HPA Project
Qubo Project
The folowing instructions are made so that you can use this library
The dataset is provided by Kaggle
However, kaggle api is not very easy to use on remote server
Please use this chrome plugging to get cookie.txt file: Here
After you upload your cookie.txt file to your remote server, use command(provided by CarlosSouza)
Please run the following command in your ~/RedstoneTorch directory
cd ~/RedstoneTorch/data/DATASET
wget -x --load-cookies ~/cookies.txt -nH --cut-dirs=5 LINK
The DATASET can be replaced with human-protein-atlas-image-classification
The command above will create a file named data and put your file download-all in it.
So you need to unzip the doanload-all
To do so, run the following command
unzip ~/RedstoneTorch/data/download-all -d ~/RedstoneTorch/data/DATASET
and then you need to unzip the train.zip and test.zip
unzip ~/RedstoneTorch/data/DATASET/train.zip -d ~/RedstoneTorch/data/DATASET/train
unzip ~/RedstoneTorch/data/DATASET/test.zip -d ~/RedstoneTorch/data/DATASET/test
Please use sudo in front of these command if the terminal says that you don't have permissions to do so
However, you may not have the full permission to read download file, use
sudo chmod -R a+rwx train.csv
to give yourself permission to read.
If you want to connect to your machine
ssh -i '/home/koke_cacao/.ssh/google_compute_engine' koke_cacao@35.229.123.118
You can also use rsync to upload data to your server like:
rsync -P --rsh=ssh -r /home/koke_cacao/Documents/Koke_Cacao/Python/WorkSpace/RedstoneTorch/data/qubo_dataset/preprocessed koke_cacao@xxx.xxx.xxx.xxx:/home/k1412042720/RedstoneTorch/data/qubo_dataset/preprocessed
If you get errors about mkdir, you probably does not have access to other user's account using ssh.
So you should upload to the folder you have access to and then copy back on cloud.
By using this command
python preprocess.py
You can preprocess the data.
- You can calculate the mean and standard deviation of train and test data
- The image will transformed to .npy so that it load faster
You can start trainning by type command python train.py
Make sure you have everything setup
You can also use the following flags to train
| Flag | Function | Default |
|---|---|---|
| --projecttag | specify the project's tag | "" |
| --versiontag | specify the version's tag | "" |
| --loadfile | file name you want to load | None |
| --resume | resume or not | False |
We strongly recommend you use some tags to make sure the program runs correctly
cd ~/RedstoneTorch
python train.py --projecttag mem --versiontag mem1 --resume False
If you want to load from previous model to continue trainning progress:
python train.py --projecttag 2018-10-30-04-07-40-043900-test --versiontag test2 --resume True --loadfile test1-CP1.pth
The above information can be obtained in the command line during trainning, like this:
Validation Dice Coeff: 0.0754207968712
Checkpoint: 1 epoch; 13.0-13.0 step; dir: model/2018-10-30-04-07-40-043900-test/test1-CP1.pth
(The epoch starts from #1, whereas fold start from #0. Only Epoch got saved.)
The program use tensorboardX to display tensors
Use command
python .local/lib/python2.7/site-packages/tensorboard/main.py --logdir=~/RedstoneTorch/model/PROJECTTAG --port=6006
to open tensorboad's display on port 6006 of your server after you run train.py where PROJECTTAG can be replaced with your project tag.
Use predict.py to get the submit data table
python predict.py --projecttag 2018-10-30-04-07-40-043900-test --versiontag test2 --loadfile test1-CP1.pth
After the prediction, you probably want to download the .csv file, the directory is here:
RedstoneTorch/model/2018-10-30-04-07-40-043900-test/test1-CP1.pth-test-0.csv
# To install the Stackdriver monitoring agent:
$ curl -sSO https://dl.google.com/cloudagents/install-monitoring-agent.sh
$ sudo bash install-monitoring-agent.sh
# To install the Stackdriver logging agent:
$ curl -sSO https://dl.google.com/cloudagents/install-logging-agent.sh
$ sudo bash install-logging-agent.sh
This package depends on
matplotlib
pydensecrf
numpy
Pillow
torch
torchvision
augmentor
tensorboardX
psutil
tensorboard
tensorflow
Please use pip install to install these dependencies.
.
