def test_cnt_windows_iterator_oversample():
iterator = CntWindowsFromCntIterator(batch_size=3,
input_time_length=6,
n_sample_preds=4,
oversample_targets=True)
iterator.reset_rng()
y = np.append(np.zeros((16, 4)), np.ones((4, 4)), axis=0)
in_set = DenseDesignMatrixWrapper(topo_view=to_4d_time_array(
range(20)).swapaxes(2, 0),
y=y)
batches = list(iterator.get_batches(in_set, shuffle=True))
assert 3 == len(batches)
# Note that 14,20 is oversampled
assert np.array_equal(
batches[0][0].squeeze(), [range(
6, 12), range(10, 16), range(14, 20)])
assert np.array_equal(
batches[1][0].squeeze(),
[range(14, 20), range(14, 20),
range(14, 20)])
assert np.array_equal(
batches[2][0].squeeze(),
[[18, 19] + range(4), range(14, 20),
range(2, 8)])
def test_stride_reshape_layer_with_padding():
"""Testign with padding... actually should never be any problem, as
Stide reshape layer is independent of padding before..."""
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network = lasagne.layers.InputLayer(shape=[None,1,15,1], input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=1, pad='same', filter_size=[3, 1],
W=lasagne.init.Constant(1), stride=(1,1),
)
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
preds_cnt = lasagne.layers.get_output(lasagne.layers.get_all_layers(network)[1:])
pred_cnt_func = theano.function([input_var], preds_cnt)
layer_activations = pred_cnt_func(to_4d_time_array([range(1,16), range(16,31)]))
assert equal_without_nans(np.array([[[[ 3.], [ 6.], [ 9.], [ 12.], [ 15.], [ 18.], [ 21.],
[ 24.], [ 27.], [ 30.], [ 33.], [ 36.], [ 39.],
[ 42.], [ 29.]]],
[[[ 33.], [ 51.], [ 54.], [ 57.], [ 60.], [ 63.], [ 66.],
[ 69.], [ 72.], [75.], [ 78.], [ 81.], [ 84.],
[ 87.], [59. ]]]],
dtype=np.float32),
layer_activations[0])
assert equal_without_nans(np.array(
[[[[3.], [ 9.], [ 15.], [ 21.], [ 27.], [ 33.], [ 39.], [29.]]],
[[[33.], [ 54.], [ 60.], [ 66.], [ 72.], [ 78.], [ 84.], [59.]]],
[[[ 6.], [ 12.], [ 18.], [ 24.], [ 30.], [ 36.], [ 42.], [ np.nan]]],
[[[ 51.], [ 57.], [ 63.], [ 69.], [ 75.], [ 81.], [ 87.], [ np.nan]]]],
dtype=np.float32),
layer_activations[1])
def test_inverse_dilation_layer():
from convvisual.receptive_field.convvisual import receptive_field_build_deconv_layers
from braindecode.veganlasagne.layer_util import print_layers
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network1 = lasagne.layers.InputLayer(shape=[None, 1, 15, 1],
input_var=input_var)
network2 = StrideReshapeLayer(network1,
n_stride=2,
invalid_fill_value=np.nan)
network3 = StrideReshapeLayer(network2,
n_stride=2,
invalid_fill_value=np.nan)
network = FinalReshapeLayer(network3, remove_invalids=True, flatten=False)
print_layers(network)
#network = DilationSeparate2DLayer(network,(4,1),((0,1),(0,0)),fill_value=np.nan)
#network = DilationMerge2DLayer(network,(2,1),((0,0),(0,0)))
#network = DilationMerge2DLayer(network,(2,1),((0,1),(0,0)))
deconv_network = receptive_field_build_deconv_layers(network, network2)
print_layers(deconv_network)
input = to_4d_time_array([range(1, 16), range(16, 31)])
preds_cnt = lasagne.layers.get_output(
lasagne.layers.get_all_layers(network))
pred_cnt_func = theano.function([input_var], preds_cnt)
layer_activations = pred_cnt_func(input)
print layer_activations
layer_activations2 = lasagne.layers.get_output(
lasagne.layers.get_all_layers(deconv_network), layer_activations[-1])
print layer_activations[-1].shape
print layer_activations2[3].eval()
assert (np.array_equal(layer_activations2[3].eval(), layer_activations[0]))
def test_stride_reshape_layer_with_padding():
"""Testing with padding... actually should never be any problem, as
Stide reshape layer is independent of padding before..."""
