diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c6290ff4..48fe7547 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://gitlab.com/pycqa/flake8.git - rev: 3.8.3 + rev: 4.0.0 hooks: - id: flake8 exclude: thirdparty/|examples/ diff --git a/.pre-commit-config_local.yaml b/.pre-commit-config_local.yaml index 138561e3..0b2e2f39 100644 --- a/.pre-commit-config_local.yaml +++ b/.pre-commit-config_local.yaml @@ -1,6 +1,6 @@ repos: - repo: /home/admin/pre-commit/flake8 - rev: 3.8.3 + rev: 4.0.0 hooks: - id: flake8 exclude: thirdparty/|examples/ diff --git a/data/test/audios/asr_example_8K.wav b/data/test/audios/asr_example_8K.wav new file mode 100644 index 00000000..956aad27 --- /dev/null +++ b/data/test/audios/asr_example_8K.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e999c247bfebb03d556a31722f0ce7145cac20a67fac9da813ad336e1f549f9f +size 38954 diff --git a/data/test/audios/asr_example_cn_dialect.wav b/data/test/audios/asr_example_cn_dialect.wav new file mode 100644 index 00000000..e18fb05d --- /dev/null +++ b/data/test/audios/asr_example_cn_dialect.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32eb8d4d537941bf0edea69cd6723e8ba489fa3df64e13e29f96e4fae0b856f4 +size 93676 diff --git a/data/test/audios/asr_example_cn_en.wav b/data/test/audios/asr_example_cn_en.wav new file mode 100644 index 00000000..8baf3193 --- /dev/null +++ b/data/test/audios/asr_example_cn_en.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f57aee13ade70be6b2c6e4f5e5c7404bdb03057b63828baefbaadcf23855a4cb +size 472012 diff --git a/data/test/audios/asr_example_en.wav b/data/test/audios/asr_example_en.wav new file mode 100644 index 00000000..fa996eec --- /dev/null +++ b/data/test/audios/asr_example_en.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fee8e0460ca707f108782be0d93c555bf34fb6b1cb297e5fceed70192cc65f9b +size 71244 diff --git a/data/test/audios/asr_example_es.wav b/data/test/audios/asr_example_es.wav new file mode 100644 index 00000000..95b22dc3 --- /dev/null +++ b/data/test/audios/asr_example_es.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:450e31f9df8c5b48c617900625f01cb64c484f079a9843179fe9feaa7d163e61 +size 181964 diff --git a/data/test/audios/asr_example_id.wav b/data/test/audios/asr_example_id.wav new file mode 100644 index 00000000..54c30614 --- /dev/null +++ b/data/test/audios/asr_example_id.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:255494c41bc1dfb0c954d827ec6ce775900e4f7a55fb0a7881bdf9d66a03b425 +size 112078 diff --git a/data/test/audios/asr_example_ja.wav b/data/test/audios/asr_example_ja.wav new file mode 100644 index 00000000..e953fee2 --- /dev/null +++ b/data/test/audios/asr_example_ja.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22a55277908bbc3ef60a0cf56b230eb507b9e837574e8f493e93644b1d21c281 +size 200556 diff --git a/data/test/audios/asr_example_ko.wav b/data/test/audios/asr_example_ko.wav new file mode 100644 index 00000000..0dad1be3 --- /dev/null +++ b/data/test/audios/asr_example_ko.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee92191836c76412463d8b282a7ab4e1aa57386ba699ec011a3e2c4d64f32f4b +size 162636 diff --git a/data/test/audios/asr_example_ru.wav b/data/test/audios/asr_example_ru.wav new file mode 100644 index 00000000..b0cb8f2f --- /dev/null +++ b/data/test/audios/asr_example_ru.wav @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:77d1537fc584c1505d8aa10ec8c86af57ab661199e4f28fd7ffee3c22d1e4e61 +size 160204 diff --git a/data/test/regression/sbert-base-tnews.bin b/data/test/regression/sbert-base-tnews.bin new file mode 100644 index 00000000..1546860f --- /dev/null +++ b/data/test/regression/sbert-base-tnews.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bce1341f4b55d536771dad6e2b280458579f46c3216474ceb8a926022ab53d0 +size 151572 diff --git a/data/test/regression/sbert_nli.bin b/data/test/regression/sbert_nli.bin index a5f680bb..68efb778 100644 --- a/data/test/regression/sbert_nli.bin +++ b/data/test/regression/sbert_nli.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:44e3925c15d86d8596baeb6bd1d153d86f57b7489798b2cf988a1248e110fd62 -size 62231 +oid sha256:6af5024a26337a440c7ea2935fce84af558dd982ee97a2f027bb922cc874292b +size 61741 diff --git a/data/test/regression/sbert_sen_sim.bin b/data/test/regression/sbert_sen_sim.bin index a59cbe0b..362f762c 100644 --- a/data/test/regression/sbert_sen_sim.bin +++ b/data/test/regression/sbert_sen_sim.bin @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1ff17a0272752de4c88d4254b2e881f97f8ef022f03609d03ee1de0ae964368a -size 62235 +oid sha256:bbce084781342ca7274c2e4d02ed5c5de43ba213a3b76328d5994404d6544c41 +size 61745 diff --git a/modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py b/modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py index dc1e2b92..52dab4bc 100644 --- a/modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py +++ b/modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py @@ -23,12 +23,14 @@ class SbertForSequenceClassificationExporter(TorchModelExporter): def generate_dummy_inputs(self, shape: Tuple = None, + pair: bool = False, **kwargs) -> Dict[str, Any]: """Generate dummy inputs for model exportation to onnx or other formats by tracing. @param shape: A tuple of input shape which should have at most two dimensions. shape = (1, ) batch_size=1, sequence_length will be taken from the preprocessor. shape = (8, 128) batch_size=1, sequence_length=128, which will cover the config of the preprocessor. + @param pair: Generate sentence pairs or single sentences for dummy inputs. @return: Dummy inputs. """ @@ -55,7 +57,7 @@ class SbertForSequenceClassificationExporter(TorchModelExporter): **sequence_length }) preprocessor: Preprocessor = build_preprocessor(cfg, field_name) - if preprocessor.pair: + if pair: first_sequence = preprocessor.tokenizer.unk_token second_sequence = preprocessor.tokenizer.unk_token else: diff --git a/modelscope/hub/api.py b/modelscope/hub/api.py index dc4d0ab2..f8ca683a 100644 --- a/modelscope/hub/api.py +++ b/modelscope/hub/api.py @@ -1,8 +1,11 @@ # Copyright (c) Alibaba, Inc. and its affiliates. +# yapf: disable +import datetime import os import pickle import shutil +import tempfile from collections import defaultdict from http import HTTPStatus from http.cookiejar import CookieJar @@ -16,17 +19,25 @@ from modelscope.hub.constants import (API_RESPONSE_FIELD_DATA, API_RESPONSE_FIELD_GIT_ACCESS_TOKEN, API_RESPONSE_FIELD_MESSAGE, API_RESPONSE_FIELD_USERNAME, - DEFAULT_CREDENTIALS_PATH) + DEFAULT_CREDENTIALS_PATH, Licenses, + ModelVisibility) +from modelscope.hub.errors import (InvalidParameter, NotExistError, + NotLoginException, RequestError, + datahub_raise_on_error, + handle_http_post_error, + handle_http_response, is_ok, raise_on_error) +from modelscope.hub.git import GitCommandWrapper +from modelscope.hub.repository import Repository +from modelscope.hub.utils.utils import (get_endpoint, + model_id_to_group_owner_name) from modelscope.utils.config_ds import DOWNLOADED_DATASETS_PATH from modelscope.utils.constant import (DEFAULT_DATASET_REVISION, DEFAULT_MODEL_REVISION, DatasetFormations, DatasetMetaFormats, - DownloadMode) + DownloadMode, ModelFile) from modelscope.utils.logger import get_logger -from .errors import (InvalidParameter, NotExistError, RequestError, - datahub_raise_on_error, handle_http_post_error, - handle_http_response, is_ok, raise_on_error) -from .utils.utils import get_endpoint, model_id_to_group_owner_name + +# yapf: enable logger = get_logger() @@ -169,11 +180,106 @@ class HubApi: else: r.raise_for_status() - def list_model(self, - owner_or_group: str, - page_number=1, - page_size=10) -> dict: - """List model in owner or group. + def push_model(self, + model_id: str, + model_dir: str, + visibility: int = ModelVisibility.PUBLIC, + license: str = Licenses.APACHE_V2, + chinese_name: Optional[str] = None, + commit_message: Optional[str] = 'upload model', + revision: Optional[str] = DEFAULT_MODEL_REVISION): + """ + Upload model from a given directory to given repository. A valid model directory + must contain a configuration.json file. + + This function upload the files in given directory to given repository. If the + given repository is not exists in remote, it will automatically create it with + given visibility, license and chinese_name parameters. If the revision is also + not exists in remote repository, it will create a new branch for it. + + This function must be called before calling HubApi's login with a valid token + which can be obtained from ModelScope's website. + + Args: + model_id (`str`): + The model id to be uploaded, caller must have write permission for it. + model_dir(`str`): + The Absolute Path of the finetune result. + visibility(`int`, defaults to `0`): + Visibility of the new created model(1-private, 5-public). If the model is + not exists in ModelScope, this function will create a new model with this + visibility and this parameter is required. You can ignore this parameter + if you make sure the model's existence. + license(`str`, defaults to `None`): + License of the new created model(see License). If the model is not exists + in ModelScope, this function will create a new model with this license + and this parameter is required. You can ignore this parameter if you + make sure the model's existence. + chinese_name(`str`, *optional*, defaults to `None`): + chinese name of the new created model. + commit_message(`str`, *optional*, defaults to `None`): + commit message of the push request. + revision (`str`, *optional*, default to DEFAULT_MODEL_REVISION): + which branch to push. If the branch is not exists, It will create a new + branch and push to it. + """ + if model_id is None: + raise InvalidParameter('model_id cannot be empty!') + if model_dir is None: + raise InvalidParameter('model_dir cannot be empty!') + if not os.path.exists(model_dir) or os.path.isfile(model_dir): + raise InvalidParameter('model_dir must be a valid directory.') + cfg_file = os.path.join(model_dir, ModelFile.CONFIGURATION) + if not os.path.exists(cfg_file): + raise ValueError(f'{model_dir} must contain a configuration.json.') + cookies = ModelScopeConfig.get_cookies() + if cookies is None: + raise NotLoginException('Must login before upload!') + files_to_save = os.listdir(model_dir) + try: + self.get_model(model_id=model_id) + except Exception: + if visibility is None or license is None: + raise InvalidParameter( + 'visibility and license cannot be empty if want to create new repo' + ) + logger.info('Create new model %s' % model_id) + self.create_model( + model_id=model_id, + visibility=visibility, + license=license, + chinese_name=chinese_name) + tmp_dir = tempfile.mkdtemp() + git_wrapper = GitCommandWrapper() + try: + repo = Repository(model_dir=tmp_dir, clone_from=model_id) + branches = git_wrapper.get_remote_branches(tmp_dir) + if revision not in branches: + logger.info('Create new branch %s' % revision) + git_wrapper.new_branch(tmp_dir, revision) + git_wrapper.checkout(tmp_dir, revision) + for f in files_to_save: + if f[0] != '.': + src = os.path.join(model_dir, f) + if