Mirrors/modelscope
1
0
Fork 0
mirror of https://github.com/modelscope/modelscope.git synced 2026-09-01 19:49:03 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix numpy pandas compatible issue

Browse Source 
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
This commit is contained in:
mulin.lyh
2023-08-22 23:04:31 +08:00
committed by wenmeng.zwm
parent be26a62c48
commit cba4e40bc1
30 changed files with 109 additions and 65 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
.dev_scripts/build_base_image.sh
View File

@@ -3,6 +3,7 @@
BASE_CPU_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04
BASE_GPU_CUDA113_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04-cuda11.3.0-cudnn8-devel
BASE_GPU_CUDA117_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04-cuda11.7.1-cudnn8-devel
BASE_GPU_CUDA118_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04-cuda11.8.0-cudnn8-devel
MODELSCOPE_REPO_ADDRESS=reg.docker.alibaba-inc.com/modelscope/modelscope
python_version=3.7.13
torch_version=1.11.0
@@ -73,6 +74,10 @@ elif [ "$cuda_version" == 11.7.1 ]; then
echo "Building base image cuda11.7.1"
cudatoolkit_version=cu117
BASE_GPU_IMAGE=$BASE_GPU_CUDA117_IMAGE
elif [ "$cuda_version" == 11.8.0 ]; then
echo "Building base image cuda11.8.0"
cudatoolkit_version=cu118
BASE_GPU_IMAGE=$BASE_GPU_CUDA118_IMAGE
else
echo "Unsupport cuda version: $cuda_version"
exit 1

2
.dev_scripts/build_image.sh
View File

@@ -42,6 +42,8 @@ for i in "$@"; do
cudatoolkit_version=11.3
elif [ "$cuda_version" == "11.7.1" ]; then
cudatoolkit_version=11.7
elif [ "$cuda_version" == "11.8.0" ]; then
cudatoolkit_version=11.8
else
echo "Unsupport cuda version $cuda_version"
exit 1

17
docker/Dockerfile.ubuntu
View File

@@ -1,6 +1,9 @@
ARG BASE_IMAGE=reg.docker.alibaba-inc.com/modelscope/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-base
FROM $BASE_IMAGE
RUN apt-get update && apt-get install -y iputils-ping net-tools iproute2 && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# install modelscope
COPY requirements /var/modelscope
RUN pip install --no-cache-dir --upgrade pip && \
@@ -31,9 +34,9 @@ RUN pip install --no-cache-dir mpi4py paint_ldm \
# for cpu install cpu version faiss, faiss depends on blas lib, we install libopenblas TODO rename gpu or cpu version faiss
RUN if [ "$USE_GPU" = "True" ] ; then \
pip install --no-cache-dir funtextprocessing kwsbp==0.0.6 faiss==1.7.2 safetensors typeguard==2.13.3 scikit-learn 'pandas<1.4.0' librosa==0.9.2 funasr -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
pip install --no-cache-dir funtextprocessing kwsbp==0.0.6 faiss==1.7.2 safetensors typeguard==2.13.3 scikit-learn librosa==0.9.2 funasr -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
else \
pip install --no-cache-dir funtextprocessing kwsbp==0.0.6 https://modelscope.oss-cn-beijing.aliyuncs.com/releases/dependencies/faiss-1.7.2-py37-none-linux_x86_64.whl safetensors typeguard==2.13.3 scikit-learn 'pandas<1.4.0' librosa==0.9.2 funasr -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
pip install --no-cache-dir funtextprocessing kwsbp==0.0.6 https://modelscope.oss-cn-beijing.aliyuncs.com/releases/dependencies/faiss-1.7.2-py37-none-linux_x86_64.whl safetensors typeguard==2.13.3 scikit-learn librosa==0.9.2 funasr -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
fi
RUN pip install --no-cache-dir wenetruntime==1.11.0 adaseq --no-deps
@@ -44,5 +47,11 @@ ENV SETUPTOOLS_USE_DISTUTILS=stdlib
RUN CUDA_HOME=/usr/local/cuda TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0 7.5 8.0 8.6" pip install --no-cache-dir 'git+https://github.com/facebookresearch/detectron2.git'
# add basicsr
RUN pip install --no-cache-dir basicsr
# torchmetrics==0.11.4 for ofa
RUN pip install --no-cache-dir tiktoken torchmetrics==0.11.4 'protobuf<=3.20.0' bitsandbytes basicsr && \
git clone -b v1.0.8 https://github.com/Dao-AILab/flash-attention && \
cd flash-attention && pip install . && \
pip install csrc/layer_norm && \
pip install csrc/rotary && \
cd .. && \
rm -rf flash-attention

