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Merge remote-tracking branch 'origin' into ofa/finetune

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Yingda Chen
2022-10-20 17:40:03 +08:00
parent 63a62c3151 8ec90ccbf8
commit b559ea50ca
61 changed files with 882 additions and 511 deletions
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2
.pre-commit-config.yaml
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@@ -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/

2
.pre-commit-config_local.yaml
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@@ -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/

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data/test/regression/sbert_sen_sim.bin
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4
modelscope/exporters/nlp/sbert_for_sequence_classification_exporter.py
View File

@@ -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:

136
modelscope/hub/api.py
View File

@@ -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()

3
modelscope/hub/file_download.py
View File

@@ -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

11
modelscope/hub/git.py
View File

@@ -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']

4
modelscope/hub/repository.py
View File

@@ -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:

2
modelscope/hub/snapshot_download.py
View File

@@ -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,

117
modelscope/hub/upload.py
View File

@@ -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)

6
modelscope/metainfo.py
View File

@@ -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'

1
modelscope/metrics/builder.py
View File

@@ -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:

78
modelscope/models/audio/tts/voice.py
View File

@@ -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))

2
modelscope/models/cv/text_driven_segmentation/lseg_model.py
View File

@@ -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])

2
modelscope/models/cv/tinynas_detection/__init__.py
View File

@@ -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

29
modelscope/models/cv/tinynas_detection/backbone/tinynas.py
View File

@@ -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

5
modelscope/models/cv/tinynas_detection/detector.py
View File

@@ -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)

51
modelscope/models/cv/tinynas_detection/head/gfocal_v2_tiny.py
View File

@@ -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.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)
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)

5
modelscope/models/cv/tinynas_detection/neck/giraffe_fpn_v2.py
View File

@@ -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)

15
modelscope/models/cv/tinynas_detection/tinynas_damoyolo.py Normal file
View File

@@ -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)

2
modelscope/models/cv/tinynas_detection/tinynas_detector.py
View File

@@ -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)

78
modelscope/models/nlp/bert/modeling_bert.py
View File

@@ -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']

13
modelscope/msdatasets/cv/easycv_base.py
View File

@@ -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)

8
modelscope/msdatasets/utils/dataset_utils.py
View File

@@ -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,

19
modelscope/pipelines/audio/asr_inference_pipeline.py
View File

@@ -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'],

4
modelscope/pipelines/audio/kws_kwsbp_pipeline.py
View File

@@ -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)

22
modelscope/pipelines/cv/tinynas_detection_pipeline.py
View File

@@ -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)

6
modelscope/preprocessors/asr.py
View File

@@ -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'

7
modelscope/preprocessors/nlp/nlp_base.py
View File

@@ -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)

23
modelscope/preprocessors/video.py
View File

@@ -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)

2
modelscope/trainers/hooks/lr_scheduler_hook.py
View File

@@ -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:

2
modelscope/trainers/trainer.py
View File

@@ -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)

13
modelscope/utils/audio/audio_utils.py
View File

@@ -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(
'<I', data[16:20])[0]
header_fields['SampleRate'] = struct.unpack('<I',
data[24:28])[0]
sample_rate = header_fields['SampleRate']
if header_fields['SubChunk1Size'] == 16:
frame_len = 44
elif header_fields['SubChunk1Size'] == 18:
frame_len = 46
else:
return data
return data, sample_rate
data = wav[frame_len:file_len]
except Exception:
# no treatment
pass
return data
return data, sample_rate
def load_bytes_from_url(url: str) -> 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

7
modelscope/utils/constant.py
View File

@@ -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
"""

11
modelscope/utils/device.py
View File

@@ -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:

8
modelscope/utils/file_utils.py
View File

@@ -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

9
modelscope/utils/registry.py
View File

@@ -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

94
modelscope/utils/regress_test_utils.py
View File

@@ -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

2
requirements/cv.txt
View File

@@ -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

2
tests/hub/test_hub_operation.py
View File

@@ -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

54
tests/hub/test_hub_upload.py
View File

@@ -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(
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)
except Exception as e:
logger.info(e)
self.api.login(TEST_ACCESS_TOKEN1)
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)
@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__':

3
tests/msdatasets/test_ms_dataset.py
View File

@@ -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')

297
tests/pipelines/test_automatic_speech_recognition.py
View File

@@ -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)
@@ -136,46 +304,7 @@ 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
"""
logger.info('Run ASR test with waveform file (pytorch)...')
wav_file_path = 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
"""
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):
"""run with single waveform file
"""
logger.info('Run ASR test with waveform file (tensorflow)...')
wav_file_path = os.path.join(os.getcwd(), WAV_FILE)
rec_result = self.run_pipeline(
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):
def test_run_with_pcm(self):
"""run with wav data
"""
@@ -187,8 +316,33 @@ class AutomaticSpeechRecognitionTest(unittest.TestCase,
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)...')
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_url_tf(self):
def test_run_with_wav(self):
"""run with single waveform file
"""
logger.info('Run ASR test with waveform file (tensorflow)...')
wav_file_path = os.path.join(os.getcwd(), WAV_FILE)
rec_result = self.run_pipeline(
model_id=self.am_tf_model_id, audio_in=wav_file_path)
self.check_result('test_run_with_wav_tf', rec_result)
logger.info('Run ASR test with waveform file (pytorch)...')
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_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:
<dataset_path>
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 ...')
logger.info('Run ASR test with all models')
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)
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):

14
tests/pipelines/test_csanmt_translation.py
View File

@@ -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。'

29
tests/pipelines/test_tinynas_detection.py
View File

@@ -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__':

71
tests/trainers/easycv/test_easycv_trainer_face_2d_keypoints.py Normal file
View File

@@ -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()

33
tests/trainers/test_finetune_sequence_classification.py
View File

@@ -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,

4
tests/trainers/test_image_denoise_trainer.py
View File

@@ -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(

24
tests/trainers/test_trainer_with_nlp.py
View File

@@ -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)

19
tests/utils/test_compatibility.py Normal file
View File

@@ -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()