os.path.isdir(src): + shutil.copytree(src, os.path.join(tmp_dir, f)) + else: + shutil.copy(src, tmp_dir) + if not commit_message: + date = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S') + commit_message = '[automsg] push model %s to hub at %s' % ( + model_id, date) + repo.push(commit_message=commit_message, branch=revision) + except Exception: + raise + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) + + def list_models(self, + owner_or_group: str, + page_number=1, + page_size=10) -> dict: + """List models in owner or group. Args: owner_or_group(`str`): owner or group. @@ -390,11 +496,13 @@ class HubApi: return resp['Data'] def list_oss_dataset_objects(self, dataset_name, namespace, max_limit, - is_recursive, is_filter_dir, revision, - cookies): + is_recursive, is_filter_dir, revision): url = f'{self.endpoint}/api/v1/datasets/{namespace}/{dataset_name}/oss/tree/?' \ f'MaxLimit={max_limit}&Revision={revision}&Recursive={is_recursive}&FilterDir={is_filter_dir}' - cookies = requests.utils.dict_from_cookiejar(cookies) + + cookies = ModelScopeConfig.get_cookies() + if cookies: + cookies = requests.utils.dict_from_cookiejar(cookies) resp = requests.get(url=url, cookies=cookies) resp = resp.json() diff --git a/modelscope/hub/file_download.py b/modelscope/hub/file_download.py index 1cc5645b..8ffc60bc 100644 --- a/modelscope/hub/file_download.py +++ b/modelscope/hub/file_download.py @@ -11,13 +11,12 @@ from typing import Dict, Optional, Union from uuid import uuid4 import requests -from filelock import FileLock from tqdm import tqdm from modelscope import __version__ +from modelscope.hub.api import HubApi, ModelScopeConfig from modelscope.utils.constant import DEFAULT_MODEL_REVISION from modelscope.utils.logger import get_logger -from .api import HubApi, ModelScopeConfig from .constants import FILE_HASH from .errors import FileDownloadError, NotExistError from .utils.caching import ModelFileSystemCache diff --git a/modelscope/hub/git.py b/modelscope/hub/git.py index db76506e..fe1d1554 100644 --- a/modelscope/hub/git.py +++ b/modelscope/hub/git.py @@ -1,13 +1,10 @@ # Copyright (c) Alibaba, Inc. and its affiliates. import os -import re import subprocess from typing import List -from xmlrpc.client import Boolean from modelscope.utils.logger import get_logger -from .api import ModelScopeConfig from .errors import GitError logger = get_logger() @@ -132,6 +129,7 @@ class GitCommandWrapper(metaclass=Singleton): return response def add_user_info(self, repo_base_dir, repo_name): + from modelscope.hub.api import ModelScopeConfig user_name, user_email = ModelScopeConfig.get_user_info() if user_name and user_email: # config user.name and user.email if exist @@ -184,8 +182,11 @@ class GitCommandWrapper(metaclass=Singleton): info = [ line.strip() for line in rsp.stdout.decode('utf8').strip().split(os.linesep) - ][1:] - return ['/'.join(line.split('/')[1:]) for line in info] + ] + if len(info) == 1: + return ['/'.join(info[0].split('/')[1:])] + else: + return ['/'.join(line.split('/')[1:]) for line in info[1:]] def pull(self, repo_dir: str): cmds = ['-C', repo_dir, 'pull'] diff --git a/modelscope/hub/repository.py b/modelscope/hub/repository.py index d92089ed..35c831a9 100644 --- a/modelscope/hub/repository.py +++ b/modelscope/hub/repository.py @@ -7,7 +7,6 @@ from modelscope.hub.errors import GitError, InvalidParameter, NotLoginException from modelscope.utils.constant import (DEFAULT_DATASET_REVISION, DEFAULT_MODEL_REVISION) from modelscope.utils.logger import get_logger -from .api import ModelScopeConfig from .git import GitCommandWrapper from .utils.utils import get_endpoint @@ -47,6 +46,7 @@ class Repository: err_msg = 'a non-default value of revision cannot be empty.' raise InvalidParameter(err_msg) + from modelscope.hub.api import ModelScopeConfig if auth_token: self.auth_token = auth_token else: @@ -166,7 +166,7 @@ class DatasetRepository: err_msg = 'a non-default value of revision cannot be empty.' raise InvalidParameter(err_msg) self.revision = revision - + from modelscope.hub.api import ModelScopeConfig if auth_token: self.auth_token = auth_token else: diff --git a/modelscope/hub/snapshot_download.py b/modelscope/hub/snapshot_download.py index cde6ad34..ac57d1b1 100644 --- a/modelscope/hub/snapshot_download.py +++ b/modelscope/hub/snapshot_download.py @@ -5,9 +5,9 @@ import tempfile from pathlib import Path from typing import Dict, Optional, Union +from modelscope.hub.api import HubApi, ModelScopeConfig from modelscope.utils.constant import DEFAULT_MODEL_REVISION from modelscope.utils.logger import get_logger -from .api import HubApi, ModelScopeConfig from .constants import FILE_HASH from .errors import NotExistError from .file_download import (get_file_download_url, http_get_file, diff --git a/modelscope/hub/upload.py b/modelscope/hub/upload.py deleted file mode 100644 index 9dffc60e..00000000 --- a/modelscope/hub/upload.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) Alibaba, Inc. and its affiliates. - -import datetime -import os -import shutil -import tempfile -import uuid -from typing import Dict, Optional -from uuid import uuid4 - -from filelock import FileLock - -from modelscope import __version__ -from modelscope.hub.api import HubApi, ModelScopeConfig -from modelscope.hub.errors import InvalidParameter, NotLoginException -from modelscope.hub.git import GitCommandWrapper -from modelscope.hub.repository import Repository -from modelscope.utils.constant import DEFAULT_MODEL_REVISION, ModelFile -from modelscope.utils.logger import get_logger - -logger = get_logger() - - -def upload_folder(model_id: str, - model_dir: str, - visibility: int = 0, - license: str = None, - chinese_name: Optional[str] = None, - commit_message: Optional[str] = None, - revision: Optional[str] = DEFAULT_MODEL_REVISION): - """ - Upload model from a given directory to given repository. A valid model directory - must contain a configuration.json file. - - This function upload the files in given directory to given repository. If the - given repository is not exists in remote, it will automatically create it with - given visibility, license and chinese_name parameters. If the revision is also - not exists in remote repository, it will create a new branch for it. - - This function must be called before calling HubApi's login with a valid token - which can be obtained from ModelScope's website. - - Args: - model_id (`str`): - The model id to be uploaded, caller must have write permission for it. - model_dir(`str`): - The Absolute Path of the finetune result. - visibility(`int`, defaults to `0`): - Visibility of the new created model(1-private, 5-public). If the model is - not exists in ModelScope, this function will create a new model with this - visibility and this parameter is required. You can ignore this parameter - if you make sure the model's existence. - license(`str`, defaults to `None`): - License of the new created model(see License). If the model is not exists - in ModelScope, this function will create a new model with this license - and this parameter is required. You can ignore this parameter if you - make sure the model's existence. - chinese_name(`str`, *optional*, defaults to `None`): - chinese name of the new created model. - commit_message(`str`, *optional*, defaults to `None`): - commit message of the push request. - revision (`str`, *optional*, default to DEFAULT_MODEL_REVISION): - which branch to push. If the branch is not exists, It will create a new - branch and push to it. - """ - if model_id is None: - raise InvalidParameter('model_id cannot be empty!') - if model_dir is None: - raise InvalidParameter('model_dir cannot be empty!') - if not os.path.exists(model_dir) or os.path.isfile(model_dir): - raise InvalidParameter('model_dir must be a valid directory.') - cfg_file = os.path.join(model_dir, ModelFile.CONFIGURATION) - if not os.path.exists(cfg_file): - raise ValueError(f'{model_dir} must contain a configuration.json.') - cookies = ModelScopeConfig.get_cookies() - if cookies is None: - raise NotLoginException('Must login before upload!') - files_to_save = os.listdir(model_dir) - api = HubApi() - try: - api.get_model(model_id=model_id) - except Exception: - if visibility is None or license is None: - raise InvalidParameter( - 'visibility and license cannot be empty if want to create new repo' - ) - logger.info('Create new model %s' % model_id) - api.create_model( - model_id=model_id, - visibility=visibility, - license=license, - chinese_name=chinese_name) - tmp_dir = tempfile.mkdtemp() - git_wrapper = GitCommandWrapper() - try: - repo = Repository(model_dir=tmp_dir, clone_from=model_id) - branches = git_wrapper.get_remote_branches(tmp_dir) - if revision not in branches: - logger.info('Create new branch %s' % revision) - git_wrapper.new_branch(tmp_dir, revision) - git_wrapper.checkout(tmp_dir, revision) - for f in files_to_save: - if f[0] != '.': - src = os.path.join(model_dir, f) - if os.path.isdir(src): - shutil.copytree(src, os.path.join(tmp_dir, f)) - else: - shutil.copy(src, tmp_dir) - if not commit_message: - date = datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S') - commit_message = '[automsg] push model %s to hub at %s' % ( - model_id, date) - repo.push(commit_message=commit_message, branch=revision) - except Exception: - raise - finally: - shutil.rmtree(tmp_dir, ignore_errors=True) diff --git a/modelscope/metainfo.py b/modelscope/metainfo.py index c3fe5594..03f1f92a 100644 --- a/modelscope/metainfo.py +++ b/modelscope/metainfo.py @@ -9,7 +9,9 @@ class Models(object): Model name should only contain model info but not task info. """ + # tinynas models tinynas_detection = 'tinynas-detection' + tinynas_damoyolo = 'tinynas-damoyolo' # vision models detection = 'detection' @@ -454,9 +456,9 @@ class Datasets(object): """ Names for different datasets. """ ClsDataset = 'ClsDataset' - Face2dKeypointsDataset = 'Face2dKeypointsDataset' + Face2dKeypointsDataset = 'FaceKeypointDataset' HandCocoWholeBodyDataset = 'HandCocoWholeBodyDataset' - HumanWholeBodyKeypointDataset = 'HumanWholeBodyKeypointDataset' + HumanWholeBodyKeypointDataset = 'WholeBodyCocoTopDownDataset' SegDataset = 'SegDataset' DetDataset = 'DetDataset' DetImagesMixDataset = 'DetImagesMixDataset' diff --git a/modelscope/metrics/builder.py b/modelscope/metrics/builder.py index ee4d2840..1c8e16d7 100644 --- a/modelscope/metrics/builder.py +++ b/modelscope/metrics/builder.py @@ -32,6 +32,7 @@ task_default_metrics = { Tasks.sentiment_classification: [Metrics.seq_cls_metric], Tasks.token_classification: [Metrics.token_cls_metric], Tasks.text_generation: [Metrics.text_gen_metric], + Tasks.text_classification: [Metrics.seq_cls_metric], Tasks.image_denoising: [Metrics.image_denoise_metric], Tasks.image_color_enhancement: [Metrics.image_color_enhance_metric], Tasks.image_portrait_enhancement: diff --git a/modelscope/models/audio/tts/voice.py b/modelscope/models/audio/tts/voice.py index dc830db5..b7240088 100644 --- a/modelscope/models/audio/tts/voice.py +++ b/modelscope/models/audio/tts/voice.py @@ -2,6 +2,7 @@ import os import pickle as pkl +from threading import Lock import json import numpy as np @@ -27,6 +28,7 @@ class Voice: self.