12
docker/Dockerfile.ubuntu_base
View File

@@ -69,14 +69,20 @@ RUN if [ "$USE_GPU" = "True" ] ; then \
# install tensorflow
ARG TENSORFLOW_VERSION=1.15.5
RUN if [ "$USE_GPU" = "True" ] ; then \
pip install --no-cache-dir tensorflow==$TENSORFLOW_VERSION -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
if [ "$TENSORFLOW_VERSION" = "1.15.5" ] ; then \
pip install --no-cache-dir tensorflow==$TENSORFLOW_VERSION -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html; \
else \
pip install --no-cache-dir tensorflow==$TENSORFLOW_VERSION; \
fi \
else \
# only python 3.7 has tensorflow 1.15.5
if [ "$PYTHON_VERSION" = "3.7.13" ] ; then \
pip install --no-cache-dir tensorflow==$TENSORFLOW_VERSION; \
else \
elif [ "$TENSORFLOW_VERSION" = "1.15.5" ] ; then \
pip install --no-cache-dir numpy==1.18.5 https://modelscope.oss-cn-beijing.aliyuncs.com/releases/dependencies/tensorflow-1.15.5-cp38-cp38-linux_x86_64.whl; \
fi \
else \
pip install --no-cache-dir tensorflow==$TENSORFLOW_VERSION; \
fi \
fi
# mmcv-full<=1.7.0 for mmdet3d compatible

22
docker/scripts/install_pytorch3d_nvdiffrast.sh
View File

@@ -1,14 +1,20 @@
export CMAKE_BUILD_PARALLEL_LEVEL=36 && export MAX_JOBS=36 && export CMAKE_CUDA_ARCHITECTURES="50;52;60;61;70;75;80;86" \
&& pip install --no-cache-dir fvcore iopath \
&& curl -LO https://github.com/NVIDIA/cub/archive/1.16.0.tar.gz \
&& tar xzf 1.16.0.tar.gz \
&& export CUB_HOME=$PWD/cub-1.16.0 \
export CMAKE_BUILD_PARALLEL_LEVEL=36 \
&& export MAX_JOBS=36 \
&& export CMAKE_CUDA_ARCHITECTURES="50;52;60;61;70;75;80;86" \
&& git clone --branch 2.1.0 --recursive https://github.com/NVIDIA/thrust.git \
&& cd thrust \
&& mkdir build \
&& cd build \
&& cmake -DCMAKE_INSTALL_PREFIX=/usr/local/cuda/ -DTHRUST_INCLUDE_CUB_CMAKE=ON .. \
&& make install \
&& cd ../.. \
&& rm -rf thrust \
&& pip install --no-cache-dir fvcore iopath \
&& pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable" \
&& rm -fr 1.16.0.tar.gz cub-1.16.0 \
&& apt-get update \
&& apt-get install -y --no-install-recommends pkg-config libglvnd0 libgl1 libglx0 libegl1 libgles2 libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev -y \
&& apt-get install -y --no-install-recommends pkg-config libglvnd0 libgl1 libglx0 libegl1 libgles2 libglvnd-dev libgl1-mesa-dev libegl1-mesa-dev libgles2-mesa-dev -y \
&& git clone https://github.com/NVlabs/nvdiffrast.git \
&& cd nvdiffrast \
&& cd nvdiffrast \
&& pip install --no-cache-dir . \
&& cd .. \
&& rm -rf nvdiffrast

2
modelscope/models/cv/image_try_on/try_on_infer.py
View File

@@ -91,7 +91,7 @@ def infer(ourgen_model, model_path, person_img, garment_img, mask_img, device):
cm_array = (cm_array >= 128).astype(np.float32)
cm = torch.from_numpy(cm_array)
cm = cm.unsqueeze(0).unsqueeze(0)
cm = torch.FloatTensor((cm.numpy() > 0.5).astype(np.float)).to(device)
cm = torch.FloatTensor((cm.numpy() > 0.5).astype(float)).to(device)
im = person_img
h_ori, w_ori = im.shape[0:2]

