How to get or use network from nnp

[goofmint]

Hi.

Neural Network Console of Windows provides h5 file and Python code. And Cloud version provides just nnp file. I know the nnp file has h5 and network of protocol buffers. But I don't know how to use network in nnp file.

Usally we can get the python code like below.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

def network(x, y, test=False):
    # Input:x -> 1,28,28
    # MaxPooling -> 1,14,14
    h = F.max_pooling(x, (2,2), (2,2))
    # Affine -> 100
    h = PF.affine(h, (100,), name='Affine')
    # ReLU
    h = F.relu(h, True)
    # Affine_2 -> 1
    h = PF.affine(h, (1,), name='Affine_2')
    # Sigmoid
    h = F.sigmoid(h)
    # BinaryCrossEntropy
    h = F.binary_cross_entropy(h, y)
    return h

And we don't neet BinaryCrossEntropy, so delete it. The nnp file's network has it.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
from nnabla.utils import nnp_graph

nnp = nnp_graph.NnpLoader('./result.nnp')
graph = nnp.get_network('MainValidation')
y = graph.outputs
print(y)

This code outputs below.

{'BinaryCrossEntropy': <Variable((64, 1), need_grad=True) at 0x114f926d8>}

I hope use the nnp in Python like below.

import nnabla as nn

nn.load_parameters('./result.nnp')
graph = nn.get_network('MainValidation')
x = graph.inputs['x']
y = graph.outputs['y']
y.forward(x, test=True)
y.d

How to use network in the nnp file?

I know nnabla_cli can do it. But I don't know how to do it.

Thanks

[goofmint]

I can use the nnp file like below.

def image():
     :
    x = nn.Variable((1,1,28,28))
    x.d = np.array(img) * (1.0 / 255.0)
    y = network(x, test=True)
    y.forward()
     :

def network(x, test=False):
    # Input:x -> 1,28,28
    # MaxPooling -> 1,14,14
    h = F.max_pooling(x, (2,2), (2,2))
    # Affine -> 100
    h = PF.affine(h, (100,), name='Affine')
    # ReLU
    h = F.relu(h, True)
    # Affine_2
    h = PF.affine(h, (1,), name='Affine_2')
    # Sigmoid
    h = F.sigmoid(h)
    return h

But I think I don't need to write network method if I'm using nnp file. 'cause it is contained network by protocol buffer.

[goofmint]

This is NOT completely code.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
from nnabla.utils import nnp_graph

nnp = nnp_graph.NnpLoader('./result.nnp')
print(nnp.get_network_names())
graph = nnp.get_network('MainRuntime', batch_size=1)
x = graph.inputs['Input'][0]
print(x)
y = graph.outputs
print(y)

Output:

# print(nnp.get_network_names())
['Main', 'MainValidation', 'MainRuntime']
# print(x)
<Variable((1, 28, 28), need_grad=False) at 0x11e7a6728>
# print(y)
{'Sigmoid': <Variable((1, 1), need_grad=True) at 0x11e7a6638>}

If I'm using Main or MainValidation network, output is below.

# print(x)
<Variable((1, 28, 28), need_grad=False) at 0x11a08c818>
# print(y)
{'BinaryCrossEntropy': <Variable((1, 1), need_grad=True) at 0x11a08c728>}

I can't find out variable x and y from graph.inputs and graph.outputs looks like sample code. And I don't know what is Nnp method.

# Read a .nnp file.
nnp = Nnp('/path/to/nnp.nnp')
# Assume a graph `graph_a` is in the nnp file.
net = nnp.get_network(network_name, batch_size=1)
# `x` is an input of the graph.
x = net.inputs['x']
# 'y' is an outputs of the graph.
y = net.outputs['y']
# Set random data as input and perform forward prop.
x.d = np.random.randn(x.shape)
y.forward(clear_buffer=True)
print('output:', y.d)

https://nnabla.readthedocs.io/en/latest/_modules/nnabla/utils/nnp_graph.html

I have attached my nnp file.

Thanks.

result.nnp.zip

[goofmint]

I did it.

from nnabla.utils import nnp_graph
import numpy as np
from PIL import Image

nnp = nnp_graph.NnpLoader('./result.nnp')
graph = nnp.get_network('MainRuntime', batch_size=1)
# Input variable name
input = list(graph.inputs.keys())[0]
# Output variable name
output = list(graph.outputs.keys())[0]
# Get input and output
x = graph.inputs[input]
y = graph.outputs[output]
# Open image
img = Image.open('001.png')
# Transform
x.d = np.array(img) * (1.0 / 255.0)
# Forward
y.forward(clear_buffer=True)
print(y.d)