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network = lasagne.layers.InputLayer(shape=[None,1,15,1], input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=1, pad='same', filter_size=[3, 1],
W=lasagne.init.Constant(1), stride=(1,1),
)
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
preds_cnt = lasagne.layers.get_output(lasagne.layers.get_all_layers(network)[1:])
pred_cnt_func = theano.function([input_var], preds_cnt)
layer_activations = pred_cnt_func(to_4d_time_array([range(1,16), range(16,31)]))
assert equal_without_nans(np.array([[[[ 3.], [ 6.], [ 9.], [ 12.], [ 15.], [ 18.], [ 21.],
[ 24.], [ 27.], [ 30.], [ 33.], [ 36.], [ 39.],
[ 42.], [ 29.]]],
[[[ 33.], [ 51.], [ 54.], [ 57.], [ 60.], [ 63.], [ 66.],
[ 69.], [ 72.], [75.], [ 78.], [ 81.], [ 84.],
[ 87.], [59. ]]]],
dtype=np.float32),
layer_activations[0])
assert equal_without_nans(np.array(
[[[[3.], [ 9.], [ 15.], [ 21.], [ 27.], [ 33.], [ 39.], [29.]]],
[[[33.], [ 54.], [ 60.], [ 66.], [ 72.], [ 78.], [ 84.], [59.]]],
[[[ 6.], [ 12.], [ 18.], [ 24.], [ 30.], [ 36.], [ 42.], [ np.nan]]],
[[[ 51.], [ 57.], [ 63.], [ 69.], [ 75.], [ 81.], [ 87.], [ np.nan]]]],
dtype=np.float32),
layer_activations[1])
def test_cnt_windows_iterator_oversample():
iterator = CntWindowsFromCntIterator(batch_size=3, input_time_length=6, n_sample_preds=4, oversample_targets=True)
iterator.reset_rng()
y = np.append(np.zeros((16,4)), np.ones((4,4)), axis=0)
in_set = DenseDesignMatrixWrapper(topo_view=to_4d_time_array(range(20)).swapaxes(2,0), y=y)
batches = list(iterator.get_batches(in_set, shuffle=True))
assert 3 == len(batches)
# Note that 14,20 is oversampled
assert np.array_equal(batches[0][0].squeeze(), [range(6,12), range(10,16), range(14,20)])
assert np.array_equal(batches[1][0].squeeze(), [range(14,20), range(14,20), range(14,20)])
assert np.array_equal(batches[2][0].squeeze(), [[18,19] + range(4), range(14,20), range(2,8)])
def test_cnt_windows_iterator_shuffle():
#Random Regression test, values should not change unless randomization changes...
iterator = CntWindowsFromCntIterator(batch_size=2, input_time_length=6, n_sample_preds=4)
iterator.reset_rng()
in_topo = to_4d_time_array(range(20)).swapaxes(2,0)
y = np.outer(range(20), np.ones(4))
in_set = DenseDesignMatrixWrapper(topo_view=in_topo, y=y)
batches = list(iterator.get_batches(in_set, shuffle=True))
assert 2 == len(batches)
assert np.array_equal([range(6,12), [18,19] + range(4), range(10,16)], batches[0][0].squeeze())
assert np.array_equal(np.outer([8,9,10,11,0,1,2,3,12,13,14,15], np.ones(4)), batches[0][1])
assert np.array_equal([range(14,20), range(2,8)], batches[1][0].squeeze())
assert np.array_equal(np.outer([16,17,18,19,4,5,6,7], np.ones(4)), batches[1][1])
def test_cnt_windows_iterator():
iterator = CntWindowsFromCntIterator(batch_size=2, input_time_length=6,
n_sample_preds=4)
in_topo = to_4d_time_array(range(20)).swapaxes(2,0)
y = np.outer(range(20), np.ones(4))
in_set = DenseDesignMatrixWrapper(topo_view=in_topo, y=y)
batches = list(iterator.get_batches(in_set, shuffle=False))
assert 2 == len(batches)
# we have two lost samples so expect wraparound from back
assert np.array_equal([[18,19] + range(4), range(2,8), range(6,12)], batches[0][0].squeeze())
assert np.array_equal(np.outer([0,1,2,3,4,5,6,7,8,9,10,11], np.ones(4)), batches[0][1])
assert np.array_equal([range(10,16), range(14,20)], batches[1][0].squeeze())
assert np.array_equal(np.outer([12,13,14,15,16,17,18,19], np.ones(4)), batches[1][1])
def test_cnt_windows_iterator_shuffle():
#Random Regression test, values should not change unless randomization changes...
