def test_simple_conv(self):
bs = 10
r = 2
n_channels = 3
n_filters = 5
w, h = 32, 32
# define a simple model
x = nn.Tensor((bs, n_channels, h, w))
W = nn.Constant(
np.random.randn(n_filters, n_channels, 2 * r + 1,
2 * r + 1).astype(np.float32))
y = nn.conv(x, W)
# check shapes
self.assertEqual(y.output.shape, (bs, n_filters, h, w))
# compile model
mdl = nn.compile_model(x, y)
# emulate model with numpy
npx = np.random.randn(bs, n_channels, h, w).astype(np.float32)
npy = np_pad(npx, r)
npy = np_conv(npy, W.value)
# results must be very similar
res = mdl(npx)
self.assertLess(np.max(np.abs(npy - res)), 1e-4)
def main():
tot_images = 50000
bs = 200
n_filters = 1024
x = nn.Tensor((bs, 3, 32, 32))
w_1 = nn.Constant(np.random.randn(n_filters, 3, 5, 5).astype(np.float32))
y = nn.conv(x, w_1)
# y = nn.activation(y, lambda x: "exp(-({x}))")
mdl = nn.compile_model(x, y)
# s = time.time()
npx = np.random.randn(bs, 3, 32, 32).astype(np.float32)
# npy = np_pad(npx, 2)
# npy = np_conv(npy, w_1.value)
# e = time.time()
# print("Numpy time", e - s)
s = time.time()
res = mdl(npx)
e = time.time()
print("OpenCL time", e - s)
print(
f"Expected time for {tot_images} is {(tot_images / bs) * (e - s)} seconds ({(tot_images / (bs * 60)) * (e - s)} minutes)"
)
def test_variable_with_tensor(self, size, U):
a = U.random(size)
p = tf.convert_to_tensor(a)
x = nn.variable(a)
assert U.equal(x, p)
assert nn.tf(x) is x.tf()
x = nn.variable(p)
assert U.equal(x, p)
assert x.tf() is not p
y = nn.variable(x)
assert y is x
y = nn.Tensor(x)
assert y is not x
assert U.equal(y, x)
assert y.tf() is x.tf()
self.assert_properties(y, x)