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How to add a Validation and Test Set in Pytorch Model?


By : MylesM
Date : September 17 2020, 05:00 AM
Hope that helps Train/validation/test splits of data are "orthogonal" to the model.
To manage your data for training/testing you might want to use pytorch's TensorDataset. Then you might find Subset to be useful for splitting the dataset into train/validation/test subsets.
code :


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Pytorch model accuracy test


By : Kapy Lazarin Zavala
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Just in case it helps someone.
If you don't have a GPU system (say you are developing on a laptop and will eventually test on a server with GPU) you can do the same using:
code :
if torch.cuda.is_available():
        inputs =inputs.to('cuda')
    else:
        inputs = inputs.to('cuda')

pytorch vgg model test on one image


By : user2172961
Date : March 29 2020, 07:55 AM
Hope this helps Your image is [h, w, 3] where 3 means the rgb channel, and pytorch expects [b, 3, h, w] where b is batch size. So you can reshape it by calling do that by calling reshaped = img.permute(2, 0, 1).unsqueeze(0). I think there is also a utility function for that somewhere, but I can't find it right now.
So in your case
code :
tensor = torch.from_numpy(test_image)
reshaped = tensor.permute(2, 0 1).unsqueeze(0)
your_result = vgg16(reshaped)

Got Very Different Scores After Translating Simple Test Model from Keras to PyTorch


By : user2677569
Date : March 29 2020, 07:55 AM
To fix the issue you can do You need to call optimizer.zero_grad() at the start of each iteration, otherwise the gradients from different batches just keep getting accumulated.

PyTorch: Different Forward Methods for Train and Test/Validation


By : user3352043
Date : March 29 2020, 07:55 AM
hop of those help? First of all you should always use and define forward not some other methods that you call on the torch.nn.Module instance.
Definitely do not overload eval() as shown by trsvchn as it's evaluation method defined by PyTorch (see here). This method allows layers inside your model to be put into evaluation mode (e.g. specific changes to layers like inference mode for Dropout or BatchNorm).
code :
import torch


class Network(torch.nn.Module):
    def __init__(self):
        super().__init__()
        ...

    # You could split it into two functions but both should be called by forward
    def forward(
        self, src_tokens=None, src_lengths=None, prev_output_tokens=None, **kwargs
    ):
        encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
        if self.train:
            return self.decoder(prev_output_tokens, encoder_out=encoder_out, **kwargs)
        autoencoder_out = self.encoder(tgt_tokens, src_lengths=src_lengths, **kwargs)
        concat = some_concatination_func(encoder_out, autoencoder_out)
        return self.decoder(prev_output_tokens, encoder_out=concat, **kwargs)

In Pytorch, how to test simple image with my loaded model?


By : user3448240
Date : March 29 2020, 07:55 AM
I wish this helpful for you You have already written the function test to test your net. The only thing you should do — create batch with one image with same preprocessing as images in your dataset.
code :
def test_one_image(I, model):
    '''
    I - 28x28 uint8 numpy array
    '''

    # test phase
    model.eval()

    # convert image to torch tensor and add batch dim
    batch = torch.tensor(I / 255).unsqueeze(0)

    # We don't need gradients for test, so wrap in 
    # no_grad to save memory
    with torch.no_grad():
        batch = batch.to(device)

        # forward propagation
        output = model( batch )

        # get prediction
        output = torch.argmax(output, 1)

    return output
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