def filter(self, video_input):
"""
Performs RF filtering on input video
for all the rfs
"""
if len(video_input.shape) == 2:
# if input has 2 dimensions
assert video_input.shape[1] == self.size
else:
# if input has 3 dimensions
assert (video_input.shape[1]*video_input.shape[2] ==
self.size)
# rasterizing inputs
video_input.resize((video_input.shape[0], self.size))
d_video = parray.to_gpu(video_input)
d_output = parray.empty((self.num_neurons, video_input.shape[0]),
self.dtype)
free, total = cuda.mem_get_info()
self.ONE_TIME_FILTERS = ((free // self.dtype.itemsize)
* 3 // 4 // self.size)
self.ONE_TIME_FILTERS -= self.ONE_TIME_FILTERS % 2
self.ONE_TIME_FILTERS = min(self.ONE_TIME_FILTERS, self.num_neurons)
handle = la.cublashandle()
for i in np.arange(0, self.num_neurons, self.ONE_TIME_FILTERS):
Nfilters = min(self.ONE_TIME_FILTERS, self.num_neurons - i)
self.generate_filters(startbias=i, N_filters=Nfilters)
la.dot(self.filters, d_video, opb='t',
C=d_output[i: i+Nfilters],
handle=handle)
del self.filters
return d_output.T()
def filter_image(self, image_input):
"""
Performs RF filtering on input video
for all the rfs
"""
# video dimensions should match screen dimensions
# numpy resize operation doesn,t make any checks
if len(image_input.shape) == 2:
# if input has 2 dimensions
assert image_input.shape[1] == self.size
else:
# if input has 3 dimensions
assert (image_input.shape[1]*image_input.shape[2] ==
self.size)
# rasterizing inputs
image_input.resize((1, self.size))
d_image = parray.to_gpu(image_input)
d_output = parray.empty((self.num_neurons, image_input.shape[0]),
self.dtype)
free, total = cuda.mem_get_info()
self.ONE_TIME_FILTERS = ((free // self.dtype.itemsize)
* 3 // 4 // self.size)
self.ONE_TIME_FILTERS -= self.ONE_TIME_FILTERS % 2
self.ONE_TIME_FILTERS = min(self.ONE_TIME_FILTERS, self.num_neurons)
handle = la.cublashandle()
for i in np.arange(0, self.num_neurons, self.ONE_TIME_FILTERS):
Nfilters = min(self.ONE_TIME_FILTERS, self.num_neurons - i)
self.generate_filters(startbias=i, N_filters=Nfilters)
la.dot(self.filters, d_image, opb='t',
C=d_output[i: i+Nfilters],
handle=handle)
del self.filters
return d_output.T()
def rnn3(G, q, dt = 1e-6, alpha = 5000, steps = 4000, XOUTPUTSTEPS = None):
"""
Solving the decoding problem using a recurrent neural network.
Parameters
----------
G: PitchArray
Must be real and positive semidefinite.
q: PitchArray
The measurements from spikes
dt: float (optional)
the time step in simulating the continuous network
alpha: float (optional)
scaling factor
steps: int (optional)
the number of steps to run the network
XOUTPUTSTEPS: int (optional)
The number of steps that are returned.
If using default None, only return the final result.
