def test_grad(self):
x = T.vector('x')
a = np.random.random(50).astype(config.floatX)
theano.function([x], T.grad(T.sum(diff(x)), x))
utt.verify_grad(self.op, [a])
for k in range(TestDiffOp.nb):
theano.function([x], T.grad(T.sum(diff(x, n=k)), x))
utt.verify_grad(DiffOp(n=k), [a], eps=7e-3)
def test_grad(self):
x = T.vector('x')
a = np.random.random(50).astype(config.floatX)
theano.function([x], T.grad(T.sum(diff(x)), x))
utt.verify_grad(self.op, [a])
for k in range(TestDiffOp.nb):
theano.function([x], T.grad(T.sum(diff(x, n=k)), x))
utt.verify_grad(DiffOp(n=k), [a], eps=7e-3)
def test_diffOp(self):
x = T.matrix('x')
a = np.random.random((30, 50)).astype(config.floatX)
f = theano.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
g = theano.function([x], diff(x, n=k, axis=axis))
assert np.allclose(np.diff(a, n=k, axis=axis), g(a))
def test_diffOp(self):
x = T.matrix('x')
a = np.random.random((30, 50)).astype(config.floatX)
f = theano.function([x], diff(x))
assert np.allclose(np.diff(a), f(a))
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
g = theano.function([x], diff(x, n=k, axis=axis))
assert np.allclose(np.diff(a, n=k, axis=axis), g(a))
def toChw(self, position):
samples, targetDim = K.shape(position)
position = K.reshape(position, (samples, 2, 2))
centroid = K.sum(position, axis=1) / 2.0
hw = K.abs(THEO.diff(position, axis=1)[:, 0, :])
chw = K.concatenate((centroid, hw), axis=1)
chw = chw[:, [0, 1, 3, 2]] # Changing from cwh to chw
return chw
def test_infer_shape(self):
x = T.matrix("x")
a = np.random.random((30, 50)).astype(config.floatX)
self._compile_and_check([x], [self.op(x)], [a], self.op_class)
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
self._compile_and_check([x], [diff(x, n=k, axis=axis)], [a], self.op_class)
def toChw(self, position):
samples, targetDim = K.shape(position)
position = K.reshape(position, (samples, 2, 2))
centroid = K.sum(position, axis=1) / 2.0
hw = K.abs(THEO.diff(position, axis=1)[:,0,:])
chw = K.concatenate((centroid, hw), axis=1)
chw = chw[:, [0, 1, 3, 2]] # Changing from cwh to chw
return chw
def test_infer_shape(self):
x = T.matrix('x')
a = np.random.random((30, 50)).astype(config.floatX)
self._compile_and_check([x], [self.op(x)], [a], self.op_class)
for axis in range(len(a.shape)):
for k in range(TestDiffOp.nb):
self._compile_and_check([x], [diff(x, n=k, axis=axis)], [a],
self.op_class)