__am_config = AttrDict(**am_config) self.__voc_config = AttrDict(**voc_config) self.__model_loaded = False + self.__lock = Lock() if 'am' not in self.__am_config: raise TtsModelConfigurationException( 'modelscope error: am configuration invalid') @@ -71,34 +73,35 @@ class Voice: self.__generator.remove_weight_norm() def __am_forward(self, symbol_seq): - with torch.no_grad(): - inputs_feat_lst = self.__ling_unit.encode_symbol_sequence( - symbol_seq) - inputs_sy = torch.from_numpy(inputs_feat_lst[0]).long().to( - self.__device) - inputs_tone = torch.from_numpy(inputs_feat_lst[1]).long().to( - self.__device) - inputs_syllable = torch.from_numpy(inputs_feat_lst[2]).long().to( - self.__device) - inputs_ws = torch.from_numpy(inputs_feat_lst[3]).long().to( - self.__device) - inputs_ling = torch.stack( - [inputs_sy, inputs_tone, inputs_syllable, inputs_ws], - dim=-1).unsqueeze(0) - inputs_emo = torch.from_numpy(inputs_feat_lst[4]).long().to( - self.__device).unsqueeze(0) - inputs_spk = torch.from_numpy(inputs_feat_lst[5]).long().to( - self.__device).unsqueeze(0) - inputs_len = torch.zeros(1).to(self.__device).long( - ) + inputs_emo.size(1) - 1 # minus 1 for "~" - res = self.__am_net(inputs_ling[:, :-1, :], inputs_emo[:, :-1], - inputs_spk[:, :-1], inputs_len) - postnet_outputs = res['postnet_outputs'] - LR_length_rounded = res['LR_length_rounded'] - valid_length = int(LR_length_rounded[0].item()) - postnet_outputs = postnet_outputs[ - 0, :valid_length, :].cpu().numpy() - return postnet_outputs + with self.__lock: + with torch.no_grad(): + inputs_feat_lst = self.__ling_unit.encode_symbol_sequence( + symbol_seq) + inputs_sy = torch.from_numpy(inputs_feat_lst[0]).long().to( + self.__device) + inputs_tone = torch.from_numpy(inputs_feat_lst[1]).long().to( + self.__device) + inputs_syllable = torch.from_numpy( + inputs_feat_lst[2]).long().to(self.__device) + inputs_ws = torch.from_numpy(inputs_feat_lst[3]).long().to( + self.__device) + inputs_ling = torch.stack( + [inputs_sy, inputs_tone, inputs_syllable, inputs_ws], + dim=-1).unsqueeze(0) + inputs_emo = torch.from_numpy(inputs_feat_lst[4]).long().to( + self.__device).unsqueeze(0) + inputs_spk = torch.from_numpy(inputs_feat_lst[5]).long().to( + self.__device).unsqueeze(0) + inputs_len = torch.zeros(1).to(self.__device).long( + ) + inputs_emo.size(1) - 1 # minus 1 for "~" + res = self.__am_net(inputs_ling[:, :-1, :], inputs_emo[:, :-1], + inputs_spk[:, :-1], inputs_len) + postnet_outputs = res['postnet_outputs'] + LR_length_rounded = res['LR_length_rounded'] + valid_length = int(LR_length_rounded[0].item()) + postnet_outputs = postnet_outputs[ + 0, :valid_length, :].cpu().numpy() + return postnet_outputs def __vocoder_forward(self, melspec): dim0 = list(melspec.shape)[-1] @@ -118,14 +121,15 @@ class Voice: return audio def forward(self, symbol_seq): - if not self.__model_loaded: - torch.manual_seed(self.__am_config.seed) - if torch.cuda.is_available(): + with self.__lock: + if not self.__model_loaded: torch.manual_seed(self.__am_config.seed) - self.__device = torch.device('cuda') - else: - self.__device = torch.device('cpu') - self.__load_am() - self.__load_vocoder() - self.__model_loaded = True + if torch.cuda.is_available(): + torch.manual_seed(self.__am_config.seed) + self.__device = torch.device('cuda') + else: + self.__device = torch.device('cpu') + self.__load_am() + self.__load_vocoder() + self.__model_loaded = True return self.__vocoder_forward(self.__am_forward(symbol_seq)) diff --git a/modelscope/models/cv/text_driven_segmentation/lseg_model.py b/modelscope/models/cv/text_driven_segmentation/lseg_model.py index 9a5754c6..ec381356 100644 --- a/modelscope/models/cv/text_driven_segmentation/lseg_model.py +++ b/modelscope/models/cv/text_driven_segmentation/lseg_model.py @@ -93,7 +93,7 @@ class TextDrivenSeg(TorchModel): """ with torch.no_grad(): if self.device_id == -1: - output = self.model(image) + output = self.model(image, [text]) else: device = torch.device('cuda', self.device_id) output = self.model(image.to(device), [text]) diff --git a/modelscope/models/cv/tinynas_detection/__init__.py b/modelscope/models/cv/tinynas_detection/__init__.py index 13532d10..6d696ac4 100644 --- a/modelscope/models/cv/tinynas_detection/__init__.py +++ b/modelscope/models/cv/tinynas_detection/__init__.py @@ -7,10 +7,12 @@ from modelscope.utils.import_utils import LazyImportModule if TYPE_CHECKING: from .tinynas_detector import Tinynas_detector + from .tinynas_damoyolo import DamoYolo else: _import_structure = { 'tinynas_detector': ['TinynasDetector'], + 'tinynas_damoyolo': ['DamoYolo'], } import sys diff --git a/modelscope/models/cv/tinynas_detection/backbone/tinynas.py b/modelscope/models/cv/tinynas_detection/backbone/tinynas.py index 814ee550..87a28a2f 100755 --- a/modelscope/models/cv/tinynas_detection/backbone/tinynas.py +++ b/modelscope/models/cv/tinynas_detection/backbone/tinynas.py @@ -4,6 +4,7 @@ import torch import torch.nn as nn +from modelscope.utils.file_utils import read_file from ..core.base_ops import Focus, SPPBottleneck, get_activation from ..core.repvgg_block import RepVggBlock @@ -49,12 +50,16 @@ class ResConvK1KX(nn.Module): kernel_size, stride, force_resproj=False, - act='silu'): + act='silu', + reparam=False): super(ResConvK1KX, self).__init__() self.stride = stride self.conv1 = ConvKXBN(in_c, btn_c, 1, 1) - self.conv2 = RepVggBlock( - btn_c, out_c, kernel_size, stride, act='identity') + if not reparam: + self.conv2 = ConvKXBN(btn_c, out_c, 3, stride) + else: + self.conv2 = RepVggBlock( + btn_c, out_c, kernel_size, stride, act='identity') if act is None: self.activation_function = torch.relu @@ -97,7 +102,8 @@ class SuperResConvK1KX(nn.Module): stride, num_blocks, with_spp=False, - act='silu'): + act='silu', + reparam=False): super(SuperResConvK1KX, self).__init__() if act is None: self.act = torch.relu @@ -124,7 +130,8 @@ class SuperResConvK1KX(nn.Module): this_kernel_size, this_stride, force_resproj, - act=act) + act=act, + reparam=reparam) self.block_list.append(the_block) if block_id == 0 and with_spp: self.block_list.append( @@ -248,7 +255,8 @@ class TinyNAS(nn.Module): with_spp=False, use_focus=False, need_conv1=True, - act='silu'): + act='silu', + reparam=False): super(TinyNAS, self).__init__() assert len(out_indices) == len(out_channels) self.out_indices = out_indices @@ -281,7 +289,8 @@ class TinyNAS(nn.Module): block_info['s'], block_info['L'], spp, - act=act) + act=act, + reparam=reparam) self.block_list.append(the_block) elif the_block_class == 'SuperResConvKXKX': spp = with_spp if idx == len(structure_info) - 1 else False @@ -325,8 +334,8 @@ class TinyNAS(nn.Module): def load_tinynas_net(backbone_cfg): # load masternet model to path import ast - - struct_str = ''.join([x.strip() for x in backbone_cfg.net_structure_str]) + net_structure_str = read_file(backbone_cfg.structure_file) + struct_str = ''.join([x.strip() for x in net_structure_str]) struct_info = ast.literal_eval(struct_str) for layer in struct_info: if 'nbitsA' in layer: @@ -342,6 +351,6 @@ def load_tinynas_net(backbone_cfg): use_focus=backbone_cfg.use_focus, act=backbone_cfg.act, need_conv1=backbone_cfg.need_conv1, - ) + reparam=backbone_cfg.reparam) return model diff --git a/modelscope/models/cv/tinynas_detection/detector.py b/modelscope/models/cv/tinynas_detection/detector.py index 615b13a8..42a71381 100644 --- a/modelscope/models/cv/tinynas_detection/detector.py +++ b/modelscope/models/cv/tinynas_detection/detector.py @@ -30,7 +30,7 @@ class SingleStageDetector(TorchModel): """ super().__init__(model_dir, *args, **kwargs) - config_path = osp.join(model_dir, 'airdet_s.py') + config_path = osp.join(model_dir, self.config_name) config = parse_config(config_path) self.cfg = config model_path = osp.join(model_dir, config.model.name) @@ -41,6 +41,9 @@ class SingleStageDetector(TorchModel): self.conf_thre = config.model.head.nms_conf_thre self.nms_thre = config.model.head.nms_iou_thre + if self.cfg.model.backbone.name == 'TinyNAS': + self.cfg.model.backbone.structure_file = osp.join( + model_dir, self.cfg.model.backbone.structure_file) self.backbone = build_backbone(self.cfg.model.backbone) self.neck = build_neck(self.cfg.model.neck) self.head = build_head(self.cfg.model.head) diff --git a/modelscope/models/cv/tinynas_detection/head/gfocal_v2_tiny.py b/modelscope/models/cv/tinynas_detection/head/gfocal_v2_tiny.py index 41f35968..66904ed1 100644 --- a/modelscope/models/cv/tinynas_detection/head/gfocal_v2_tiny.py +++ b/modelscope/models/cv/tinynas_detection/head/gfocal_v2_tiny.py @@ -124,11 +124,13 @@ class GFocalHead_Tiny(nn.Module): simOTA_iou_weight=3.0, octbase=8, simlqe=False, + use_lqe=True, **kwargs): self.simlqe = simlqe self.num_classes = num_classes self.in_channels = in_channels self.strides = strides + self.use_lqe = use_lqe self.feat_channels = feat_channels if isinstance(feat_channels, list) \ else [feat_channels] * len(self.strides) @@ -181,15 +183,20 @@ class GFocalHead_Tiny(nn.Module): groups=self.conv_groups, norm=self.norm, act=self.act)) - if not self.simlqe: - conf_vector = [nn.Conv2d(4 * self.total_dim, self.reg_channels, 1)] + if self.use_lqe: + if not self.simlqe: + conf_vector = [ + nn.Conv2d(4 * self.total_dim, self.reg_channels, 1) + ] + else: + conf_vector = [ + nn.Conv2d(4 * (self.reg_max + 1), self.reg_channels, 1) + ] + conf_vector += [self.relu] + conf_vector += [nn.Conv2d(self.reg_channels, 1, 1), nn.Sigmoid()] + reg_conf = nn.Sequential(*conf_vector) else: - conf_vector = [ - nn.Conv2d(4 * (self.reg_max + 1), self.reg_channels, 1) - ] - conf_vector += [self.relu] - conf_vector += [nn.Conv2d(self.reg_channels, 1, 1), nn.Sigmoid()] - reg_conf = nn.Sequential(*conf_vector) + reg_conf = None return cls_convs, reg_convs, reg_conf @@ -290,21 +297,27 @@ class GFocalHead_Tiny(nn.Module): N, C, H, W = bbox_pred.size() prob = F.softmax( bbox_pred.reshape(N, 4, self.reg_max + 1, H, W), dim=2) - if not self.simlqe: - prob_topk, _ = prob.topk(self.reg_topk, dim=2) - - if self.add_mean: - stat = torch.cat( - [prob_topk, prob_topk.mean(dim=2, keepdim=True)], dim=2) + if self.use_lqe: + if not self.simlqe: + prob_topk, _ = prob.topk(self.reg_topk, dim=2) + + if self.add_mean: + stat = torch.cat( + [prob_topk, + prob_topk.mean(dim=2, keepdim=True)], + dim=2) + else: + stat = prob_topk + + quality_score = reg_conf( + stat.reshape(N, 4 * self.total_dim, H, W)) else: - stat = prob_topk + quality_score = reg_conf( + bbox_pred.reshape(N, 4 * (self.reg_max + 1), H, W)) - quality_score = reg_conf(stat.reshape(N, 4 * self.total_dim, H, W)) + cls_score = gfl_cls(cls_feat).sigmoid() * quality_score else: - quality_score = reg_conf( - bbox_pred.reshape(N, 4 * (self.reg_max + 1), H, W)) - - cls_score = gfl_cls(cls_feat).sigmoid() * quality_score + cls_score = gfl_cls(cls_feat).sigmoid() flatten_cls_score = cls_score.flatten(start_dim=2).transpose(1, 2) flatten_bbox_pred = bbox_pred.flatten(start_dim=2).transpose(1, 2) diff --git a/modelscope/models/cv/tinynas_detection/neck/giraffe_fpn_v2.py b/modelscope/models/cv/tinynas_detection/neck/giraffe_fpn_v2.py index b710572f..b88c39f2 100644 --- a/modelscope/models/cv/tinynas_detection/neck/giraffe_fpn_v2.py +++ b/modelscope/models/cv/tinynas_detection/neck/giraffe_fpn_v2.py @@ -14,7 +14,6 @@ class GiraffeNeckV2(nn.Module): self, depth=1.0, width=1.0, - in_features=[2, 3, 4], in_channels=[256, 512, 1024], out_channels=[256, 512, 1024], depthwise=False, @@ -24,7 +23,6 @@ class GiraffeNeckV2(nn.Module): block_name='BasicBlock', ): super().