def check_conv(precision):
cgt.reset_config()
cgt.set_precision(precision)
f = cgt.function([], nn.conv2d(cgt.constant(x), cgt.constant(filt), kernelshape=(filtrows,filtcols), pad=(filtrows-1, filtcols-1)))
out1 = f()
# out1 = cgt.numeric_eval1(nn.conv2d(cgt.constant(x), cgt.constant(f), kersize=(filtrows,filtcols)), {})
np.testing.assert_allclose(out, out1, atol={"single":1e-3,"double":1e-6}[precision])
def check_scalar_grads(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
x = cgt.scalar('x')
y = cgt.scalar('y')
z = cgt.scalar('z')
vars = [x,y,z] #pylint: disable=W0622
vals = nr.rand(len(vars))+1
PROB2RESULT = {}
for ((key,_), cls) in it.chain(
it.izip(core.UNARY_INFO.items(),it.repeat(core.ElwiseUnary)),
it.izip(core.BINARY_INFO.items(),it.repeat(core.ElwiseBinary))
):
if key == "conj":
print "skipping conj"
continue
utils.colorprint(utils.Color.YELLOW, "Testing %s\n"%key)
if cls == core.ElwiseUnary:
n_in = 1
op = cls(key)
else:
n_in = 2
op = cls(key, (True,True))
inputvars = vars[0:n_in]
inputvals = vals[0:n_in]
out = core.Result(op, inputvars)
f = cgt.function(inputvars, out)
try:
grads = cgt.grad(out, inputvars)
except core.NonDifferentiable:
print "nondiff"
continue
if DISPLAY:
print "Function:"
cgt.print_tree(out)
print "Gradient original:"
cgt.print_tree(grads)
print "Gradient simplified:"
grads_simple = core.simplify(grads)
if DISPLAY: cgt.print_tree(grads_simple)
gradf = cgt.function(inputvars, grads)
eps = {"single":1e-4,"double":1e-9}[precision]
nugrad = numeric_grad(lambda li: f(*li), inputvals,eps=eps) #pylint: disable=W0640
cgtgrad = gradf(*inputvals)
np.testing.assert_almost_equal(nugrad,cgtgrad,decimal={"single":3,"double":6}[precision])
grad_count = core.count_nodes(grads_simple)
PROB2RESULT[key] = {}
PROB2RESULT[key]["grad"] = grad_count
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key,val["grad"]] for (key,val) in PROB2RESULT.iteritems()],headers=["funcname","gradcount"])
def test_multi_output():
cgt.reset_config()
cgt.set_precision("single")
for x in (cgt.scalar('x'), cgt.vector('x'), cgt.matrix('x')):
for cls in (SinCos, SinCos2):
y,z = core.unpack(core.Result(cls(), [x]))
xnum = np.ones((3,)*x.ndim, cgt.floatX)
correct = (np.sin(xnum),np.cos(xnum))
yznum = cgt.numeric_eval([y,z], {x:xnum})
np.testing.assert_allclose(yznum, correct)
f = cgt.function([x],[y,z])
np.testing.assert_allclose(f(xnum), correct)
def test_multi_output():
cgt.reset_config()
cgt.set_precision("single")
for x in (cgt.scalar('x'), cgt.vector('x'), cgt.matrix('x')):
for cls in (SinCos, SinCos2):
y, z = core.unpack(core.Result(cls(), [x]))
xnum = np.ones((3, ) * x.ndim, cgt.floatX)
correct = (np.sin(xnum), np.cos(xnum))
yznum = cgt.numeric_eval([y, z], {x: xnum})
np.testing.assert_allclose(yznum, correct)
f = cgt.function([x], [y, z])
np.testing.assert_allclose(f(xnum), correct)
def check_conv(precision):
cgt.reset_config()
cgt.set_precision(precision)
f = cgt.function([],
nn.conv2d(cgt.constant(x),
cgt.constant(filt),
kernelshape=(filtrows, filtcols),
pad=(filtrows - 1, filtcols - 1)))
