使用代码github链接：https://github.com/di-dimitrov/propaganda-techniques-in-memes
该代码相当在mmf-master上展开自己的任务，之前想直接跑通facebook的MMF，但是被两个问题困扰：
- 环境配置
- 新数据集的构造
后来被推荐了这套代码，才跑通了visualBert模型。
实现过程
1 将项目下载并存放到服务器
2 Install MMF
- Prerequisites – generating image caption features for VisualBERT and ViLBERT:
i. Install MMF according to the instructions here:
https://mmf.readthedocs.io/en/website/notes/installation.html
该链接中给出了安装MMF的两种方法：
2.
我使用方法1安装总会在后期出现各种环境报错，方法2安装就成功了，所以建议安装不成功的试试方法2安装。
; 3 Install the following packages
ii. Install the following packages: pip install yacs, opencv-python, cython (if using ‘pip’, any package manager works)
4 Clone vqa-maskrcnn-benchmark repository
iii. Clone vqa-maskrcnn-benchmark repository: https://gitlab.com/vedanuj/vqa-maskrcnn-benchmark
a. Run python setup.py build
b. Run python setup.py develop
5 feature extraction
c. Run the feature extraction script from the following path:
mmf/tools/scripts/features/extract_features_vmb.py
这一步对图片进行处理，先将图片存放到 data/datasets/propaganda/defaults/images下，执行
python tools/scripts/features/extract_features_vmb.py
控制台显示如下即为正在处理（这里，”/”后的数字应该和images文件夹下的图片总数一致）：
如果图片文件夹的布局不寻常，可在
extract_features_vmb.py的 line273以下稍作修改。该步骤的生成文件默认存储在 ./output文件夹下。
5 convert the features to a .mdb file
d. After feature extraction is done convert the features to a .mdb file with the following script: mmf/tools/scripts/features/extract_features_vmb.py
此处存在笔误，实际运行程序为：
python tools/scripts/features/lmdb_conversion.py --mode convert --lmdb_path ./save --features_folder ./output
其中，传参内容的含义可见 lmdb_conversion.py：
parser.add_argument(
"--mode",
required=True,
type=str,
help="Mode can either be convert (for conversion of \n"
+ "features to an LMDB file) or extract (extract \n"
+ "raw features from a LMDB file)",default="convert"
)
parser.add_argument(
"--lmdb_path", required=True, type=str, help="LMDB file path",default="./save"
)
parser.add_argument(
"--features_folder", required=True, type=str, help="Features folder",default="./output"
)
该步骤会在./save文件夹下生成 data.mdb文件。
6 Rename the .mdb features file to deceptron.lmdb and move it
Rename the .mdb features file to deceptron.lmdb and move it to /root/.cache/torch/mmf/data/datasets/propaganda/defaults/features/
7 Running the models
2.Running the models – open ‘Propaganda_Detection.ipynb’ and run the code inside.
程序默认是多GPU的，指定服务器上的固定节点跑程序：
CUDA_VISIBLE_DEVICES=1 !mmf_run config=./projects/propaganda/configs/visual_bert/direct.yaml \
datasets=propaganda \
model=visual_bert
visualBert：对于关键修改位置的备注
参数修改位置
/root/propaganda-techniques-in-memes-main/projects/propaganda/configs/visual_bert/defaults.yaml
修改数据集标签数
/root/propaganda-techniques-in-memes-main/mmf/datasets/builders/propaganda/dataset.py
line81, line147修改标签数量
数据集存放位置
/root/.cache/torch/mmf/data/datasets/propaganda/defaults/annotations/
Bug记录
显示’mmf_run’是不可识别的语法
mmf安装不成功，注意安装过程中出现的bug
ERROR: Cannot uninstall ‘certifi’. It is a distutils installed project and …
直接pip uninstall certifi不成功，执行：
pip install certifi --ignore-installed
参考博客：ERROR: Cannot uninstall ‘certifi’. It is a distutils installed project and thus we cannot accurately
实现环境备注
torch == 1.7.1+cu110
torchtext == 0.5.0
torchvision == 0.8.2+cu110
pytorch-lightning == 1.4.9
Original: https://blog.csdn.net/xiyou__/article/details/121792839
Author: xiyou__
Title: 跑通mmf：visualBert实现过程
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/545056/
转载文章受原作者版权保护。转载请注明原作者出处！