def __init__(
self,
origin_dataset_path: str = "",
tfrecord_dataset_path: str = "",
model_save_path: str = "",
validation_split: float = 0.2,
batch_size: int = 32,
epochs: int = 30,
project_id: int = 0,
image_size: int = 224,
do_fine_tune=False,
with_image_net=True,
**kwargs,
):
"""
Args:
origin_dataset_path (str): 处理前的数据集路径
tfrecord_dataset_path (str): 处理后的数据集路径
model_save_path (str): 模型保存路径
validation_split (float): 验证集切割比例
batch_size (int): mini batch 大小
epochs (int): 训练epoch数
project_id (int): 训练项目编号
with_image_net (bool): 是否使用imagenet的均值初始化数据
"""
self._call_code = ""
self.project_id = project_id
self.do_fine_tune = do_fine_tune
self.with_image_net = with_image_net
origin_dataset_path = file_util.abspath(origin_dataset_path)
tfrecord_dataset_path = file_util.abspath(tfrecord_dataset_path)
model_save_path = file_util.abspath(model_save_path)
self.image_size = image_size
self.origin_dataset_path = origin_dataset_path
# 当未给定处理后数据集的路径时，默认保存到原始数据集相同路径
if tfrecord_dataset_path:
self.tfrecord_dataset_path = tfrecord_dataset_path
else:
self.tfrecord_dataset_path = origin_dataset_path
# 当未给定模型保存路径时，默认保存到处理后数据集相同路径
if self.project_id:
self.project_save_name = f"luwu-classification-project-{self.project_id}"
else:
self.project_save_name = f"luwu-classification-project"
if model_save_path:
self.project_save_path = os.path.join(model_save_path,
self.project_save_name)
else:
self.project_save_path = os.path.join(self.tfrecord_dataset_path,
self.project_save_name)
self.model_save_path = os.path.join(self.project_save_path,
"best_weights.h5")
self.validation_split = validation_split
self.batch_size = batch_size
self.epochs = epochs
file_util.mkdirs(self.project_save_path)
file_util.mkdirs(self.tfrecord_dataset_path)
def upload_dataset(self):
origin_dataset_path = self.kwargs.get("origin_dataset_path", "")
if os.path.exists(origin_dataset_path):
# 先复制一份数据集
# 创建一个文件夹
dataset_path = os.path.join(self.tmp_dir_path, "kaggle-data")
copy_path = os.path.join(dataset_path, "data")
logger.info(f"创建文件夹 {copy_path} ...")
file_util.mkdirs(copy_path)
# 复制数据集到临时目录
logger.info(f"复制数据集到临时目录...")
if os.path.isdir(origin_dataset_path):
cmd = f'cp -r {os.path.join(origin_dataset_path,"*")} {copy_path}'
else:
cmd = f"cp -r {origin_dataset_path} {copy_path}"
cmd_util.run_cmd(cmd)
# 使用kaggle api初始化数据集
logger.info("使用kaggle api初始化数据集...")
cmd = f"kaggle datasets init -p {dataset_path}"
cmd_util.run_cmd(cmd)
# 配置dataset meta
dataset_meta_path = os.path.join(dataset_path,
"dataset-metadata.json")
with open(dataset_meta_path, "r") as f:
dataset_meta = json.load(f)
dataset_meta["title"] = f"luwu-dataset-{self.uuid}"
dataset_meta["id"] = (dataset_meta["id"].split("/")[0] + "/" +
f"luwu-dataset-{self.uuid}")
with open(dataset_meta_path, "w") as f:
json.dump(dataset_meta, f, ensure_ascii=False, indent=2)
# 上传数据集
logger.info("上传数据集...")
cmd = f"kaggle datasets create -r zip -p {dataset_path}"
cmd_util.run_cmd(cmd)
logger.info("数据集上传完成！")
logger.info("等待 kaggle 处理数据集，这可能需要几分钟时间 ...")
self.dataset_id = dataset_meta["id"]
self.dataset_title = dataset_meta["title"]
cmd = f"kaggle datasets status {self.dataset_id}"
while True:
code, output = subprocess.getstatusoutput(cmd)
if code != 0:
logger.error(output)
raise Exception("查询数据集状态失败！")
if output:
if "ready" in output:
logger.info("数据集准备完成！")
else:
logger.warning(output)
break
else:
logger.info("暂未查询到数据，等待中 ...")