iterator = CntWindowsFromCntIterator(batch_size=2,
input_time_length=6,
n_sample_preds=4)
iterator.reset_rng()
in_topo = to_4d_time_array(range(20)).swapaxes(2, 0)
y = np.outer(range(20), np.ones(4))
in_set = DenseDesignMatrixWrapper(topo_view=in_topo, y=y)
batches = list(iterator.get_batches(in_set, shuffle=True))
assert 2 == len(batches)
assert np.array_equal([range(6, 12), [18, 19] + range(4),
range(10, 16)], batches[0][0].squeeze())
assert np.array_equal(
np.outer([8, 9, 10, 11, 0, 1, 2, 3, 12, 13, 14, 15], np.ones(4)),
batches[0][1])
assert np.array_equal([range(14, 20), range(2, 8)],
batches[1][0].squeeze())
assert np.array_equal(np.outer([16, 17, 18, 19, 4, 5, 6, 7], np.ones(4)),
batches[1][1])
def test_cnt_windows_iterator():
iterator = CntWindowsFromCntIterator(batch_size=2,
input_time_length=6,
n_sample_preds=4)
in_topo = to_4d_time_array(range(20)).swapaxes(2, 0)
y = np.outer(range(20), np.ones(4))
in_set = DenseDesignMatrixWrapper(topo_view=in_topo, y=y)
batches = list(iterator.get_batches(in_set, shuffle=False))
assert 2 == len(batches)
# we have two lost samples so expect wraparound from back
assert np.array_equal(
[[18, 19] + range(4), range(2, 8),
range(6, 12)], batches[0][0].squeeze())
assert np.array_equal(
np.outer([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], np.ones(4)),
batches[0][1])
assert np.array_equal([range(10, 16), range(14, 20)],
batches[1][0].squeeze())
assert np.array_equal(
np.outer([12, 13, 14, 15, 16, 17, 18, 19], np.ones(4)), batches[1][1])
def test_stride_reshape_layer():
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network = lasagne.layers.InputLayer(shape=[None,1,15,1], input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[3, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[2, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=4, filter_size=[2, 1],
W=to_4d_time_array([[1,1], [-1,-1], [0.1,0.1], [-0.1,-0.1]]), stride=(1,1),
nonlinearity=lasagne.nonlinearities.identity)
network = FinalReshapeLayer(network, remove_invalids=False)
preds_cnt = lasagne.layers.get_output(lasagne.layers.get_all_layers(network)[1:])
pred_cnt_func = theano.function([input_var], preds_cnt)
layer_activations = pred_cnt_func(to_4d_time_array([range(1,16), range(16,31)]))
assert equal_without_nans(np.array([[[[ 6.], [ 9.], [ 12.], [ 15.], [ 18.], [ 21.],
[ 24.], [ 27.], [ 30.], [ 33.], [ 36.], [ 39.],
[ 42.]]],
[[[ 51.], [ 54.], [ 57.], [ 60.], [ 63.], [ 66.],
[ 69.], [ 72.], [75.], [ 78.], [ 81.], [ 84.],
[ 87.]]]],
dtype=np.float32),
layer_activations[0])
assert equal_without_nans(np.array(
[[[[ 6.], [ 12.], [ 18.], [ 24.], [ 30.], [ 36.], [ 42.]]],
[[[ 51.], [ 57.], [ 63.], [ 69.], [ 75.], [ 81.], [ 87.]]],
[[[ 9.], [ 15.], [ 21.], [ 27.], [ 33.], [ 39.], [ np.nan]]],
[[[ 54.], [ 60.], [ 66.], [ 72.], [ 78.], [ 84.], [ np.nan]]]],