__init__() - self.in_features = in_features self.in_channels = in_channels Conv = DWConv if depthwise else BaseConv @@ -169,8 +167,7 @@ class GiraffeNeckV2(nn.Module): """ # backbone - features = [out_features[f] for f in self.in_features] - [x2, x1, x0] = features + [x2, x1, x0] = out_features # node x3 x13 = self.bu_conv13(x1) diff --git a/modelscope/models/cv/tinynas_detection/tinynas_damoyolo.py b/modelscope/models/cv/tinynas_detection/tinynas_damoyolo.py new file mode 100644 index 00000000..9effad3a --- /dev/null +++ b/modelscope/models/cv/tinynas_detection/tinynas_damoyolo.py @@ -0,0 +1,15 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. + +from modelscope.metainfo import Models +from modelscope.models.builder import MODELS +from modelscope.utils.constant import Tasks +from .detector import SingleStageDetector + + +@MODELS.register_module( + Tasks.image_object_detection, module_name=Models.tinynas_damoyolo) +class DamoYolo(SingleStageDetector): + + def __init__(self, model_dir, *args, **kwargs): + self.config_name = 'damoyolo_s.py' + super(DamoYolo, self).__init__(model_dir, *args, **kwargs) diff --git a/modelscope/models/cv/tinynas_detection/tinynas_detector.py b/modelscope/models/cv/tinynas_detection/tinynas_detector.py index e6f144df..92acf3fa 100644 --- a/modelscope/models/cv/tinynas_detection/tinynas_detector.py +++ b/modelscope/models/cv/tinynas_detection/tinynas_detector.py @@ -12,5 +12,5 @@ from .detector import SingleStageDetector class TinynasDetector(SingleStageDetector): def __init__(self, model_dir, *args, **kwargs): - + self.config_name = 'airdet_s.py' super(TinynasDetector, self).__init__(model_dir, *args, **kwargs) diff --git a/modelscope/models/nlp/bert/modeling_bert.py b/modelscope/models/nlp/bert/modeling_bert.py index e91a6433..7c1dfcf5 100755 --- a/modelscope/models/nlp/bert/modeling_bert.py +++ b/modelscope/models/nlp/bert/modeling_bert.py @@ -15,7 +15,6 @@ """PyTorch BERT model. """ import math -import os import warnings from dataclasses import dataclass from typing import Optional, Tuple @@ -41,7 +40,6 @@ from transformers.modeling_utils import (PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer) -from modelscope.models.base import TorchModel from modelscope.utils.logger import get_logger from .configuration_bert import BertConfig @@ -50,81 +48,6 @@ logger = get_logger(__name__) _CONFIG_FOR_DOC = 'BertConfig' -def load_tf_weights_in_bert(model, config, tf_checkpoint_path): - """Load tf checkpoints in a pytorch model.""" - try: - import re - - import numpy as np - import tensorflow as tf - except ImportError: - logger.error( - 'Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see ' - 'https://www.tensorflow.org/install/ for installation instructions.' - ) - raise - tf_path = os.path.abspath(tf_checkpoint_path) - logger.info(f'Converting TensorFlow checkpoint from {tf_path}') - # Load weights from TF model - init_vars = tf.train.list_variables(tf_path) - names = [] - arrays = [] - for name, shape in init_vars: - logger.info(f'Loading TF weight {name} with shape {shape}') - array = tf.train.load_variable(tf_path, name) - names.append(name) - arrays.append(array) - - for name, array in zip(names, arrays): - name = name.split('/') - # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v - # which are not required for using pretrained model - if any(n in [ - 'adam_v', 'adam_m', 'AdamWeightDecayOptimizer', - 'AdamWeightDecayOptimizer_1', 'global_step' - ] for n in name): - logger.info(f"Skipping {'/'.join(name)}") - continue - pointer = model - for m_name in name: - if re.fullmatch(r'[A-Za-z]+_\d+', m_name): - scope_names = re.split(r'_(\d+)', m_name) - else: - scope_names = [m_name] - if scope_names[0] == 'kernel' or scope_names[0] == 'gamma': - pointer = getattr(pointer, 'weight') - elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta': - pointer = getattr(pointer, 'bias') - elif scope_names[0] == 'output_weights': - pointer = getattr(pointer, 'weight') - elif scope_names[0] == 'squad': - pointer = getattr(pointer, 'classifier') - else: - try: - pointer = getattr(pointer, scope_names[0]) - except AttributeError: - logger.info(f"Skipping {'/'.join(name)}") - continue - if len(scope_names) >= 2: - num = int(scope_names[1]) - pointer = pointer[num] - if m_name[-11:] == '_embeddings': - pointer = getattr(pointer, 'weight') - elif m_name == 'kernel': - array = np.transpose(array) - try: - if pointer.shape != array.shape: - raise ValueError( - f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' - ) - except AssertionError as e: - e.args += (pointer.shape, array.shape) - raise - logger.info(f'Initialize PyTorch weight {name}') - pointer.data = torch.from_numpy(array) - return model - - class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" @@ -750,7 +673,6 @@ class BertPreTrainedModel(PreTrainedModel): """ config_class = BertConfig - load_tf_weights = load_tf_weights_in_bert base_model_prefix = 'bert' supports_gradient_checkpointing = True _keys_to_ignore_on_load_missing = [r'position_ids'] diff --git a/modelscope/msdatasets/cv/easycv_base.py b/modelscope/msdatasets/cv/easycv_base.py index a45827a3..7b6df6e0 100644 --- a/modelscope/msdatasets/cv/easycv_base.py +++ b/modelscope/msdatasets/cv/easycv_base.py @@ -26,11 +26,16 @@ class EasyCVBaseDataset(object): if self.split_config is not None: self._update_data_source(kwargs['data_source']) + def _update_data_root(self, input_dict, data_root): + for k, v in input_dict.items(): + if isinstance(v, str) and self.DATA_ROOT_PATTERN in v: + input_dict.update( + {k: v.replace(self.DATA_ROOT_PATTERN, data_root)}) + elif isinstance(v, dict): + self._update_data_root(v, data_root) + def _update_data_source(self, data_source): data_root = next(iter(self.split_config.values())) data_root = data_root.rstrip(osp.sep) - for k, v in data_source.items(): - if isinstance(v, str) and self.DATA_ROOT_PATTERN in v: - data_source.update( - {k: v.replace(self.DATA_ROOT_PATTERN, data_root)}) + self._update_data_root(data_source, data_root) diff --git a/modelscope/msdatasets/utils/dataset_utils.py b/modelscope/msdatasets/utils/dataset_utils.py index db9d1fee..c7aa7682 100644 --- a/modelscope/msdatasets/utils/dataset_utils.py +++ b/modelscope/msdatasets/utils/dataset_utils.py @@ -7,7 +7,7 @@ from typing import Any, Mapping, Optional, Sequence, Union from datasets.builder import DatasetBuilder from modelscope.hub.api import HubApi -from modelscope.utils.constant import DEFAULT_DATASET_REVISION, DownloadParams +from modelscope.utils.constant import DEFAULT_DATASET_REVISION from modelscope.utils.logger import get_logger from .dataset_builder import MsCsvDatasetBuilder, TaskSpecificDatasetBuilder @@ -95,15 +95,13 @@ def list_dataset_objects(hub_api: HubApi, max_limit: int, is_recursive: bool, res (list): List of objects, i.e., ['train/images/001.png', 'train/images/002.png', 'val/images/001.png', ...] """ res = [] - cookies = hub_api.check_cookies_upload_data(use_cookies=True) objects = hub_api.list_oss_dataset_objects( dataset_name=dataset_name, namespace=namespace, max_limit=max_limit, is_recursive=is_recursive, is_filter_dir=True, - revision=version, - cookies=cookies) + revision=version) for item in objects: object_key = item.get('Key') @@ -174,7 +172,7 @@ def get_dataset_files(subset_split_into: dict, modelscope_api = HubApi() objects = list_dataset_objects( hub_api=modelscope_api, - max_limit=DownloadParams.MAX_LIST_OBJECTS_NUM.value, + max_limit=-1, is_recursive=True, dataset_name=dataset_name, namespace=namespace, diff --git a/modelscope/pipelines/audio/asr_inference_pipeline.py b/modelscope/pipelines/audio/asr_inference_pipeline.py index 4e8b658d..6a4864bf 100644 --- a/modelscope/pipelines/audio/asr_inference_pipeline.py +++ b/modelscope/pipelines/audio/asr_inference_pipeline.py @@ -47,22 +47,28 @@ class AutomaticSpeechRecognitionPipeline(Pipeline): if isinstance(audio_in, str): # load pcm data from url if audio_in is url str - self.audio_in = load_bytes_from_url(audio_in) + self.audio_in, checking_audio_fs = load_bytes_from_url(audio_in) elif isinstance(audio_in, bytes): # load pcm data from wav data if audio_in is wave format - self.audio_in = extract_pcm_from_wav(audio_in) + self.audio_in, checking_audio_fs = extract_pcm_from_wav(audio_in) else: self.audio_in = audio_in + # set the sample_rate of audio_in if checking_audio_fs is valid + if checking_audio_fs is not None: + self.audio_fs = checking_audio_fs + if recog_type is None or audio_format is None: self.recog_type, self.audio_format, self.audio_in = asr_utils.type_checking( audio_in=self.audio_in, recog_type=recog_type, audio_format=audio_format) - if hasattr(asr_utils, 'sample_rate_checking') and audio_fs is None: - self.audio_fs = asr_utils.sample_rate_checking( + if hasattr(asr_utils, 'sample_rate_checking'): + checking_audio_fs = asr_utils.sample_rate_checking( self.audio_in, self.audio_format) + if checking_audio_fs is not None: + self.audio_fs = checking_audio_fs if self.preprocessor is None: self.preprocessor = WavToScp() @@ -80,7 +86,7 @@ class AutomaticSpeechRecognitionPipeline(Pipeline): logger.info(f"Decoding with {inputs['audio_format']} files ...") - data_cmd: Sequence[Tuple[str, str]] + data_cmd: Sequence[Tuple[str, str, str]] if inputs['audio_format'] == 'wav' or inputs['audio_format'] == 'pcm': data_cmd = ['speech', 'sound'] elif inputs['audio_format'] == 'kaldi_ark': @@ -88,6 +94,9 @@ class AutomaticSpeechRecognitionPipeline(Pipeline): elif inputs['audio_format'] == 'tfrecord': data_cmd = ['speech', 'tfrecord'] + if inputs.