jdit 0.0.11

Make it easy to do research on pytorch

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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/tag/jdit

Docs: https://jdit.readthedocs.io/en/latest/index.html

If you have any problems, or you find bugs you can contact the author.

E-mail: [e-mail]

Install

Requires:

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

Install requirement.

pip install -r requirements.txt

From source

This method is recommended, because you can keep the newest version.

1. Clone from github

   git clone https://github.com/dingguanglei/jdit
   

2. Setup By using setup.py to install the package.

   python setup.py bdist_wheel
   

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

   pip install dist/jdit-0.0.x-py3-none-any.whl
   

From pip

pip install jdit

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.)

from jdit.trainer.instances.fashingClassification import start_fashingClassTrainer
start_fashingClassTrainer()

The following is the accomplishment of start_fashingClassTrainer()

# 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.

===> 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.

Guide List:

• Classification

• Generative Adversarial Networks

• Gan for Generation

• Gan for Pix2pix

• Parallel Task

• ......