def task1(cufft, d_image_complex, d_response_complex):
### Task1 ###
# Implement a inplace CUDA FFT convolution
# Pseduocode:
# freq_imag = fft(image)
# freq_resp = fft(response)
# freq_out = fftimag * fftresp
# output = ifft(freq_out)
#
# Use the cuFFT functions:
# - cufft.fft_inplace(ary)
# - cufft.ifft_inplace(ary)
#
# Call `vmult` which is our elementwise complex multiplication.
# Do a inplace operation on `d_image_complex`.
# Hints:
# - keyword argument 'out' specify the output array
# - length of d_image_complex and d_response_complex has the same length.
cufft.fft_inplace(d_image_complex)
cufft.fft_inplace(d_response_complex)
vmult(d_image_complex, d_response_complex, out=d_image_complex)
cufft.ifft_inplace(d_image_complex)
# At this point, we have applied the filter onto d_image_complex
return # Does not return anything
def task1(cufft, d_image_complex, d_response_complex):
### Task1 ###
# Implement a inplace CUDA FFT convolution
# Pseduocode:
# freq_imag = fft(image)
# freq_resp = fft(response)
# freq_out = fftimag * fftresp
# output = ifft(freq_out)
#
# Use the cuFFT functions:
# - cufft.fft_inplace(ary)
# - cufft.ifft_inplace(ary)
#
# Call `vmult` which is our elementwise complex multiplication.
# Do a inplace operation on `d_image_complex`.
# Hints:
# - keyword argument 'out' specify the output array
# - length of d_image_complex and d_response_complex has the same length.
cufft.fft_inplace(d_image_complex)
cufft.fft_inplace(d_response_complex)
vmult(d_image_complex, d_response_complex, out=d_image_complex)
cufft.ifft_inplace(d_image_complex)
# At this point, we have applied the filter onto d_image_complex
return # Does not return anything
def main():
# Build Filter
laplacian_pts = '''
-4 -1 0 -1 -4
-1 2 3 2 -1
0 3 4 3 0
-1 2 3 2 -1
-4 -1 0 -1 -4
'''.split()
laplacian = np.array(laplacian_pts, dtype=np.float32).reshape(5, 5)
# Build Image
try:
filename = sys.argv[1]
image = ndimage.imread(filename, flatten=True).astype(np.float32)
except IndexError:
image = misc.lena().astype(np.float32)
print("Image size: %s" % (image.shape, ))
response = np.zeros_like(image)
response[:5, :5] = laplacian
# CPU
ts = timer()
cvimage_cpu = fftconvolve(image, laplacian, mode='same')
te = timer()
print('CPU: %.2fs' % (te - ts))
# GPU
threadperblock = 32, 8
blockpergrid = best_grid_size(tuple(reversed(image.shape)), threadperblock)
print('kernel config: %s x %s' % (blockpergrid, threadperblock))
# Trigger initialization the cuFFT system.
# This takes significant time for small dataset.
# We should not be including the time wasted here
cufft.FFTPlan(shape=image.shape, itype=np.complex64, otype=np.complex64)
# Start GPU timer
ts = timer()
image_complex = image.astype(np.complex64)
response_complex = response.astype(np.complex64)
d_image_complex = cuda.to_device(image_complex)
d_response_complex = cuda.to_device(response_complex)
cufft.fft_inplace(d_image_complex)
cufft.fft_inplace(d_response_complex)
vmult(d_image_complex, d_response_complex, out=d_image_complex)
cufft.ifft_inplace(d_image_complex)
cvimage_gpu = d_image_complex.copy_to_host().real / np.prod(image.shape)
te = timer()
print('GPU: %.2fs' % (te - ts))
# Plot the results
plt.subplot(1, 2, 1)
plt.title('CPU')
plt.imshow(cvimage_cpu, cmap=plt.cm.gray)
plt.axis('off')
plt.subplot(1, 2, 2)
plt.title('GPU')
plt.imshow(cvimage_gpu, cmap=plt.cm.gray)
plt.axis('off')
plt.show()
def main():
# Build Filter
laplacian_pts = '''
-4 -1 0 -1 -4
-1 2 3 2 -1
0 3 4 3 0
-1 2 3 2 -1
-4 -1 0 -1 -4
'''.split()
laplacian = np.array(laplacian_pts, dtype=np.float32).reshape(5, 5)
# Build Image
try:
filename = sys.argv[1]
image = ndimage.imread(filename, flatten=True).astype(np.float32)
except IndexError:
image = misc.lena().astype(np.float32)
print("Image size: %s" % (image.shape,))
response = np.zeros_like(image)
response[:5, :5] = laplacian
# CPU
ts = timer()
cvimage_cpu = fftconvolve(image, laplacian, mode='same')
te = timer()
print('CPU: %.2fs' % (te - ts))
# GPU
threadperblock = 32, 8
blockpergrid = best_grid_size(tuple(reversed(image.shape)), threadperblock)
print('kernel config: %s x %s' % (blockpergrid, threadperblock))
# Trigger initialization the cuFFT system.
# This takes significant time for small dataset.
# We should not be including the time wasted here
cufft.FFTPlan(shape=image.shape, itype=np.complex64, otype=np.complex64)
# Start GPU timer
ts = timer()
image_complex = image.astype(np.complex64)
response_complex = response.astype(np.complex64)
d_image_complex = cuda.to_device(image_complex)
d_response_complex = cuda.to_device(response_complex)
cufft.fft_inplace(d_image_complex)
cufft.fft_inplace(d_response_complex)
vmult(d_image_complex, d_response_complex, out=d_image_complex)
cufft.ifft_inplace(d_image_complex)
cvimage_gpu = d_image_complex.copy_to_host().real / np.prod(image.shape)
te = timer()
print('GPU: %.2fs' % (te - ts))
# Plot the results
plt.subplot(1, 2, 1)
plt.title('CPU')
plt.imshow(cvimage_cpu, cmap=plt.cm.gray)
plt.axis('off')
plt.subplot(1, 2, 2)
plt.title('GPU')
plt.imshow(cvimage_gpu, cmap=plt.cm.gray)
plt.axis('off')
plt.show()