Skip to main content

A pytorch based deep learning solver framework.

Project description


A pytorch based deep learning solver framework.


pip install torchsolver


import torch
from torch import nn, optim
from torchvision.datasets import MNIST
from torchvision.transforms import *

from torchsolver.module import Module
from torchsolver.metrics import accuracy

class LeNet(nn.Module):
    def __init__(self, classes_num):
        super(LeNet, self).__init__()

        self.conv1 = nn.Conv2d(1, 32, 5)
        self.pool1 = nn.MaxPool2d(2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, 5)
        self.pool2 = nn.MaxPool2d(2, stride=2)

        self.act = nn.ReLU()

        self.fc1 = nn.Linear(1024, 512)
        self.dropout = nn.Dropout(0.5)
        self.out = nn.Linear(512, classes_num)

    def forward(self, x):
        x = self.pool1(self.act(self.conv1(x)))
        x = self.pool2(self.act(self.conv2(x)))

        x = torch.flatten(x, start_dim=1)

        x = self.fc1(x)
        x = self.dropout(x)
        x = self.out(x)

        x = torch.softmax(x, dim=-1)
        return x

class MnistSolver(Module):
    def __init__(self, **kwargs):
        super(MnistSolver, self).__init__(**kwargs)

        self.model = LeNet(10)
        self.loss = nn.CrossEntropyLoss()
        self.optimizer = optim.Adam(self.model.parameters())

        if self.num_device > 1:
            self.model = torch.nn.DataParallel(self.model)

    def forward(self, img, label):
        pred = self.model(img)

        acc = accuracy(pred, label)
            loss = self.loss(pred, label)
            return loss, {"loss": loss, "acc": acc}
            return acc, {}

if __name__ == '__main__':
    train_data = MNIST("data", train=True, transform=ToTensor())
    val_data = MNIST("data", train=False, transform=ToTensor())

    MnistSolver(batch_size=128).fit(train_data=train_data, val_data=val_data)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for torchsolver, version 1.5.1
Filename, size File type Python version Upload date Hashes
Filename, size torchsolver-1.5.1.tar.gz (12.2 kB) File type Source Python version None Upload date Hashes View
Filename, size torchsolver-1.5.1-py3-none-any.whl (16.6 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page