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test_ml_nn.py
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test_ml_nn.py
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# encoding: utf-8
# pylint: skip-file
"""
This file contains tests for the madmom.ml.nn module.
"""
from __future__ import absolute_import, division, print_function
import unittest
from madmom.ml.nn import *
from madmom.ml.nn.activations import *
from madmom.ml.nn.layers import *
from madmom.models import *
class TestRNNClass(unittest.TestCase):
def setUp(self):
# uni-directional RNN
self.rnn = NeuralNetwork.load(ONSETS_RNN[0])
self.data = np.zeros((4, self.rnn.layers[0].weights.shape[0]))
self.data[1] = 1.
self.result = [1.78801871e-04, 8.00144131e-01,
3.30476369e-05, 1.36037513e-04]
def test_process(self):
# process the whole sequence at once
result = self.rnn.process(self.data)
self.assertTrue(np.allclose(result, self.result))
# two runs must produce the same output
result_1 = self.rnn.process(self.data)
self.assertTrue(np.allclose(result_1, self.result))
# after resetting the RNN, it must produce the same output as before
self.rnn.reset()
result_2 = [self.rnn.process(np.atleast_2d(d), reset=False)
for d in self.data]
self.assertTrue(np.allclose(np.hstack(result_2), self.result))
# without resetting it produces different results
result_3 = [self.rnn.process(np.atleast_2d(d), reset=False)
for d in self.data]
self.assertTrue(np.allclose(np.hstack(result_3),
[9.15636891e-04, 9.74331021e-01,
4.83996118e-05, 2.72355013e-04]))
class TestLSTMClass(unittest.TestCase):
def setUp(self):
# uni-directional LSTM-RNN
self.rnn = NeuralNetwork.load(BEATS_LSTM[0])
self.data = np.zeros((4, self.rnn.layers[0].cell.weights.shape[0]))
self.data[1] = 1.
self.result = [0.00126955, 0.03134079, 0.01535073, 0.00207471]
def test_process(self):
# process the whole sequence at once
result = self.rnn.process(self.data)
self.assertTrue(np.allclose(result, self.result))
# two runs must produce the same output
result_1 = self.rnn.process(self.data)
self.assertTrue(np.allclose(result_1, self.result))
# after resetting the RNN, it must produce the same output
self.rnn.reset()
result_2 = [self.rnn.process(np.atleast_2d(d), reset=False)
for d in self.data]
self.assertTrue(np.allclose(np.hstack(result_2), self.result))
# without resetting it produces different output
result_3 = [self.rnn.process(np.atleast_2d(d), reset=False)
for d in self.data]
self.assertTrue(np.allclose(np.hstack(result_3),
[0.00054101, 0.05323271,
0.0548761, 0.00785541]))
# class for testing all other (offline-only) networks
class TestNeuralNetworkClass(unittest.TestCase):
def test_brnn(self):
rnn = NeuralNetwork.load(ONSETS_BRNN[0])
input_size = rnn.layers[0].fwd_layer.weights.shape[0]
data = np.zeros((4, input_size))
data[1] = 1.
result = rnn.process(data)
self.assertTrue(np.allclose(result, [0.00461393, 0.46032878,
0.04824624, 0.00083493]))
def test_brnn_pp(self):
rnn = NeuralNetwork.load(ONSETS_BRNN_PP[0])
input_size = rnn.layers[0].fwd_layer.weights.shape[0]
data = np.zeros((4, input_size))
data[1] = 1.
result = rnn.process(data)
self.assertTrue(np.allclose(result,
[3.88076517e-03, 1.67354920e-03,
1.14450835e-03, 5.01533471e-05]))
def test_brnn_regression(self):
rnn = NeuralNetwork.load(NOTES_BRNN[0])
input_size = rnn.layers[0].fwd_layer.weights.shape[0]
data = np.zeros((4, input_size))
data[1] = 1.
