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modelscope/tests/pipelines/test_image_denoise.py

62 lines
2.5 KiB
Python

# Copyright (c) Alibaba, Inc. and its affiliates.
import unittest
from PIL import Image
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.models import Model
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.pipelines.cv import ImageDenoisePipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
class ImageDenoiseTest(unittest.TestCase):
model_id = 'damo/cv_nafnet_image-denoise_sidd'
demo_image_path = 'data/test/images/noisy-demo-1.png'
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_by_direct_model_download(self):
cache_path = snapshot_download(self.model_id)
pipeline = ImageDenoisePipeline(cache_path)
denoise_img = pipeline(
input=self.demo_image_path)[OutputKeys.OUTPUT_IMG]
denoise_img = Image.fromarray(denoise_img)
w, h = denoise_img.size
print('pipeline: the shape of output_img is {}x{}'.format(h, w))
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_model_from_modelhub(self):
model = Model.from_pretrained(self.model_id)
pipeline_ins = pipeline(task=Tasks.image_denoising, model=model)
denoise_img = pipeline_ins(
input=self.demo_image_path)[OutputKeys.OUTPUT_IMG]
denoise_img = Image.fromarray(denoise_img)
w, h = denoise_img.size
print('pipeline: the shape of output_img is {}x{}'.format(h, w))
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_run_with_model_name(self):
pipeline_ins = pipeline(
task=Tasks.image_denoising, model=self.model_id)
denoise_img = pipeline_ins(
input=self.demo_image_path)[OutputKeys.OUTPUT_IMG]
denoise_img = Image.fromarray(denoise_img)
w, h = denoise_img.size
print('pipeline: the shape of output_img is {}x{}'.format(h, w))
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
def test_run_with_default_model(self):
pipeline_ins = pipeline(task=Tasks.image_denoising)
denoise_img = pipeline_ins(
input=self.demo_image_path)[OutputKeys.OUTPUT_IMG]
denoise_img = Image.fromarray(denoise_img)
w, h = denoise_img.size
print('pipeline: the shape of output_img is {}x{}'.format(h, w))
if __name__ == '__main__':
unittest.main()