time.sleep(10)
else:
raise FileNotFoundError(
f"指定的 origin_dataset_path 不存在！{origin_dataset_path}")
def download_result_from_kaggle(self):
output_path = os.path.join(self.tmp_dir_path, "kaggle-output")
logger.info(f"创建文件夹 {output_path} ...")
file_util.mkdirs(output_path)
logger.info("从kaggle拉取运行结果...")
cmd = f"kaggle kernels output {self.kernel_id} -p {output_path}"
cmd_util.run_cmd(cmd)
model_save_path = self.kwargs.get("model_save_path", "")
if not model_save_path:
model_save_path = "luwu-output"
project_path = file_util.abspath(model_save_path)
file_util.mkdirs(project_path)
output_files_path = os.path.join(output_path, "*")
logger.info(f"将运行结果移动到指定目录 {project_path} ...")
cmd = f"cp -r {output_files_path} {project_path}"
cmd_util.run_cmd(cmd)
logger.info("Done.")
def run_project(project):
params = project["params"]
model_save_path = params["model_save_path"]
log_path = os.path.join(
model_save_path,
f"luwu-classification-project-{project['id']}",
f"train.log",
)
file_util.mkdirs(os.path.dirname(log_path))
curdir = os.path.abspath(os.path.dirname(__file__))
cd_path = os.path.abspath(os.path.join(os.path.join(curdir, ".."), ".."))
py_path = os.path.join("." + curdir[len(cd_path) :], "train_project.py")
cmd = f"""cd {cd_path};python {py_path} {project['id']} > {log_path} 2>&1"""
st, out = subprocess.getstatusoutput(cmd)
if st == 0:
logger.info("处理成功！")
else:
logger.info("处理失败！")
raise Exception(out)
def __init__(
self,
origin_dataset_path: str,
validation_dataset_path: str = "",
test_dataset_path: str = "",
model_save_path: str = "",
validation_split: float = 0.1,
test_split: float = 0.1,
batch_size: int = 32,
epochs: int = 30,
learning_rate: float = 0.01,
project_id: int = 0,
maxlen: int = 128,
frezee_pre_trained_model=False,
optimizer: str = "Adam",
optimize_with_piecewise_linear_lr: bool = False,
simplified_tokenizer: bool = False,
pre_trained_model_type: str = "bert_base",
language: str = "chinese",
*args,
**kwargs,
):
"""
Args:
origin_dataset_path (str): 处理前的数据集路径
validation_dataset_path (str): 验证数据集路径。如不指定，
则从origin_dataset_path中进行切分。
test_dataset_path (str): 测试数据集路径。如不指定，则从
origin_dataset_path中进行切分。
model_save_path (str): 模型保存路径
validation_split (float): 验证集切割比例
test_split (float): 测试集切割比例
batch_size (int): mini batch 大小
learning_rate (float): 学习率大小
epochs (int): 训练epoch数
project_id (int): 训练项目编号
maxlen (int, optional): 单个文本的最大长度. Defaults to 128.
frezee_pre_trained_model (bool, optional): 在训练下游网络时，是否冻结预训练模型权重. Defaults to False.
optimizer (str, optional): 优化器类别. Defaults to "Adam".
