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k_winners2d_global_test.py
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k_winners2d_global_test.py
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# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2019, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
import unittest
import torch
import nupic.torch.functions as F
from nupic.torch.modules import KWinners2d
class KWinners2DTest(unittest.TestCase):
""""""
def setUp(self):
# Tests will use 3 filters and image width, height = 2 X 2
# Batch size 1
x = torch.rand(1, 3, 2, 2) / 2.0
x[0, 0, 1, 0] = 1.10
x[0, 0, 1, 1] = 1.20
x[0, 1, 0, 1] = 1.21
x[0, 2, 1, 0] = 1.30
self.x = x
# Batch size 2
x = torch.rand(2, 3, 2, 2) / 2.0
x[0, 0, 1, 0] = 1.10
x[0, 0, 1, 1] = 1.20
x[0, 1, 0, 1] = 1.21
x[0, 2, 1, 0] = 1.30
x[1, 0, 0, 0] = 1.40
x[1, 1, 0, 0] = 1.50
x[1, 1, 0, 1] = 1.60
x[1, 2, 1, 1] = 1.70
self.x2 = x
self.gradient2 = torch.rand(x.shape)
# All equal
self.duty_cycle = torch.zeros((1, 3, 1, 1))
self.duty_cycle[:] = 1.0 / 3.0
def test_one(self):
"""Equal duty cycle, boost factor 0, k=4, batch size 1."""
x = self.x
expected = torch.zeros_like(x)
expected[0, 0, 1, 0] = x[0, 0, 1, 0]
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
result = F.kwinners2d(x, self.duty_cycle, k=4,
boost_strength=0.0, local=False,
break_ties=break_ties)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_two(self):
"""Equal duty cycle, boost factor 0, k=3."""
x = self.x
expected = torch.zeros_like(x)
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
result = F.kwinners2d(x, self.duty_cycle, k=3,
boost_strength=0.0, local=False,
break_ties=break_ties)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_three(self):
"""Equal duty cycle, boost factor=0, k=4, batch size=2."""
x = self.x2
expected = torch.zeros_like(x)
expected[0, 0, 1, 0] = x[0, 0, 1, 0]
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
expected[1, 0, 0, 0] = x[1, 0, 0, 0]
expected[1, 1, 0, 0] = x[1, 1, 0, 0]
expected[1, 1, 0, 1] = x[1, 1, 0, 1]
expected[1, 2, 1, 1] = x[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
result = F.kwinners2d(x, self.duty_cycle, k=4,
boost_strength=0.0, local=False,
break_ties=break_ties)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_four(self):
"""Equal duty cycle, boost factor=0, k=3, batch size=2."""
x = self.x2
expected = torch.zeros_like(x)
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
expected[1, 1, 0, 0] = x[1, 1, 0, 0]
expected[1, 1, 0, 1] = x[1, 1, 0, 1]
expected[1, 2, 1, 1] = x[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
result = F.kwinners2d(x, self.duty_cycle, k=3,
boost_strength=0.0, local=False,
break_ties=break_ties)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
def test_k_winners2d_module_one(self):
x = self.x2
expected = torch.zeros_like(x)
expected[0, 0, 1, 0] = x[0, 0, 1, 0]
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 1, 0, 1] = x[0, 1, 0, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
expected[1, 0, 0, 0] = x[1, 0, 0, 0]
expected[1, 1, 0, 0] = x[1, 1, 0, 0]
expected[1, 1, 0, 1] = x[1, 1, 0, 1]
expected[1, 2, 1, 1] = x[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.333,
channels=3,
k_inference_factor=0.5,
boost_strength=1.0,
boost_strength_factor=0.5,
duty_cycle_period=1000,
local=False,
break_ties=break_ties,
)
result = kw(x)
self.assertEqual(result.shape, expected.shape)
num_correct = (result == expected).sum()
self.assertEqual(num_correct, result.reshape(-1).size()[0])
new_duty = torch.tensor([1.5000, 1.5000, 1.0000]) / 4.0
diff = (kw.duty_cycle.reshape(-1) - new_duty).abs().sum()
self.assertLessEqual(diff, 0.001)
def test_k_winners2d_module_two(self):
"""
Test a series of calls on the module in training mode.
"""
x = self.x2
expected = torch.zeros_like(x)
expected[0, 0, 1, 0] = x[0, 0, 1, 0]
expected[0, 0, 1, 1] = x[0, 0, 1, 1]
expected[0, 2, 1, 0] = x[0, 2, 1, 0]
expected[1, 0, 0, 0] = x[1, 0, 0, 0]
expected[1, 1, 0, 1] = x[1, 1, 0, 1]
expected[1, 2, 1, 1] = x[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.25,
channels=3,
k_inference_factor=0.5,
boost_strength=1.0,
boost_strength_factor=0.5,
duty_cycle_period=1000,
local=False,
break_ties=break_ties,
)
kw.train(mode=True)
result = kw(x)
result = kw(x)
result = kw(x)
result = kw(x)
result = kw(x)
result = kw(x)
self.assertTrue(result.eq(expected).all())
def test_k_winners2d_relu(self):
x = torch.zeros(2, 4, 2, 2)
x[0, :, 0, 0] = torch.FloatTensor([-6, -7, -8, -9])
x[0, :, 0, 1] = torch.FloatTensor([0, 3, -42, -19])
x[0, :, 1, 0] = torch.FloatTensor([-1, -2, 3, -4])
x[0, :, 1, 1] = torch.FloatTensor([-10, -11, -12, -13])
x[1, :, 0, 0] = torch.FloatTensor([-10, -12, -31, -42])
x[1, :, 0, 1] = torch.FloatTensor([0, -1, 0, -6])
x[1, :, 1, 0] = torch.FloatTensor([-2, -10, -11, -4])
x[1, :, 1, 1] = torch.FloatTensor([-7, -1, -10, -3])
expected = torch.zeros(2, 4, 2, 2)
expected[0, 1, 0, 1] = 3
expected[0, 2, 1, 0] = 3
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=0.25,
channels=4,
k_inference_factor=0.5,
boost_strength=1.0,
boost_strength_factor=0.5,
duty_cycle_period=1000,
local=False,
break_ties=break_ties,
relu=True,
)
result = kw(x)
self.assertTrue(result.eq(expected).all())
def test_k_winners2d_global_grad(self):
"""
Test gradient
"""
x = self.x2.clone().requires_grad_(True)
n, c, h, w = x.shape
grad = self.gradient2
expected = torch.zeros_like(x)
expected[0, 0, 1, 0] = grad[0, 0, 1, 0]
expected[0, 0, 1, 1] = grad[0, 0, 1, 1]
expected[0, 1, 0, 1] = grad[0, 1, 0, 1]
expected[0, 2, 1, 0] = grad[0, 2, 1, 0]
expected[1, 0, 0, 0] = grad[1, 0, 0, 0]
expected[1, 1, 0, 0] = grad[1, 1, 0, 0]
expected[1, 1, 0, 1] = grad[1, 1, 0, 1]
expected[1, 2, 1, 1] = grad[1, 2, 1, 1]
for break_ties in [True, False]:
with self.subTest(break_ties=break_ties):
kw = KWinners2d(
percent_on=(1 / 3),
channels=c,
k_inference_factor=1.0,
boost_strength=0.0,
duty_cycle_period=1000,
local=False,
break_ties=break_ties,
)
kw.train(mode=True)
y = kw(x)
y.backward(grad)
torch.testing.assert_allclose(x.grad, expected)
x.grad.zero_()
if __name__ == "__main__":
unittest.main()