python类Apply()的实例源码

def make_node(self, img, kern, output, desc, alpha=None, beta=None):
        img = as_cuda_ndarray_variable(img)
        kern = as_cuda_ndarray_variable(kern)
        output = as_cuda_ndarray_variable(output)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if kern.type.ndim != 4:
            raise TypeError('kern must be 4D tensor')
        if output.type.ndim != 4:
            raise TypeError('output must be a 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')

        alpha = ensure_float(alpha, _one, 'alpha')
        beta = ensure_float(beta, _zero, 'beta')

        return Apply(self, [img, kern, output, desc, alpha, beta],
                     [output.type()])

def make_node(self, img, kern, output, desc, alpha=None, beta=None):

        img = as_cuda_ndarray_variable(img)
        kern = as_cuda_ndarray_variable(kern)
        output = as_cuda_ndarray_variable(output)
        if img.type.ndim != 5:
            raise TypeError('img must be 5D tensor')
        if kern.type.ndim != 5:
            raise TypeError('kern must be 5D tensor')
        if output.type.ndim != 5:
            raise TypeError('output must be a 5D tensor')
        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')
        alpha = ensure_float(alpha, _one, 'alpha')
        beta = ensure_float(beta, _zero, 'beta')

        return Apply(self, [img, kern, output, desc, alpha, beta],
                     [output.type()])

def make_node(self, img, topgrad, output, desc, alpha=None, beta=None):
        img = as_cuda_ndarray_variable(img)
        topgrad = as_cuda_ndarray_variable(topgrad)
        output = as_cuda_ndarray_variable(output)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if output.type.ndim != 4:
            raise TypeError('output must be 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')

        alpha = ensure_float(alpha, _one, 'alpha')
        beta = ensure_float(beta, _zero, 'beta')

        return Apply(self, [img, topgrad, output, desc, alpha, beta],
                     [output.type()])

def make_node(self, kern, topgrad, output, desc, alpha=None, beta=None):
        kern = as_cuda_ndarray_variable(kern)
        topgrad = as_cuda_ndarray_variable(topgrad)
        output = as_cuda_ndarray_variable(output)
        if kern.type.ndim != 4:
            raise TypeError('kern must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if output.type.ndim != 4:
            raise TypeError('output must be 4D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')

        alpha = ensure_float(alpha, _one, 'alpha')
        beta = ensure_float(beta, _zero, 'beta')

        return Apply(self, [kern, topgrad, output, desc, alpha, beta],
                     [output.type()])

def make_node(self, kern, topgrad, output, desc, alpha=None, beta=None):
        kern = as_cuda_ndarray_variable(kern)
        topgrad = as_cuda_ndarray_variable(topgrad)
        output = as_cuda_ndarray_variable(output)
        if kern.type.ndim != 5:
            raise TypeError('kern must be 5D tensor')
        if topgrad.type.ndim != 5:
            raise TypeError('topgrad must be 5D tensor')
        if output.type.ndim != 5:
            raise TypeError('output must be 5D tensor')

        if not isinstance(desc.type, CDataType) \
                or desc.type.ctype != 'cudnnConvolutionDescriptor_t':
            raise TypeError('desc must be cudnnConvolutionDescriptor_t')

        alpha = ensure_float(alpha, _one, 'alpha')
        beta = ensure_float(beta, _zero, 'beta')

        return Apply(self, [kern, topgrad, output, desc, alpha, beta],
                     [output.type()])

def make_node(self, inp, out, inp_grad, ws, stride, pad):
        inp = as_cuda_ndarray_variable(inp)
        assert (inp.ndim in [4, 5])
        inp_grad = as_cuda_ndarray_variable(inp_grad)
        assert (inp_grad.ndim in [4, 5])
        out = as_cuda_ndarray_variable(out)
        assert(out.ndim in [4, 5])

        assert (inp_grad.ndim == inp.ndim)
        assert (inp.ndim == out.ndim)

        ws = tensor.as_tensor_variable(ws)
        stride = tensor.as_tensor_variable(stride)
        pad = tensor.as_tensor_variable(pad)
        assert ws.type.ndim == stride.type.ndim and ws.type.ndim == pad.type.ndim
        assert ws.type.ndim == 1

