def test_neg_output(self):
inpl = theano_var([[[[1, 1], [1, 1]]]])
inpu = theano_var([[[[2, 2], [2, 2]]]])
iinp = TheanoInterval(inpl, inpu)
idout = theano_interval([[[[-3]]]])
shp = (1, 1, 2, 2)
din = d_pool(idout, iinp, shp, poolsize=(2, 2), mode='max')
l, u = din.eval()
array_almost_equal(l, A([[[[-3, -3], [-3, -3]]]]))
array_almost_equal(u, A([[[[0, 0], [0, 0]]]]))
def test_channels_batch(self):
inpl = theano_var([[
[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [3, 0, 0]]
],
[
[[0, 3, 3], [4, 5, 6], [7, 8, 4]],
[[-3, -3, -3], [-3, -3, -3], [3, 3, 3]]
]])
inpu = theano_var([[
[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1], [4, 1, 1]]
],
[
[[2, 4, 4], [9, 9, 9], [9, 9, 9]],
[[2, 2, 2], [2, 2, 2], [5, 5, 5]]
]])
iinp = TheanoInterval(inpl, inpu)
doutl = theano_var([[
[[-1, -2], [-3, -4]],
[[1, 2], [-3, -2]]
],
[
[[1, 2], [-3, -2]],
[[-1, 1], [-1, 1]]
]])
doutu = theano_var([[
[[5, 4], [3, 2]],
[[4, 4], [4, 4]],
],
[
[[4, 5], [0, 1]],
[[0, 2], [0, 2]]
]])
idout = TheanoInterval(doutl, doutu)
shp = (2, 2, 3, 3)
din = d_pool(idout, iinp, shp, poolsize=(2, 2), mode='avg')
l, u = din.eval()
array_almost_equal(l, A([[
[[-1, -3, -2], [-4, -10, -6], [-3, -7, -4]],
[[1, 3, 2], [-2, -2, 0], [-3, -5, -2]]
],
[
[[1, 3, 2], [-2, -2, 0], [-3, -5, -2]],
[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
]]) / 4.0)
array_almost_equal(u, A([[
[[5, 9, 4], [8, 14, 6], [3, 5, 2]],
[[4, 8, 4], [8, 16, 8], [4, 8, 4]]
],
[
[[4, 9, 5], [4, 10, 6], [0, 1, 1]],
[[0, 2, 2], [0, 4, 4], [0, 2, 2]]
]]) / 4.0)
def test_simple(self):
inpl = theano_var([[[[0, 0, 0], [0, 0, 0], [0, 0, 0]]]])
inpu = theano_var([[[[1, 1, 1], [1, 1, 1], [1, 1, 1]]]])
iinp = TheanoInterval(inpl, inpu)
doutl = theano_var([[[[-1, -2], [-3, -4]]]])
doutu = theano_var([[[[5, 4], [3, 2]]]])
idout = TheanoInterval(doutl, doutu)
shp = (1, 1, 3, 3)
din = d_pool(idout, iinp, shp, poolsize=(2, 2), mode='avg')
l, u = din.eval()
array_almost_equal(l, A([[[[-1, -3, -2], [-4, -10, -6],
[-3, -7, -4]]]]) / 4.0)
array_almost_equal(u, A([[[[5, 9, 4], [8, 14, 6], [3, 5, 2]]]]) / 4.0)
def test_padding(self):
inpl = theano_var([[[[0, 0, 0], [0, 0, 0], [0, 0, 0]]]])
inpu = theano_var([[[[1, 1, 1], [1, 1, 1], [1, 1, 1]]]])
iinp = TheanoInterval(inpl, inpu)
doutl = theano_var([[[[-1, -2], [-3, -4]]]])
doutu = theano_var([[[[5, 4], [3, 2]]]])
idout = TheanoInterval(doutl, doutu)
shp = (1, 1, 3, 3)
din = d_pool(idout, iinp, shp, poolsize=(2, 2), padding=(1, 1),
stride=(3, 3), mode='max')
l, u = din.eval()
array_almost_equal(l, A([[[[-1, 0, -2], [0, 0, 0], [-3, 0, -4]]]]))
array_almost_equal(u, A([[[[5, 0, 4], [0, 0, 0], [3, 0, 2]]]]))
def test_stride(self):
tinpl = theano.shared(np.arange(25).reshape((1, 1, 5, 5)))
tinpu = theano.shared(np.arange(25).reshape((1, 1, 5, 5)) + 2)
iinp = TheanoInterval(tinpl, tinpu)
idout = theano_interval([[[[-1, 2], [-3, 4]]]])
shp = (1, 1, 5, 5)
din = d_pool(idout, iinp, shp, poolsize=(2, 2), stride=(3, 3),
mode='avg')
l, u = din.eval()
array_almost_equal(l, A([[[[-1, -1, 0, 2, 2],
[-1, -1, 0, 2, 2],
[0, 0, 0, 0, 0],
[-3, -3, 0, 4, 4],
[-3, -3, 0, 4, 4]]]]) / 4.0)
array_almost_equal(u, A([[[[-1, -1, 0, 2, 2],
[-1, -1, 0, 2, 2],
[0, 0, 0, 0, 0],
[-3, -3, 0, 4, 4],
[-3, -3, 0, 4, 4]]]]) / 4.0)
