def test_complex_graph(self):
# easier debugging, start with small dimensions
x = Variable((5, 5))
cx = conv_nofft(
np.array([[1, 1, 1]]) / 3,
conv_nofft(np.array([[1], [1], [1]]) / 3, x))
scx = subsample(cx, (2, 2))
ed = scx - np.reshape(np.arange(3 * 3), (3, 3))
w = Variable(x.shape + (2, ))
gw = grad(w, 2)
Ew = gw + transpose(gw, (0, 1, 3, 2))
gx = grad(x, 2)
tgx = pxwise_matrixmult(
np.reshape(np.arange(5 * 5 * 2 * 2), (5, 5, 2, 2)), gx)
e1 = tgx - w
inshape = (5 * 5 + 5 * 5 * 2, )
outshape = (3 * 3 + 5 * 5 * 2 * 2 + 5 * 5 * 2, )
self._generic_check_adjoint(lambda x: (ed, e1, Ew),
inshape,
outshape,
"complex",
eps=5e-4)
# continue with more values
K1 = np.abs(random.rand(1, 5, 1))
K2 = np.abs(random.rand(5, 1, 1))
x = Variable((320, 240, 2))
cx = conv_nofft(K1, conv_nofft(K2, x))
scx = subsample(cx, (5, 5, 1))
ed = scx - random.rand(64, 48, 2)
w = Variable(x.shape + (2, ))
gw = grad(w, 2)
Ew = gw + transpose(gw, (0, 1, 2, 4, 3))
gx = grad(x, 2)
tgx = pxwise_matrixmult(random.rand(320, 240, 2, 2, 2), gx)
e1 = tgx - w
inshape = (320 * 240 * 2 + 320 * 240 * 2 * 2, )
outshape = (64 * 48 * 2 + 320 * 240 * 2 * 2 * 2 + 320 * 240 * 2 * 2, )
self._generic_check_adjoint(lambda x: (ed, e1, Ew),
inshape,
outshape,
"complex2",
eps=5e-4)
def test_group_norm1(self):
random.seed(0)
x = Variable((10, 10, 2, 3))
f = group_norm1(x, [2, 3])
v = np.reshape(np.arange(10 * 10 * 2 * 3),
(10, 10, 2, 3)).astype(np.float32)
xhat1 = f.prox(1, v.copy())
xhat2 = f.prox_cuda(1, v.copy()).get()
if not np.all(np.abs(xhat1 - xhat2) < 1e-4):
logging.error(f.cuda_code)
logging.error("failed: %f" % np.amax(np.abs(xhat1 - xhat2)))
self.assertTrue(np.all(np.abs(xhat1 - xhat2) < 1e-4))
eps = 1e-5
maxeps = 0
for i in range(50):
v = random.rand(10, 10, 2, 3).astype(np.float32)
rho = np.abs(random.rand(1))
xhat1 = f.prox(rho, v.copy())
xhat2 = f.prox_cuda(rho, v.copy()).get()
err = np.amax(np.abs(xhat1 - xhat2))
if not err < eps:
logging.error(f.cuda_code)
logging.error("failed: %f" % np.amax(np.abs(xhat1 - xhat2)))
self.assertTrue(err < eps)
maxeps = max(err, maxeps)
for i in range(50):
v = random.rand(10, 10, 2, 3).astype(np.float32)
rho = np.abs(random.rand(1))
alpha = np.abs(random.rand(1))
beta = np.abs(random.rand(1))
gamma = np.abs(random.rand(1))
c = np.abs(random.rand(*f.c.shape))
b = np.abs(random.rand(*f.b.shape))
xhat1 = f.prox(rho,
v.copy(),
alpha=alpha,
beta=beta,
gamma=gamma,
c=c,
b=b)
xhat2 = f.prox_cuda(rho, v.copy()).get()
err = np.amax(np.abs(xhat1 - xhat2))
if not err < eps:
logging.error(f.cuda_code)
logging.error("failed: %f" % np.amax(np.abs(xhat1 - xhat2)))
self.assertTrue(err < eps)
maxeps = max(err, maxeps)
logging.info("Max proxfn error: %.2e" % maxeps)
def test_performance(self):
c = random.rand(2000, 2000)
x = Variable([2000, 2000])
K = np.abs(random.rand(9, 9))
G = CompGraph(
vstack([subsample((conv_nofft(K, x) - c) * 5, [2, 4]), x * 10]))
xtest1 = random.rand(2000 * 2000).astype(np.float32)
ytest1 = np.zeros(G.output_size, dtype=np.float32)
t1_cpu = time.time()
for i in range(10):
ytest1 = G.forward(xtest1, ytest1)
t2_cpu = time.time()
xtest = gpuarray.to_gpu(xtest1.astype(np.float32))
ytest = gpuarray.to_gpu(ytest1.astype(np.float32))
t1_gpu = time.time()
for i in range(10):
ytest = G.forward_cuda(xtest, ytest)
t2_gpu = time.time()