dtype=np.float32),
layer_activations[1])
assert equal_without_nans(np.array([[[[ 18.], [ 30.], [ 42.], [ 54.], [ 66.], [ 78.]]],
[[[ 108.], [ 120.], [ 132.], [ 144.], [ 156.], [ 168.]]],
[[[ 24.], [ 36.], [ 48.], [ 60.], [ 72.], [ np.nan]]],
[[[ 114.], [ 126.], [ 138.], [ 150.], [ 162.], [ np.nan]]]],
dtype=np.float32),
layer_activations[2])
assert equal_without_nans(np.array([[[[ 18.], [ 42.], [ 66.]]],
[[[ 108.], [ 132.], [ 156.]]],
[[[ 24.], [ 48.], [ 72.]]],
[[[ 114.], [ 138.], [ 162.]]],
[[[ 30.], [ 54.], [ 78.]]],
[[[ 120.], [ 144.], [ 168.]]],
[[[ 36.], [ 60.], [ np.nan]]],
[[[ 126.], [ 150.], [ np.nan]]]],
dtype=np.float32),
layer_activations[3])
assert allclose_without_nans(np.array(
[[[[ 60. ], [ 108. ]], [[ -60. ], [-108. ]],
[[ 6.00000048], [ 10.80000019]], [[ -6.00000048], [ -10.80000019]]],
[[[ 240. ], [ 288. ]], [[-240. ], [-288. ]],
[[ 24. ], [ 28.80000114]], [[ -24. ], [ -28.80000114]]],
[[[ 72. ], [ 120. ]], [[ -72. ], [-120. ]],
[[ 7.20000029], [ 12. ]], [[ -7.20000029], [ -12. ]]],
[[[ 252. ], [ 300. ]], [[-252. ], [-300. ]],
[[ 25.20000076], [ 30.00000191]], [[ -25.20000076], [ -30.00000191]]],
[[[ 84. ], [ 132. ]], [[ -84. ], [-132. ]],
[[ 8.40000057], [ 13.19999981]], [[ -8.40000057], [ -13.19999981]]],
[[[ 264. ], [ 312. ]], [[-264. ], [-312. ]],
[[ 26.40000153], [ 31.20000076]], [[ -26.40000153], [ -31.20000076]]],
[[[ 96. ], [ np.nan]], [[ -96. ], [ np.nan]],
[[ 9.60000038], [ np.nan]], [[ -9.60000038], [ np.nan]]],
[[[ 276. ], [ np.nan]], [[-276. ], [ np.nan]],
[[ 27.60000038], [ np.nan]], [[ -27.60000038], [ np.nan]]]],
dtype=np.float32),
layer_activations[4])
assert allclose_without_nans(np.array(
[[ 60. , -60. , 6.0, -6.],
[ 72. , -72. , 7.2, -7.2],
[ 84. , -84. , 8.4, -8.4],
[ 96. , -96. , 9.6, -9.6],
[ 108. , -108. , 10.8, -10.8],
[ 120. , -120. , 12. , -12. ],
[ 132. , -132. , 13.2, -13.2],
[ np.nan, np.nan, np.nan, np.nan],
[ 240. , -240. , 24. , -24. ],
[ 252. , -252. , 25.2, -25.2],
[ 264. , -264. , 26.4, -26.4],
[ 276. , -276. , 27.6, -27.6],
[ 288. , -288. , 28.8, -28.8],
[ 300. , -300. , 30.0, -30.0],
[ 312. , -312. , 31.2, -31.2],
[ np.nan, np.nan, np.nan, np.nan]],
dtype=np.float32),
layer_activations[5])
def test_stride_reshape_layer():
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network = lasagne.layers.InputLayer(shape=[None,1,15,1], input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[3, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[2, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=4, filter_size=[2, 1],
W=to_4d_time_array([[1,1], [-1,-1], [0.1,0.1], [-0.1,-0.1]]), stride=(1,1),
nonlinearity=lasagne.nonlinearities.identity)
network = FinalReshapeLayer(network, remove_invalids=False)
preds_cnt = lasagne.layers.get_output(lasagne.layers.get_all_layers(network)[1:])
pred_cnt_func = theano.function([input_var], preds_cnt)