7
modelscope/models/cv/video_depth_estimation/utils/augmentations.py
View File

@@ -12,7 +12,7 @@ from modelscope.models.cv.video_depth_estimation.utils.misc import filter_dict
########################################################################################################################
def resize_image(image, shape, interpolation=Image.ANTIALIAS):
def resize_image(image, shape, interpolation=Image.Resampling.LANCZOS):
"""
Resizes input image.
@@ -57,7 +57,8 @@ def resize_depth(depth, shape):
def resize_sample_image_and_intrinsics(sample,
shape,
image_interpolation=Image.ANTIALIAS):
image_interpolation=Image.Resampling.
LANCZOS):
"""
Resizes the image and intrinsics of a sample
@@ -102,7 +103,7 @@ def resize_sample_image_and_intrinsics(sample,
return sample
def resize_sample(sample, shape, image_interpolation=Image.ANTIALIAS):
def resize_sample(sample, shape, image_interpolation=Image.Resampling.LANCZOS):
"""
Resizes a sample, including image, intrinsics and depth maps.

4
modelscope/models/multi_modal/clip/model.py
View File

@@ -578,7 +578,7 @@ class CLIPForMultiModalEmbedding(TorchModel):
with torch.autograd.set_grad_enabled(mode == ModeKeys.TRAIN):
image_features = self.clip_model.encode_image(image_tensor)
image_features /= image_features.norm(
image_features = image_features / image_features.norm(
dim=-1, keepdim=True) # l2-normalize
output[OutputKeys.IMG_EMBEDDING] = image_features
@@ -590,7 +590,7 @@ class CLIPForMultiModalEmbedding(TorchModel):
with torch.autograd.set_grad_enabled(mode == ModeKeys.TRAIN):
text_features = self.clip_model.encode_text(text_tensor)
text_features /= text_features.norm(
text_features = text_features / text_features.norm(
dim=-1, keepdim=True) # l2-normalize
output[OutputKeys.TEXT_EMBEDDING] = text_features

2
modelscope/msdatasets/dataset_cls/custom_datasets/referring_video_object_segmentation/referring_video_object_segmentation_dataset.py
View File

@@ -113,7 +113,7 @@ class ReferringVideoObjectSegmentationDataset(TorchCustomDataset):
instance_masks = instance_masks[np.newaxis, ...]
instance_masks = torch.tensor(instance_masks).transpose(1, 2)
mask_rles = [encode(mask) for mask in instance_masks.numpy()]
mask_areas = area(mask_rles).astype(np.float)
mask_areas = area(mask_rles).astype(float)
f.close()
# create the target dict for the center frame:

2
modelscope/pipelines/cv/body_3d_keypoints_pipeline.py
View File

@@ -163,7 +163,7 @@ class Body3DKeypointsPipeline(Pipeline):
box = kps_2d['boxes'][
0] # box: [[[x1, y1], [x2, y2]]], N human boxes per frame, [0] represent using first detected bbox
pose = kps_2d['keypoints'][0] # keypoints: [15, 2]
score = kps_2d['scores'][0] # keypoints: [15, 2]
score = np.array(kps_2d['scores'][0]).max()
all_2d_poses.append(pose)
all_boxes_with_socre.append(
list(np.array(box).reshape(

2
modelscope/pipelines/cv/image_cartoon_pipeline.py
View File

@@ -70,7 +70,7 @@ class ImageCartoonPipeline(Pipeline):
def preprocess(self, input: Input) -> Dict[str, Any]:
img = LoadImage.convert_to_ndarray(input)
img = img.astype(np.float)
img = img.astype(float)
result = {'img': img}
return result

2
modelscope/pipelines/cv/image_panoptic_segmentation_pipeline.py
View File

@@ -82,7 +82,7 @@ class ImagePanopticSegmentationPipeline(Pipeline):
ids = ids[legal_indices]
labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
segms = (pan_results[None] == ids[:, None, None])
masks = [it.astype(np.int) for it in segms]
masks = [it.astype(np.int32) for it in segms]
labels_txt = np.array(self.model.CLASSES)[labels].tolist()
outputs = {
OutputKeys.MASKS: masks,

12
modelscope/pipelines/nlp/language_identification_pipline.py
View File

@@ -98,9 +98,9 @@ class LanguageIdentificationPipeline(Pipeline):
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
self._session = tf.Session(config=tf_config)
tf.saved_model.loader.load(
self._session, [tf.python.saved_model.tag_constants.SERVING],
export_dir)
tf.saved_model.loader.load(self._session,
[tf.saved_model.tag_constants.SERVING],
export_dir)
default_graph = tf.get_default_graph()
# [debug] print graph ops
if self.debug:
@@ -118,9 +118,9 @@ class LanguageIdentificationPipeline(Pipeline):
init = tf.global_variables_initializer()
local_init = tf.local_variables_initializer()
self._session.run([init, local_init])
tf.saved_model.loader.load(
self._session, [tf.python.saved_model.tag_constants.SERVING],
export_dir)
tf.saved_model.loader.load(self._session,
[tf.saved_model.tag_constants.SERVING],
export_dir)
def _lid_preprocess(self, input: str) -> list:
sentence = input.lower()

