def test_max_pool_masked(self):
xval = np.random.random((100, 20, 50)).astype("float32")
maskid = np.random.randint(1, 18, (100, ))
mask = np.ones((xval.shape[:2]))
for i in range(mask.shape[0]):
mask[i, maskid[i]:] = 0
#xval[:, :, -1] = 100
x = Val(xval)
x.mask = Val(mask)
pool = GlobalPool1D(mode="max")
pred = pool(x)
predval = pred.eval()
xval = xval - 1e9 * np.tensordot(1 - mask, np.ones(
(xval.shape[-1], )), 0)
predvalexp = np.max(xval, axis=1)
self.assertTrue(np.allclose(predval, predvalexp))
def test_output_mask_strided(self):
xval = np.random.random((100, 20, 50)).astype("float32")
maskid = np.random.randint(3, 20, (100, ))
mask = np.ones((xval.shape[:2]))
for i in range(mask.shape[0]):
mask[i, maskid[i]:] = 0
conv = Conv1D(indim=50,
outdim=40,
window=5,
stride=4,
border_mode="valid")
x = Val(xval)
x.mask = Val(mask)
pred = conv(x)
predmask = pred.mask
print predmask.eval().shape
print predmask.eval()[:5]
print mask[:5]
def test_output_shape_masked(self):
xval = np.random.random((100, 20, 50)).astype("float32")
maskid = np.random.randint(3, 20, (100, ))
mask = np.ones((xval.shape[:2]))
for i in range(mask.shape[0]):
mask[i, maskid[i]:] = 0
conv = Conv1D(indim=50, outdim=40, window=5)
x = Val(xval)
x.mask = Val(mask)
pred = conv(x)
predmask = pred.mask
predval = pred.eval()
predvalmask = (predval != 0.0) * 1
predvalexpmask = np.ones_like(predvalmask)
for i in range(predvalexpmask.shape[0]):
predvalexpmask[i,
min(maskid[i] + 2, predvalexpmask.shape[1]):, :] = 0
self.assertTrue(np.sum(predvalexpmask - predvalmask) == 0)
self.assertEqual(predval.shape[:2], xval.shape[:2])
self.assertEqual(predval.shape[2], 40)