optimize_with_piecewise_linear_lr (bool): 是否使用分段的线性学习率进行优化. 默认 False
simplified_tokenizer (bool): 是否对分词器的词表进行精简，默认False
pre_trained_model_type (str): 使用何种预训练模型
language (str): 预训练语料的语言
"""
self._call_code = ""
self.project_id = project_id
self.frezee_pre_trained_model = frezee_pre_trained_model
self.learning_rate = learning_rate
self.optimize_with_piecewise_linear_lr = optimize_with_piecewise_linear_lr
self.optimizer_cls = self.get_optimizer_cls(optimizer)
origin_dataset_path = file_util.abspath(origin_dataset_path)
model_save_path = file_util.abspath(model_save_path)
self.simplified_tokenizer = simplified_tokenizer
self.pre_trained_model_type = pre_trained_model_type
self.language = language
if self.pre_trained_model_type not in self.model_lang_weights_dict:
raise Exception(
f"指定模型 {self.pre_trained_model_type} 不存在！当前支持的模型为：{list(self.model_lang_weights_dict.keys())}"
)
if (self.language not in self.model_lang_weights_dict[
self.pre_trained_model_type]):
languages = list(self.model_lang_weights_dict[
self.pre_trained_model_type].keys())
raise Exception(
f"指定语料 {self.language} 的预训练模型 {self.pre_trained_model_type} 不存在！支持的语料为：{languages}"
)
self.maxlen = maxlen
self.origin_dataset_path = origin_dataset_path
if validation_dataset_path:
self.validation_dataset_path = file_util.abspath(
validation_dataset_path)
else:
self.validation_dataset_path = validation_dataset_path
if test_dataset_path:
self.test_dataset_path = file_util.abspath(test_dataset_path)
else:
self.test_dataset_path = test_dataset_path
# 当未给定模型保存路径时，默认保存到origin数据集相同路径
self.project_save_name = self.init_project_save_name(project_id)
if model_save_path:
self.project_save_path = os.path.join(model_save_path,
self.project_save_name)
else:
self.project_save_path = os.path.join(
os.path.dirname(self.origin_dataset_path),
self.project_save_name)
self.model_save_path = os.path.join(self.project_save_path,
"best_weights.h5")
self.validation_split = validation_split
self.test_split = test_split
self.batch_size = batch_size
self.epochs = epochs
file_util.mkdirs(self.project_save_path)
self.model = None
def train_on_kaggle(self, task_type):
# 生成训练代码
# 创建文件夹
kernel_path = os.path.join(self.tmp_dir_path, "kaggle-kernel")
logger.info(f"创建文件夹 {kernel_path} ...")
file_util.mkdirs(kernel_path)
# 初始化kernel
logger.info("使用 kaggle api 初始化 kernel ...")
cmd = f"kaggle kernels init -p {kernel_path}"
cmd_util.run_cmd(cmd)
# 生成训练脚本
override_params = {"project_id", "cmd", "luwu_version"}
train_cmd_params = []
if task_type == "classification":
project_name = "luwu-classification-project"
override_params.update(["net_name", "network_name"])
# tfrecord数据集路径
tfrecord_dataset_path = "./dataset"
train_cmd_params.append(
f"--tfrecord_dataset_path {tfrecord_dataset_path}")
override_params.add("tfrecord_dataset_path")
elif task_type == "detection":
project_name = "luwu-object-detection-project"
override_params.update([
"label_map_path",
"fine_tune_checkpoint_path",
])
# tfrecord数据集路径
tfrecord_dataset_path = "./dataset"
train_cmd_params.append(
f"--tfrecord_dataset_path {tfrecord_dataset_path}")
override_params.add("tfrecord_dataset_path")
elif task_type == "text_classification":
project_name = "luwu-text-classification-project"
else:
raise Exception(f"不支持的任务类型! {task_type}")
# 原始数据集路径
origin_dataset_path = os.path.join("../input", self.dataset_title)
if self.kwargs.get("cmd") == "text_classification":
filename = self.kwargs.get("origin_dataset_path").split("/")[-1]
origin_dataset_path = os.path.join(origin_dataset_path, filename)
train_cmd_params.append(f"--origin_dataset_path {origin_dataset_path}")
override_params.add("origin_dataset_path")
# 模型保存路径
model_save_path = "./project"
train_cmd_params.append(f"--model_save_path {model_save_path}")
