dtype = np.float16
cpu = 1
repeat = 1
ng = NervanaGPU(stochastic_round=False, bench=True)
pool = ng.pool_layer(
"max",
64, # N
64,
1,
64,
64, # C,D,H,W
4,
1,
2,
2, # J,T,R,S
0,
0,
0,
0, # padding
4,
1,
2,
2) # strides
dimI = pool.dimI
dimO = pool.dimO
# colapse pooling dimensions into one
dtype = np.float32
cpu = 1
repeat = 1
ng = NervanaGPU(stochastic_round=False, bench=True)
pool = ng.pool_layer(
dtype,
"max",
32, # N
32,
1,
32,
32, # C,D,H,W
2,
1,
3,
3, # J,T,R,S
0,
0,
0,
0, # padding
2,
1,
2,
2) # strides
dimI = pool.dimI
dimO = pool.dimO
# colapse pooling dimensions into one
from operator import mul
print context.get_device().name()
np.set_printoptions(threshold=8193, linewidth=600, formatter={'int':lambda x: "%10d" % x,'float':lambda x: "% .3f" % x})
dtype = np.float16
cpu = 1
repeat = 1
ng = NervanaGPU(stochastic_round=False, bench=True)
pool = ng.pool_layer(
"max",
64, # N
64,1,64,64, # C,D,H,W
4,1,2,2, # J,T,R,S
0,0,0,0, # padding
4,1,2,2) # strides
dimI = pool.dimI
dimO = pool.dimO
# colapse pooling dimensions into one
# this allows for easy cpu pooling in numpy
def slicable(dim, pad=0):
dim0 = reduce(mul, dim[:-1], 1) + pad
return (dim0, dim[-1])
# cpu input arrays
cpuI = np.random.uniform(0.0, 9.4, slicable(dimI,1)).astype(np.float16).astype(np.float32)
from operator import mul
print(context.get_device().name())
np.set_printoptions(threshold=8193, linewidth=600, formatter={'int':lambda x: "%10d" % x,'float':lambda x: "% .3f" % x})
dtype = np.float32
cpu = 1
repeat = 1
ng = NervanaGPU(stochastic_round=False, bench=True)
pool = ng.pool_layer(dtype,
"max",
32, # N
32,1,32,32, # C,D,H,W
2,1,3,3, # J,T,R,S
0,0,0,0, # padding
2,1,2,2) # strides
dimI = pool.dimI
dimO = pool.dimO
# colapse pooling dimensions into one
# this allows for easy cpu pooling in numpy
def slicable(dim, pad=0):
dim0 = reduce(mul, dim[:-1], 1) + pad
return (dim0, dim[-1])
# cpu input arrays
cpuI = np.random.uniform(0.0, 1.0, slicable(dimI,1)).astype(np.float16).astype(np.float32)
