keras4torch 1.2.4a1

A compatible-with-keras wrapper for training PyTorch models✨

A compatible-with-keras wrapper for training PyTorch models✨

Documentations • Dev Logs • Mini Version

keras4torch provides a high-level API to train PyTorch models compatible with Keras. This project is designed for beginner with these objectives:

• Help people who are new to PyTorch but familar with Keras
• Reduce the cost for migrating Keras model implementation to PyTorch

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Use keras4torch for Kaggle's code competition! Check this package dataset and starter notebook.

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Installation

pip install keras4torch

PyTorch 1.6+ and Python 3.6+ is required.

Quick start

Suppose you have a nn.Module to train.

model = torchvision.models.resnet18(num_classes=10)

All you need to do is wrapping it via k4t.Model().

import keras4torch as k4t

model = k4t.Model(model)

Now, there're two workflows can be used for training.

The NumPy workflow is compatible with Keras.

• .compile(optimizer, loss, metrics) for settings of optimizer, loss and metrics
• .fit(x, y, epochs, batch_size, ...) takes raw numpy input for training
• .evaluate(x, y) outputs a dict result of your metrics
• .predict(x) for doing predictions

And DataLoader workflow is more flexible and of pytorch style.

• .compile(optimizer, loss, metrics) same as NumPy workflow
• .fit_dl(train_loader, val_loader, epochs) for training the model via DataLoader
• .evaluate_dl(data_loader) same as NumPy workflow but takes DataLoader
• .predict_dl(data_loader) same as NumPy workflow but takes DataLoader

The two workflows can be mixed.

MNIST example

Here we show a complete example of training a ConvNet on MNIST.

import torch
import torchvision
from torch import nn

import keras4torch as k4t

Step1: Preprocess data

mnist = torchvision.datasets.MNIST(root='./', download=True)
x, y = mnist.train_data.unsqueeze(1), mnist.train_labels

x = x.float() / 255.0    # scale the pixels to [0, 1]

x_train, y_train = x[:40000], y[:40000]
x_test, y_test = x[40000:], y[40000:]

Step2: Define the model

If you have a nn.Module already, just wrap it via k4t.Model. For example,

model = torchvision.models.resnet50(num_classes=10)

model = k4t.Model(model)

For building models from scratch, you can use KerasLayer (located in k4t.layers) for automatic shape inference, which can free you from calculating the input channels.

As is shown below, k4t.layers.Conv2d(32, kernel_size=3) equals nn.Conv2d(?, 32, kernel_size=3) where the first parameter ? (i.e. in_channels) will be determined by itself.

model = torch.nn.Sequential(
    k4t.layers.Conv2d(32, kernel_size=3), nn.ReLU(),
    nn.MaxPool2d(2, 2), 
    k4t.layers.Conv2d(64, kernel_size=3), nn.ReLU(),
    nn.Flatten(),
    k4t.layers.Linear(10)
)

A model containing KerasLayer needs an extra .build(input_shape) operation.

model = k4t.Model(model).build([1, 28, 28])

Step3: Summary the model

model.summary()

=========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
=========================================================================================
├─Conv2d*: 1-1                           [-1, 32, 26, 26]          320
├─ReLU: 1-2                              [-1, 32, 26, 26]          --
├─MaxPool2d: 1-3                         [-1, 32, 13, 13]          --
├─Conv2d*: 1-4                           [-1, 64, 11, 11]          18,496
├─ReLU: 1-5                              [-1, 64, 11, 11]          --
├─Flatten: 1-6                           [-1, 7744]                --
├─Linear*: 1-7                           [-1, 10]                  77,450
=========================================================================================
Total params: 96,266
Trainable params: 96,266
Non-trainable params: 0
Total mult-adds (M): 2.50
=========================================================================================

Step4: Config optimizer, loss and metrics

model.compile(optimizer='adam', loss=nn.CrossEntropyLoss(), metrics=['acc'])

If GPU is available, it will be used automatically. You can also pass device parameter to .compile() explicitly.

Step5: Training

history = model.fit(x_train, y_train,
                	epochs=30,
                	batch_size=512,
                	validation_split=0.2,
                	)

Train on 32000 samples, validate on 8000 samples:
Epoch 1/30 - 2.8s - loss: 0.6109 - acc: 0.8372 - val_loss: 0.2712 - val_acc: 0.9235 - lr: 1e-03
Epoch 2/30 - 1.5s - loss: 0.2061 - acc: 0.9402 - val_loss: 0.1494 - val_acc: 0.9579 - lr: 1e-03
Epoch 3/30 - 1.5s - loss: 0.1202 - acc: 0.9653 - val_loss: 0.0974 - val_acc: 0.9719 - lr: 1e-03
Epoch 4/30 - 1.5s - loss: 0.0835 - acc: 0.9757 - val_loss: 0.0816 - val_acc: 0.9769 - lr: 1e-03
... ...

Step6: Plot learning curve

history.plot(kind='line', y=['loss', 'val_loss'])

Step7: Evaluate on test set

model.evaluate(x_test, y_test)

{'loss': 0.06655170023441315, 'acc': 0.9839999675750732}

Communication

We have activated Github Discussion for Q&A and most general topics!

For bugs report, please use Github Issues.