jdit 0.0.3

Make it easy to do research on pytorch


---

[](https://github.com/dingguanglei/jdit)
[](https://jdit.readthedocs.io/en/latest/?badge=latest)
[](https://codebeat.co/projects/github-com-dingguanglei-jdit-master)

**Jdit** is a research processing oriented framework based on pytorch.
Only care about your ideas. You don't need to build a long boring code
to run a deep learning project to verify your ideas.

You only need to implement you ideas and don't do anything with training
framework, multiply-gpus, checkpoint, process visualization, performance
evaluation and so on.

Guide: [https://dingguanglei.com/jdit](https://dingguanglei.com/jdit)

Docs: [https://jdit.readthedocs.io/en/latest/index.html](https://jdit.readthedocs.io/en/latest/index.html)

## Install
Requires:

tensorboard >= 1.12.0
tensorboardX >= 1.4
pytorch >= 0.4.1

By using `setup.py` to install the package.

```
python setup.py bdist_wheel
```

You will find packages in `jdit/dist/`. Use pip to install.

```
pip install jdit-0.0.3-py3-none-any.whl
```


## Quick start

After building and installing jdit package, you can make a new directory
for a quick test. Assuming that you get a new directory example. run
this code in ipython cmd.(Create a main.py file is also acceptable.)

``` {.sourceCode .python}
from jdit.trainer.instances.fashingClassification import start_fashingClassTrainer
start_fashingClassTrainer()
```
The following is the accomplishment of ``start_fashingClassTrainer()``

``` {.sourceCode .python}
# coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from jdit.trainer.classification import ClassificationTrainer
from jdit import Model
from jdit.optimizer import Optimizer
from jdit.dataset import FashionMNIST

# This is your model. Defined by torch.nn.Module
class SimpleModel(nn.Module):
def __init__(self, depth=64, num_class=10):
super(SimpleModel, self).__init__()
self.num_class = num_class
self.layer1 = nn.Conv2d(1, depth, 3, 1, 1)
self.layer2 = nn.Conv2d(depth, depth * 2, 4, 2, 1)
self.layer3 = nn.Conv2d(depth * 2, depth * 4, 4, 2, 1)
self.layer4 = nn.Conv2d(depth * 4, depth * 8, 4, 2, 1)
self.layer5 = nn.Conv2d(depth * 8, num_class, 4, 1, 0)

def forward(self, input):
out = F.relu(self.layer1(input))
out = F.relu(self.layer2(out))
out = F.relu(self.layer3(out))
out = F.relu(self.layer4(out))
out = self.layer5(out)
out = out.view(-1, self.num_class)
return out

# A trainer, you need to rewrite the loss and valid function.
class FashingClassTrainer(ClassificationTrainer):
def __init__(self, logdir, nepochs, gpu_ids, net, opt, datasets, num_class):
super(FashingClassTrainer, self).__init__(logdir, nepochs, gpu_ids, net, opt, datasets, num_class)
data, label = self.datasets.samples_train
# plot samples of dataset in tensorboard.
self.watcher.embedding(data, data, label, 1)

def compute_loss(self):
var_dic = {}
var_dic["CEP"] = loss = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())

_, predict = torch.max(self.output.detach(), 1) # 0100=>1 0010=>2
total = predict.size(0) * 1.0
labels = self.labels.squeeze().long()
correct = predict.eq(labels).cpu().sum().float()
acc = correct / total
var_dic["ACC"] = acc
return loss, var_dic

def compute_valid(self):
var_dic = {}
var_dic["CEP"] = cep = nn.CrossEntropyLoss()(self.output, self.labels.squeeze().long())

_, predict = torch.max(self.output.detach(), 1) # 0100=>1 0010=>2
total = predict.size(0) * 1.0
labels = self.labels.squeeze().long()
correct = predict.eq(labels).cpu().sum().float()
acc = correct / total
var_dic["ACC"] = acc
return var_dic


def start_fashingClassTrainer(gpus=(), nepochs=10, run_type="train"):
num_class = 10
depth = 32
gpus = gpus
batch_size = 64
nepochs = nepochs
logdir = "log/fashion_classify"
opt_hpm = {"optimizer": "Adam",
"lr_decay": 0.94,
"decay_position": 10,
"decay_type": "epoch",
"lr": 1e-3,
"weight_decay": 2e-5,
"betas": (0.9, 0.99)}

print('===> Build dataset')
mnist = FashionMNIST(batch_size=batch_size)
torch.backends.cudnn.benchmark = True
print('===> Building model')
net = Model(SimpleModel(depth=depth), gpu_ids_abs=gpus, init_method="kaiming", check_point_pos=1)
print('===> Building optimizer')
opt = Optimizer(net.parameters(), **opt_hpm)
print('===> Training')
print("using `tensorboard --logdir=log` to see learning curves and net structure."
"training and valid_epoch data, configures info and checkpoint were save in `log` directory.")
Trainer = FashingClassTrainer(logdir, nepochs, gpus, net, opt, mnist, num_class)
if run_type == "train":
Trainer.train()
elif run_type == "debug":
Trainer.debug()

if __name__ == '__main__':
start_fashingClassTrainer()
```

Then you will see something like this as following.

``` {.sourceCode .python}
===> Build dataset
use 8 thread
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Processing...
Done
===> Building model
ResNet Total number of parameters: 2776522
ResNet model use CPU
apply kaiming weight init
===> Building optimizer
===> Training
using `tensorboard --logdir=log` to see learning curves and net structure.
training and valid_epoch data, configures info and checkpoint were save in `log` directory.
0%| | 0/10 [00:00<?, ?epoch/s]
0step [00:00, step?/s]
```
To see learning curves in tensorboard. Pay attention to your code about ``var_dic["ACC"], var_dic["CEP"]``.
This will be shown in the tensorboard.

### Learning curves


### Model structure


### Dataset

You need to apply ``self.watcher.embedding(data, data, label)``)


### Log
All of these will be saved in ``log/fashion_classify``, because of parameter ``logdir = "log/fashion_classify"``.


#### Process data
For the most thing that we care about are training process and valid process data.
They are saved in ``Train.csv`` and ``Valid.csv``. The following are the content.

**Train.csv**


**Valid.csv**


#### Model
The info of model will be saved in ``net.csv``. (The file name is given by your variable name(``net``).)
If your model changes during the process, it will be recorded in this file.


#### Learning rate trace
From file ``opt.csv`` you can see the learning rate variation. It will be saved only feature changed.


#### Dataset info
From file ``datasets.csv`` you can see the information of your dataset.


#### Others

* For file ``performance.csv``, it saves the memory cost during the training.
* For file ``FashingClassTrainer.csv``, it saves some parameters, such as the amount of epochs.
* Model checkpoint in the ``checkpoint`` directory.

### Conclusion
Due to this simple classification example, there are many interesting features not showed here.
But you can get a intuitive feeling from this simple example.
It seems that your code doesn't have these functions. So, this is what Jdit did.

Although it is just an example, you still can build your own project
easily by using jdit framework. Jdit framework can deal with
* Data visualization. (learning curves, images in pilot process)
* CPU, GPU or GPUs. (Training your model on specify devices)
* Intermediate data storage. (Saving training data into a csv file)
* Model checkpoint automatically.
* Flexible templates can be used to integrate and custom overrides.
* ...

## More
For other templates, you can see and learn form here.

[Classification Guide](https://dingguanglei.com/jdit)

[Gan for Generation Guide](https://dingguanglei.com/jdit)

[Gan for Pix2pix Guide](https://dingguanglei.com/jdit)

[Parallel Task Guide](https://dingguanglei.com/jdit)

......