out1 = f()
# out1 = cgt.numeric_eval1(nn.conv2d(cgt.constant(x), cgt.constant(f), kersize=(filtrows,filtcols)), {})
np.testing.assert_allclose(out,
out1,
atol={
"single": 1e-3,
"double": 1e-6
}[precision])
def test_linreg():
cgt.reset_config()
cgt.set_precision('double')
N = 10
K = 3
Xval = np.random.randn(N, K)
wval = np.random.randn(K)
bval = np.random.randn()
yval = np.random.randn(N)
X_nk = cgt.matrix("X")
y_n = cgt.vector("y")
w_k = cgt.vector("w")
b = cgt.scalar(name="b")
ypred = cgt.dot(X_nk, w_k) + b
err = cgt.sum(cgt.square(ypred - y_n))
g = cgt.grad(err, [w_k, b])
g_simple, an, _ = cgt.core.simplify_and_analyze(g)
print "Loss function:"
cgt.print_tree([err])
print "Gradient:"
cgt.print_tree(g)
print "Gradient simplified"
cgt.print_tree(
g_simple,
nodefn=lambda node, o: o.write(" " + an["node2hash"][node][:5]))
print "-------"
d = {X_nk: Xval, w_k: wval, b: bval, y_n: yval}
np.testing.assert_allclose(cgt.numeric_eval(err, d),
np.linalg.norm(Xval.dot(wval) + bval - yval)**2)
np.testing.assert_allclose(cgt.numeric_eval(g[0], d),
2 * Xval.T.dot(Xval.dot(wval) + bval - yval))
np.testing.assert_allclose(cgt.numeric_eval(g[1], d),
2 * np.sum(Xval.dot(wval) + bval - yval, 0))
def test_einsum():
cgt.reset_config()
cgt.set_precision("double")
x = cgt.tensor3()
y = cgt.tensor3()
sizes = {'i':2,'j':3,'k':5,'l':7}
xaxes = 'ijk'
yaxes = 'ikl'
zaxes = 'ijl'
for i in xrange(10):
xperm = xaxes
(yperm,zperm) = permaxes = [[chars[i] for i in np.random.permutation(3)] for chars in [yaxes,zaxes]]
desc = "%s,%s->%s"%tuple("".join(chars) for chars in [xperm] + permaxes)
z = cgt.einsum(desc, x, y)
xval = nr.randn(*(sizes[c] for c in xperm))
yval = nr.randn(*(sizes[c] for c in yperm))
np.testing.assert_allclose(
cgt.numeric_eval(z, {x : xval, y : yval}),
np.einsum(desc, xval, yval))
def test_linreg():
cgt.reset_config()
cgt.set_precision('double')
N = 10
K = 3
Xval = np.random.randn(N,K)
wval = np.random.randn(K)
bval = np.random.randn()
yval = np.random.randn(N)
X_nk = cgt.matrix("X")
y_n = cgt.vector("y")
w_k = cgt.vector("w")
b = cgt.scalar(name="b")
ypred = cgt.dot(X_nk, w_k) + b
err = cgt.sum(cgt.square(ypred - y_n))
g = cgt.grad(err, [w_k, b])
g_simple,an,_ = cgt.core.simplify_and_analyze(g)
print "Loss function:"
cgt.print_tree([err])
print "Gradient:"
cgt.print_tree(g)
print "Gradient simplified"
cgt.print_tree(g_simple, nodefn=lambda node,o: o.write(" " + an["node2hash"][node][:5]))
print "-------"
d = {X_nk : Xval, w_k : wval, b : bval, y_n : yval}
np.testing.assert_allclose(cgt.numeric_eval(err,d), np.linalg.norm(Xval.dot(wval) + bval - yval)**2)
np.testing.assert_allclose(cgt.numeric_eval(g[0],d), 2 * Xval.T.dot(Xval.dot(wval) + bval - yval))
np.testing.assert_allclose(cgt.numeric_eval(g[1],d), 2 * np.sum(Xval.dot(wval) + bval - yval, 0))
def check_affine_funcs(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
sA = np.array(nr.rand())
sB = np.array(nr.rand())
sC = np.array(nr.rand())
mA = nr.randn(2, 3)
mB = nr.randn(2, 3)
mC = nr.randn(2, 3)
