( 64, 64, 64, 1, 224,224, 1, 3, 3, 0,1,1, 1,1,1),
( 64, 64,128, 1, 112,112, 1, 3, 3, 0,1,1, 1,1,1),
( 64,128,128, 1, 112,112, 1, 3, 3, 0,1,1, 1,1,1),
( 64,128,256, 1, 56, 56, 1, 3, 3, 0,1,1, 1,1,1),
( 64,256,256, 1, 56, 56, 1, 3, 3, 0,1,1, 1,1,1),
( 64,256,512, 1, 28, 28, 1, 3, 3, 0,1,1, 1,1,1),
( 64,512,512, 1, 28, 28, 1, 3, 3, 0,1,1, 1,1,1),
( 64,512,512, 1, 14, 14, 1, 3, 3, 0,1,1, 1,1,1),
(128, 3, 64, 1, 224,224, 1,11,11, 0,3,3, 1,4,4), #Alexnet
(128, 64,192, 1, 27, 27, 1, 5, 5, 0,2,2, 1,1,1),
(128,192,384, 1, 13, 13, 1, 3, 3, 0,1,1, 1,1,1),
(128,384,256, 1, 13, 13, 1, 3, 3, 0,1,1, 1,1,1),
(128,256,256, 1, 13, 13, 1, 3, 3, 0,1,1, 1,1,1),):
conv = ng.conv_layer(dtype, *dims)
N,C,K = conv.NCK
D,H,W = conv.DHW
T,R,S = conv.TRS
M,P,Q = conv.MPQ
pad_d, pad_h, pad_w = conv.padding
str_d, str_h, str_w = conv.strides
alpha, beta = (1.0, 0.0)
dimI = conv.dimI2
dimF = conv.dimF2
dimO = conv.dimO2
print "cudnn:"
def run():
ng = NervanaGPU(stochastic_round=False)
dt = np.float32
# N: Number of images in mini-batch
# C: Number of input feature maps
# K: Number of output feature maps
# D: Depth of input image
# H: Height of input image
# W: Width of input image
# T: Depth of filter kernel
# R: Height of filter kernel
# S: Width of filter kernel
#
# * images: (numColors, imgSizeY, imgSizeX, numImages) with stride given
# * filters: (numColors, filterPixels, numFilters) if conv
# * (numModules, numColors, filterPixels, numFilters) otherwise
# *
# * targets: (numFilters, numModulesY, numModulesX, numImages)
N = 128
C = 3
K = 64
D = 1
H = 64
W = 64
T = 1
R = 8
S = 8
pad_h = pad_w = 0
str_h = str_w = 4
layer = ng.conv_layer(dt, N, C, K,
D=D, H=H, W=W,
T=T, R=R, S=S,
pad_d=0, pad_h=pad_h, pad_w=pad_w,
str_d=1, str_h=str_h, str_w=str_w,
grid_P=0, grid_Q=0, update_size=None)
numImages = N
numFilters = K
numModulesY = int(math.ceil(float(H - R + 1 + 2*pad_h) / str_h))
numModulesX = int(math.ceil(float(W - S + 1 + 2*pad_w) / str_w))
print "Num Modules ", numModulesX, numModulesY
# Set up images, filters, and outputs
# imgd = np.loadtxt("im1.txt")
# img = np.zeros((64, 64, 3))
# print imgd.shape
# for i in range(3):
# img[:, :, i] = imgd[i*64:(i+1)*64, :]
# hostImages = np.tile(img)
hostImages = np.random.rand(C, H, W, N)
hostFilters = np.random.uniform(low=0.0, high=1.0, size=(C, S*R, numFilters)) #np.ones((C, S*R, numFilters)) #
hostOutputs = np.zeros((numFilters, numModulesY, numModulesX, N))
print "Input sum", np.sum(hostImages)
# Run cc2 kernel
devI = ng.array(hostImages, dtype=dt)
devF = ng.array(hostFilters, dtype=dt)
devO = ng.array(hostOutputs, dtype=dt)
ng.fprop_cuda_conv(layer, devI, devF, devO)
print "CC2 input sum: ", np.sum(devI.asnumpyarray())
print "CC2 output sum: ", np.sum(devO.asnumpyarray())
# Run maxwel kernel
# images: (C * H * W, N)
# filters: (C * S * R , numFilters)
# outputs: (numFilters * numModulesX * numModulesY, N)
devI = ng.array(hostImages.reshape((C*H*W, N)), dtype=dt)
devF = ng.array(hostFilters.reshape((C*S*R, numFilters)), dtype=dt)