result = rnn.process(data)
self.assertEqual(result.shape, (4, 88))
self.assertTrue(np.allclose(result[:, :2],
[[6.50841586e-05, 4.06891153e-04],
[-9.74552809e-04, -3.86762259e-03],
[1.09878686e-04, 1.54044293e-04],
[-8.16427571e-04, 4.62550714e-04]]))
def test_blstm(self):
rnn = NeuralNetwork.load(BEATS_BLSTM[0])
input_size = rnn.layers[0].fwd_layer.cell.weights.shape[0]
data = np.zeros((4, input_size))
data[1] = 1.
result = rnn.process(data)
self.assertTrue(np.allclose(result, [0.0815198, 0.24451593,
0.08786312, 0.01776425]))
def test_cnn(self):
cnn = NeuralNetwork.load(ONSETS_CNN[0])
data = np.zeros((19, 80, 3))
data[10] = 1.
result = cnn.process(data)
self.assertTrue(np.allclose(result, [0.0021432, 0.02647826, 0.92750794,
0.84207922, 0.21631248]))
class TestFeedForwardLayerClass(unittest.TestCase):
def setUp(self):
# borrow an FeedForwardLayer from an existing network
rnn = NeuralNetwork.load(ONSETS_RNN[0])
self.layer = rnn.layers[-1]
input_size = self.layer.weights.shape[0]
self.data = np.zeros((4, input_size))
self.data[1] = 1.
self.result = np.array([[0.16283005], [0.14362903],
[0.16283005], [0.16283005]])
def test_types(self):
self.assertTrue(isinstance(self.layer, FeedForwardLayer))
self.assertTrue(self.layer.activation_fn == sigmoid)
def test_init(self):
self.assertFalse(hasattr(self.layer, 'init'))
self.assertFalse(hasattr(self.layer, '_prev'))
def test_activate(self):
# test result
result_1 = self.layer(self.data)
self.assertTrue(np.allclose(result_1, self.result))
# two runs must yield identical results
result_2 = self.layer(self.data)
self.assertTrue(np.allclose(result_1, result_2))
# calling frame-by-frame must yield identical results
result_3 = [self.layer.activate(d) for d in self.data]
self.assertTrue(np.allclose(result_3, self.result))
# calling frame-by-frame without resetting also
result_4 = [self.layer.activate(d, reset=False) for d in self.data]
self.assertTrue(np.allclose(result_4, self.result))
class TestRecurrentLayerClass(unittest.TestCase):
def setUp(self):
# borrow an RecurrentLayer from an existing network
rnn = NeuralNetwork.load(ONSETS_RNN[0])
self.layer = rnn.layers[0]
input_size = self.layer.weights.shape[0]
self.data = np.zeros((4, input_size))
self.data[1] = 1.
def test_types(self):
self.assertTrue(isinstance(self.layer, RecurrentLayer))
self.assertTrue(self.layer.activation_fn == tanh)
def test_init(self):
self.assertTrue(hasattr(self.layer, 'init'))
self.assertTrue(hasattr(self.layer, '_prev'))
def test_activate(self):
# test result
result_1 = self.layer(self.data)
self.assertTrue(np.allclose(result_1[0, :2],
[-0.33919713, -0.02091585]))
self.assertTrue(np.allclose(result_1[-1, -2:],
[0.4419672, -0.261151]))
# two runs must yield identical results
result_2 = self.layer(self.data)
self.assertTrue(np.allclose(result_2, result_1))
# last step must be preserved
self.assertTrue(np.allclose(self.layer._prev, result_2[-1]))
# initialisation must not change
self.assertTrue(np.allclose(self.layer.init, np.zeros(25)))
# reset layer, activate framewise must yield the same result
self.layer.reset()
result_3 = [self.layer.activate(np.atleast_2d(d), reset=False)
for d in self.data]
result_3 = np.vstack(result_3)
self.assertTrue(np.allclose(result_3, result_1))
self.assertTrue(np.allclose(self.layer._prev, result_3[-1]))
# activate framewise without resetting must yield a different result
result_4 = [self.layer.activate(np.atleast_2d(d), reset=False)
for d in self.data]
result_4 = np.vstack(result_4)
self.assertFalse(np.allclose(result_4, result_1))
self.assertTrue(np.allclose(result_4[0, :2],
[-3.14807342e-01, -2.22700375e-01]))
self.assertTrue(np.allclose(result_4[-1, -2:],
[4.40259654e-01, -2.59141315e-01]))
# last step must be preserved
self.assertTrue(np.allclose(self.layer._prev, result_4[-1]))
# initialisation must not change
self.assertTrue(np.allclose(self.layer.init, np.zeros(25)))
class TestLSTMLayerClass(unittest.TestCase):
def setUp(self):
# borrow an LSTMLayer from an existing network
rnn = NeuralNetwork.load(BEATS_BLSTM[0])
self.layer = rnn.layers[0].fwd_layer
input_size = self.layer.cell.weights.shape[0]
self.data = np.zeros((4, input_size))
self.data[1] = 1.