__contains__('mvn_file'): + data_cmd.append(inputs['mvn_file']) + # generate asr inference command cmd = { 'model_type': inputs['model_type'], diff --git a/modelscope/pipelines/audio/kws_kwsbp_pipeline.py b/modelscope/pipelines/audio/kws_kwsbp_pipeline.py index 5555c9e6..db6fc65d 100644 --- a/modelscope/pipelines/audio/kws_kwsbp_pipeline.py +++ b/modelscope/pipelines/audio/kws_kwsbp_pipeline.py @@ -51,10 +51,10 @@ class KeyWordSpottingKwsbpPipeline(Pipeline): if isinstance(audio_in, str): # load pcm data from url if audio_in is url str - audio_in = load_bytes_from_url(audio_in) + audio_in, audio_fs = load_bytes_from_url(audio_in) elif isinstance(audio_in, bytes): # load pcm data from wav data if audio_in is wave format - audio_in = extract_pcm_from_wav(audio_in) + audio_in, audio_fs = extract_pcm_from_wav(audio_in) output = self.preprocessor.forward(self.model.forward(), audio_in) output = self.forward(output) diff --git a/modelscope/pipelines/cv/tinynas_detection_pipeline.py b/modelscope/pipelines/cv/tinynas_detection_pipeline.py index b2063629..d35d4d36 100644 --- a/modelscope/pipelines/cv/tinynas_detection_pipeline.py +++ b/modelscope/pipelines/cv/tinynas_detection_pipeline.py @@ -12,6 +12,8 @@ from modelscope.pipelines.base import Input, Pipeline from modelscope.pipelines.builder import PIPELINES from modelscope.preprocessors import LoadImage from modelscope.utils.constant import Tasks +from modelscope.utils.cv.image_utils import \ + show_image_object_detection_auto_result from modelscope.utils.logger import get_logger logger = get_logger() @@ -52,10 +54,18 @@ class TinynasDetectionPipeline(Pipeline): bboxes, scores, labels = self.model.postprocess(inputs['data']) if bboxes is None: - return None - outputs = { - OutputKeys.SCORES: scores, - OutputKeys.LABELS: labels, - OutputKeys.BOXES: bboxes - } + outputs = { + OutputKeys.SCORES: [], + OutputKeys.LABELS: [], + OutputKeys.BOXES: [] + } + else: + outputs = { + OutputKeys.SCORES: scores, + OutputKeys.LABELS: labels, + OutputKeys.BOXES: bboxes + } return outputs + + def show_result(self, img_path, result, save_path=None): + show_image_object_detection_auto_result(img_path, result, save_path) diff --git a/modelscope/preprocessors/asr.py b/modelscope/preprocessors/asr.py index facaa132..91bf5860 100644 --- a/modelscope/preprocessors/asr.py +++ b/modelscope/preprocessors/asr.py @@ -133,6 +133,12 @@ class WavToScp(Preprocessor): else: inputs['asr_model_config'] = asr_model_config + if inputs['model_config'].__contains__('mvn_file'): + mvn_file = os.path.join(inputs['model_workspace'], + inputs['model_config']['mvn_file']) + assert os.path.exists(mvn_file), 'mvn_file does not exist' + inputs['mvn_file'] = mvn_file + elif inputs['model_type'] == Frameworks.tf: assert inputs['model_config'].__contains__( 'vocab_file'), 'vocab_file does not exist' diff --git a/modelscope/preprocessors/nlp/nlp_base.py b/modelscope/preprocessors/nlp/nlp_base.py index 267dbb8c..bc96f569 100644 --- a/modelscope/preprocessors/nlp/nlp_base.py +++ b/modelscope/preprocessors/nlp/nlp_base.py @@ -2,7 +2,7 @@ import os.path as osp import re -from typing import Any, Dict, Iterable, Optional, Tuple, Union +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union import numpy as np import sentencepiece as spm @@ -217,7 +217,7 @@ class NLPTokenizerPreprocessorBase(Preprocessor): return isinstance(label, str) or isinstance(label, int) if labels is not None: - if isinstance(labels, Iterable) and all([label_can_be_mapped(label) for label in labels]) \ + if isinstance(labels, (tuple, list)) and all([label_can_be_mapped(label) for label in labels]) \ and self.label2id is not None: output[OutputKeys.LABELS] = [ self.label2id[str(label)] for label in labels @@ -314,8 +314,7 @@ class SequenceClassificationPreprocessor(NLPTokenizerPreprocessorBase): def __init__(self, model_dir: str, mode=ModeKeys.INFERENCE, **kwargs): kwargs['truncation'] = kwargs.get('truncation', True) - kwargs['padding'] = kwargs.get( - 'padding', False if mode == ModeKeys.INFERENCE else 'max_length') + kwargs['padding'] = kwargs.get('padding', 'max_length') kwargs['max_length'] = kwargs.pop('sequence_length', 128) super().__init__(model_dir, mode=mode, **kwargs) diff --git a/modelscope/preprocessors/video.py b/modelscope/preprocessors/video.py index f693cd9e..794033b5 100644 --- a/modelscope/preprocessors/video.py +++ b/modelscope/preprocessors/video.py @@ -1,5 +1,10 @@ import math +import os import random +import uuid +from os.path import exists +from tempfile import TemporaryDirectory +from urllib.parse import urlparse import numpy as np import torch @@ -9,6 +14,7 @@ import torchvision.transforms._transforms_video as transforms from decord import VideoReader from torchvision.transforms import Compose +from modelscope.hub.file_download import http_get_file from modelscope.metainfo import Preprocessors from modelscope.utils.constant import Fields, ModeKeys from modelscope.utils.type_assert import type_assert @@ -30,7 +36,22 @@ def ReadVideoData(cfg, Returns: data (Tensor): the normalized video clips for model inputs """ - data = _decode_video(cfg, video_path, num_temporal_views_override) + url_parsed = urlparse(video_path) + if url_parsed.scheme in ('file', '') and exists( + url_parsed.path): # Possibly a local file + data = _decode_video(cfg, video_path, num_temporal_views_override) + else: + with TemporaryDirectory() as temporary_cache_dir: + random_str = uuid.uuid4().hex + http_get_file( + url=video_path, + local_dir=temporary_cache_dir, + file_name=random_str, + cookies=None) + temp_file_path = os.path.join(temporary_cache_dir, random_str) + data = _decode_video(cfg, temp_file_path, + num_temporal_views_override) + if num_spatial_crops_override is not None: num_spatial_crops = num_spatial_crops_override transform = kinetics400_tranform(cfg, num_spatial_crops_override) diff --git a/modelscope/trainers/hooks/lr_scheduler_hook.py b/modelscope/trainers/hooks/lr_scheduler_hook.py index ca0ec01b..32fb0250 100644 --- a/modelscope/trainers/hooks/lr_scheduler_hook.py +++ b/modelscope/trainers/hooks/lr_scheduler_hook.py @@ -47,7 +47,7 @@ class LrSchedulerHook(Hook): return lr def before_train_iter(self, trainer): - if not self.by_epoch: + if not self.by_epoch and trainer.iter > 0: if self.warmup_lr_scheduler is not None: self.warmup_lr_scheduler.step() else: diff --git a/modelscope/trainers/trainer.py b/modelscope/trainers/trainer.py index 916a6def..35caed0d 100644 --- a/modelscope/trainers/trainer.py +++ b/modelscope/trainers/trainer.py @@ -656,7 +656,7 @@ class EpochBasedTrainer(BaseTrainer): # TODO: support MsDataset load for cv if hasattr(data_cfg, 'name'): dataset = MsDataset.load( - dataset_name=data_cfg.name, + dataset_name=data_cfg.pop('name'), **data_cfg, ) cfg = ConfigDict(type=self.cfg.model.type, mode=mode) diff --git a/modelscope/utils/audio/audio_utils.py b/modelscope/utils/audio/audio_utils.py index 647d9521..32e2fa54 100644 --- a/modelscope/utils/audio/audio_utils.py +++ b/modelscope/utils/audio/audio_utils.py @@ -57,6 +57,7 @@ def update_conf(origin_config_file, new_config_file, conf_item: [str, str]): def extract_pcm_from_wav(wav: bytes) -> bytes: data = wav + sample_rate = None if len(data) > 44: frame_len = 44 file_len = len(data) @@ -70,29 +71,33 @@ def extract_pcm_from_wav(wav: bytes) -> bytes: 'Subchunk1ID'] == 'fmt ': header_fields['SubChunk1Size'] = struct.unpack( ' Union[bytes, str]: + sample_rate = None result = urlparse(url) if result.scheme is not None and len(result.scheme) > 0: storage = HTTPStorage() data = storage.read(url) - data = extract_pcm_from_wav(data) + data, sample_rate = extract_pcm_from_wav(data) else: data = url - return data + return data, sample_rate diff --git a/modelscope/utils/constant.py b/modelscope/utils/constant.py index 6ba58c19..6c0f3e98 100644 --- a/modelscope/utils/constant.py +++ b/modelscope/utils/constant.py @@ -231,13 +231,6 @@ class DownloadMode(enum.Enum): FORCE_REDOWNLOAD = 'force_redownload' -class DownloadParams(enum.Enum): - """ - Parameters for downloading dataset. - """ - MAX_LIST_OBJECTS_NUM = 50000 - - class DatasetFormations(enum.Enum): """ How a dataset is organized and interpreted """ diff --git a/modelscope/utils/device.py b/modelscope/utils/device.py index 4bbd09d8..83faa261 100644 --- a/modelscope/utils/device.py +++ b/modelscope/utils/device.py @@ -61,8 +61,8 @@ def device_placement(framework, device_name='gpu:0'): if framework == Frameworks.tf: import tensorflow as tf if device_type == Devices.gpu and not tf.test.is_gpu_available(): - logger.warning( - 'tensorflow cuda is not available, using cpu instead.') + logger.debug( + 'tensorflow: cuda is not available, using cpu instead.') device_type = Devices.cpu if device_type == Devices.cpu: with tf.device('/CPU:0'): @@ -78,7 +78,8 @@ def device_placement(framework, device_name='gpu:0'): if torch.cuda.is_available(): torch.cuda.set_device(f'cuda:{device_id}') else: - logger.warning('cuda is not available, using cpu instead.') + logger.debug( + 'pytorch: cuda is not available, using cpu instead.') yield else: yield @@ -96,9 +97,7 @@ def create_device(device_name): if device_type == Devices.gpu: use_cuda = True if not torch.cuda.is_available(): - logger.warning( - 'cuda is not available, create gpu device failed, using cpu instead.' - ) + logger.info('cuda is not available, using cpu instead.') use_cuda = False if use_cuda: diff --git a/modelscope/utils/file_utils.py b/modelscope/utils/file_utils.py index 9b82f8d2..cf59dc57 100644 --- a/modelscope/utils/file_utils.py +++ b/modelscope/utils/file_utils.py @@ -1,6 +1,7 @@ # Copyright (c) Alibaba, Inc. and its affiliates. import inspect +import os from pathlib import Path @@ -35,3 +36,10 @@ def get_default_cache_dir(): """ default_cache_dir = Path.home().joinpath('.cache', 'modelscope') return default_cache_dir + + +def read_file(path): + + with open(path, 'r') as f: + text = f.read() + return text diff --git a/modelscope/utils/registry.py b/modelscope/utils/registry.py index 7a9c79e2..73e94b3c 100644 --- a/modelscope/utils/registry.py +++ b/modelscope/utils/registry.py @@ -176,7 +176,7 @@ def build_from_cfg(cfg, raise TypeError('default_args must be a dict or None, ' f'but got {type(default_args)}') - # dynamic load installation reqruiements for this module + # dynamic load installation requirements for this module from modelscope.utils.import_utils import LazyImportModule sig = (registry.name.upper(), group_key, cfg['type']) LazyImportModule.import_module(sig) @@ -193,8 +193,11 @@ def build_from_cfg(cfg, if isinstance(obj_type, str): obj_cls = registry.get(obj_type, group_key=group_key) if obj_cls is None: - raise KeyError(f'{obj_type} is not in the {registry.name}' - f' registry group {group_key}') + raise KeyError( + f'{obj_type} is not in the {registry.name}' + f' registry group {group_key}. Please make' + f' sure the correct version of 1qqQModelScope library is used.' + ) obj_cls.group_key = group_key elif inspect.isclass(obj_type) or inspect.isfunction(obj_type): obj_cls = obj_type diff --git a/modelscope/utils/regress_test_utils.py b/modelscope/utils/regress_test_utils.py index 47bbadfe..3c1e5c1c 100644 --- a/modelscope/utils/regress_test_utils.py +++ b/modelscope/utils/regress_test_utils.py @@ -65,7 +65,8 @@ class RegressTool: def monitor_module_single_forward(self, module: nn.Module, file_name: str, - compare_fn=None): + compare_fn=None, + **kwargs): """Monitor a pytorch module in a single forward. @param module: A torch module @@ -107,7 +108,7 @@ class RegressTool: baseline = os.path.join(tempfile.gettempdir(), name) self.load(baseline, name) with open(baseline, 'rb') as f: - baseline_json = pickle.load(f) + base = pickle.load(f) class NumpyEncoder(json.JSONEncoder): """Special json encoder for numpy types @@ -122,9 +123,9 @@ class RegressTool: return obj.tolist() return json.JSONEncoder.default(self, obj) - print(f'baseline: {json.dumps(baseline_json, cls=NumpyEncoder)}') + print(f'baseline: {json.dumps(base, cls=NumpyEncoder)}') print(f'latest : {json.dumps(io_json, cls=NumpyEncoder)}') - if not