layer_activations = pred_cnt_func(to_4d_time_array([range(1,16), range(16,31)]))
assert equal_without_nans(np.array([[[[ 6.], [ 9.], [ 12.], [ 15.], [ 18.], [ 21.],
[ 24.], [ 27.], [ 30.], [ 33.], [ 36.], [ 39.],
[ 42.]]],
[[[ 51.], [ 54.], [ 57.], [ 60.], [ 63.], [ 66.],
[ 69.], [ 72.], [75.], [ 78.], [ 81.], [ 84.],
[ 87.]]]],
dtype=np.float32),
layer_activations[0])
assert equal_without_nans(np.array(
[[[[ 6.], [ 12.], [ 18.], [ 24.], [ 30.], [ 36.], [ 42.]]],
[[[ 51.], [ 57.], [ 63.], [ 69.], [ 75.], [ 81.], [ 87.]]],
[[[ 9.], [ 15.], [ 21.], [ 27.], [ 33.], [ 39.], [ np.nan]]],
[[[ 54.], [ 60.], [ 66.], [ 72.], [ 78.], [ 84.], [ np.nan]]]],
dtype=np.float32),
layer_activations[1])
assert equal_without_nans(np.array([[[[ 18.], [ 30.], [ 42.], [ 54.], [ 66.], [ 78.]]],
[[[ 108.], [ 120.], [ 132.], [ 144.], [ 156.], [ 168.]]],
[[[ 24.], [ 36.], [ 48.], [ 60.], [ 72.], [ np.nan]]],
[[[ 114.], [ 126.], [ 138.], [ 150.], [ 162.], [ np.nan]]]],
dtype=np.float32),
layer_activations[2])
assert equal_without_nans(np.array([[[[ 18.], [ 42.], [ 66.]]],
[[[ 108.], [ 132.], [ 156.]]],
[[[ 24.], [ 48.], [ 72.]]],
[[[ 114.], [ 138.], [ 162.]]],
[[[ 30.], [ 54.], [ 78.]]],
[[[ 120.], [ 144.], [ 168.]]],
[[[ 36.], [ 60.], [ np.nan]]],
[[[ 126.], [ 150.], [ np.nan]]]],
dtype=np.float32),
layer_activations[3])
assert allclose_without_nans(np.array(
[[[[ 60. ], [ 108. ]], [[ -60. ], [-108. ]],
[[ 6.00000048], [ 10.80000019]], [[ -6.00000048], [ -10.80000019]]],
[[[ 240. ], [ 288. ]], [[-240. ], [-288. ]],
[[ 24. ], [ 28.80000114]], [[ -24. ], [ -28.80000114]]],
[[[ 72. ], [ 120. ]], [[ -72. ], [-120. ]],
[[ 7.20000029], [ 12. ]], [[ -7.20000029], [ -12. ]]],
[[[ 252. ], [ 300. ]], [[-252. ], [-300. ]],
[[ 25.20000076], [ 30.00000191]], [[ -25.20000076], [ -30.00000191]]],
[[[ 84. ], [ 132. ]], [[ -84. ], [-132. ]],
[[ 8.40000057], [ 13.19999981]], [[ -8.40000057], [ -13.19999981]]],
[[[ 264. ], [ 312. ]], [[-264. ], [-312. ]],
[[ 26.40000153], [ 31.20000076]], [[ -26.40000153], [ -31.20000076]]],
[[[ 96. ], [ np.nan]], [[ -96. ], [ np.nan]],
[[ 9.60000038], [ np.nan]], [[ -9.60000038], [ np.nan]]],
[[[ 276. ], [ np.nan]], [[-276. ], [ np.nan]],
[[ 27.60000038], [ np.nan]], [[ -27.60000038], [ np.nan]]]],
dtype=np.float32),
layer_activations[4])
assert allclose_without_nans(np.array(
[[ 60. , -60. , 6.0, -6.],
[ 72. , -72. , 7.2, -7.2],
[ 84. , -84. , 8.4, -8.4],
[ 96. , -96. , 9.6, -9.6],
[ 108. , -108. , 10.8, -10.8],
[ 120. , -120. , 12. , -12. ],
[ 132. , -132. , 13.2, -13.2],
[ np.nan, np.nan, np.nan, np.nan],
[ 240. , -240. , 24. , -24. ],
[ 252. , -252. , 25.2, -25.2],
[ 264. , -264. , 26.4, -26.4],
[ 276. , -276. , 27.6, -27.6],
[ 288. , -288. , 28.8, -28.8],
[ 300. , -300. , 30.0, -30.0],
[ 312. , -312. , 31.2, -31.2],
[ np.nan, np.nan, np.nan, np.nan]],
dtype=np.float32),