for fn in [xplusx, _2x_plus_3x, xm1, onemx]:
for arg in [sA, mA]:
check_affine(fn, arg)
check_affine(elem_mult2, mA, mB, mC)
check_affine(elem_mult2, sA, sB, sC)
check_affine(pyramid, sA, sB, sC)
check_affine(pyramid, mA, mB, mC)
check_affine(slisum1, mA)
check_affine(slisum2, mA)
check_affine(slisum3, mA)
check_affine(slisum4, mA)
check_affine(max0, mA)
check_affine(max1, mA)
check_affine(max2, mA)
check_affine(fancysli0, mA)
check_affine(sum10, mA)
check_affine(sum01, mA)
check_affine(repeat0, mA[0:1, :], nr.randn(7, 3))
check_affine(repeat1, mA[:, 0:1], nr.randn(2, 7))
M23 = mA
M35 = nr.randn(3, 5)
v3 = nr.randn(3)
v13 = v3.reshape(1, 3) #XXX
v5 = nr.randn(5)
v15 = v5.reshape(1, 5) #XXX
v3b = nr.randn(3)
check_affine(matmat00, M23, M35)
check_affine(matmat01, M23, M35.T)
check_affine(matmat10, M23.T, M35)
check_affine(matmat11, M23.T, M35.T)
check_affine(matmat00a, M23, M35)
check_affine(matmat01a, M23, M35.T)
# check_affine(matmat10a, M23.T, M35)
check_affine(matmat11a, M23.T, M35.T)
check_affine(matvec, M23, v3)
check_affine(vecvec, v3, v3b)
check_affine(bcadd, M23, v13)
check_affine(matmatplusvec, M23, M35, v15)
check_affine(transpose, M23, nr.randn(3, 2))
T235 = nr.randn(2, 3, 5)
T235a = nr.randn(2, 3, 5)
T257 = nr.randn(2, 5, 7)
T2357 = nr.randn(2, 3, 5, 7)
T2357a = nr.randn(2, 3, 5, 7)
check_affine(transpose012, T235, T235a)
check_affine(transpose021, T235, T235a.transpose(0, 2, 1))
check_affine(transpose102, T235, T235a.transpose(1, 0, 2))
check_affine(transpose0312, T2357, T2357a.transpose(0, 3, 1, 2))
check_affine(transpose0231, T2357, T2357a.transpose(0, 2, 3, 1))
check_affine(batchedmatmul, T235, T257)
check_affine(flip0, M23, nr.randn(2, 3))
check_affine(flip1, M23, nr.randn(2, 3))
# check_affine(negsli0, M23, nr.randn(2,3))
# check_affine(negsli1, M23, nr.randn(2,3))
# check_affine(negsli01, M23, nr.randn(2,3))
# check_affine(rfft, M35)
check_affine(convlike, T2357, nr.randn(11, 3 * 5 * 7), nr.randn(2, 11))
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key, val["fn"], val["grad"]]
for (key, val) in sorted(PROB2RESULT.items())],
headers=["funcname", "fncount", "gradcount"])
def check_affine_funcs(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
sA = np.array(nr.rand())
sB = np.array(nr.rand())
sC = np.array(nr.rand())
mA = nr.randn(2,3)
mB = nr.randn(2,3)
mC = nr.randn(2,3)
for fn in [xplusx, _2x_plus_3x, xm1, onemx]:
for arg in [sA, mA]:
check_affine(fn, arg)
check_affine(elem_mult2, mA, mB, mC)
check_affine(elem_mult2, sA, sB, sC)
check_affine(pyramid, sA, sB, sC)
check_affine(pyramid, mA, mB, mC)
check_affine(slisum1, mA)
check_affine(slisum2, mA)
check_affine(slisum3, mA)
check_affine(slisum4, mA)
check_affine(max0, mA)
check_affine(max1, mA)
check_affine(max2, mA)
check_affine(fancysli0, mA)
check_affine(sum10, mA)
check_affine(sum01, mA)
check_affine(repeat0, mA[0:1, :], nr.randn(7,3))
check_affine(repeat1, mA[:, 0:1], nr.randn(2,7))
M23 = mA
M35 = nr.randn(3,5)
v3 = nr.randn(3)
v13 = v3.reshape(1,3) #XXX
v5 = nr.randn(5)
v15 = v5.reshape(1,5) #XXX
v3b = nr.randn(3)