override_params.add("model_save_path")
# 其他参数
for arg_name, arg_value in self.kwargs.items():
if "kaggle" in arg_name:
continue
if arg_name in override_params:
continue
# 兼容bool类型参数
if arg_value != False:
train_cmd_params.append(f'--{arg_name} "{arg_value}"')
# else:
# train_cmd_params.append(f"--{arg_name}")
if task_type == "classification":
train_cmd = f"!luwu {task_type} {' '.join(train_cmd_params)} {self.luwu_model_class.__name__}\n"
elif task_type == "detection":
train_cmd = f"!luwu {task_type} {' '.join(train_cmd_params)}\n"
elif task_type == "text_classification":
train_cmd = f"!luwu {task_type} {' '.join(train_cmd_params)}\n"
else:
raise Exception(f"不支持的任务类型! {task_type}")
project_path = os.path.join(model_save_path, project_name)
if task_type == "classification":
zip_cmd = (
f"!mv {project_path} ./ "
f"&& zip -r {project_name}-{self.uuid}.zip ./{project_name} "
f"&& rm -rf {tfrecord_dataset_path} "
f"&& rm -rf ./{project_name} "
f"&& rm -rf {model_save_path} \n")
elif task_type == "detection":
zip_cmd = (
f"!mv {project_path} ./ "
f'&& rm -rf {os.path.join(project_name,"train_models")} '
f"&& zip -r {project_name}-{self.uuid}.zip ./{project_name} "
f"&& rm -rf {tfrecord_dataset_path} "
f"&& rm -rf ./{project_name} "
f"&& rm -rf {model_save_path} \n")
elif task_type == "text_classification":
zip_cmd = (
f"!mv {project_path} ./ "
f"&& zip -r {project_name}-{self.uuid}.zip ./{project_name} "
f"&& rm -rf ./{project_name} "
f"&& rm -rf {model_save_path} \n")
luwu_version = self.kwargs.get("luwu_version")
if luwu_version:
install_cmd = f"!pip install luwu=={luwu_version}\n"
else:
install_cmd = "!pip install luwu\n"
codes = [
"# 安装 luwu\n",
install_cmd,
"# 执行训练指令\n",
train_cmd,
"# 打包待下载文件的指令\n",
zip_cmd,
" ",
]
script_metadata = self.load_notebook_metadata()
self.update_notebook_codes(script_metadata, codes)
kernel_file_path = os.path.join(kernel_path,
f"luwu-kernel-{self.uuid}.ipynb")
with open(kernel_file_path, "w") as f:
json.dump(script_metadata, f, ensure_ascii=False, indent=2)
# 修改 kernel-metadata.json
kernel_metadata_path = os.path.join(kernel_path,
"kernel-metadata.json")
with open(kernel_metadata_path, "r") as f:
kernel_metadata = json.load(f)
kernel_metadata["id"] = (kernel_metadata["id"].split("/")[0] + "/" +
f"luwu-kernel-{self.uuid}")
kernel_metadata["title"] = f"luwu-kernel-{self.uuid}"
kernel_metadata["code_file"] = kernel_file_path
kernel_metadata["language"] = "python"
kernel_metadata["kernel_type"] = "notebook"
kaggle_accelerator = self.kwargs.get("kaggle_accelerator", False)
if kaggle_accelerator:
kernel_metadata["enable_gpu"] = "true"
else:
kernel_metadata["enable_gpu"] = "false"
kernel_metadata["dataset_sources"] = [
self.dataset_id,
]
with open(kernel_metadata_path, "w") as f:
json.dump(kernel_metadata, f, ensure_ascii=False, indent=2)
logger.info(f"kernel metadata :{kernel_metadata}")
self.kernel_id = kernel_metadata["id"]
self.kernel_title = kernel_metadata["title"]
# 推送并运行kernel
logger.info("将 kernel 推送到 Kaggle 并运行 ...")
cmd = f"kaggle kernels push -p {kernel_path}"
logger.debug(cmd)
cmd_util.run_cmd(cmd)
logger.info("推送完成！等待运行中 ...")
running = False
error_cnt = 0
while True:
cmd = f"kaggle kernels status {self.kernel_id}"
code, output = subprocess.getstatusoutput(cmd)
if code != 0:
logger.error(output)
raise Exception(output)
pattern = 'has status "([^"]*)"'
matches = re.findall(pattern, output)
if not matches:
logger.error(f"未查询到状态！{output}")
error_cnt += 1
if error_cnt > 10:
raise Exception(
f"连续10次未获取到 kernel {self.kernel_id} 的运行状态！")
else:
status = matches[0]
# 运行之前，所有的状态都忽略
if not running:
if status == "running":
logger.info(f"{self.kernel_id} running ...")
running = True
else:
# 运行之后，找到第一次非 running 状态就退出
if status == "running":
logger.info(f"{self.kernel_id} running ...")