layer_activations[5])
def test_stride_reshape_layer_with_nonempty_3rd_dim():
input_var = T.tensor4('inputs').astype(theano.config.floatX)
network = lasagne.layers.InputLayer(shape=[None,1,15,2], input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[3, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=1,filter_size=[2, 1],
W=lasagne.init.Constant(1), stride=(1,1))
network = StrideReshapeLayer(network, n_stride=2, invalid_fill_value=np.nan)
network = lasagne.layers.Conv2DLayer(network, num_filters=4, filter_size=[2, 1],
W=to_4d_time_array([[1,1], [-1,-1], [0.1,0.1], [-0.1,-0.1]]), stride=(1,1),
nonlinearity=lasagne.nonlinearities.identity)
network = lasagne.layers.Conv2DLayer(network, num_filters=3, filter_size=[1, 2],
W=np.array([[[[1,1]],[[0.5,0.5]], [[1,1]], [[0.5,0.5]]],
[[[-1,-1]],[[-0.5,-0.5]], [[-1,-1]], [[0,0]]],
[[[0,0]],[[0,0]], [[-1,1]], [[0,0]]]], dtype=np.float32), stride=(1,1),
nonlinearity=lasagne.nonlinearities.identity)
network = FinalReshapeLayer(network, remove_invalids=False)
preds_cnt = lasagne.layers.get_output(lasagne.layers.get_all_layers(network)[1:])
pred_cnt_func = theano.function([input_var], preds_cnt)
inputs= np.array([[range(1,16), range(101,116)],
[range(16,31), range(116,131)]]).astype(np.float32)
inputs = inputs.swapaxes(1,2)[:,np.newaxis,:,:]
layer_activations = pred_cnt_func(inputs)
assert equal_without_nans(np.array([[[[ 6., 306], [ 9., 309], [ 12., 312], [ 15.,315], [ 18.,318], [ 21.,321],
[ 24.,324], [ 27.,327], [ 30.,330], [ 33.,333], [ 36.,336], [ 39.,339],
[ 42.,342]]],
[[[ 51.,351], [ 54.,354], [ 57.,357], [ 60.,360], [ 63.,363], [ 66.,366],
[ 69.,369], [ 72.,372], [75.,375], [ 78.,378], [ 81.,381], [ 84.,384],
[ 87.,387]]]],
dtype=np.float32),
layer_activations[0])
assert equal_without_nans(np.array(
[[[[ 6., 306], [ 12.,312], [ 18.,318], [ 24.,324], [ 30.,330], [ 36.,336], [ 42.,342]]],
[[[ 51., 351], [ 57., 357], [ 63., 363], [ 69., 369], [ 75., 375], [ 81., 381], [ 87., 387]]],
[[[ 9., 309], [ 15., 315], [ 21., 321], [ 27., 327], [ 33., 333], [ 39., 339], [ np.nan, np.nan]]],
[[[ 54., 354], [ 60., 360], [ 66., 366], [ 72., 372], [ 78., 378], [ 84., 384], [ np.nan, np.nan]]]],
dtype=np.float32),
layer_activations[1])
assert equal_without_nans(np.array([[[[ 18.,618.], [ 30., 630], [ 42., 642], [ 54., 654], [ 66., 666], [ 78., 678]]],
[[[ 108., 708], [ 120., 720], [ 132., 732], [ 144., 744], [ 156., 756], [ 168., 768]]],
[[[ 24., 624], [ 36., 636], [ 48., 648], [ 60., 660], [ 72., 672], [ np.nan, np.nan]]],
[[[ 114., 714], [ 126., 726], [ 138., 738], [ 150., 750], [ 162., 762], [ np.nan, np.nan]]]],
dtype=np.float32),
layer_activations[2])
assert equal_without_nans(np.array([[[[ 18., 618], [ 42.,642], [ 66.,666]]],
[[[ 108.,708], [ 132.,732], [ 156.,756]]],
[[[ 24.,624], [ 48.,648], [ 72.,672]]],
[[[ 114.,714], [ 138.,738], [ 162.,762]]],