def test_types(self):
self.assertTrue(isinstance(self.layer, LSTMLayer))
self.assertTrue(isinstance(self.layer.input_gate, Gate))
self.assertTrue(isinstance(self.layer.forget_gate, Gate))
self.assertTrue(isinstance(self.layer.cell, Cell))
self.assertTrue(isinstance(self.layer.output_gate, Gate))
self.assertTrue(self.layer.activation_fn == tanh)
self.assertTrue(self.layer.input_gate.activation_fn == sigmoid)
self.assertTrue(self.layer.forget_gate.activation_fn == sigmoid)
self.assertTrue(self.layer.cell.activation_fn == tanh)
self.assertTrue(self.layer.output_gate.activation_fn == sigmoid)
def test_init(self):
self.assertTrue(hasattr(self.layer, 'init'))
self.assertTrue(hasattr(self.layer, '_prev'))
def test_activate(self):
# test result
result_1 = self.layer(self.data)
self.assertTrue(np.allclose(result_1[0, :2],
[8.15829188e-02, 1.14209838e-02]))
self.assertTrue(np.allclose(result_1[-1, -2:],
[-1.81968838e-01, -2.32963227e-02]))
# two runs must yield identical results
result_2 = self.layer(self.data)
self.assertTrue(np.allclose(result_2, result_1))
# last step must be preserved
self.assertTrue(np.allclose(self.layer._prev, result_2[-1]))
# reset layer, activate framewise must yield the same result
self.layer.reset()
result_3 = [self.layer.activate(np.atleast_2d(d), reset=False)
for d in self.data]
self.assertTrue(np.allclose(np.vstack(result_3), result_1))
# activate framewise without resetting must yield a different result
result_4 = [self.layer.activate(np.atleast_2d(d), reset=False)
for d in self.data]
result_4 = np.vstack(result_4)
self.assertFalse(np.allclose(result_4, result_1))
self.assertTrue(np.allclose(result_4[0, :2],
[2.34878659e-01, -1.26488507e-03]))
self.assertTrue(np.allclose(result_4[-1, -2:],
[-2.45573238e-01, -2.92062219e-02]))
# last step must be preserved
self.assertTrue(np.allclose(self.layer._prev, result_4[-1]))
# initialisation must not change
self.assertTrue(np.allclose(self.layer.init, np.zeros(25)))
class TestGRUClass(unittest.TestCase):
W_xr = np.array([[-0.42948743, -1.29989187],
[0.77213901, 0.86070993],
[1.13791823, -0.87066225]])
W_xu = np.array([[0.44875312, 0.07172084],
[-0.24292999, 1.318794],
[1.0270179, 0.16293946]])
W_xhu = np.array([[0.8812559, 1.35859991],
[1.04311944, -0.25449358],
[-1.09539597, 1.19808424]])
W_hr = np.array([[0.96696973, 0.1384294],
[-0.09561655, -1.23413809]])
W_hu = np.array([[0.04664641, 0.59561686],
[1.00325841, -0.11574791]])
W_hhu = np.array([[1.19742848, 1.07850016],
[0.35234964, -1.45348681]])
b_r = np.array([1.41851288, -0.39743243])
b_u = np.array([-0.78729095, 0.83385797])
b_hu = np.array([1.25143065, -0.97715625])
IN = np.array([[0.91298812, -1.47626202, -1.08667502],
[0.49814883, -0.0104938, 0.93869008],
[-1.12282135, 0.3780883, 1.42017503],
[0.62669439, 0.89438929, -0.69354132],
[0.16162221, -1.00166208, 0.23579985]])
