Files
modelscope/tests/pipelines/easycv_pipelines/test_segmentation_pipeline.py
pengyu.lpy 3ed0c9c8d8 [to #42322933] relax un-determinsitic test validation constraints
放松了tests/pipelines/easycv_pipelines/test_segmentation_pipeline.py里面对于识别非determinsitic的校验条件,一方便后续模型更新
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10245940
2022-09-24 17:58:42 +08:00

87 lines
3.2 KiB
Python

# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest
from distutils.version import LooseVersion
import easycv
import numpy as np
from PIL import Image
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
class EasyCVSegmentationPipelineTest(unittest.TestCase):
img_path = 'data/test/images/image_segmentation.jpg'
def _internal_test_(self, model_id):
img = np.asarray(Image.open(self.img_path))
semantic_seg = pipeline(task=Tasks.image_segmentation, model=model_id)
outputs = semantic_seg(self.img_path)
self.assertEqual(len(outputs), 1)
results = outputs[0]
self.assertListEqual(
list(img.shape)[:2], list(results['seg_pred'].shape))
def _internal_test_batch_(self, model_id, num_samples=2, batch_size=2):
# TODO: support in the future
img = np.asarray(Image.open(self.img_path))
num_samples = num_samples
batch_size = batch_size
semantic_seg = pipeline(
task=Tasks.image_segmentation,
model=model_id,
batch_size=batch_size)
outputs = semantic_seg([self.img_path] * num_samples)
self.assertEqual(semantic_seg.predict_op.batch_size, batch_size)
self.assertEqual(len(outputs), num_samples)
for output in outputs:
self.assertListEqual(
list(img.shape)[:2], list(output['seg_pred'].shape))
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b0(self):
model_id = 'damo/cv_segformer-b0_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b1(self):
model_id = 'damo/cv_segformer-b1_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b2(self):
model_id = 'damo/cv_segformer-b2_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b3(self):
model_id = 'damo/cv_segformer-b3_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b4(self):
model_id = 'damo/cv_segformer-b4_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_segformer_b5(self):
model_id = 'damo/cv_segformer-b5_image_semantic-segmentation_coco-stuff164k'
self._internal_test_(model_id)
self._internal_test_batch_(model_id)
if __name__ == '__main__':
unittest.main()