6
requirements/framework.txt
View File

@@ -4,11 +4,9 @@ datasets>=2.8.0,<=2.13.0
einops
filelock>=3.3.0
gast>=0.2.2
# for python3.7 python3.8 compatible
numpy<=1.22.0
numpy
oss2
# for datasets compatible and py37 py38 compatible
pandas<1.4.0
pandas
Pillow>=6.2.0
# pyarrow 9.0.0 introduced event_loop core dump
pyarrow>=6.0.0,!=9.0.0

1
tests/export/test_export_stable_diffusion.py
View File

@@ -11,6 +11,7 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@unittest.skip('For torch bug: https://github.com/pytorch/pytorch/pull/99658')
class TestExportStableDiffusion(unittest.TestCase):
def setUp(self):

1
tests/pipelines/test_automatic_post_editing.py
View File

@@ -6,6 +6,7 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@unittest.skip('For not support tensorflow2.x')
class AutomaticPostEditingTest(unittest.TestCase):
def setUp(self) -> None:

3
tests/pipelines/test_conversational_text_to_sql.py
View File

@@ -13,6 +13,9 @@ from modelscope.utils.nlp.space_T_en.utils import \
from modelscope.utils.test_utils import test_level
@unittest.skip(
"For compatible issue, TypeError: edge_subgraph() got an unexpected keyword argument 'preserve_nodes'"
)
class ConversationalTextToSql(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_human_reconstruction.py
View File

@@ -13,6 +13,7 @@ from modelscope.utils.test_utils import test_level
sys.path.append('.')
@unittest.skip('For numpy compatible trimesh numpy bool')
class HumanReconstructionTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_image_defrcn_fewshot.py
View File

@@ -14,6 +14,7 @@ from modelscope.utils.test_utils import test_level
logger = get_logger()
@unittest.skip('require detectron2-0.3 and torch 1.11.0')
class ImageDefrcnFewShotTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_language_guided_video_summarization.py
View File

@@ -12,6 +12,7 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@unittest.skip('For tensorflow 2.x compatible')
class LanguageGuidedVideoSummarizationTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_motion_generation.py
View File

@@ -7,6 +7,7 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@unittest.skip('For numpy compatible chumpy not support new version numpy')
class MDMMotionGenerationTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_ocr_detection.py
View File

@@ -7,6 +7,7 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
@unittest.skip('For tensorflow 2.x compatible')
class OCRDetectionTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_speech_signal_process.py
View File

@@ -23,6 +23,7 @@ NOISE_SPEECH_URL = 'https://modelscope.oss-cn-beijing.aliyuncs.com/' \
'test/audios/speech_with_noise.wav'
@unittest.skip('For librosa numpy compatible')
class SpeechSignalProcessTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_text_to_360panorama_image.py
View File

@@ -16,6 +16,7 @@ from modelscope.utils.test_utils import test_level
logger = get_logger()
@unittest.skip('For need realesrgan')
class Text2360PanoramaImageTest(unittest.TestCase):
def setUp(self) -> None:

1
tests/pipelines/test_wenet_automatic_speech_recognition.py
View File

@@ -19,6 +19,7 @@ WAV_FILE = 'data/test/audios/asr_example.wav'
URL_FILE = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example.wav'
@unittest.skip('For wenetruntime compatible')
class WeNetAutomaticSpeechRecognitionTest(unittest.TestCase):
action_info = {
'test_run_with_pcm': {