compare_io_and_print(baseline_json, io_json, compare_fn): + if not compare_io_and_print(base, io_json, compare_fn, **kwargs): raise ValueError('Result not match!') @contextlib.contextmanager @@ -136,7 +137,8 @@ class RegressTool: ignore_keys=None, compare_random=True, reset_dropout=True, - lazy_stop_callback=None): + lazy_stop_callback=None, + **kwargs): """Monitor a pytorch module's backward data and cfg data within a step of the optimizer. This is usually useful when you try to change some dangerous code @@ -265,14 +267,15 @@ class RegressTool: baseline_json = pickle.load(f) if level == 'strict' and not compare_io_and_print( - baseline_json['forward'], io_json, compare_fn): + baseline_json['forward'], io_json, compare_fn, **kwargs): raise RuntimeError('Forward not match!') if not compare_backward_and_print( baseline_json['backward'], bw_json, compare_fn=compare_fn, ignore_keys=ignore_keys, - level=level): + level=level, + **kwargs): raise RuntimeError('Backward not match!') cfg_opt1 = { 'optimizer': baseline_json['optimizer'], @@ -286,7 +289,8 @@ class RegressTool: 'cfg': summary['cfg'], 'state': None if not compare_random else summary['state'] } - if not compare_cfg_and_optimizers(cfg_opt1, cfg_opt2, compare_fn): + if not compare_cfg_and_optimizers(cfg_opt1, cfg_opt2, compare_fn, + **kwargs): raise RuntimeError('Cfg or optimizers not match!') @@ -303,7 +307,8 @@ class MsRegressTool(RegressTool): compare_fn=None, ignore_keys=None, compare_random=True, - lazy_stop_callback=None): + lazy_stop_callback=None, + **kwargs): if lazy_stop_callback is None: @@ -319,7 +324,7 @@ class MsRegressTool(RegressTool): trainer.register_hook(EarlyStopHook()) - def _train_loop(trainer, *args, **kwargs): + def _train_loop(trainer, *args_train, **kwargs_train): with self.monitor_module_train( trainer, file_name, @@ -327,9 +332,11 @@ class MsRegressTool(RegressTool): compare_fn=compare_fn, ignore_keys=ignore_keys, compare_random=compare_random, - lazy_stop_callback=lazy_stop_callback): + lazy_stop_callback=lazy_stop_callback, + **kwargs): try: - return trainer.train_loop_origin(*args, **kwargs) + return trainer.train_loop_origin(*args_train, + **kwargs_train) except MsRegressTool.EarlyStopError: pass @@ -530,7 +537,8 @@ def compare_arguments_nested(print_content, ) return False if not all([ - compare_arguments_nested(None, sub_arg1, sub_arg2) + compare_arguments_nested( + None, sub_arg1, sub_arg2, rtol=rtol, atol=atol) for sub_arg1, sub_arg2 in zip(arg1, arg2) ]): if print_content is not None: @@ -551,7 +559,8 @@ def compare_arguments_nested(print_content, print(f'{print_content}, key diff:{set(keys1) - set(keys2)}') return False if not all([ - compare_arguments_nested(None, arg1[key], arg2[key]) + compare_arguments_nested( + None, arg1[key], arg2[key], rtol=rtol, atol=atol) for key in keys1 ]): if print_content is not None: @@ -574,7 +583,7 @@ def compare_arguments_nested(print_content, raise ValueError(f'type not supported: {type1}') -def compare_io_and_print(baseline_json, io_json, compare_fn=None): +def compare_io_and_print(baseline_json, io_json, compare_fn=None, **kwargs): if compare_fn is None: def compare_fn(*args, **kwargs): @@ -602,10 +611,10 @@ def compare_io_and_print(baseline_json, io_json, compare_fn=None): else: match = compare_arguments_nested( f'unmatched module {key} input args', v1input['args'], - v2input['args']) and match + v2input['args'], **kwargs) and match match = compare_arguments_nested( f'unmatched module {key} input kwargs', v1input['kwargs'], - v2input['kwargs']) and match + v2input['kwargs'], **kwargs) and match v1output = numpify_tensor_nested(v1['output']) v2output = numpify_tensor_nested(v2['output']) res = compare_fn(v1output, v2output, key, 'output') @@ -615,8 +624,11 @@ def compare_io_and_print(baseline_json, io_json, compare_fn=None): ) match = match and res else: - match = compare_arguments_nested(f'unmatched module {key} outputs', - v1output, v2output) and match + match = compare_arguments_nested( + f'unmatched module {key} outputs', + arg1=v1output, + arg2=v2output, + **kwargs) and match return match @@ -624,7 +636,8 @@ def compare_backward_and_print(baseline_json, bw_json, level, ignore_keys=None, - compare_fn=None): + compare_fn=None, + **kwargs): if compare_fn is None: def compare_fn(*args, **kwargs): @@ -653,18 +666,26 @@ def compare_backward_and_print(baseline_json, data2, grad2, data_after2 = bw_json[key]['data'], bw_json[key][ 'grad'], bw_json[key]['data_after'] match = compare_arguments_nested( - f'unmatched module {key} tensor data', data1, data2) and match + f'unmatched module {key} tensor data', + arg1=data1, + arg2=data2, + **kwargs) and match if level == 'strict': match = compare_arguments_nested( - f'unmatched module {key} grad data', grad1, - grad2) and match + f'unmatched module {key} grad data', + arg1=grad1, + arg2=grad2, + **kwargs) and match match = compare_arguments_nested( f'unmatched module {key} data after step', data_after1, - data_after2) and match + data_after2, **kwargs) and match return match -def compare_cfg_and_optimizers(baseline_json, cfg_json, compare_fn=None): +def compare_cfg_and_optimizers(baseline_json, + cfg_json, + compare_fn=None, + **kwargs): if compare_fn is None: def compare_fn(*args, **kwargs): @@ -686,12 +707,12 @@ def compare_cfg_and_optimizers(baseline_json, cfg_json, compare_fn=None): print( f"Optimizer type not equal:{optimizer1['type']} and {optimizer2['type']}" ) - match = compare_arguments_nested('unmatched optimizer defaults', - optimizer1['defaults'], - optimizer2['defaults']) and match - match = compare_arguments_nested('unmatched optimizer state_dict', - optimizer1['state_dict'], - optimizer2['state_dict']) and match + match = compare_arguments_nested( + 'unmatched optimizer defaults', optimizer1['defaults'], + optimizer2['defaults'], **kwargs) and match + match = compare_arguments_nested( + 'unmatched optimizer state_dict', optimizer1['state_dict'], + optimizer2['state_dict'], **kwargs) and match res = compare_fn(lr_scheduler1, lr_scheduler2, None, 'lr_scheduler') if res is not None: @@ -703,16 +724,17 @@ def compare_cfg_and_optimizers(baseline_json, cfg_json, compare_fn=None): print( f"Optimizer type not equal:{lr_scheduler1['type']} and {lr_scheduler2['type']}" ) - match = compare_arguments_nested('unmatched lr_scheduler state_dict', - lr_scheduler1['state_dict'], - lr_scheduler2['state_dict']) and match + match = compare_arguments_nested( + 'unmatched lr_scheduler state_dict', lr_scheduler1['state_dict'], + lr_scheduler2['state_dict'], **kwargs) and match res = compare_fn(cfg1, cfg2, None, 'cfg') if res is not None: print(f'cfg compared with user compare_fn with result:{res}\n') match = match and res else: - match = compare_arguments_nested('unmatched cfg', cfg1, cfg2) and match + match = compare_arguments_nested( + 'unmatched cfg', arg1=cfg1, arg2=cfg2, **kwargs) and match res = compare_fn(state1, state2, None, 'state') if res is not None: @@ -721,6 +743,6 @@ def compare_cfg_and_optimizers(baseline_json, cfg_json, compare_fn=None): match = match and res else: match = compare_arguments_nested('unmatched random state', state1, - state2) and match + state2, **kwargs) and match return match diff --git a/requirements/cv.txt b/requirements/cv.txt index d23fab3a..f29b296b 100644 --- a/requirements/cv.txt +++ b/requirements/cv.txt @@ -19,7 +19,7 @@ moviepy>=1.0.3 networkx>=2.5 numba onnxruntime>=1.10 -pai-easycv>=0.6.3.7 +pai-easycv>=0.6.3.9 pandas psutil regex diff --git a/tests/hub/test_hub_operation.py b/tests/hub/test_hub_operation.py index c96db986..f2bdb2d3 100644 --- a/tests/hub/test_hub_operation.py +++ b/tests/hub/test_hub_operation.py @@ -127,7 +127,7 @@ class HubOperationTest(unittest.TestCase): return None def test_list_model(self): - data = self.api.list_model(TEST_MODEL_ORG) + data = self.api.list_models(TEST_MODEL_ORG) assert len(data['Models']) >= 1 diff --git a/tests/hub/test_hub_upload.py b/tests/hub/test_hub_upload.py index 2250164b..e1f61467 100644 --- a/tests/hub/test_hub_upload.py +++ b/tests/hub/test_hub_upload.py @@ -7,12 +7,12 @@ import uuid from modelscope.hub.api import HubApi from modelscope.hub.constants import Licenses, ModelVisibility +from modelscope.hub.errors import HTTPError, NotLoginException from modelscope.hub.repository import Repository -from modelscope.hub.upload import upload_folder from modelscope.utils.constant import ModelFile from modelscope.utils.logger import get_logger from modelscope.utils.test_utils import test_level -from .test_utils import TEST_ACCESS_TOKEN1, delete_credential +from .test_utils import TEST_ACCESS_TOKEN1, TEST_MODEL_ORG, delete_credential logger = get_logger() @@ -22,7 +22,7 @@ class HubUploadTest(unittest.TestCase): def setUp(self): logger.info('SetUp') self.api = HubApi() - self.user = os.environ.get('TEST_MODEL_ORG', 'citest') + self.user = TEST_MODEL_ORG logger.info(self.user) self.create_model_name = '%s/%s_%s' % (self.user, 'test_model_upload', uuid.uuid4().hex) @@ -39,7 +39,10 @@ class HubUploadTest(unittest.TestCase): def tearDown(self): logger.info('TearDown') shutil.rmtree(self.model_dir, ignore_errors=True) - self.api.delete_model(model_id=self.create_model_name) + try: + self.api.delete_model(model_id=self.create_model_name) + except Exception: + pass def test_upload_exits_repo_master(self): logger.info('basic test for upload!') @@ -50,14 +53,14 @@ class HubUploadTest(unittest.TestCase): license=Licenses.APACHE_V2) os.system("echo '111'>%s" % os.path.join(self.finetune_path, 'add1.py')) - upload_folder( + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path) Repository(model_dir=self.repo_path, clone_from=self.create_model_name) assert os.path.exists(os.path.join(self.repo_path, 'add1.py')) shutil.rmtree(self.repo_path, ignore_errors=True) os.system("echo '222'>%s" % os.path.join(self.finetune_path, 'add2.py')) - upload_folder( + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path, revision='new_revision/version1') @@ -69,7 +72,7 @@ class HubUploadTest(unittest.TestCase): shutil.rmtree(self.repo_path, ignore_errors=True) os.system("echo '333'>%s" % os.path.join(self.finetune_path, 'add3.py')) - upload_folder( + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path, revision='new_revision/version2', @@ -84,7 +87,7 @@ class HubUploadTest(unittest.TestCase): add4_path = os.path.join(self.finetune_path, 'temp') os.mkdir(add4_path) os.system("echo '444'>%s" % os.path.join(add4_path, 'add4.py')) - upload_folder( + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path, revision='new_revision/version1') @@ -101,7 +104,7 @@ class HubUploadTest(unittest.TestCase): self.api.login(TEST_ACCESS_TOKEN1) os.system("echo '111'>%s" % os.path.join(self.finetune_path, 'add1.py')) - upload_folder( + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path, revision='new_model_new_revision', @@ -119,48 +122,23 @@ class HubUploadTest(unittest.TestCase): logger.info('test upload without login!') self.api.login(TEST_ACCESS_TOKEN1) delete_credential() - try: - upload_folder( - model_id=self.create_model_name, - model_dir=self.finetune_path, - visibility=ModelVisibility.PUBLIC, - license=Licenses.APACHE_V2) - except