Return
------
c: PitchArray
The approximate solution to the decoding problem
output: PitchArray (optional)
If XOUTPUTSTEPS is not None, the full output specified
"""
if G.dtype != q.dtype:
raise TypeError("matrix multiplication must have same dtype")
if np.iscomplexobj(G):
raise TypeError("RNN currently only solves real types")
if (len(G.shape) != 2) | (len(q.shape) != 2):
raise TypeError("G, q must both be matrices")
if XOUTPUTSTEPS is None:
XOUTPUTSTEPS = min(20, steps)
x_steps = steps / XOUTPUTSTEPS
fullout = False
else:
fullout = True
x_steps = steps / int(XOUTPUTSTEPS)
output = parray.empty((XOUTPUTSTEPS, q.size), q.dtype)
c = parray.zeros_like(q)
update_func = get_rnn3_update_func(G.dtype)
dt = float(dt)
alpha = float(alpha)
y = parray.empty_like(q)
if y.dtype == np.float64:
normfunc = cublasDnrm2
else:
normfunc = cublasSnrm2
grid = (6 * cuda.Context.get_device().MULTIPROCESSOR_COUNT, 1)
handle = la.cublashandle()
start = time.time()
for i in range(0,steps+1):
Gc = la.dot(G, c, handle = handle)
launch_kernel(update_func, (256,1,1), grid,
[c, dt*alpha, q, Gc, y, c.size, 1],
prepared = True)
if i%x_steps == 0:
ynorm = normfunc(handle.handle, y.size, y.gpudata, 1)
print "%d, norm = %.10f, time=%f(ms)" % (i / x_steps, ynorm,
(time.time()-start)*1000);
if fullout:
cuda.memcpy_dtod(
int(output.gpudata) +
output.dtype.itemsize*output.ld*int(i/x_steps-1),
c.gpudata, c.dtype.itemsize * c.size)
#cuda.memcpy_dtod(q.gpudata, c.gpudata, c.dtype.itemsize*c.size)
if fullout:
return c,output
else:
return c
def rnn3(G, q, dt=1e-6, alpha=5000, steps=4000, XOUTPUTSTEPS=None):
"""
Solving the decoding problem using a recurrent neural network.
Parameters
----------
G: PitchArray
Must be real and positive semidefinite.
q: PitchArray
The measurements from spikes
dt: float (optional)
the time step in simulating the continuous network
alpha: float (optional)
scaling factor
steps: int (optional)
the number of steps to run the network
XOUTPUTSTEPS: int (optional)
The number of steps that are returned.
If using default None, only return the final result.
Return
------
c: PitchArray
The approximate solution to the decoding problem
output: PitchArray (optional)
If XOUTPUTSTEPS is not None, the full output specified
"""
if G.dtype != q.dtype:
raise TypeError("matrix multiplication must have same dtype")
if np.iscomplexobj(G):
raise TypeError("RNN currently only solves real types")
if (len(G.shape) != 2) | (len(q.shape) != 2):
raise TypeError("G, q must both be matrices")
if XOUTPUTSTEPS is None:
XOUTPUTSTEPS = min(20, steps)
x_steps = steps / XOUTPUTSTEPS
fullout = False
else:
fullout = True
x_steps = steps / int(XOUTPUTSTEPS)
output = parray.empty((XOUTPUTSTEPS, q.size), q.dtype)
c = parray.zeros_like(q)
update_func = get_rnn3_update_func(G.dtype)
dt = float(dt)
alpha = float(alpha)
y = parray.empty_like(q)
if y.dtype == np.float64:
normfunc = cublasDnrm2
else:
normfunc = cublasSnrm2
grid = (6 * cuda.Context.get_device().MULTIPROCESSOR_COUNT, 1)
handle = la.cublashandle()
start = time.time()
for i in range(0, steps + 1):
Gc = la.dot(G, c, handle=handle)
launch_kernel(update_func, (256, 1, 1),
grid, [c, dt * alpha, q, Gc, y, c.size, 1],
prepared=True)
if i % x_steps == 0:
ynorm = normfunc(handle.handle, y.size, y.gpudata, 1)
print "%d, norm = %.10f, time=%f(ms)" % (i / x_steps, ynorm,
(time.time() - start) *
1000)
if fullout:
cuda.memcpy_dtod(
int(output.gpudata) +
output.dtype.itemsize * output.ld * int(i / x_steps - 1),
c.gpudata, c.dtype.itemsize * c.size)
#cuda.memcpy_dtod(q.gpudata, c.gpudata, c.dtype.itemsize*c.size)
if fullout:
return c, output
else:
return c