Korch
Simple tools to provide a Keras-like interface to PyTorch.
Install
pip install korch
Example of usage
We can perform a very simple example using the Fashion MNIST dataset (as is done in the official PyTorch docs.
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
# Create datasets for training & validation, download if necessary
training_set = torchvision.datasets.FashionMNIST('./data', train=True, transform=transform, download=True)
validation_set = torchvision.datasets.FashionMNIST('./data', train=False, transform=transform, download=True)
# Create data loaders for our datasets; shuffle for training, not for validation
training_loader = torch.utils.data.DataLoader(training_set, batch_size=4, shuffle=True, num_workers=2)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=4, shuffle=False, num_workers=2)
See that the only different with respect to basic PyTorch is that we're inhereting from our custom Module, not from PyTorch's nn.Module:
class SimpleModel(Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
Following the usual Keras way, we instantiate the model and compile it, providing the loss and the optimizer. Metrics can be provided as well, and are expected as torchvision.MetricCollection:
model = SimpleModel()
model.compile(loss=torch.nn.CrossEntropyLoss(),
optimizer=torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9),
metrics = MetricCollection([Accuracy()]))
model
SimpleModel(
(conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(fc1): Linear(in_features=256, out_features=120, bias=True)
(fc2): Linear(in_features=120, out_features=84, bias=True)
(fc3): Linear(in_features=84, out_features=10, bias=True)
(loss_fn): CrossEntropyLoss()
)
model.evaluate(training_loader), model.evaluate(validation_loader)
(2.3070596095879874, 2.307082461261749)
model.fit(trainloader=training_loader, epochs=1, validationloader=validation_loader)
model.evaluate(training_loader), model.evaluate(validation_loader)
(0.39349932589025605, 0.42693415356237674)