[[[ 30.,630], [ 54.,654], [ 78.,678]]],
[[[ 120.,720], [ 144.,744], [ 168.,768]]],
[[[ 36.,636], [ 60.,660], [ np.nan, np.nan]]],
[[[ 126., 726], [ 150., 750], [ np.nan, np.nan]]]],
dtype=np.float32),
layer_activations[3])
assert allclose_without_nans(np.array(
[[[[ 60. ,1260], [ 108. , 1308]], [[ -60. , -1260], [-108. ,-1308]],
[[ 6.00000048, 126], [ 10.80000019, 130.80000305]], [[ -6.00000048, -126], [ -10.80000019, -130.80000305]]],
[[[ 240. , 1440. ], [ 288. , 1488. ]], [[ -240. , -1440. ],
[ -288. , -1488. ]], [[ 24. , 144. ], [ 28.79999924, 148.80000305]],
[[ -24. , -144. ], [ -28.79999924, -148.80000305]]],
[[[ 72. , 1272. ], [ 120. , 1320. ]], [[ -72. , -1272. ],
[ -120. , -1320. ]], [[ 7.20000029, 127.20000458], [ 12. , 132. ]],
[[ -7.20000029, -127.20000458], [ -12. , -132. ]]],
[[[ 252. , 1452. ], [ 300. , 1500. ]], [[ -252. , -1452. ],
[ -300. , -1500. ]], [[ 25.20000076, 145.19999695], [ 30. , 150. ]],
[[ -25.20000076, -145.19999695], [ -30. , -150. ]]],
[[[ 84. , 1284. ], [ 132. , 1332. ]], [[ -84. , -1284. ],
[ -132. , -1332. ]], [[ 8.39999962, 128.3999939 ], [ 13.19999981, 133.19999695]],
[[ -8.39999962, -128.3999939 ], [ -13.19999981, -133.19999695]]],
[[[ 264. , 1464. ], [ 312. , 1512. ]], [[ -264. , -1464. ],
[ -312. , -1512. ]], [[ 26.39999962, 146.3999939 ], [ 31.20000076, 151.19999695]],
[[ -26.39999962, -146.3999939 ], [ -31.20000076, -151.19999695]]],
[[[ 96. , 1296. ], [np.nan, np.nan]], [[ -96. , -1296. ],
[np.nan, np.nan]],
[[ 9.60000038, 129.6000061 ], [np.nan, np.nan]], [[ -9.60000038, -129.6000061 ],
[np.nan, np.nan]]],
[[[ 276. , 1476. ], [np.nan, np.nan]],
[[ -276. , -1476. ], [np.nan, np.nan]],
[[ 27.60000038, 147.6000061 ], [np.nan, np.nan]],
[[ -27.60000038, -147.6000061 ], [np.nan, np.nan]]]], dtype=np.float32),
layer_activations[4])
expected_5 = [np.sum(layer_activations[4][:,[0,2]] * 1 + layer_activations[4][:,[1,3]] * 0.5, axis=(1,3)),
np.sum(layer_activations[4][:,[0,2]] * (-1), axis=(1,3)) +
np.sum(layer_activations[4][:,[1]] * (-0.5), axis=(1,3)),
layer_activations[4][:,2,:,0] - layer_activations[4][:,2,:,1]]
expected_5 = np.array(expected_5).swapaxes(0,1)[:,:,:,np.newaxis]
assert allclose_without_nans(expected_5, layer_activations[5])
assert allclose_without_nans(layer_activations[6][:,0],
np.concatenate((np.linspace(726,805.2, 7), [np.nan], np.linspace(924, 1003.2, 7), [np.nan])))
assert allclose_without_nans(layer_activations[6][:,1],
np.concatenate((np.linspace(-792,-878.4, 7), [np.nan], np.linspace(-1008, -1094.4, 7), [np.nan])))
assert allclose_without_nans(layer_activations[6][:,2],
np.concatenate(([-120] * 7, [np.nan], [-120] * 7, [np.nan],)))
for elem in layer_activations[6].flatten():
assert np.isnan(elem) or elem in layer_activations[5]
assert np.sum(np.isnan(layer_activations[5])) == np.sum(np.isnan(layer_activations[6]))
