def test_cudnn():
compile_info = get_compile_info()
if not (compile_info["CGT_ENABLE_CUDNN"] and compile_info["CGT_ENABLE_CUDA"]):
raise SkipTest("CUDNN not enabled. Skipping this test")
Xval = nr.randn(2,3,19,18)
Wval = nr.randn(5,3,3,3)
bval = nr.randn(1,5,1,1)
X = cgt.tensor4("X", fixed_shape=Xval.shape)
W = cgt.tensor4("W", fixed_shape=Wval.shape)
b = cgt.tensor4("b", fixed_shape=bval.shape)
Y = cgt.core.Result(cudnn_ops.CudnnConvForward(1,1,1,1),[X, W, b])
Y2 = nr.randn(*cgt.core.infer_shape(Y))
fY = cgt.function([X,W,b],Y)
Yval = fY(Xval,Wval,bval)
cost = (Y*Y2).sum()
fcost = cgt.function([X,W,b],cost)
fgrad = cgt.function([X,W,b],cgt.grad(cost, [X,W,b]))
angrads = fgrad(Xval,Wval,bval)
nugrads = numeric_grad_multi(fcost, [Xval, Wval, bval],eps=1e-3)
for (nugrad,angrad) in zip(nugrads,angrads):
assert np.allclose(nugrad, angrad, rtol=9e-3, atol=1e-7)
def test_cudnn():
if not get_compile_info()["CGT_ENABLE_CUDNN"]:
raise SkipTest("CUDNN not enabled. Skipping this test")
Xval = nr.randn(2, 3, 19, 18)
Wval = nr.randn(5, 3, 3, 3)
bval = nr.randn(1, 5, 1, 1)
X = cgt.tensor4("X", fixed_shape=Xval.shape)
W = cgt.tensor4("W", fixed_shape=Wval.shape)
b = cgt.tensor4("b", fixed_shape=bval.shape)
Y = cgt.core.Result(cudnn_ops.CudnnConvForward(1, 1, 1, 1), [X, W, b])
Y2 = nr.randn(*cgt.core.infer_shape(Y))
fY = cgt.function([X, W, b], Y)
Yval = fY(Xval, Wval, bval)
cost = (Y * Y2).sum()
fcost = cgt.function([X, W, b], cost)
fgrad = cgt.function([X, W, b], cgt.grad(cost, [X, W, b]))
angrads = fgrad(Xval, Wval, bval)
nugrads = numeric_grad_multi(fcost, [Xval, Wval, bval], eps=1e-3)
for (nugrad, angrad) in zip(nugrads, angrads):
assert np.allclose(nugrad, angrad)
def test_cudnn():
with cgt.scoped_update_config(precision="double",backend="native"):
if not get_compile_info()["CGT_ENABLE_CUDNN"]:
raise SkipTest("CUDNN not enabled. Skipping this test")
Xval = nr.randn(2,3,19,18)
Wval = nr.randn(5,3,3,3)
bval = nr.randn(1,5,1,1)
X = cgt.tensor4("X", fixed_shape=Xval.shape)
W = cgt.tensor4("W", fixed_shape=Wval.shape)
b = cgt.tensor4("b", fixed_shape=bval.shape)
Y = cgt.core.Result(cudnn_ops.CudnnConvForward(1,1,1,1),[X, W, b])
Y2 = nr.randn(*cgt.core.infer_shape(Y))
fY = cgt.function([X,W,b],Y)
Yval = fY(Xval,Wval,bval)
cost = (Y*Y2).sum()
fcost = cgt.function([X,W,b],cost)
fgrad = cgt.function([X,W,b],cgt.grad(cost, [X,W,b]))
angrads = fgrad(Xval,Wval,bval)
nugrads = numeric_grad_multi(fcost, [Xval, Wval, bval],eps=1e-3)
for (nugrad,angrad) in zip(nugrads,angrads):
assert np.allclose(nugrad, angrad)
def test_lrn():
if not get_compile_info()["CGT_ENABLE_CUDA"]:
raise SkipTest("Skipping because CUDA disabled")
nr.seed(0)
Xval = nr.randn(4,8,16,16)
X = cgt.shared(Xval, name="X", fixed_shape_mask="all")
# X = cgt.tensor4(name='X')
y = cross_channel_lrn(X, localsize=4, alpha=.1, beta=.5)
f = cgt.function([],y)
print f().sum()
print f().sum()
print f().sum()
assert np.isfinite(f().sum())
# print f(Xval).sum()
a = nr.rand(*cgt.infer_shape(y))
loss = (y*a).sum()
gradcheck_model(loss, [X],eps=1e-5)
def test_lrn():
if not get_compile_info()["CGT_ENABLE_CUDA"]:
raise SkipTest("Skipping because CUDA disabled")
nr.seed(0)
Xval = nr.randn(4, 8, 16, 16)
X = cgt.shared(Xval, name="X", fixed_shape_mask="all")
# X = cgt.tensor4(name='X')
y = cross_channel_lrn(X, localsize=4, alpha=.1, beta=.5)
f = cgt.function([], y)
print f().sum()
print f().sum()
print f().sum()
assert np.isfinite(f().sum())
# print f(Xval).sum()
a = nr.rand(*cgt.infer_shape(y))
loss = (y * a).sum()
gradcheck_model(loss, [X], eps=1e-5)
def test_cpu_pool():
with cgt.scoped_update_config(precision="quad", backend="native"):
print cgt.get_precision()
ci = get_compile_info()
np.random.seed(0)
x = cgt.tensor4("x", fixed_shape=(2, 3, 5, 7))
y = max_pool_2d(x, (4, 4), (0, 0), (1, 1))
xval = np.random.randn(2, 3, 5, 7)
hval = np.random.randn(*cgt.infer_shape(y))
h = cgt.constant(hval)
cost = (y * h).sum()
fcost = cgt.function([x], cost)
fgrad = cgt.function([x], cgt.grad(cost, [x])[0])
from cgt.numeric_diff import numeric_grad
gnum = numeric_grad(fcost, xval)
gana = fgrad(xval)
assert np.allclose(gnum, gana)
def test_cpu_pool():
with cgt.scoped_update_config(precision="quad",backend="native"):
print cgt.get_precision()
ci = get_compile_info()
np.random.seed(0)
x = cgt.tensor4("x", fixed_shape=(2,3,5,7))
y = max_pool_2d(x, (4,4),(0,0),(1,1))
xval = np.random.randn(2,3,5,7)
hval = np.random.randn(*cgt.infer_shape(y))
h = cgt.constant(hval)
cost = (y*h).sum()
fcost = cgt.function([x], cost)
fgrad = cgt.function([x], cgt.grad(cost, [x])[0])
from cgt.numeric_diff import numeric_grad
gnum = numeric_grad(fcost, xval)
gana = fgrad(xval)
assert np.allclose(gnum,gana)