else:
self.kernel_exit_status = status
logger.info(output)
logger.info(
f"{self.kernel_id} 终止状态：{self.kernel_exit_status} . 已退出！"
)
break
time.sleep(10)
logger.info("kernel 运行已结束！")
def __init__(
self,
origin_dataset_path: str = "",
validation_dataset_path: str = "",
test_dataset_path: str = "",
tfrecord_dataset_path: str = "",
model_save_path: str = "",
validation_split: float = 0.1,
test_split: float = 0.1,
batch_size: int = 32,
epochs: int = 30,
learning_rate: float = 0.01,
project_id: int = 0,
image_size: int = 224,
do_fine_tune=False,
with_image_net=True,
optimizer: str = "Adam",
freeze_epochs_ratio: float = 0.1,
image_augmentation_random_flip_horizontal: bool = False,
image_augmentation_random_flip_vertival: bool = False,
image_augmentation_random_crop: bool = False,
image_augmentation_random_brightness: bool = False,
image_augmentation_random_hue: bool = False,
**kwargs,
):
"""
Args:
origin_dataset_path (str): 处理前的数据集路径
validation_dataset_path (str): 验证数据集路径。如不指定，
则从origin_dataset_path中进行切分。
test_dataset_path (str): 测试数据集路径。如不指定，则从
origin_dataset_path中进行切分。
tfrecord_dataset_path (str): 处理后的数据集路径
model_save_path (str): 模型保存路径
validation_split (float): 验证集切割比例
test_split (float): 测试集切割比例
batch_size (int): mini batch 大小
learning_rate (float): 学习率大小
epochs (int): 训练epoch数
project_id (int): 训练项目编号
with_image_net (bool): 是否使用imagenet的均值初始化数据
optimizer (str): 优化器类别
freeze_epochs_ratio (float): 当进行fine_tune时，会先冻结预训练模型进行训练一定epochs，
再解冻全部参数训练一定epochs，此参数表示冻结训练epochs占
全部epochs的比例（此参数仅当 do_fine_tune = True 时有效）。
默认 0.1（当总epochs>1时，只要设置了比例，至少会训练一个epoch）
image_augmentation_random_flip_horizontal (bool): 数据增强选项，是否做随机左右镜像。默认False.
image_augmentation_random_flip_vertival (bool): 数据增强选项，是否做随机上下镜像。默认False.
image_augmentation_random_crop (bool): 数据增强选项，是否做随机剪裁，剪裁尺寸为原来比例的0.9。默认False.
image_augmentation_random_brightness (bool): 数据增强选项，是否做随机饱和度调节。默认False.
image_augmentation_random_hue (bool): 数据增强选项，是否做随机色调调节。默认False.
"""
self._call_code = ""
self.project_id = project_id
self.do_fine_tune = do_fine_tune
self.with_image_net = with_image_net
self.learning_rate = learning_rate
self.freeze_epochs_ratio = freeze_epochs_ratio
self.image_augmentation_random_flip_horizontal = (
image_augmentation_random_flip_horizontal)
self.image_augmentation_random_flip_vertival = (
image_augmentation_random_flip_vertival)
self.image_augmentation_random_crop = image_augmentation_random_crop
self.image_augmentation_random_brightness = image_augmentation_random_brightness
self.image_augmentation_random_hue = image_augmentation_random_hue
self.optimizer_cls = self.get_optimizer_cls(optimizer)
origin_dataset_path = file_util.abspath(origin_dataset_path)
tfrecord_dataset_path = file_util.abspath(tfrecord_dataset_path)
model_save_path = file_util.abspath(model_save_path)
self.image_size = image_size
self.origin_dataset_path = origin_dataset_path
if validation_dataset_path:
self.validation_dataset_path = file_util.abspath(
validation_dataset_path)
else:
self.validation_dataset_path = validation_dataset_path
if test_dataset_path:
self.test_dataset_path = file_util.abspath(test_dataset_path)
else:
self.test_dataset_path = test_dataset_path
# 当未给定处理后数据集的路径时，默认保存到原始数据集相同路径
if tfrecord_dataset_path:
self.tfrecord_dataset_path = tfrecord_dataset_path
else:
self.tfrecord_dataset_path = origin_dataset_path
# 当未给定模型保存路径时，默认保存到处理后数据集相同路径
if self.project_id:
self.project_save_name = f"luwu-classification-project-{self.project_id}"
else:
self.project_save_name = f"luwu-classification-project"
if model_save_path:
self.project_save_path = os.path.join(model_save_path,
self.project_save_name)
else:
self.project_save_path = os.path.join(self.tfrecord_dataset_path,
self.project_save_name)
self.model_save_path = os.path.join(self.project_save_path,
"best_weights.h5")
self.validation_split = validation_split
self.test_split = test_split
self.batch_size = batch_size
self.epochs = epochs
file_util.mkdirs(self.project_save_path)
file_util.mkdirs(self.tfrecord_dataset_path)