OUT = np.array([[0.22772433, -0.13181415],
[0.49479958, 0.51224858],
[0.08539771, -0.56119639],
[0.1946809, -0.50421363],
[0.17403202, -0.27258521]])
H = np.array([0.02345737, 0.34454183])
def setUp(self):
self.reset_gate = Gate(
TestGRUClass.W_xr, TestGRUClass.b_r, TestGRUClass.W_hr,
activation_fn=sigmoid)
self.update_gate = Gate(
TestGRUClass.W_xu, TestGRUClass.b_u, TestGRUClass.W_hu,
activation_fn=sigmoid)
self.gru_cell = GRUCell(
TestGRUClass.W_xhu, TestGRUClass.b_hu, TestGRUClass.W_hhu)
self.gru_1 = GRULayer(self.reset_gate, self.update_gate,
self.gru_cell)
self.gru_2 = GRULayer(self.reset_gate, self.update_gate,
self.gru_cell, init=TestGRUClass.H)
def test_activate(self):
self.assertTrue(
np.allclose(self.reset_gate.activate(TestGRUClass.IN[0, :],
TestGRUClass.H), np.array([0.20419282, 0.08861294])))
self.assertTrue(
np.allclose(self.update_gate.activate(TestGRUClass.IN[0, :],
TestGRUClass.H), np.array([0.31254834, 0.2226105])))
self.assertTrue(
np.allclose(self.gru_cell.activate(TestGRUClass.IN[0, :],
TestGRUClass.H, TestGRUClass.H),
np.array([0.9366396, -0.67876764])))
# activating the layer normally
self.assertTrue(np.allclose(self.gru_1.activate(TestGRUClass.IN),
TestGRUClass.OUT))
# activating the layer a second time must give the same results
self.assertTrue(np.allclose(self.gru_1.activate(TestGRUClass.IN),
TestGRUClass.OUT))
# activating the other layer
self.assertTrue(
np.allclose(self.gru_2.activate(TestGRUClass.IN),
np.array([[0.30988133, 0.13258138],
[0.60639685, 0.55714613],
[0.21366976, -0.55568963],
[0.30860096, -0.43686554],
[0.28866628, -0.23025239]])))
# reset layer, activate framewise must yield the same result
self.gru_1.reset()
result_1 = [self.gru_1.activate(np.atleast_2d(d), reset=False)
for d in self.IN]
self.assertTrue(np.allclose(np.vstack(result_1), self.OUT))
# the previous state must be the last output
self.assertTrue(np.allclose(self.gru_1._prev, self.OUT[-1]))
# activate with same data without resetting
result_2 = [self.gru_1.activate(np.atleast_2d(d), reset=False)
for d in self.IN]
# results must differ
self.assertFalse(np.allclose(result_1, result_2))
self.assertTrue(np.allclose(np.vstack(result_2),
[[0.3254016, -0.33195618],
[0.53048187, 0.51293057],
[0.12495148, -0.559039],
[0.22988503, -0.48455557],
[0.20934506, -0.25959042]]))
# the previous state must be the last output
self.assertTrue(np.allclose(self.gru_1._prev,
[0.20934506, -0.25959042]))
# initialisation must not change
self.assertTrue(np.allclose(self.gru_1.init, [0, 0]))
class TestMaxPoolLayerClass(unittest.TestCase):
def test_time_pooling(self):
layer = MaxPoolLayer(size=(2, 1))
out = layer(np.arange(20).reshape(5, 4))
self.assertEqual(out.shape, (2, 4))
self.assertTrue(np.allclose(out, [[4, 5, 6, 7],
[12, 13, 14, 15]]))
def test_freq_pooling(self):
