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_conv():
try:
import scipy.signal
except ImportError:
raise SkipTest("skipping because we don't have ndimage")
np.random.seed(0)
x = np.random.randn(2,2,5,17)
filt = np.random.randn(3,2,4,7)
filtrows = filt.shape[2]
filtcols = filt.shape[3]
batchsize = x.shape[0]
outchans = filt.shape[0]
out = np.zeros((batchsize,outchans,x.shape[2]+filtrows-1,x.shape[3]+filtcols-1))
for b in xrange(x.shape[0]):
for inchan in xrange(x.shape[1]):
for outchan in xrange(outchans):
out[b,outchan] += scipy.signal.convolve2d(x[b,inchan],filt[outchan,inchan][::-1,::-1],mode='full')
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}[cgt.get_precision()])
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_conv():
try:
import scipy.signal
except ImportError:
raise SkipTest("skipping because we don't have ndimage")
np.random.seed(0)
x = np.random.randn(2, 2, 5, 17)
filt = np.random.randn(3, 2, 4, 7)
filtrows = filt.shape[2]
filtcols = filt.shape[3]
batchsize = x.shape[0]
outchans = filt.shape[0]
out = np.zeros((batchsize, outchans, x.shape[2] + filtrows - 1,
x.shape[3] + filtcols - 1))
for b in xrange(x.shape[0]):
for inchan in xrange(x.shape[1]):
for outchan in xrange(outchans):
out[b, outchan] += scipy.signal.convolve2d(
x[b, inchan],
filt[outchan, inchan][::-1, ::-1],
mode='full')
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
}[cgt.get_precision()])
def test_im2col():
for settings in [ ((4,4),(0,0),(1,1)), ((3,3),(1,1),(2,2)), ((3,3),(1,1),(3,3)) ]:
xval = np.arange(2*1*28*28).reshape(2,1,28,28).astype(cgt.floatX)
x = cgt.tensor4("x", fixed_shape=xval.shape)
y = im2col(x, *settings)
h = cgt.constant(np.random.randn(*cgt.infer_shape(y)))
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,eps=1e-5)
gana = fgrad(xval)
assert np.allclose(gnum, gana)
def test_pool(**kwargs):
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_im2col():
for settings in [((4, 4), (0, 0), (1, 1)), ((3, 3), (1, 1), (2, 2)),
((3, 3), (1, 1), (3, 3))]:
xval = np.arange(2 * 1 * 28 * 28).reshape(2, 1, 28,
28).astype(cgt.floatX)
x = cgt.tensor4("x", fixed_shape=xval.shape)
y = im2col(x, *settings)
h = cgt.constant(np.random.randn(*cgt.infer_shape(y)))
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, eps=1e-5)
gana = fgrad(xval)
assert np.allclose(gnum, gana)
def test_cpu_pool(**kwargs):
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)
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 constant(x):
return cgt.constant(x)
def constant(x):
return cgt.constant(x)
