test/python/test_net.py

#
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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
from __future__ import division
from builtins import zip

import unittest
import math
import numpy as np

from singa import net
from singa import layer
from singa import tensor
from singa import loss

layer.engine = 'singacpp'
# net.verbose = True


class TestFeedForwardNet(unittest.TestCase):

    def test_single_input_output(self):
        ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
        ffn.add(layer.Activation('relu1', input_sample_shape=(2,)))
        ffn.add(layer.Activation('relu2'))
        x = np.array([[-1, 1], [1, 1], [-1, -2]], dtype=np.float32)
        x = tensor.from_numpy(x)
        y = tensor.Tensor((3,))
        y.set_value(0)
        out, _ = ffn.evaluate(x, y)
        self.assertAlmostEqual(out * 3,
                               - math.log(1.0 / (1+math.exp(1))) -
                               math.log(0.5) - math.log(0.5),
                               5)

    def test_mult_inputs(self):
        ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
        s1 = ffn.add(layer.Activation('relu1', input_sample_shape=(2,)), [])
        s2 = ffn.add(layer.Activation('relu2', input_sample_shape=(2,)), [])
        ffn.add(layer.Merge('merge', input_sample_shape=(2,)), [s1, s2])
        x1 = tensor.Tensor((2, 2))
        x1.set_value(1.1)
        x2 = tensor.Tensor((2, 2))
        x2.set_value(0.9)
        out = ffn.forward(False, {'relu1': x1, 'relu2': x2})
        out = tensor.to_numpy(out)
        self.assertAlmostEqual(np.average(out), 2)

    def test_mult_outputs(self):
        ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
        s1 = ffn.add(layer.Activation('relu1', input_sample_shape=(2,)), [])
        s2 = ffn.add(layer.Activation('relu2', input_sample_shape=(2,)), [])
        ffn.add(layer.Merge('merge', input_sample_shape=(2,)), [s1, s2])
        split = ffn.add(layer.Split('split', 2))
        ffn.add(layer.Dummy('split1'), split)
        ffn.add(layer.Dummy('split2'), split)
        x1 = tensor.Tensor((2, 2))
        x1.set_value(1.1)
        x2 = tensor.Tensor((2, 2))
        x2.set_value(0.9)
        out = ffn.forward(False, {'relu1': x1, 'relu2': x2})
        out = tensor.to_numpy(out['split1'])
        self.assertAlmostEqual(np.average(out), 2)

    def test_save_load(self):
        ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
        ffn.add(layer.Conv2D('conv', 4, 3, input_sample_shape=(3, 12, 12)))
        ffn.add(layer.Flatten('flat'))
        # ffn.add(layer.BatchNorm('bn'))
        ffn.add(layer.Dense('dense', num_output=4))
        for pname, pval in zip(ffn.param_names(), ffn.param_values()):
            pval.set_value(0.1)
        ffn.save('test_snaphost')
        ffn.save('test_pickle', use_pickle=True)

        ffn.load('test_snaphost')
        ffn.load('test_pickle', use_pickle=True)

    def test_train_one_batch(self):
        ffn = net.FeedForwardNet(loss.SoftmaxCrossEntropy())
        ffn.add(layer.Conv2D('conv', 4, 3, input_sample_shape=(3, 12, 12)))
        ffn.add(layer.Flatten('flat'))
        ffn.add(layer.Dense('dense', num_output=4))
        for pname, pval in zip(ffn.param_names(), ffn.param_values()):
            pval.set_value(0.1)
        x = tensor.Tensor((4, 3, 12, 12))
        x.gaussian(0, 0.01)
        y = np.asarray([[1, 0, 0],
                        [0, 0, 1],
                        [0, 0, 1],
                        [0, 1, 0]], dtype=np.int32)
        y = tensor.from_numpy(y)
        o = ffn.forward(True, x)
        ffn.loss.forward(True, o, y)
        g = ffn.loss.backward()
        for pname, pvalue, pgrad, _ in ffn.backward(g):
            self.assertEqual(len(pvalue), len(pgrad))
            for p, g in zip(pvalue, pgrad):
                self.assertEqual(p.size(), g.size())


if __name__ == '__main__':
    unittest.main()