32 from daal.algorithms.neural_networks
import layers
33 from daal.data_management
import HomogenTensor
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder
not in sys.path:
37 sys.path.insert(0, utils_folder)
38 from utils
import printTensor, printNumericTable
42 dataArray = np.array([[[[2, 4, 6, 8],
49 [-10, -12, -14, -16]],
50 [[-18, -20, -22, -24],
51 [-26, -28, -30, -32]],
52 [[-34, -36, -38, -40],
53 [-42, -44, -46, -48]]]],
56 if __name__ ==
"__main__":
57 data = HomogenTensor(dataArray)
60 printTensor(data,
"Forward two-dimensional spatial pyramid average pooling layer input (first 10 rows):", 10)
63 forwardLayer = layers.spatial_average_pooling2d.forward.Batch(2, nDim)
64 forwardLayer.input.setInput(layers.forward.data, data)
68 forwardResult = forwardLayer.compute()
70 printTensor(forwardResult.getResult(layers.forward.value),
71 "Forward two-dimensional spatial pyramid average pooling layer result (first 5 rows):",
73 printNumericTable(forwardResult.getLayerData(layers.spatial_average_pooling2d.auxInputDimensions),
74 "Forward two-dimensional spatial pyramid average pooling layer input dimensions:")
77 backwardLayer = layers.spatial_average_pooling2d.backward.Batch(2, nDim)
78 backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
79 backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
83 backwardResult = backwardLayer.compute()
85 printTensor(backwardResult.getResult(layers.backward.gradient),
86 "Backward two-dimensional spatial pyramid average pooling layer result (first 10 rows):",