PNG Generation about pytorchviz HOT 7 OPEN

@szagoruyko @willprice

from pytorchviz.

Hi,

The from_numpy() call looks very suspicious here! If pred is a numpy array, it cannot have autograd info associated with it.

from pytorchviz.

so the .from_numpy() bascially converts numpy array into torch.tensor

from pytorchviz.

how can I remedy this issue? @albanD

from pytorchviz.

so when I do predict on the model, am I to return the prediction as a torch tensor?

from pytorchviz.

Also, one other question I have is this:

Suppose that my model architecture is of the form:

import torch
import torch.nn as nn
from torch.autograd import Variable

class DLModel(nn.Module):
    def __init__(self, layer_list):
        """
        Function to initialize Deep Learning model
        given a user specified layer list

        Args:
            layer_list (list): list of nn.Module layers from parser.py
        """
        super().__init__()
        self.model = nn.Sequential(*layer_list)
    
    def forward(self, x: torch.Tensor):
        pred = self.model(x) #apply model on input x
        return pred

How can I use pytorchviz to visualize the model architecture where we want to see each layer/module in nn.Sequential? @albanD @szagoruyko

from pytorchviz.

so when I do predict on the model, am I to return the prediction as a torch tensor?

PyTorch autograd and training in general will only work with torch Tensors. So yes, you will have to use Tensor to be able to train this model and to visualize it as well.

How can I use pytorchviz to visualize the model architecture where we want to see each layer/module in nn.Sequential?

If you want nn.Module as well, I would recommend torchrecorder: https://torchrecorder.readthedocs.io/en/latest/

from pytorchviz.