How to print the full contents of a PyTorch tensor

 refer to code:

..

import torch

# create a tensor

x = torch.randn(3, 4)

# set print options to display full tensor

torch.set_printoptions(precision=10, threshold=None, edgeitems=None, linewidth=None, profile=None)

# print the full tensor

print(x)

..

In this example, we set the precision to 10 to display up to 10 decimal places, and set threshold, edgeitems, linewidth, and profile to None to display all the elements of the tensor. You can adjust these settings to your preference, depending on the size and precision of your tensor.

Thank you. 

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RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

 refer to code:

use

tensor.detach().numpy()

..

import torch

tensor1 = torch.tensor([1.0,2.0],requires_grad=True)

print(tensor1)

print(type(tensor1))

tensor1 = tensor1.detach().numpy()

print(tensor1)

print(type(tensor1))

..

Thank you.

flatten, unflatten Pytorch, torch, tensor reshape

 flatten -> unflatten

..

#batch, ch, row, col

input_size=torch.empty(10, 1, 256, 256)

print('input shape: ',input_size.shape)

#flatten batch, ch*row*col

reshape = torch.flatten(input_size, start_dim=1)

# reshape = input_size.reshape(-1, 1*256*256)

print('reshape or flatten: ',reshape.shape)

#unflatten batch, ch, row, col

reshape = reshape.reshape(-1,1,256,256)

print('unflatten: ',reshape.shape)

..

input shape:  torch.Size([10, 1, 256, 256])
reshape or flatten:  torch.Size([10, 65536])
unflatten:  torch.Size([10, 1, 256, 256])

Thank you.

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In Tensorflow, get the names of all the Tensors in a graph

To get all nodes in the graph: (type tensorflow.core.framework.node_def_pb2.NodeDef)

all_nodes = [n for n in tf.get_default_graph().as_graph_def().node]

To get all ops in the graph: (type tensorflow.python.framework.ops.Operation)

all_ops = tf.get_default_graph().get_operations()

To get all variables in the graph: (type tensorflow.python.ops.resource_variable_ops.ResourceVariable)

all_vars = tf.global_variables()

To get all tensors in the graph: (type tensorflow.python.framework.ops.Tensor)

all_tensors = [tensor for op in tf.get_default_graph().get_operations() for tensor in op.values()]

To get all placeholders in the graph: (type tensorflow.python.framework.ops.Tensor)

all_placeholders = [placeholder for op in tf.get_default_graph().get_operations() if op.type=='Placeholder' for placeholder in op.values()]

Tensorflow 2

To get the graph in Tensorflow 2, instead of tf.get_default_graph() you need to instantiate a tf.function first and access the graph attribute, for example:

graph = func.get_concrete_function().graph

where func is a tf.function

sparse tensor to csr_matrix

from scipy.sparse import csr_matrix

import numpy as np

x = val_data.x

dim = len(x)

print(dim)

edge_index = val_data.edge_index

print(edge_index) #sparse tensor

row = edge_index[0].numpy()

col = edge_index[1].numpy()

edge_num = len(row)

data = np.ones( edge_num )

mtx = csr_matrix((data, (row, col)), shape=(dim, dim))

#print( type(mtx.toarray()), mtx.toarray().shape)

print( mtx.toarray(), type(mtx.toarray()), mtx.toarray().shape) 

let's image 

val_data.x is node features ex) 13x1000

val_data.edge_index is sparse edge index stored by torch tensor

now we want to convert it to csr_matrix

The above code is example for this case.

The print out is like this:

tensor([[ 0,  0,  0,  1,  1,  1,  1,  1,  2,  2,  2,  2,  3,  3,  3,  3,  3,  4,
          4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  7,  7,  7,
          8,  8,  8,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11,
         11, 11, 12, 12, 12, 12],
        [ 1,  3, 10,  0,  2,  3, 10, 11,  1,  3, 11, 12,  0,  1,  2, 11, 12,  5,
          6,  8,  9, 11, 12,  4,  6,  7,  8,  9,  4,  5,  7,  9, 10,  5,  6,  8,
          4,  5,  7,  4,  5,  6, 10, 11,  0,  1,  6,  9, 11,  1,  2,  3,  4,  9,
         10, 12,  2,  3,  4, 11]])
[[0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0.]
 [0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
 [1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0.]
 [0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0.]
 [1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0.]
 [0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1.]
 [0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0.]] <class 'numpy.ndarray'> (13, 13)

Thank you

Enjoy Pytorch!

RuntimeError: set_sizes_contiguous is not allowed on Tensor created from .data or .detach(), in Pytorch 1.1.0

change old -> new 

old

v.data.resize_(data.size()).copy_(data)

NEW

with torch.no_grad():

    v.resize_(data.size()).copy_(data)