def test_fwd_same_as_numpy_3D3D_memsave(self):
a = Variable(torch.FloatTensor(np.random.random((10, 5, 20))))
b = Variable(torch.FloatTensor(np.random.random((10, 4, 20))))
dist = ForwardDistance(20, 20, 10, activation=None, use_bias=False)
dist.memsave = True
d = dist(a, b).data.numpy()
lw, rw, aggw = dist.lblock.weight.data.numpy(
), dist.rblock.weight.data.numpy(), dist.agg.data.numpy()
a = a.data.numpy()
b = b.data.numpy()
npd = np.zeros((10, 5, 4))
for i in range(a.shape[0]):
for j in range(a.shape[1]):
for k in range(b.shape[1]):
x = np.dot(np.dot(lw, a[i, j]) + np.dot(rw, b[i, k]), aggw)
npd[i, j, k] = x
print(np.argwhere(abs(d - npd) > 1e-8))
#print(d[np.argwhere(abs(d - npd) > 1e-8)])
#print(npd[np.argwhere(abs(d - npd) > 1e-8)])
print(np.argwhere(abs(d - npd) > 1e-7))
print(np.argwhere(abs(d - npd) > 1e-6))
print(np.argwhere(abs(d - npd) > 1e-5))
print(d[1])
print(npd[1])
print((d - npd)[1])
self.assertTrue(np.allclose(d, npd, atol=1e-6))
def test_all_dist_shape_3D3D(self):
self.m = DotDistance()
self.dorun_shape_tst_3D3D()
self.m = CosineDistance()
self.dorun_shape_tst_3D3D()
self.m = ForwardDistance(4, 4, 8)
self.dorun_shape_tst_3D3D()
self.m = BilinearDistance(4, 4)
self.dorun_shape_tst_3D3D()
self.m = TrilinearDistance(4, 4, 8)
self.dorun_shape_tst_3D3D()
def forward_gen(self,
ldim,
rdim,
aggdim,
activation="tanh",
use_bias=True):
self.attgen.dist = ForwardDistance(ldim,
rdim,
aggdim,
activation=activation,
use_bias=use_bias)
return self
def test_fwd_same_as_numpy_2D2D(self):
a = Variable(torch.FloatTensor(np.random.random((100, 200))))
b = Variable(torch.FloatTensor(np.random.random((100, 200))))
dist = ForwardDistance(200, 200, 100, activation=None, use_bias=False)
d = dist(a, b).data.numpy()
lw, rw, aggw = dist.lblock.weight.data.numpy(
), dist.rblock.weight.data.numpy(), dist.agg.data.numpy()
a = a.data.numpy()
b = b.data.numpy()
npd = np.zeros((100, ))
for i in range(a.shape[0]):
x = np.dot(np.dot(lw, a[i]) + np.dot(rw, b[i]), aggw)
npd[i] = x
self.assertTrue(np.allclose(d, npd, atol=1e-6))