Python NervanaGPU.bprop_pool方法代码示例

# 需要导入模块: from nervanagpu import NervanaGPU [as 别名]
# 或者: from nervanagpu.NervanaGPU import bprop_pool [as 别名]

#.........这里部分代码省略.........
        produce output pre-activations (ready for transformation by an
        activation function).

        Arguments:
            out (GPUTensor): Where to store the forward propagated results.
            inputs (GPUTensor): Will be either the dataset input values (first
                                layer), or the outputs from the previous layer.
            op (string): The type of pooling operation to apply.  We support
                         "max", "avg", "l2" currently.
            ofmshape (tuple): Dimensions of each output feature map (typically
                              number of height and width neurons).
            ofmsize (int): Total size of each output feature map.
            ofmlocs (GPUTensor): Indices giving the location of each element in
                                 each output feature map stored in out.
            fshape (tuple): Dimensions of each filter (typically height and
                            width).
            ifmshape (tuple): Dimensions of each input feature map (typically
                              number of height and width neurons).
            links (GPUTensor): Input receptive field indices.
            nifm (int): Total number of input feature maps.
            padding (int): Number of additional elements to include along each
                           dimension of each local receptive field during the
                           pooling operation.
            stride (int): Number of neurons to shift the filter at each step.
            fpropbuf (GPUTensor): Temporary storage buffer used to hold the
                                  pooled outputs for a single receptive field.
        """
        op = op.lower()
        if op == "max":
            self.ng.fprop_pool(layer=fpropbuf, I=inputs, O=out, repeat=1)
        else:
            raise AttributeError("unexpected pooling op type: %s", op)

    def bprop_pool(self, out, fouts, inputs, deltas, op, ofmshape, ofmsize,
                   ofmlocs, fshape, fpsize, ifmshape, links, nifm, padding,
                   stride, bpropbuf):
        """
        Backward propagate the error through a pooling network layer.

        Arguments:
            out (GPUTensor): Where to store the backward propagated errors.
            fouts (GPUTensor): Forward propagated outputs from the previous
                               layer.
            inputs (GPUTensor): Will be either the dataset input values (first
                                layer), or the outputs from the previous layer.
            deltas (GPUTensor): The error values for this layer
            op (string): The type of pooling operation to apply.  We support
                         "max", "avg", "l2" currently.
            ofmshape (tuple): Dimensions of each output feature map (typically
                              height and width).
            ofmsize (int): Total size of each output feature map.
            ofmlocs (GPUTensor): Indices giving the location of each element in
                              each output feature map stored in out.
            fshape (tuple): Dimensions of each filter (typically height and
                            width).
            fpsize (int): The size of each filter.
            ifmshape (tuple): Dimensions of each input feature map (typically
                              height and width).
            links (GPUTensor): Input receptive field indices.
            nifm (int): Total number of input feature maps.
            padding (int): Number of additional elements to include along each
                           dimension of each local receptive field during the
                           pooling operation.
            stride (int): Number of neurons to shift the filter at each step.
            bpropbuf (GPUTensor): Temporary storage buffer used to hold the
                                  backpropagated error for a single receptive

# 需要导入模块: from nervanagpu import NervanaGPU [as 别名]
# 或者: from nervanagpu.NervanaGPU import bprop_pool [as 别名]
    cpuI[-1,:] = np.finfo(cpuI.dtype).min
else:
    cpuI[-1,:] = 0

# cpu output arrays
cpuO = np.empty(dimO, dtype=np.float32)
cpuB = np.zeros(slicable(dimI,1), dtype=np.float32)

# give gpu the input array without zero padding (not needed)
devI = ng.array(cpuI[:-1,:].reshape(dimI), dtype=dtype)
devO = ng.zeros(dimO, dtype=dtype)
devB = ng.empty(dimI, dtype=dtype)

ng.fprop_pool(pool, devI, devO, repeat=repeat)

ng.bprop_pool(pool, devI, devO, devB, repeat=repeat)

def pixel_indices(kj, mt, pr, qs):

    C       = pool.C
    J,T,R,S = pool.JTRS
    D,H,W = pool.DHW
    HW    = H*W
    DHW   = D*H*W
    imax  = C*D*H*W
    idx   = []

    for j in range(J):
        c  = kj + j
        ci = c*DHW
        cb = c >= 0 and c < C