(self, shared_outputs=None)
Allocate output buffer to store activations from fprop.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into
Allocate output buffer to store activations from fprop.
Allocate output buffer to store activations from fprop .
def allocate(self, shared_outputs=None):
        """
        Allocate output buffer to store activations from fprop.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into
        """
        super(RoiPooling, self).allocate(shared_outputs)
        self.owns_output = True
        self.error = self.be.iobuf(self.in_shape)
        self.outputs = self.be.iobuf(self.out_shape, shared=shared_outputs)
        self.max_idx = self.be.iobuf(self.out_shape, dtype=np.int32)
def allocate ( self , shared_outputs = None ) : super ( RoiPooling , self ) . allocate ( shared_outputs ) self . owns_output = True self . error = self . be . iobuf ( self . in_shape ) self . outputs = self . be . iobuf ( self . out_shape , shared = shared_outputs ) self . max_idx = self . be . iobuf ( self . out_shape , dtype = np . int32 )