEqual(
|
|
[
|
|
[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
|
|
[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
|
|
[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
|
|
[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
|
|
[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
|
|
],
|
|
outputs,
|
|
f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
|
|
)
|
|
|
|
@require_tf
|
|
@unittest.skip("Image segmentation not implemented in TF")
|
|
def test_small_model_tf(self):
|
|
pass
|
|
|
|
@require_torch
|
|
@unittest.skip("No weights found for hf-internal-testing/tiny-detr-mobilenetsv3-panoptic")
|
|
def test_small_model_pt(self):
|
|
model_id = "hf-internal-testing/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",
|
|
task="panoptic",
|
|
threshold=0.0,
|
|
overlap_mask_area_threshold=0.0,
|
|
)
|
|
|
|
# Shortening by hashing
|
|
for o in outputs:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
],
|
|
)
|
|
|
|
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"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
],
|
|
[
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
{
|
|
"score": 0.004,
|
|
"label": "LABEL_215",
|
|
"mask": "34eecd16bbfb0f476083ef947d81bf66",
|
|
},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
def test_small_model_pt_semantic(self):
|
|
model_id = "hf-internal-testing/tiny-random-beit-pipeline"
|
|
image_segmenter = pipeline(model=model_id)
|
|
outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
|
|
for o in outputs:
|
|
# shortening by hashing
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{
|
|
"score": None,
|
|
"label": "LABEL_0",
|
|
"mask": "775518a7ed09eea888752176c6ba8f38",
|
|
},
|
|
{
|
|
"score": None,
|
|
"label": "LABEL_1",
|
|
"mask": "a12da23a46848128af68c63aa8ba7a02",
|
|
},
|
|
],
|
|
)
|
|
|
|
@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",
|
|
task="panoptic",
|
|
threshold=0,
|
|
overlap_mask_area_threshold=0.0,
|
|
)
|
|
|
|
# Shortening by hashing
|
|
for o in outputs:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
|
|
{"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
|
|
{"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
|
|
{"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
|
|
{"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
|
|
],
|
|
)
|
|
|
|
outputs = image_segmenter(
|
|
[
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg",
|
|
],
|
|
task="panoptic",
|
|
threshold=0.0,
|
|
overlap_mask_area_threshold=0.0,
|
|
)
|
|
|
|
# Shortening by hashing
|
|
for output in outputs:
|
|
for o in output:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
|
|
{"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
|
|
{"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
|
|
{"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
|
|
{"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
|
|
],
|
|
[
|
|
{"score": 0.9094, "label": "blanket", "mask": "dcff19a97abd8bd555e21186ae7c066a"},
|
|
{"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
|
|
{"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
|
|
{"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
|
|
{"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
|
|
],
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_threshold(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", task="panoptic", threshold=0.999
|
|
)
|
|
# Shortening by hashing
|
|
for o in outputs:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9995, "label": "remote", "mask": "d02404f5789f075e3b3174adbc3fd5b8"},
|
|
{"score": 0.9994, "label": "cat", "mask": "eaa115b40c96d3a6f4fe498963a7e470"},
|
|
],
|
|
)
|
|
|
|
outputs = image_segmenter(
|
|
"http://images.cocodataset.org/val2017/000000039769.jpg", task="panoptic", threshold=0.5
|
|
)
|
|
|
|
for o in outputs:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9941, "label": "cat", "mask": "9c0af87bd00f9d3a4e0c8888e34e70e2"},
|
|
{"score": 0.9987, "label": "remote", "mask": "c7870600d6c02a1f6d96470fc7220e8e"},
|
|
{"score": 0.9995, "label": "remote", "mask": "ef899a25fd44ec056c653f0ca2954fdd"},
|
|
{"score": 0.9722, "label": "couch", "mask": "37b8446ac578a17108aa2b7fccc33114"},
|
|
{"score": 0.9994, "label": "cat", "mask": "6a09d3655efd8a388ab4511e4cbbb797"},
|
|
],
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_maskformer(self):
|
|
threshold = 0.8
|
|
model_id = "facebook/maskformer-swin-base-ade"
|
|
|
|
model = AutoModelForInstanceSegmentation.from_pretrained(model_id)
|
|
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
|
|
|
|
image_segmenter = pipeline("image-segmentation", model=model, feature_extractor=feature_extractor)
|
|
|
|
image = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
|
|
file = image[0]["file"]
|
|
outputs = image_segmenter(file, task="panoptic", threshold=threshold)
|
|
|
|
# Shortening by hashing
|
|
for o in outputs:
|
|
o["mask"] = hashimage(o["mask"])
|
|
|
|
self.assertEqual(
|
|
nested_simplify(outputs, decimals=4),
|
|
[
|
|
{"score": 0.9974, "label": "wall", "mask": "a547b7c062917f4f3e36501827ad3cd6"},
|
|
{"score": 0.949, "label": "house", "mask": "0da9b7b38feac47bd2528a63e5ea7b19"},
|
|
{"score": 0.9995, "label": "grass", "mask": "1d07ea0a263dcf38ca8ae1a15fdceda1"},
|
|
{"score": 0.9976, "label": "tree", "mask": "6cdc97c7daf1dc596fa181f461ddd2ba"},
|
|
{"score": 0.8239, "label": "plant", "mask": "1ab4ce378f6ceff57d428055cfbd742f"},
|
|
{"score": 0.9942, "label": "road, route", "mask": "39c5d17be53b2d1b0f46aad8ebb15813"},
|
|
{"score": 1.0, "label": "sky", "mask": "a3756324a692981510c39b1a59510a36"},
|
|
],
|
|
)
|