layer = MaxPoolLayer(size=(1, 2))
out = layer(np.arange(20).reshape(5, 4))
self.assertEqual(out.shape, (5, 2))
self.assertTrue(np.allclose(out, [[1, 3], [5, 7], [9, 11],
[13, 15], [17, 19]]))
def test_max_pooling(self):
layer = MaxPoolLayer((3, 3))
out = layer(np.arange(20).reshape((5, 4, 1)))
self.assertTrue(np.allclose(out, [[[10]]]))
out = layer(np.arange(60).reshape((5, 4, 3)))
self.assertTrue(np.allclose(out, [[[30, 31, 32]]]))
out = layer(np.arange(80).reshape((5, 4, 4)))
self.assertTrue(np.allclose(out, [[[40, 41, 42, 43]]]))
class TestBatchNormLayerClass(unittest.TestCase):
IN = np.array([[[0.32400414, 0.31483042],
[0.38269293, 0.04822304],
[0.03791266, 0.34776369]],
[[0.87113619, 0.62172854],
[0.87353969, 0.92837042],
[0.70359915, 0.49917081]],
[[0.42643583, 0.74653631],
[0.08519834, 0.35423595],
[0.34863797, 0.44895086]]])
BN_PARAMS = [np.array([-0.00098404, 0.00185387]),
np.array([-0.0068268, 0.0068859]),
np.array([-0.00289366, 0.00742069]),
np.array([-0.00177374, -0.00444383])]
OUT = np.array([[[-0.00098008, 0.00184446],
[-0.00097937, 0.00185262],
[-0.00098355, 0.00184345]],
[[-0.00097346, 0.00183507],
[-0.00097343, 0.00182569],
[-0.00097549, 0.00183882]],
[[-0.00097884, 0.00183125],
[-0.00098297, 0.00184326],
[-0.00097978, 0.00184036]]])
def test_batch_norm(self):
params = TestBatchNormLayerClass.BN_PARAMS
x = TestBatchNormLayerClass.IN
y_true = TestBatchNormLayerClass.OUT
bnl = BatchNormLayer(
params[0], params[1], params[2], params[3], linear)
y = bnl.activate(x)
self.assertTrue(np.allclose(y, y_true))
class TestAverageLayerClass(unittest.TestCase):
IN = np.array([[[0.32400414, 0.31483042],
[0.38269293, 0.04822304],
[0.03791266, 0.34776369]],
[[0.87113619, 0.62172854],
[0.87353969, 0.92837042],
[0.70359915, 0.49917081]],
[[0.42643583, 0.74653631],
[0.08519834, 0.35423595],
[0.34863797, 0.44895086]]])
OUT_AVG = 0.46460927444444444
OUT_02 = np.array([0.55077857, 0.44537673, 0.39767252])
OUT_02_KD = np.array([[[0.55077857], [0.44537673], [0.39767252]]])
def test_average_layer(self):
al = AverageLayer()
out = al(TestAverageLayerClass.IN)
self.assertAlmostEqual(out, TestAverageLayerClass.OUT_AVG)
al = AverageLayer(axis=(0, 2))
out = al(TestAverageLayerClass.IN)
self.assertEqual(out.shape, (3,))
self.assertTrue(np.allclose(out, TestAverageLayerClass.OUT_02))
al = AverageLayer(axis=(0, 2), keepdims=True)
out = al(TestAverageLayerClass.IN)
self.assertEqual(out.shape, (1, 3, 1))
self.assertTrue(np.allclose(out, TestAverageLayerClass.OUT_02_KD))
al = AverageLayer(axis=(0, 2), dtype=np.float32)
out = al(TestAverageLayerClass.IN)
self.assertEqual(out.dtype, np.float32)
class TestReshapeLayerClass(unittest.TestCase):
IN = np.random.random((2, 3, 4))
def test_reshape_layer(self):
rl = ReshapeLayer(newshape=(3, 4, 2))
self.assertEqual(rl(TestReshapeLayerClass.IN).shape, (3, 4, 2))
