如何安装 torchcrf 并修复导入错误？答案

【问题标题】：How to install torchcrf and fix import error?如何安装 torchcrf 并修复导入错误？
【发布时间】：2022-09-27 22:25:03
【问题描述】：

我正在使用 Windows 11 pro x64，PyCharm 2022.2.2（专业版）- Build #PY-222.4167.33，构建于 2022 年 9 月 15 日。蟒蛇版本：

Microsoft Windows [Version 10.0.22621.521]
(c) Microsoft Corporation. All rights reserved.

C:\\Users\\donhu>python
Python 3.10.7 (tags/v3.10.7:6cc6b13, Sep  5 2022, 14:08:36) [MSC v.1933 64 bit (AMD64)] on win32
Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.
>>>

我的代码

import argparse
import yaml
import pandas as pd
import torch
import torchcrf
import transformers
from data import Dataset
from engines import train_fn
import warnings

warnings.filterwarnings(\"ignore\")
parser = argparse.ArgumentParser()
parser.add_argument(\"--data_file\", type=str)
parser.add_argument(\"--hyps_file\", type=str)
args = parser.parse_args()
data_file = yaml.load(open(args.data_file), Loader=yaml.FullLoader)
hyps_file = yaml.load(open(args.hyps_file), Loader=yaml.FullLoader)
train_loader = torch.utils.data.DataLoader(
    Dataset(
        df=pd.read_csv(data_file[\"train_df_path\"]),
        tag_names=data_file[\"tag_names\"],
        tokenizer=transformers.AutoTokenizer.from_pretrained(hyps_file[\"encoder\"], use_fast=False),
    ),
    num_workers=hyps_file[\"num_workers\"],
    batch_size=hyps_file[\"batch_size\"],
    shuffle=True,
)
val_loader = torch.utils.data.DataLoader(
    Dataset(
        df=pd.read_csv(data_file[\"val_df_path\"]),
        tag_names=data_file[\"tag_names\"],
        tokenizer=transformers.AutoTokenizer.from_pretrained(hyps_file[\"encoder\"], use_fast=False),
    ),
    num_workers=hyps_file[\"num_workers\"],
    batch_size=hyps_file[\"batch_size\"] * 2,
)
loaders = {
    \"train\": train_loader,
    \"val\": val_loader,
}
model = transformers.RobertaForTokenClassification.from_pretrained(hyps_file[\"encoder\"],
                                                                   num_labels=data_file[\"num_tags\"])
if hyps_file[\"use_crf\"]:
    criterion = torchcrf.CRF(num_tags=data_file[\"num_tags\"], batch_first=True)
else:
    criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=float(hyps_file[\"lr\"]))
train_fn(
    loaders, model, torch.device(hyps_file[\"device\"]), hyps_file[\"device_ids\"],
    criterion,
    optimizer,
    epochs=hyps_file[\"epochs\"],
    ckp_path=\"../ckps/{}.pt\".format(hyps_file[\"encoder\"].split(\"/\")[-1]),
)

我通过 PyCharm 安装

我也通过命令安装

pip install torchcrf

我也试试

pip install pytorch-crf

但我并不成功。

如何安装torchcrf 并修复导入错误？

标签： python pytorch pycharm

【解决方案1】：

安装

from TorchCRF import CRF

重写代码

import argparse
import yaml
import pandas as pd
import torch
from TorchCRF import CRF
import transformers
from data import Dataset
from engines import train_fn
import warnings

warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser()
parser.add_argument("--data_file", type=str)
parser.add_argument("--hyps_file", type=str)
args = parser.parse_args()
data_file = yaml.load(open(args.data_file), Loader=yaml.FullLoader)
hyps_file = yaml.load(open(args.hyps_file), Loader=yaml.FullLoader)
train_loader = torch.utils.data.DataLoader(
    Dataset(
        df=pd.read_csv(data_file["train_df_path"]),
        tag_names=data_file["tag_names"],
        tokenizer=transformers.AutoTokenizer.from_pretrained(hyps_file["encoder"], use_fast=False),
    ),
    num_workers=hyps_file["num_workers"],
    batch_size=hyps_file["batch_size"],
    shuffle=True,
)
val_loader = torch.utils.data.DataLoader(
    Dataset(
        df=pd.read_csv(data_file["val_df_path"]),
        tag_names=data_file["tag_names"],
        tokenizer=transformers.AutoTokenizer.from_pretrained(hyps_file["encoder"], use_fast=False),
    ),
    num_workers=hyps_file["num_workers"],
    batch_size=hyps_file["batch_size"] * 2,
)
loaders = {
    "train": train_loader,
    "val": val_loader,
}
model = transformers.RobertaForTokenClassification.from_pretrained(hyps_file["encoder"],
                                                                   num_labels=data_file["num_tags"])
if hyps_file["use_crf"]:
    criterion = CRF(num_tags=data_file["num_tags"], batch_first=True)
else:
    criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=float(hyps_file["lr"]))
train_fn(
    loaders, model, torch.device(hyps_file["device"]), hyps_file["device_ids"],
    criterion,
    optimizer,
    epochs=hyps_file["epochs"],
    ckp_path="../ckps/{}.pt".format(hyps_file["encoder"].split("/")[-1]),
)

参考https://pypi.org/project/TorchCRF/

【讨论】：