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I came across this PyTorch tutorial (in neural_networks_tutorial.py) where they construct a simple neural network and run an inference. I would like to print the contents of the entire input tensor for debugging purposes. What I get when I try to print the tensor is something like this and not the entire tensor:
I saw a similar link for numpy but was not sure about what would work for PyTorch. I can convert it to numpy and may be view it, but would like to avoid the extra overhead. Is there a way for me to print the entire tensor?
To avoid truncation and to control how much of the tensor data is printed use the same API as numpy’s
x = torch.rand(1000, 2, 2) print(x) # prints the truncated tensor torch.set_printoptions(threshold=10_000) print(x) # prints the whole tensor
If your tensor is very large, adjust the
threshold value to a higher number.
Another option is:
torch.set_printoptions(profile="full") print(x) # prints the whole tensor torch.set_printoptions(profile="default") # reset print(x) # prints the truncated tensor
All the available
set_printoptions arguments are documented here.
Though I don’t suggest to do that, if you want, then
In : torch.set_printoptions(edgeitems=1) In : a Out: tensor([[-0.7698, ..., -0.1949], ..., [-0.7321, ..., 0.8537]]) In : torch.set_printoptions(edgeitems=3) In : a Out: tensor([[-0.7698, 1.3383, 0.5649, ..., 1.3567, 0.6896, -0.1949], [-0.5761, -0.9789, -0.2058, ..., -0.5843, 2.6311, -0.0008], [ 1.3152, 1.8851, -0.9761, ..., 0.8639, -0.6237, 0.5646], ..., [ 0.2851, 0.5504, -0.9471, ..., 0.0688, -0.7777, 0.1661], [ 2.9616, -0.8685, -1.5467, ..., -1.4646, 1.1098, -1.0873], [-0.7321, 0.7610, 0.3182, ..., 2.5859, -0.9709, 0.8537]])
I came here actually looking for answers how to print the entire row of a tensor in one line of the console so I thought I’d add this.
tensor([[1.1573e+04, 6.0693e+02, 1.2436e+03, 2.7277e+04, 1.6673e+08, 2.0462e+00, 9.8891e-01], [2.0237e+04, 5.9074e+02, 1.7208e+03, 2.7449e+04, 2.1301e+08, 2.0678e+00, 1.0011e+00], [2.7456e+04, 6.1106e+02, 1.4897e+03, 2.7332e+04, 1.7310e+08, 2.0448e+00, 9.6041e-01], [1.7732e+04, 6.0232e+02, 1.2608e+03, 2.7371e+04, 1.8106e+08, 1.9594e+00, 1.0040e+00], ..., [1.1167e+04, 5.9867e+02, 1.3440e+03, 2.7263e+04, 2.3160e+08, 2.0190e+00, 1.0075e+00], [1.6003e+04, 5.9590e+02, 1.2319e+03, 2.7368e+04, 1.7155e+08, 2.0171e+00, 1.0202e+00], [1.5499e+04, 6.1471e+02, 9.4877e+02, 2.7395e+04, 1.8146e+08, 1.9016e+00, 9.5884e-01], [3.3886e+04, 6.0689e+02, 1.0777e+03, 2.7259e+04, 2.1599e+08, 2.0179e+00, 1.0201e+00]], dtype=torch.float64)
I did this using