rl = ReshapeLayer(newshape=(3, -1, 2))
self.assertEqual(rl(TestReshapeLayerClass.IN).shape, (3, 4, 2))
rl = ReshapeLayer(newshape=(-1,))
self.assertEqual(rl(TestReshapeLayerClass.IN).shape, (24,))
with self.assertRaises(ValueError):
rl = ReshapeLayer(newshape=(3, 2, 2))
rl(TestReshapeLayerClass.IN)
class TestTransposeLayerClass(unittest.TestCase):
IN = np.random.random((2, 3, 4, 5))
def test_transpose_layer(self):
tl = TransposeLayer()
self.assertEqual(tl(TestTransposeLayerClass.IN).shape, (5, 4, 3, 2))
tl = TransposeLayer(axes=(2, 0, 1, 3))
self.assertEqual(tl(TestTransposeLayerClass.IN).shape, (4, 2, 3, 5))
with self.assertRaises(ValueError):
tl = TransposeLayer(axes=(0, 1, 3))
tl(TestTransposeLayerClass.IN)
with self.assertRaises(ValueError):
tl = TransposeLayer(axes=(0, 1, 2, 3, 4))
tl(TestTransposeLayerClass.IN)
with self.assertRaises(ValueError):
tl = TransposeLayer(axes=(0, 1, 1, 2))
tl(TestTransposeLayerClass.IN)
class TestPadLayerClass(unittest.TestCase):
def test_constant_padding(self):
pl = PadLayer(width=2, axes=(0, 1), value=10.)
data = np.arange(40).reshape(5, 4, 2).astype(float)
out = pl(data)
self.assertEqual(out.shape, (9, 8, 2))
self.assertTrue(np.allclose(out[2:-2, 2:-2, :], data))
self.assertTrue(np.allclose(out[:2, :, :], 10.))
self.assertTrue(np.allclose(out[-2:, :, :], 10.))
self.assertTrue(np.allclose(out[:, :2, :], 10.))
self.assertTrue(np.allclose(out[:, -2:, :], 10.))
pl = PadLayer(width=3, axes=(2,), value=2.2)
out = pl(data)
self.assertEqual(out.shape, (5, 4, 8))
self.assertTrue(np.allclose(out[:, :, 3:-3], data))
self.assertTrue(np.allclose(out[:, :, :3], 2.2))
self.assertTrue(np.allclose(out[:, :, -3:], 2.2))
class TestConvolutionalLayerClass(unittest.TestCase):
def setUp(self):
# 1x1
tf_weights = np.array([[[[1., 0.5]]]])
weights = np.transpose(tf_weights, axes=(2, 3, 0, 1))
weights = np.flip(np.flip(weights, axis=2), axis=3)
bias = np.array([0., 2.])
self.layer1x1 = ConvolutionalLayer(weights, bias)
# 2x2
tf_weights = np.array([[[[1., 0.25]], [[0., 0.]]],
[[[1., 1.]], [[1., 0.75]]]])
weights = np.transpose(tf_weights, axes=(2, 3, 0, 1))
weights = np.flip(np.flip(weights, axis=2), axis=3)
bias = np.array([0.5, 0.1])
self.layer2x2 = ConvolutionalLayer(weights, bias)
# 3x3
tf_weights = np.array([[[[1.]], [[1.]], [[1.]]],
[[[0.]], [[1.]], [[1.]]],
[[[0.]], [[0.]], [[2.]]]])
weights = np.transpose(tf_weights, axes=(2, 3, 0, 1))
weights = np.flip(np.flip(weights, axis=2), axis=3)
bias = np.array([1.])
self.layer3x3 = ConvolutionalLayer(weights, bias)
# 3x3 multi-channel
tf_weights = np.array([[[[1.], [2]], [[1], [0]], [[1], [3]]],
[[[0.], [1]], [[1.], [-4]], [[1.], [-5]]],
[[[0.], [2]], [[0.], [-2]], [[2.], [0]]]])
weights = np.transpose(tf_weights, axes=(2, 3, 0, 1))
weights = np.flip(np.flip(weights, axis=2), axis=3)
bias = np.array([1.])