├── config.py
├── data
│ ├── sample_submission.csv
│ ├── test
│ │ └── [A LOT OF PICTURES]
│ ├── trian.csv
│ └── train
│ │ └── [A LOT OF PICTURES]
├── dataset
│ ├── hpa_dataset.py
│ ├── __init__.py
│ └── tgs_dataset.py
├── loss
│ ├── dice.py
│ ├── focal.py
│ ├── __init__.py
│ ├── iou.py
│ └── loss.py
├── model
├── net
│ ├── block.py
│ ├── __init__.py
│ ├── proteinet
│ │ ├── __init__.py
│ │ ├── proteinet_model.py
│ │ └── proteinet_parts.py
│ ├── resnet
│ │ ├── __init__.py
│ │ ├── resnet_extractor.py
│ │ └── resnet_model.py
│ ├── resunet
│ │ ├── __init__.py
│ │ ├── resunet_model.py
│ │ └── resunet_parts.py
│ ├── seinception
│ │ ├── __init__.py
│ │ ├── seinception_model.py
│ │ └── seinception_parts.py
│ ├── seresnet
│ │ ├── __init__.py
│ │ ├── seresnet_model.py
│ │ └── seresnet_parts.py
│ └── unet
│ ├── __init__.py
│ ├── unet_model.py
│ └── unet_parts.py
├── optimizer
│ ├── __init__.py
│ └── sgdw.py
├── pretained_model
│ ├── bninception.py
│ ├── inceptionresnetv2.py
│ ├── inceptionv4.py
│ ├── __init__.py
│ ├── nasnet.py
│ ├── resnext_features
│ │ ├── __init__.py
│ │ ├── resnext101_32x4d_features.py
│ │ └── resnext101_64x4d_features.py
│ ├── resnext.py
│ ├── senet.py
│ ├── torchvision_models.py
│ ├── utils.py
│ ├── vggm.py
│ ├── wideresnet.py
│ └── xception.py
├── project
│ ├── hpa_project.py
│ ├── __init__.py
│ └── tgs_project.py
├── README.md
├── requirements.txt
├── tensorboardwriter.py
├── train.py
├── tree.txt
└── utils
├── encode.py
├── __init__.py
├── memory.py
└── postprocess.py
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module.layer4.1.conv1.weight
module.layer4.1.bn1.weight
module.layer4.1.bn1.bias
module.layer4.1.conv2.weight
module.layer4.1.bn2.weight
module.layer4.1.bn2.bias
module.layer4.1.conv3.weight
module.layer4.1.bn3.weight
module.layer4.1.bn3.bias
module.layer4.1.se_module.fc1.weight
module.layer4.1.se_module.fc1.bias
module.layer4.1.se_module.fc2.weight
module.layer4.1.se_module.fc2.bias
module.layer4.2.conv1.weight
module.layer4.2.bn1.weight
module.layer4.2.bn1.bias
module.layer4.2.conv2.weight
module.layer4.2.bn2.weight
module.layer4.2.bn2.bias
module.layer4.2.conv3.weight
module.layer4.2.bn3.weight
module.layer4.2.bn3.bias
module.layer4.2.se_module.fc1.weight
module.layer4.2.se_module.fc1.bias
module.layer4.2.se_module.fc2.weight
module.layer4.2.se_module.fc2.bias
module.last_linear.weight
module.last_linear.bias
| Class | BestThreshold(Raw) | BestThreshold(Smoothed) |
|---|---|---|
| All | 0.2332 | 0.2196 |
| 0 | 0.07007 | 0.1547 |
| 1 | 0.9650 | 0.1571 |
| 2 | 0.8579 | 0.1798 |
| 3 | 0.1662 | 0.1931 |
| 4 | 0.7728 | 0.1324 |
| 5 | 0.01001 | 0.1926 |
| 6 | 0.01201 | 0.09215 |
| 7 | 0.0030030 | 0.1843 |
| 8 | 0.7978 | 0.1669 |
| 9 | 0.01602 | 0.09612 |
| 10 | 0.1982 | 0.1602 |
| 11 | 0.5325 | 0.1286 |
| 12 | 0.2152 | 0.1722 |
| 13 | 0.03103 | 0.1544 |
| 14 | 0.004004 | 0.04645 |
| 15 | 0.04304 | 0.06961 |
| 16 | 0.005005 | 0.1499 |
| 17 | 0.003003 | 0.06373 |
| 18 | 0.09810 | 0.1001 |
| 19 | 0.04204 | 0.1706 |
| 20 | 0.01101 | 0.1264 |
| 21 | 0.01101 | 0.1121 |
| 22 | 0.01702 | 0.08679 |
| 23 | 0.000 | 0.000 |
| 24 | 0.03504 | 0.08634 |
| 25 | 0.01502 | 0.1221 |
| 26 | 0.0050050 | 0.1943 |
| 27 | 0.01502 | 0.1180 |
| Input Image Size | Speed | Batch Size | Format | Device |
|---|---|---|---|---|
| 4x1728x1728 | 1.16s/img | 1 | jpg | 16CPU, 1 Nvidia Tesla P100 |
| 4x512x512 | 0.0128s/img | 64 | npy | 16CPU, 1 Nvidia Tesla P100 |
| 4x512x512 | 0.0769s/img | 1 | npy | 16CPU, 1 Nvidia Tesla P100 |
| Correct Label | Total Label | Binary Accuracy | F1-Macro Score | Precision | Recall | IOU Score | |
|---|---|---|---|---|---|---|---|
| Human | 5360 | 5880 | 91.15% | 0.1124 | 44.67% | 27.46% | 27.29% |
| Machine | 301384 | 311108 | 96.87% | 0.3407 | 67.29% | 69.23% | 63.07% |