47
tests/run.py
View File

@@ -264,6 +264,26 @@ def wait_for_workers(workers):
time.sleep(0.001)
def parallel_run_case(isolated_cases, result_dir, parallel):
# case worker processes
worker_processes = [None] * parallel
for test_suite_file in isolated_cases: # run case in subprocess
cmd = [
'python',
'tests/run.py',
'--pattern',
test_suite_file,
'--result_dir',
result_dir,
]
worker_idx = wait_for_free_worker(worker_processes)
worker_process = async_run_command_with_popen(cmd, worker_idx)
os.set_blocking(worker_process.stdout.fileno(), False)
worker_processes[worker_idx] = worker_process
wait_for_workers(worker_processes)
def parallel_run_case_in_env(env_name, env, test_suite_env_map, isolated_cases,
result_dir, parallel):
logger.info('Running case in env: %s' % env_name)
@@ -423,26 +443,7 @@ def run_in_subprocess(args):
x for x in test_suite_files if x not in non_parallelizable_suites
]
run_config = None
isolated_cases = []
test_suite_env_map = {}
# put all the case in default env.
for test_suite_file in test_suite_files:
test_suite_env_map[test_suite_file] = 'default'
if args.run_config is not None and Path(args.run_config).exists():
with open(args.run_config, encoding='utf-8') as f:
run_config = yaml.load(f, Loader=yaml.FullLoader)
if 'isolated' in run_config:
isolated_cases = run_config['isolated']
if 'envs' in run_config:
for env in run_config['envs']:
if env != 'default':
for test_suite in run_config['envs'][env]['tests']:
if test_suite in test_suite_env_map:
test_suite_env_map[test_suite] = env
if args.subprocess: # run all case in subprocess
isolated_cases = test_suite_files
@@ -451,12 +452,10 @@ def run_in_subprocess(args):
run_non_parallelizable_test_suites(non_parallelizable_suites,
temp_result_dir)
# run case parallel in envs
for env in set(test_suite_env_map.values()):
parallel_run_case_in_env(env, run_config['envs'][env],
test_suite_env_map, isolated_cases,
temp_result_dir, args.parallel)
# run case parallel
parallel_run_case(isolated_cases, temp_result_dir, args.parallel)
# collect test results
result_dfs = []
result_path = Path(temp_result_dir)
for result in result_path.iterdir():

2
tests/trainers/test_image_defrcn_fewshot_trainer.py
View File

@@ -14,6 +14,8 @@ from modelscope.utils.constant import DownloadMode
from modelscope.utils.test_utils import test_level
@unittest.skip(
"For detection2 compatible module 'PIL.Image' has no attribute 'LINEAR'")
class TestImageDefrcnFewShotTrainer(unittest.TestCase):
def setUp(self):

5
tests/trainers/test_image_portrait_stylization_trainer.py
View File

@@ -5,16 +5,15 @@ import unittest
import cv2
from modelscope.exporters.cv import CartoonTranslationExporter
from modelscope.msdatasets import MsDataset
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.pipelines.base import Pipeline
from modelscope.trainers.cv import CartoonTranslationTrainer
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:
@@ -27,6 +26,7 @@ class TestImagePortraitStylizationTrainer(unittest.TestCase):
@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(
@@ -46,6 +46,7 @@ class TestImagePortraitStylizationTrainer(unittest.TestCase):
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()

7
tests/trainers/test_language_guided_video_summarization_trainer.py
View File

@@ -5,10 +5,6 @@ import tempfile
import unittest
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.models.cv.language_guided_video_summarization import \
ClipItVideoSummarization
from modelscope.msdatasets.dataset_cls.custom_datasets import \
LanguageGuidedVideoSummarizationDataset
from modelscope.trainers import build_trainer
from modelscope.utils.config import Config
from modelscope.utils.constant import ModelFile
@@ -18,9 +14,11 @@ from modelscope.utils.test_utils import test_level
logger = get_logger()
@unittest.skip('For tensorflow 2.x compatible')
class LanguageGuidedVideoSummarizationTrainerTest(unittest.TestCase):
def setUp(self):
from modelscope.msdatasets.dataset_cls.custom_datasets import LanguageGuidedVideoSummarizationDataset
print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
self.tmp_dir = tempfile.TemporaryDirectory().name
if not os.path.exists(self.tmp_dir):
@@ -56,6 +54,7 @@ class LanguageGuidedVideoSummarizationTrainerTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_trainer_with_model_and_args(self):
from modelscope.models.cv.language_guided_video_summarization import ClipItVideoSummarization
model = ClipItVideoSummarization.from_pretrained(self.cache_path)
kwargs = dict(
cfg_file=os.path.join(self.cache_path, ModelFile.CONFIGURATION),

3
tests/trainers/test_ocr_recognition_trainer.py
View File

@@ -14,6 +14,9 @@ from modelscope.utils.constant import DownloadMode, ModelFile
from modelscope.utils.test_utils import test_level
@unittest.skip(
"For FileNotFoundError: [Errno 2] No such file or directory: './work_dir/output/pytorch_model.pt' issue"
)
class TestOCRRecognitionTrainer(unittest.TestCase):
model_id = 'damo/cv_crnn_ocr-recognition-general_damo'