wandb

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import torch
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import torch.nn as nn
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import torch.nn.functional as F  # noqa N812
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import wandb
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from torch.utils.tensorboard import SummaryWriter
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def main():
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    wandb.init(tensorboard=True)
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    class ConvNet(nn.Module):
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        def __init__(self):
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            super().__init__()
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            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
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            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
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            self.conv2_drop = nn.Dropout2d()
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            self.fc1 = nn.Linear(320, 50)
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            self.fc2 = nn.Linear(50, 10)
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        def forward(self, x):
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            x = F.relu(F.max_pool2d(self.conv1(x), 2))
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            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
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            x = x.view(-1, 320)
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            x = F.relu(self.fc1(x))
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            x = F.dropout(x, training=self.training)
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            x = self.fc2(x)
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            return F.log_softmax(x, dim=1)
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    writer = SummaryWriter()
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    net = ConvNet()
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    wandb.watch(net, log_freq=2)
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    for i in range(10):
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        output = net(torch.ones((64, 1, 28, 28)))
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        loss = F.mse_loss(output, torch.ones((64, 10)))
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        output.backward(torch.ones(64, 10))
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        writer.add_scalar("loss", loss / 64, i + 1)
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        writer.add_image("example", torch.ones((1, 28, 28)), i + 1)
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    writer.close()
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if __name__ == "__main__":
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    main()
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