t_cpu = t2_cpu - t1_cpu
t_gpu = t2_gpu - t1_gpu
logging.info("Forward timing: cpu=%.2f ms gpu=%.2f ms factor=%.3f" %
(t_cpu, t_gpu, t_gpu / t_cpu))
self.assertTrue(t_gpu < t_cpu)
t1_cpu = time.time()
for i in range(10):
xtest1 = G.adjoint(ytest1, xtest1)
t2_cpu = time.time()
t1_gpu = time.time()
for i in range(10):
xtest = G.adjoint_cuda(ytest, xtest)
t2_gpu = time.time()
t_cpu = t2_cpu - t1_cpu
t_gpu = t2_gpu - t1_gpu
logging.info("Adjoint timing: cpu=%.2f ms gpu=%.2f ms factor=%.3f" %
(t_cpu, t_gpu, t_gpu / t_cpu))
self.assertTrue(t_gpu < t_cpu)
def _generic_check_adjoint(self,
f,
inshape,
outshape,
s,
ntests=50,
eps=1e-5,
verbose=False,
in_out_sample=None):
"""
Generic tests used for all comp graph tests on a parametrizable function f
"""
x = Variable(inshape)
func = f(x)
if not type(func) is tuple:
func = (func, )
G = CompGraph(vstack(func))
nin = functools.reduce(lambda x, y: x * y, inshape, 1)
nout = functools.reduce(lambda x, y: x * y, outshape, 1)
if not in_out_sample is None:
# check against the given in/out samples
x1 = in_out_sample[0] # forward in
y1s = in_out_sample[1] # forward out
y2 = in_out_sample[2] # adjoint in
x2s = in_out_sample[3] # adjoint out
y1a = G.forward_cuda(gpuarray.to_gpu(x1.astype(np.float32)),
gpuarray.to_gpu(y1s.astype(
np.float32))).get()
#print(y1s)
#print(y1a)
self.assertTrue(np.amax(np.abs(y1a - y1s)) < eps)
x2a = G.adjoint_cuda(gpuarray.to_gpu(y2.astype(np.float32)),
gpuarray.to_gpu(x2s.astype(
np.float32))).get()
self.assertTrue(np.amax(np.abs(x2a - x2s)) < eps)
# test with random data that the forward/adjoint operators are consistent
maxerr = 0.0
random.seed(0) # make tests reproducable
for tidx in range(ntests):
x1 = random.rand(nin).astype(np.float32)
y2 = random.rand(nout).astype(np.float32)
y1 = np.zeros(nout, dtype=np.float32)
x2 = np.zeros(nin, dtype=np.float32)
if verbose:
print("forward: ", end="")
y1 = G.forward_cuda(gpuarray.to_gpu(x1),
gpuarray.to_gpu(y1),
printt=verbose).get()
if verbose:
print("adjoint: ", end="")
x2 = G.adjoint_cuda(gpuarray.to_gpu(y2),
gpuarray.to_gpu(x2),
printt=verbose).get()
self.assertTrue(not np.all(y1 == 0) and not np.all(x2 == 0))
y1o = G.forward(x1, y1.copy())
x2o = G.adjoint(y2, x2.copy())
erro = abs(
np.dot(x1.flatten().astype(np.float64),
x2o.flatten().astype(np.float64)) -
np.dot(y1o.flatten().astype(np.float64),
y2.flatten().astype(np.float64)))
err = abs(
np.dot(x1.flatten().astype(np.float64),
x2.flatten().astype(np.float64)) -
np.dot(y1.flatten().astype(np.float64),
y2.flatten().astype(np.float64)))
if err > maxerr:
maxerr = err
if verbose and err > eps:
print("forward CUDA code:")
print(G.cuda_forward_subgraphs.cuda_code)
print("backward CUDA code:")
print(G.cuda_adjoint_subgraphs.cuda_code)
print("x1\n", np.reshape(x1, inshape))
print("y1\n", np.reshape(y1, outshape))
print("y1o\n", np.reshape(y1o, outshape))
print("y2\n", np.reshape(y2, outshape))
print("x2\n", np.reshape(x2, inshape))
print("x2o\n", np.reshape(x2o, inshape))
print("(%d) Adjoint test (%s): gpu: %f, nogpu: %f" %
(tidx, s, err, erro))
print("max(abs(y1-y1o)): %f" % (np.amax(np.abs(y1 - y1o))))
print("max(abs(x2-x2o)): %f" % (np.amax(np.abs(x2 - x2o))))
self.assertTrue(err <= eps)
if verbose:
print("%s passed %d tests. Max adjoint test error: %f" %
(s, ntests, maxerr))