check_affine(matmat00, M23, M35)
check_affine(matmat01, M23, M35.T)
check_affine(matmat10, M23.T, M35)
check_affine(matmat11, M23.T, M35.T)
check_affine(matmat00a, M23, M35)
check_affine(matmat01a, M23, M35.T)
# check_affine(matmat10a, M23.T, M35)
check_affine(matmat11a, M23.T, M35.T)
check_affine(matvec, M23, v3)
check_affine(vecvec, v3, v3b)
check_affine(bcadd, M23, v13)
check_affine(matmatplusvec, M23, M35, v15)
check_affine(transpose, M23, nr.randn(3,2))
T235 = nr.randn(2,3,5)
T235a = nr.randn(2,3,5)
T257 = nr.randn(2,5,7)
T2357 = nr.randn(2,3,5,7)
T2357a = nr.randn(2,3,5,7)
check_affine(transpose012, T235, T235a)
check_affine(transpose021, T235, T235a.transpose(0,2,1))
check_affine(transpose102, T235, T235a.transpose(1,0,2))
check_affine(transpose0312, T2357, T2357a.transpose(0,3,1,2))
check_affine(transpose0231, T2357, T2357a.transpose(0,2,3,1))
check_affine(batchedmatmul, T235, T257)
check_affine(flip0, M23, nr.randn(2,3))
check_affine(flip1, M23, nr.randn(2,3))
# check_affine(negsli0, M23, nr.randn(2,3))
# check_affine(negsli1, M23, nr.randn(2,3))
# check_affine(negsli01, M23, nr.randn(2,3))
# check_affine(rfft, M35)
check_affine(convlike, T2357, nr.randn(11,3*5*7), nr.randn(2,11))
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key,val["fn"],val["grad"]] for (key,val) in sorted(PROB2RESULT.items())],headers=["funcname","fncount","gradcount"])
def check_scalar_grads(precision, backend):
cgt.reset_config()
np.random.seed(0)
cgt.set_precision(precision)
cgt.core.update_config(backend=backend)
x = cgt.scalar('x')
y = cgt.scalar('y')
z = cgt.scalar('z')
vars = [x, y, z] #pylint: disable=W0622
vals = nr.rand(len(vars)) + 1
PROB2RESULT = {}
for ((key, _), cls) in it.chain(
it.izip(core.UNARY_INFO.items(), it.repeat(core.ElwiseUnary)),
it.izip(core.BINARY_INFO.items(), it.repeat(core.ElwiseBinary))):
if key == "conj":
print "skipping conj"
continue
utils.colorprint(utils.Color.YELLOW, "Testing %s\n" % key)
if cls == core.ElwiseUnary:
n_in = 1
op = cls(key)
else:
n_in = 2
op = cls(key, (True, True))
inputvars = vars[0:n_in]
inputvals = vals[0:n_in]
out = core.Result(op, inputvars)
f = cgt.function(inputvars, out)
try:
grads = cgt.grad(out, inputvars)
except core.NonDifferentiable:
print "nondiff"
continue
if DISPLAY:
print "Function:"
cgt.print_tree(out)
print "Gradient original:"
cgt.print_tree(grads)
print "Gradient simplified:"
grads_simple = core.simplify(grads)
if DISPLAY: cgt.print_tree(grads_simple)
gradf = cgt.function(inputvars, grads)
eps = {"single": 1e-4, "double": 1e-9}[precision]
nugrad = numeric_grad(lambda li: f(*li), inputvals, eps=eps) #pylint: disable=W0640
cgtgrad = gradf(*inputvals)
np.testing.assert_almost_equal(nugrad,
cgtgrad,
decimal={
"single": 3,
"double": 6
}[precision])
grad_count = core.count_nodes(grads_simple)
PROB2RESULT[key] = {}
PROB2RESULT[key]["grad"] = grad_count
if DISPLAY:
from thirdparty.tabulate import tabulate
print tabulate([[key, val["grad"]]
for (key, val) in PROB2RESULT.iteritems()],
headers=["funcname", "gradcount"])