Exception as e: - logger.info(e) - self.api.login(TEST_ACCESS_TOKEN1) - upload_folder( + with self.assertRaises(NotLoginException): + self.api.push_model( model_id=self.create_model_name, model_dir=self.finetune_path, visibility=ModelVisibility.PUBLIC, license=Licenses.APACHE_V2) - Repository( - model_dir=self.repo_path, clone_from=self.create_model_name) - assert os.path.exists( - os.path.join(self.repo_path, 'configuration.json')) - shutil.rmtree(self.repo_path, ignore_errors=True) @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') def test_upload_invalid_repo(self): logger.info('test upload to invalid repo!') self.api.login(TEST_ACCESS_TOKEN1) - try: - upload_folder( + with self.assertRaises(HTTPError): + self.api.push_model( model_id='%s/%s' % ('speech_tts', 'invalid_model_test'), model_dir=self.finetune_path, visibility=ModelVisibility.PUBLIC, license=Licenses.APACHE_V2) - except Exception as e: - logger.info(e) - upload_folder( - model_id=self.create_model_name, - model_dir=self.finetune_path, - visibility=ModelVisibility.PUBLIC, - license=Licenses.APACHE_V2) - Repository( - model_dir=self.repo_path, clone_from=self.create_model_name) - assert os.path.exists( - os.path.join(self.repo_path, 'configuration.json')) - shutil.rmtree(self.repo_path, ignore_errors=True) if __name__ == '__main__': diff --git a/tests/msdatasets/test_ms_dataset.py b/tests/msdatasets/test_ms_dataset.py index 91a3b5c5..1e537e93 100644 --- a/tests/msdatasets/test_ms_dataset.py +++ b/tests/msdatasets/test_ms_dataset.py @@ -52,7 +52,8 @@ class MsDatasetTest(unittest.TestCase): @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') def test_ms_csv_basic(self): ms_ds_train = MsDataset.load( - 'afqmc_small', namespace='userxiaoming', split='train') + 'clue', subset_name='afqmc', + split='train').to_hf_dataset().select(range(5)) print(next(iter(ms_ds_train))) @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') diff --git a/tests/pipelines/test_automatic_speech_recognition.py b/tests/pipelines/test_automatic_speech_recognition.py index 303fb6b9..c37a6a3f 100644 --- a/tests/pipelines/test_automatic_speech_recognition.py +++ b/tests/pipelines/test_automatic_speech_recognition.py @@ -45,6 +45,10 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, 'checking_item': OutputKeys.TEXT, 'example': 'wav_example' }, + 'test_run_with_url_pytorch': { + 'checking_item': OutputKeys.TEXT, + 'example': 'wav_example' + }, 'test_run_with_url_tf': { 'checking_item': OutputKeys.TEXT, 'example': 'wav_example' @@ -74,6 +78,170 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, } } + all_models_info = [ + { + 'model_group': 'damo', + 'model_id': + 'speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': 'speech_paraformer_asr_nat-aishell1-pytorch', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1', + 'wav_path': 'data/test/audios/asr_example_8K.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_8K.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-zh-cn-8k-common-vocab8358-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_8K.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-cn-en-moe-16k-vocab8358-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_cn_en.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-cn-en-moe-16k-vocab8358-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_cn_en.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_cn_dialect.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-cn-dialect-16k-vocab8358-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_cn_dialect.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_paraformer_asr_nat-zh-cn-16k-common-vocab3444-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_paraformer_asr_nat-zh-cn-8k-common-vocab3444-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_8K.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_en.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_en.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_ru.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_ru.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_es.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_es.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_ko.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_ko.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_ja.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_ja.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online', + 'wav_path': 'data/test/audios/asr_example_id.wav' + }, + { + 'model_group': 'damo', + 'model_id': + 'speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-offline', + 'wav_path': 'data/test/audios/asr_example_id.wav' + }, + ] + def setUp(self) -> None: self.am_pytorch_model_id = 'damo/speech_paraformer_asr_nat-aishell1-pytorch' self.am_tf_model_id = 'damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8358-tensorflow1' @@ -90,7 +258,7 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, def run_pipeline(self, model_id: str, audio_in: Union[str, bytes], - sr: int = 16000) -> Dict[str, Any]: + sr: int = None) -> Dict[str, Any]: inference_16k_pipline = pipeline( task=Tasks.auto_speech_recognition, model=model_id) @@ -136,33 +304,26 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, return audio, fs @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run_with_wav_pytorch(self): - """run with single waveform file + def test_run_with_pcm(self): + """run with wav data """ - logger.info('Run ASR test with waveform file (pytorch)...') + logger.info('Run ASR test with wav data (tensorflow)...') - wav_file_path = os.path.join(os.getcwd(), WAV_FILE) + audio, sr = self.wav2bytes(os.path.join(os.getcwd(), WAV_FILE)) rec_result = self.run_pipeline( - model_id=self.am_pytorch_model_id, audio_in=wav_file_path) - self.check_result('test_run_with_wav_pytorch', rec_result) - - @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run_with_pcm_pytorch(self): - """run with wav data - """ + model_id=self.am_tf_model_id, audio_in=audio, sr=sr) + self.check_result('test_run_with_pcm_tf', rec_result) logger.info('Run ASR test with wav data (pytorch)...') - audio, sr = self.wav2bytes(os.path.join(os.getcwd(), WAV_FILE)) - rec_result = self.run_pipeline( model_id=self.am_pytorch_model_id, audio_in=audio, sr=sr) self.check_result('test_run_with_pcm_pytorch', rec_result) @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run_with_wav_tf(self): + def test_run_with_wav(self): """run with single waveform file """ @@ -174,21 +335,14 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, model_id=self.am_tf_model_id, audio_in=wav_file_path) self.check_result('test_run_with_wav_tf', rec_result) - @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run_with_pcm_tf(self): - """run with wav data - """ - - logger.info('Run ASR test with wav data (tensorflow)...') - - audio, sr = self.wav2bytes(os.path.join(os.getcwd(), WAV_FILE)) + logger.info('Run ASR test with waveform file (pytorch)...') rec_result = self.run_pipeline( - model_id=self.am_tf_model_id, audio_in=audio, sr=sr) - self.check_result('test_run_with_pcm_tf', rec_result) + model_id=self.am_pytorch_model_id, audio_in=wav_file_path) + self.check_result('test_run_with_wav_pytorch', rec_result) @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run_with_url_tf(self): + def test_run_with_url(self): """run with single url file """ @@ -198,6 +352,12 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, model_id=self.am_tf_model_id, audio_in=URL_FILE) self.check_result('test_run_with_url_tf', rec_result) + logger.info('Run ASR test with url file (pytorch)...') + + rec_result = self.run_pipeline( + model_id=self.am_pytorch_model_id, audio_in=URL_FILE) + self.check_result('test_run_with_url_pytorch', rec_result) + @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') def test_run_with_wav_dataset_pytorch(self): """run with datasets, and audio format is waveform @@ -217,7 +377,6 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, data.text # hypothesis text """ - logger.info('Run ASR test with waveform dataset (pytorch)...') logger.info('Downloading waveform testsets file ...') dataset_path = download_and_untar( @@ -225,40 +384,38 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase, LITTLE_TESTSETS_URL, self.workspace) dataset_path = os.path.join(dataset_path, 'wav', 'test') + logger.info('Run ASR test with waveform dataset (tensorflow)...') + + rec_result = self.run_pipeline( + model_id=self.am_tf_model_id, audio_in=dataset_path) + self.check_result('test_run_with_wav_dataset_tf', rec_result) + + logger.info('Run ASR test with waveform dataset (pytorch)...') + rec_result = self.run_pipeline( model_id=self.am_pytorch_model_id, audio_in=dataset_path) self.check_result('test_run_with_wav_dataset_pytorch', rec_result) - @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') - def test_run_with_wav_dataset_tf(self): - """run with datasets, and audio format is waveform - datasets directory: - - wav - test # testsets - xx.wav - ... - dev # devsets - yy.wav - ... - train # trainsets - zz.wav - ... - transcript - data.text # hypothesis text + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') + def test_run_with_all_models(self): + """run with all models """ - logger.info('Run ASR test with waveform dataset (tensorflow)...') - logger.info('Downloading waveform testsets file ...') - - dataset_path = download_and_untar( - os.path.join(self.workspace, LITTLE_TESTSETS_FILE), - LITTLE_TESTSETS_URL, self.workspace) - dataset_path = os.path.join(dataset_path, 'wav', 'test') - - rec_result = self.run_pipeline( - model_id=self.am_tf_model_id, audio_in=dataset_path) - self.check_result('test_run_with_wav_dataset_tf', rec_result) + logger.info('Run ASR test with all models') + + for item in self.all_models_info: + model_id = item['model_group'] + '/' + item['model_id'] + wav_path = item['wav_path'] + rec_result = self.run_pipeline( + model_id=model_id, audio_in=wav_path) + if rec_result.