mirror of
https://github.com/modelscope/modelscope.git
synced 2026-07-10 12:33:28 +02:00
cba4e40bc1
明确受影响的模型(damo)： ONE-PEACE-4B ModuleNotFoundError: MyCustomPipeline: MyCustomModel: No module named 'one_peace'，缺少依赖。 cv_resnet50_face-reconstruction 不兼容tf2 nlp_automatic_post_editing_for_translation_en2de tf2.0兼容性问题，tf1.x需要 cv_resnet18_ocr-detection-word-level_damo tf2.x兼容性问题 cv_resnet18_ocr-detection-line-level_damo tf兼容性问题 cv_resnet101_detection_fewshot-defrcn 模型限制必须detection0.3+torch1.11.0" speech_dfsmn_ans_psm_48k_causal "librosa， numpy兼容性问题 cv_mdm_motion-generation "依赖numpy版本兼容性问题： File ""/opt/conda/lib/python3.8/site-packages/smplx/body_models.py"", cv_resnet50_ocr-detection-vlpt numpy兼容性问题 cv_clip-it_video-summarization_language-guided_en tf兼容性问题 Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13744636 * numpy and pandas no version * modify compatible issue * fix numpy compatible issue * modify ci * fix lint issue * replace Image.ANTIALIAS to Image.Resampling.LANCZOS pillow compatible * skip uncompatible cases * fix numpy compatible issue, skip cases that can not compatbile numpy or tensorflow2.x * skip compatible cases * fix clip model issue * fix body 3d keypoints compatible issue
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import os
|
|
import shutil
|
|
import unittest
|
|
|
|
import cv2
|
|
|
|
from modelscope.msdatasets import MsDataset
|
|
from modelscope.outputs import OutputKeys
|
|
from modelscope.pipelines import pipeline
|
|
from modelscope.pipelines.base import Pipeline
|
|
from modelscope.utils.constant import Tasks
|
|
from modelscope.utils.test_utils import test_level
|
|
|
|
|
|
@unittest.skip('For tensorflow 2.x compatible')
|
|
class TestImagePortraitStylizationTrainer(unittest.TestCase):
|
|
|
|
def setUp(self) -> None:
|
|
self.task = Tasks.image_portrait_stylization
|
|
self.test_image = 'https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/image_cartoon.png'
|
|
|
|
def tearDown(self):
|
|
shutil.rmtree('exp_localtoon')
|
|
super().tearDown()
|
|
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
|
def test_run_with_model_name(self):
|
|
from modelscope.trainers.cv import CartoonTranslationTrainer
|
|
model_id = 'damo/cv_unet_person-image-cartoon_compound-models'
|
|
|
|
data_dir = MsDataset.load(
|
|
'dctnet_train_clipart_mini_ms',
|
|
namespace='menyifang',
|
|
split='train').config_kwargs['split_config']['train']
|
|
|
|
data_photo = os.path.join(data_dir, 'face_photo')
|
|
data_cartoon = os.path.join(data_dir, 'face_cartoon')
|
|
work_dir = 'exp_localtoon'
|
|
max_steps = 10
|
|
trainer = CartoonTranslationTrainer(
|
|
model=model_id,
|
|
work_dir=work_dir,
|
|
photo=data_photo,
|
|
cartoon=data_cartoon,
|
|
max_steps=max_steps)
|
|
trainer.train()
|
|
|
|
from modelscope.exporters.cv import CartoonTranslationExporter
|
|
ckpt_path = os.path.join(work_dir, 'saved_models', 'model-' + str(0))
|
|
pb_path = os.path.join(trainer.model_dir, 'cartoon_h.pb')
|
|
exporter = CartoonTranslationExporter()
|
|
exporter.export_frozen_graph_def(
|
|
ckpt_path=ckpt_path, frozen_graph_path=pb_path)
|
|
|
|
self.pipeline_person_image_cartoon(trainer.model_dir)
|
|
|
|
def pipeline_person_image_cartoon(self, model_dir):
|
|
pipeline_cartoon = pipeline(task=self.task, model=model_dir)
|
|
result = pipeline_cartoon(input=self.test_image)
|
|
if result is not None:
|
|
cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
|
|
print(f'Output written to {os.path.abspath("result.png")}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|