test_neon1.py

from neon.layers import Convolution
from neon.initializers import Gaussian
from neon.backends import gen_backend
import numpy as np
import pycuda.driver as cuda
#import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import time

image_size = 224
batch_size = 128
input_filters = 32
output_filters = 32

gen_backend(backend='gpu', batch_size=batch_size,
            datatype=np.float32, device_id=0)

init = Gaussian()

conv = Convolution((3, 3, output_filters), strides=1, padding=1, init=init)

#inputs = np.zeros((batch_size,image_size, image_size,input_filters), dtype=np.float32)
inputs = np.zeros((input_filters,image_size, image_size,batch_size), dtype=np.float32)
inputs[:] = np.random.randn(*inputs.shape)
inputs_cuda = gpuarray.to_gpu(inputs)

conv.configure((input_filters,image_size, image_size))
print('configure done')
conv.allocate()
print('allocate done')
conv.fprop(inputs_cuda)
for it in range(10):
  start = time.time()
  for i in range(10):
    conv.fprop(inputs_cuda)
  cuda.Context.synchronize()
  print('time=', time.time() - start)