self.layer3x3m = ConvolutionalLayer(weights, bias)
# data
self.x = np.arange(20, dtype=np.float32).reshape((5, 4, 1))
def test_1x1(self):
out = self.layer1x1.activate(self.x)
self.assertEqual(out.shape, (5, 4, 2))
self.assertTrue(np.allclose(
out[..., 0], np.arange(20).reshape((5, 4))))
self.assertTrue(np.allclose(
out[..., 1], np.arange(20).reshape((5, 4)) / 2. + 2))
def test_1x1_pad_same(self):
self.layer1x1.pad = 'same'
out = self.layer1x1.activate(self.x)
self.assertEqual(out.shape, (5, 4, 2))
def test_2x2(self):
out = self.layer2x2.activate(self.x)
correct = np.array([[[9.5, 7.85], [12.5, 9.85], [15.5, 11.85]],
[[21.5, 15.85], [24.5, 17.85], [27.5, 19.85]],
[[33.5, 23.85], [36.5, 25.85], [39.5, 27.85]],
[[45.5, 31.85], [48.5, 33.85], [51.5, 35.85]]])
self.assertEqual(out.shape, (4, 3, 2))
self.assertTrue(np.allclose(out, correct))
self.assertTrue(np.allclose(
out[..., 0], np.arange(9.5, 57.5, 3).reshape((4, 4))[:, :-1]))
self.assertTrue(np.allclose(
out[..., 1], np.arange(7.85, 39.85, 2).reshape((4, 4))[:, :-1]))
def test_2x2_pad_same(self):
self.layer2x2.pad = 'same'
out = self.layer2x2.activate(self.x)
correct = np.array([[[9.5, 7.85], [12.5, 9.85],
[15.5, 11.85], [10.5, 7.85]],
[[21.5, 15.85], [24.5, 17.85],
[27.5, 19.85], [18.5, 12.85]],
[[33.5, 23.85], [36.5, 25.85],
[39.5, 27.85], [26.5, 17.85]],
[[45.5, 31.85], [48.5, 33.85],
[51.5, 35.85], [34.5, 22.85]],
[[16.5, 4.10], [17.5, 4.35],
[18.5, 4.60], [19.5, 4.85]]])
self.assertEqual(out.shape, (5, 4, 2))
self.assertTrue(np.allclose(out, correct))
def test_3x3(self):
out = self.layer3x3.activate(self.x)
correct = np.array([[[35], [42]], [[63], [70]], [[91], [98]]])
self.assertEqual(out.shape, (3, 2, 1))
self.assertTrue(np.allclose(out, correct))
def test_3x3_pad_same(self):
self.layer3x3.pad = 'same'
out = self.layer3x3.activate(self.x)
correct = np.array([[[[12], [16], [20], [4]],
[[29], [35], [42], [13]],
[[53], [63], [70], [25]],
[[77], [91], [98], [37]],
[[59], [75], [80], [49]]]])
self.assertEqual(out.shape, (5, 4, 1))
self.assertTrue(np.allclose(out, correct))
def test_3x3_multi_channel(self):
# padding 'valid'
x = np.arange(40, dtype=np.float32).reshape((5, 4, 2))
out = self.layer3x3m.activate(x)
correct = np.array([[[-18], [-10]], [[14], [22]], [[46], [54]]])
self.assertEqual(out.shape, (3, 2, 1))
self.assertTrue(np.allclose(out, correct))
# with padding 'same'
self.layer3x3m.pad = 'same'
out = self.layer3x3m.activate(x)
correct = np.array([[[-14], [-9], [-17], [-20]],
[[-59], [-18], [-10], [-16]],
[[-75], [14], [22], [0]],
[[-91], [46], [54], [16]],
[[-109], [-6], [-2], [36]]])
self.assertEqual(out.shape, (5, 4, 1))
self.assertTrue(np.allclose(out, correct))