devO2 = ng.array(hostOutputs.reshape(numFilters*numModulesX*numModulesY, N), dtype=dt)
ng.fprop_conv(layer, devI, devF, devO2)
print "NG input sum: ", np.sum(devI.asnumpyarray())
print "NG output sum: ", np.sum(devO2.asnumpyarray())
hostOutputs1 = np.reshape(devO.asnumpyarray(), devO2.shape)
hostOutputs2 = devO2.asnumpyarray()
for i in xrange(hostOutputs1.shape[0]):
for j in xrange(hostOutputs1.shape[1]):
assert(abs(hostOutputs1[i, j] - hostOutputs2[i, j]) < 1e-4)
(64, 64, 64, 1, 224, 224, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 64, 128, 1, 112, 112, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 128, 128, 1, 112, 112, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 128, 256, 1, 56, 56, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 256, 256, 1, 56, 56, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 256, 512, 1, 28, 28, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 512, 512, 1, 28, 28, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(64, 512, 512, 1, 14, 14, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(128, 3, 64, 1, 224, 224, 1, 11, 11, 0, 3, 3, 1, 4, 4), #Alexnet
(128, 64, 192, 1, 27, 27, 1, 5, 5, 0, 2, 2, 1, 1, 1),
(128, 192, 384, 1, 13, 13, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(128, 384, 256, 1, 13, 13, 1, 3, 3, 0, 1, 1, 1, 1, 1),
(128, 256, 256, 1, 13, 13, 1, 3, 3, 0, 1, 1, 1, 1, 1),
):
conv = ng.conv_layer(dtype, *dims)
N, C, K = conv.NCK
D, H, W = conv.DHW
T, R, S = conv.TRS
M, P, Q = conv.MPQ
pad_d, pad_h, pad_w = conv.padding
str_d, str_h, str_w = conv.strides
alpha, beta = (1.0, 0.0)
dimI = conv.dimI2
dimF = conv.dimF2
dimO = conv.dimO2
print "cudnn:"
print context.get_device().name()
np.set_printoptions(threshold=8193, linewidth=600, formatter={'int':lambda x: "%10d" % x,'float':lambda x: "% .0f" % x})
ops = set(("update",)) # "fprop","bprop","update"
ones = 0
cpu = 0 # Set CPU to 1 to check against CPU
repeat = 1
dtype = np.float32
ng = NervanaGPU(stochastic_round=False, bench=True)
conv = ng.conv_layer(
dtype,
16,3,8, # N,C,K
1,64,64, # D,H,W
1,3,3, # T,R,S
0,1,1, # padding
1,1,1) # strides
dimI = conv.dimI
dimF = conv.dimF
dimO = conv.dimO
# colapse outer dimensions into one and preserve inner dimension
# this allows for easy cpu convolution in numpy
def slicable(dim, pad=0):
dim0 = reduce(mul, dim[:-1], 1) + pad
return (dim0, dim[-1])
print(context.get_device().name())
np.set_printoptions(threshold=8193, linewidth=600, formatter={'int':lambda x: "%10d" % x,'float':lambda x: "% .0f" % x})
ops = set(("update",)) # "fprop","bprop","update"
ones = 0
cpu = 0 # Set CPU to 1 to check against CPU
repeat = 1
dtype = np.float32
ng = NervanaGPU(stochastic_round=False, bench=True)
conv = ng.conv_layer(
dtype,
16,3,8, # N,C,K
1,64,64, # D,H,W
1,3,3, # T,R,S
0,1,1, # padding
1,1,1) # strides
dimI = conv.dimI
dimF = conv.dimF
dimO = conv.dimO
# colapse outer dimensions into one and preserve inner dimension
# this allows for easy cpu convolution in numpy
def slicable(dim, pad=0):
dim0 = reduce(mul, dim[:-1], 1) + pad
return (dim0, dim[-1])
