mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-24 03:59:23 +01:00
231f400133
co-contributed with 夕陌&雨泓 * add torch epoch based trainer and dis utils * add hooks including optimizer, lrscheduler, logging, checkpoint, evaluation, time profiling * add torch mdoel base and test * add optimizer and lrscheduler module * add sbert for text classification example * add task_dataset for dataset-level processor Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9338412
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from modelscope.models import Model
|
|
from modelscope.outputs import OutputKeys
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
class TextToImageSynthesisTest(unittest.TestCase):
|
|
model_id = 'damo/cv_imagen_text-to-image-synthesis_tiny'
|
|
test_text = {
|
|
'text': '宇航员',
|
|
'generator_ddim_timesteps': 2,
|
|
'upsampler_256_ddim_timesteps': 2,
|
|
'upsampler_1024_ddim_timesteps': 2,
|
|
'debug': True
|
|
}
|
|
|
|
@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)
|
|
pipe_line_text_to_image_synthesis = pipeline(
|
|
task=Tasks.text_to_image_synthesis, model=model)
|
|
img = pipe_line_text_to_image_synthesis(
|
|
self.test_text)[OutputKeys.OUTPUT_IMG]
|
|
print(np.sum(np.abs(img)))
|
|
|
|
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
|
|
def test_run_with_model_name(self):
|
|
pipe_line_text_to_image_synthesis = pipeline(
|
|
task=Tasks.text_to_image_synthesis, model=self.model_id)
|
|
img = pipe_line_text_to_image_synthesis(
|
|
self.test_text)[OutputKeys.OUTPUT_IMG]
|
|
print(np.sum(np.abs(img)))
|
|
|
|
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
|
def test_run_with_default_model(self):
|
|
pipe_line_text_to_image_synthesis = pipeline(
|
|
task=Tasks.text_to_image_synthesis)
|
|
img = pipe_line_text_to_image_synthesis(
|
|
self.test_text)[OutputKeys.OUTPUT_IMG]
|
|
print(np.sum(np.abs(img)))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|