def transform(n_images, n_features, n_components=0, disable_pbar=False):
opu = OPU(disable_pbar=disable_pbar, open_at_init=False)
if n_components != 0:
opu.n_components = n_components
ins = np.ones((n_images, n_features), dtype=np.uint8)
with opu:
print(opu.version())
begin = time.time()
opu.fit_transform1d(ins)
elapsed = time.time() - begin
print("{:d} transforms in {:.2f} s ({:.2f} Hz)".format(n_images,
elapsed, n_images / elapsed))
def opu_tutorial(opu: OPU):
# Rule is that OPU input is binary, so input vector elements should be 0 or 1
# To get your input as binary, see the encoders from lightonml package.
# The tutorial makes use of NumPy arrays, but you can use PyTorch tensors as input.
# The output then is a tensor as well.
# The dimension of output is governed by the n_components attribute:
print("Number of components:", opu.n_components)
# Simplest input is a batch of N 1d vectors, of arbitrary size
inp_1d = random_bin((2000, 1000))
out1 = opu.fit_transform1d(inp_1d)
print("1D transform out shape", out1.shape) # (2000, opu.n_components)
# In this example we fit the OPU with the input and then transform or linear_transform,
# but if we have several batches to be transformed, we must call fit first
# on one the batches, and then transform:
inp_1d_b = random_bin((2000, 1000))
opu.fit1d(inp_1d)
out1 = opu.transform(inp_1d)
out1_b = opu.transform(inp_1d_b)
print("transform of two separate batches", out1.shape, out1_b.shape)
# For a linear transform, use OPU.linear_transform with the same parameters:
out1_l = opu.linear_transform(inp_1d)
print("linear_transform of a batch", out1.shape, out1_l.shape)
# You can also run fit with the number of features, instead:
opu.fit1d(n_features=inp_1d.shape[1])
out1_alt = opu.transform(inp_1d)
print("transform 1d", out1_alt.shape)
# But you can transform a single vector if you wish
single = opu.fit_transform1d(inp_1d[0])
print("single transform out shape", single.shape) # n_components
# However if you have many single vectors, you will need either to use the
# online mode (see below), or have them transformed in a single batch,
# you will gain a lot of performance
# if you have 2d vectors, you'll benefit from the fact that OPU input is
# physically in 2d, so don't reshape them! Instead, use fit_transform2d:
inp_2d = random_bin((2000, 13, 10)) # 200 vectors of shape 13x10
out2 = opu.fit_transform2d(inp_2d)
print("2D transform out shape", out2.shape) # (2000, opu.n_components)
# If you're batch is 2D-shaped, transform will recognize it and return
# an output of the same shape:
inp_3d = random_bin((300, 100, 3000))
# call 1D transform on a 3D vector
out_3d = opu.fit_transform1d(inp_3d) # batch of 300x100 1D vectors
print("3D transform out shape", out_3d.shape) # (300, 100, opu.n_components)
# You can also have bit-packed input, which will optimize memory-space,
# as well as being a bit more efficient
inp_1d_p = np.packbits(inp_1d, axis=1)
print("1D packed transform in shape", inp_1d_p.shape) # (2000, 1000/8)
out1_p = opu.fit_transform1d(inp_1d_p, packed=True)
print("1D packed transform out shape", out1_p.shape) # (2000, opu.n_components)
# If your input is 2d AND bit-packed, you must tell what shape it is
inp_2d_p = np.packbits(inp_2d.reshape(2000, -1), axis=1)
print("1D packed transform in shape", inp_2d_p.shape) # (2000, 1300/8)
out2_p = opu.fit_transform2d(inp_2d_p, packed=True, n_2d_features=(13, 10))
print("2D packed transform out shape", out2_p.shape) # (2000, opu.n_components)
# Input is formatted to match OPU's input device, but you can pass directly
# formatted input of the correct input size (should it be 1d or 2d)
input_shape = tuple(opu.device.input_shape)
single_raw_2d = random_bin(input_shape)
single_raw_1d = np.reshape(single_raw_2d, -1)
out_raw_1d = opu.fit_transform1d(single_raw_1d)
print("1D raw out shape", out_raw_1d.shape) # (2000, opu.n_components)
out_raw_2d = opu.fit_transform2d(single_raw_2d)
print("1D raw out shape", out_raw_2d.shape) # (2000, opu.n_components)
# Of course, the equivalent for packed input of input device's size
# will work the same way
# It can also be a batch of them
many_raw_2d = random_bin((100, ) + input_shape)
out_many_raw_2d = opu.fit_transform2d(many_raw_2d)
print("Many raw out shape", out_many_raw_2d.shape) # (100, opu.n_components)
# The online mode allows you to run accelerate the run of single vectors:
n_features1d = 1200
opu.fit1d(n_features=n_features1d, online=True)
for _ in range(10):
online_out = opu.transform(random_bin(n_features1d))
print("Online out shape", online_out.shape)
n_features2d = (50, 50)
opu.fit2d(n_features=n_features2d, online=True)
for _ in range(10):
online_out = opu.transform(random_bin(n_features2d))
print("Online out shape", online_out.shape)