__contains__(OutputKeys.TEXT): + logger.info(ColorCodes.MAGENTA + str(item['model_id']) + ' ' + + ColorCodes.YELLOW + + str(rec_result[OutputKeys.TEXT]) + + ColorCodes.END) + else: + logger.info(ColorCodes.MAGENTA + str(rec_result) + + ColorCodes.END) @unittest.skip('demo compatibility test is only enabled on a needed-basis') def test_demo_compatibility(self): diff --git a/tests/pipelines/test_csanmt_translation.py b/tests/pipelines/test_csanmt_translation.py index f7ec81cd..83827813 100644 --- a/tests/pipelines/test_csanmt_translation.py +++ b/tests/pipelines/test_csanmt_translation.py @@ -26,6 +26,20 @@ class TranslationTest(unittest.TestCase, DemoCompatibilityCheck): pipeline_ins = pipeline(self.task, model=model_id) print(pipeline_ins(input=inputs)) + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_run_with_model_name_for_en2fr(self): + model_id = 'damo/nlp_csanmt_translation_en2fr' + inputs = 'When I was in my 20s, I saw my very first psychotherapy client.' + pipeline_ins = pipeline(self.task, model=model_id) + print(pipeline_ins(input=inputs)) + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_run_with_model_name_for_fr2en(self): + model_id = 'damo/nlp_csanmt_translation_fr2en' + inputs = "Quand j'avais la vingtaine, j'ai vu mes tout premiers clients comme psychothérapeute." + pipeline_ins = pipeline(self.task, model=model_id) + print(pipeline_ins(input=inputs)) + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') def test_run_with_default_model(self): inputs = '声明补充说，沃伦的同事都深感震惊，并且希望他能够投案自首。' diff --git a/tests/pipelines/test_tinynas_detection.py b/tests/pipelines/test_tinynas_detection.py index 63db9145..43e1842d 100644 --- a/tests/pipelines/test_tinynas_detection.py +++ b/tests/pipelines/test_tinynas_detection.py @@ -4,22 +4,45 @@ import unittest from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks +from modelscope.utils.demo_utils import DemoCompatibilityCheck from modelscope.utils.test_utils import test_level -class TinynasObjectDetectionTest(unittest.TestCase): +class TinynasObjectDetectionTest(unittest.TestCase, DemoCompatibilityCheck): + + def setUp(self) -> None: + self.task = Tasks.image_object_detection + self.model_id = 'damo/cv_tinynas_object-detection_damoyolo' @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') - def test_run(self): + def test_run_airdet(self): tinynas_object_detection = pipeline( Tasks.image_object_detection, model='damo/cv_tinynas_detection') result = tinynas_object_detection( 'data/test/images/image_detection.jpg') print(result) + @unittest.skip('will be enabled after damoyolo officially released') + def test_run_damoyolo(self): + tinynas_object_detection = pipeline( + Tasks.image_object_detection, + model='damo/cv_tinynas_object-detection_damoyolo') + result = tinynas_object_detection( + 'data/test/images/image_detection.jpg') + print(result) + @unittest.skip('demo compatibility test is only enabled on a needed-basis') def test_demo_compatibility(self): - self.test_demo() + self.compatibility_check() + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_image_object_detection_auto_pipeline(self): + test_image = 'data/test/images/image_detection.jpg' + tinynas_object_detection = pipeline( + Tasks.image_object_detection, model='damo/cv_tinynas_detection') + result = tinynas_object_detection(test_image) + tinynas_object_detection.show_result(test_image, result, + 'demo_ret.jpg') if __name__ == '__main__': diff --git a/tests/trainers/easycv/test_easycv_trainer_face_2d_keypoints.py b/tests/trainers/easycv/test_easycv_trainer_face_2d_keypoints.py new file mode 100644 index 00000000..4dffa998 --- /dev/null +++ b/tests/trainers/easycv/test_easycv_trainer_face_2d_keypoints.py @@ -0,0 +1,71 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. +import glob +import os +import shutil +import tempfile +import unittest + +import torch + +from modelscope.metainfo import Trainers +from modelscope.msdatasets import MsDataset +from modelscope.trainers import build_trainer +from modelscope.utils.constant import DownloadMode, LogKeys, Tasks +from modelscope.utils.logger import get_logger +from modelscope.utils.test_utils import test_level + + +@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest') +class EasyCVTrainerTestFace2DKeypoints(unittest.TestCase): + model_id = 'damo/cv_mobilenet_face-2d-keypoints_alignment' + + def setUp(self): + self.logger = get_logger() + self.logger.info(('Testing %s.%s' % + (type(self).__name__, self._testMethodName))) + + def _train(self, tmp_dir): + cfg_options = {'train.max_epochs': 2} + + trainer_name = Trainers.easycv + + train_dataset = MsDataset.load( + dataset_name='face_2d_keypoints_dataset', + namespace='modelscope', + split='train', + download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) + eval_dataset = MsDataset.load( + dataset_name='face_2d_keypoints_dataset', + namespace='modelscope', + split='train', + download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS) + + kwargs = dict( + model=self.model_id, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + work_dir=tmp_dir, + cfg_options=cfg_options) + + trainer = build_trainer(trainer_name, kwargs) + trainer.train() + + @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + def test_trainer_single_gpu(self): + temp_file_dir = tempfile.TemporaryDirectory() + tmp_dir = temp_file_dir.name + if not os.path.exists(tmp_dir): + os.makedirs(tmp_dir) + + self._train(tmp_dir) + + results_files = os.listdir(tmp_dir) + json_files = glob.glob(os.path.join(tmp_dir, '*.log.json')) + self.assertEqual(len(json_files), 1) + self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files) + + temp_file_dir.cleanup() + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/trainers/test_finetune_sequence_classification.py b/tests/trainers/test_finetune_sequence_classification.py index f2adfa22..27db1f18 100644 --- a/tests/trainers/test_finetune_sequence_classification.py +++ b/tests/trainers/test_finetune_sequence_classification.py @@ -16,7 +16,8 @@ from modelscope.trainers.optimizer.child_tuning_adamw_optimizer import \ calculate_fisher from modelscope.utils.constant import ModelFile, Tasks from modelscope.utils.data_utils import to_device -from modelscope.utils.regress_test_utils import MsRegressTool +from modelscope.utils.regress_test_utils import (MsRegressTool, + compare_arguments_nested) from modelscope.utils.test_utils import test_level @@ -41,6 +42,33 @@ class TestFinetuneSequenceClassification(unittest.TestCase): def test_trainer_repeatable(self): import torch # noqa + def compare_fn(value1, value2, key, type): + # Ignore the differences between optimizers of two torch versions + if type != 'optimizer': + return None + + match = (value1['type'] == value2['type']) + shared_defaults = set(value1['defaults'].keys()).intersection( + set(value2['defaults'].keys())) + match = all([ + compare_arguments_nested(f'Optimizer defaults {key} not match', + value1['defaults'][key], + value2['defaults'][key]) + for key in shared_defaults + ]) and match + match = (len(value1['state_dict']['param_groups']) == len( + value2['state_dict']['param_groups'])) and match + for group1, group2 in zip(value1['state_dict']['param_groups'], + value2['state_dict']['param_groups']): + shared_keys = set(group1.keys()).intersection( + set(group2.keys())) + match = all([ + compare_arguments_nested( + f'Optimizer param_groups {key} not match', group1[key], + group2[key]) for key in shared_keys + ]) and match + return match + def cfg_modify_fn(cfg): cfg.task = 'nli' cfg['preprocessor'] = {'type': 'nli-tokenizer'} @@ -98,7 +126,8 @@ class TestFinetuneSequenceClassification(unittest.TestCase): name=Trainers.nlp_base_trainer, default_args=kwargs) with self.regress_tool.monitor_ms_train( - trainer, 'sbert-base-tnews', level='strict'): + trainer, 'sbert-base-tnews', level='strict', + compare_fn=compare_fn): trainer.train() def finetune(self, diff --git a/tests/trainers/test_image_denoise_trainer.py b/tests/trainers/test_image_denoise_trainer.py index 68ddf616..c4abca6a 100644 --- a/tests/trainers/test_image_denoise_trainer.py +++ b/tests/trainers/test_image_denoise_trainer.py @@ -51,7 +51,7 @@ class ImageDenoiseTrainerTest(unittest.TestCase): shutil.rmtree(self.tmp_dir, ignore_errors=True) super().tearDown() - @unittest.skipUnless(test_level() >= 0, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') def test_trainer(self): kwargs = dict( model=self.model_id, @@ -65,7 +65,7 @@ class ImageDenoiseTrainerTest(unittest.TestCase): for i in range(2): self.assertIn(f'epoch_{i+1}.pth', results_files) - @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') def test_trainer_with_model_and_args(self): model = NAFNetForImageDenoise.from_pretrained(self.cache_path) kwargs = dict( diff --git a/tests/trainers/test_trainer_with_nlp.py b/tests/trainers/test_trainer_with_nlp.py index 6030ada9..8357e778 100644 --- a/tests/trainers/test_trainer_with_nlp.py +++ b/tests/trainers/test_trainer_with_nlp.py @@ -29,7 +29,8 @@ class TestTrainerWithNlp(unittest.TestCase): os.makedirs(self.tmp_dir) self.dataset = MsDataset.load( - 'afqmc_small', namespace='userxiaoming', split='train') + 'clue', subset_name='afqmc', + split='train').to_hf_dataset().select(range(2)) def tearDown(self): shutil.rmtree(self.tmp_dir) @@ -73,7 +74,7 @@ class TestTrainerWithNlp(unittest.TestCase): output_dir = os.path.join(self.tmp_dir, ModelFile.TRAIN_OUTPUT_DIR) pipeline_sentence_similarity(output_dir) - @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 3, 'skip test in current test level') def test_trainer_with_backbone_head(self): model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base' kwargs = dict( @@ -99,6 +100,8 @@ class TestTrainerWithNlp(unittest.TestCase): model_id = 'damo/nlp_structbert_sentiment-classification_chinese-base' cfg = read_config(model_id, revision='beta') cfg.train.max_epochs = 20 + cfg.preprocessor.train['label2id'] = {'0': 0, '1': 1} + cfg.preprocessor.val['label2id'] = {'0': 0, '1': 1} cfg.train.work_dir = self.tmp_dir cfg_file = os.path.join(self.tmp_dir, 'config.json') cfg.dump(cfg_file) @@ -120,22 +123,24 @@ class TestTrainerWithNlp(unittest.TestCase): checkpoint_path=os.path.join(self.tmp_dir, 'epoch_10.pth')) self.assertTrue(Metrics.accuracy in eval_results) - @unittest.skipUnless(test_level() >= 1, 'skip test in current test level') + @unittest.skipUnless(test_level() >= 2, 'skip test in current test level') def test_trainer_with_configured_datasets(self): model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base' cfg: Config = read_config(model_id) cfg.train.max_epochs = 20 + cfg.preprocessor.train['label2id'] = {'0': 0, '1': 1} + cfg.preprocessor.val['label2id'] = {'0': 0, '1': 1} cfg.train.work_dir = self.tmp_dir cfg.dataset = { 'train': { - 'name': 'afqmc_small', + 'name': 'clue', + 'subset_name': 'afqmc', 'split': 'train', - 'namespace': 'userxiaoming' }, 'val': { - 'name': 'afqmc_small', + 'name': 'clue', + 'subset_name': 'afqmc', 'split': 'train', - 'namespace': 'userxiaoming' }, } cfg_file = os.path.join(self.tmp_dir, 'config.json') @@ -159,6 +164,11 @@ class TestTrainerWithNlp(unittest.TestCase): model_id = 'damo/nlp_structbert_sentence-similarity_chinese-base' cfg: Config = read_config(model_id) cfg.train.max_epochs = 3 + cfg.preprocessor.first_sequence = 'sentence1' + cfg.preprocessor.second_sequence = 'sentence2' + cfg.preprocessor.label = 'label' + cfg.preprocessor.train['label2id'] = {'0': 0, '1': 1} + cfg.preprocessor.val['label2id'] = {'0': 0, '1': 1} cfg.train.work_dir = self.tmp_dir cfg_file = os.path.join(self.tmp_dir, 'config.json') cfg.dump(cfg_file) diff --git a/tests/utils/test_compatibility.py b/tests/utils/test_compatibility.py new file mode 100644 index 00000000..f5222261 --- /dev/null +++ b/tests/utils/test_compatibility.py @@ -0,0 +1,19 @@ +# Copyright (c) Alibaba, Inc. and its affiliates. + +import unittest + + +class CompatibilityTest(unittest.TestCase): + + def setUp(self): + print(('Testing %s.%s' % (type(self).__name__, self._testMethodName))) + + def tearDown(self): + super().tearDown() + + def test_xtcocotools(self): + from xtcocotools.coco import COCO + + +if __name__ == '__main__': + unittest.main()