        return Apply(self, [inp, out, inp_grad, ws, stride, pad],
                     [inp.type()])

def make_node(self, input):
        ib = tuple(input.type.broadcastable)
        if not ib == self.input_broadcastable:
            if len(ib) != len(self.input_broadcastable):
                raise TypeError((
                    "The number of dimensions of the "
                    "input is incorrect for this op. Expected %s, got %s."
                    % (self.input_broadcastable, ib)))
            for expected, b in zip(self.input_broadcastable, ib):
                if expected is True and b is False:
                    raise TypeError((
                        "The broadcastable pattern of the "
                        "input is incorrect for this op. Expected %s, got %s."
                        % (self.input_broadcastable, ib)))
                # else, expected == b or expected is False and b is True
                # Both case are good.
        ob = []
        if not isinstance(input.type, CudaNdarrayType):
            input = as_cuda_ndarray_variable(input)
        for value in self.new_order:
            if value == 'x':
                ob.append(True)
            else:
                ob.append(ib[value])
        return Apply(self, [input], [CudaNdarrayType(broadcastable=ob)()])

def c_code_cache_version_apply(self, node):
        version = [15]  # the version corresponding to the c code in this Op

        # now we insert versions for the ops on which we depend...
        Apply(self.scalar_op,
              [Scalar(
                  dtype=input.type.dtype)() for input in node.inputs],
              [Scalar(
                  dtype=output.type.dtype)() for output in node.outputs])
        version.extend(self.scalar_op.c_code_cache_version())
        for i in node.inputs + node.outputs:
            version.extend(
                Scalar(dtype=i.type.dtype).c_code_cache_version())
        if all(version):
            return tuple(version)
        else:
            return ()

def make_node(self, x, ilist):
        x_ = as_cuda_ndarray_variable(x)
        ilist_ = tensor.as_tensor_variable(ilist)
        if ilist_.type.dtype[:3] not in ('int', 'uin'):
            raise TypeError('index must be integers')
        if ilist_.type.ndim != 1:
            raise TypeError('index must be vector')
        if x_.type.ndim == 0:
            raise TypeError('cannot index into a scalar')

        # c code suppose it is int64
        if x.ndim in [1, 2, 3] and ilist_.dtype in [
                'int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32']:
            ilist_ = tensor.cast(ilist_, 'int64')

        bcast = (ilist_.broadcastable[0],) + x_.broadcastable[1:]
        return Apply(self, [x_, ilist_],
                     [CudaNdarrayType(dtype=x.dtype,
                                      broadcastable=bcast)()])

def make_node(self, img, topgrad, shape=None):
        img = as_cuda_ndarray_variable(img)
        topgrad = as_cuda_ndarray_variable(topgrad)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if self.subsample != (1, 1) or self.border_mode == "half":
            if shape is None:
                raise ValueError('shape must be given if subsample != (1, 1)'
                                 ' or border_mode == "half"')
            height_width = [shape[0], shape[1]]
            assert shape[0].ndim == 0
            assert shape[1].ndim == 0
        else:
            height_width = []

        broadcastable = [topgrad.type.broadcastable[1], img.type.broadcastable[1],
                         False, False]
        return Apply(self, [img, topgrad] + height_width, [CudaNdarrayType(broadcastable)()])

def make_node(self, img, topgrad, shape=None):
        img = as_cuda_ndarray_variable(img)
        topgrad = as_cuda_ndarray_variable(topgrad)
        if shape is not None:
            shape = as_tensor_variable(shape)

        if img.type.ndim != 5:
            raise TypeError('img must be 5D tensor')
        if topgrad.type.ndim != 5:
            raise TypeError('topgrad must be 5D tensor')
        if self.subsample != (1, 1, 1) or self.border_mode == "half":
            if shape is None:
                raise ValueError('shape must be given if subsample != (1, 1, 1)'
                                 ' or border_mode == "half"')
            height_width_depth = [shape[0], shape[1], shape[2]]
            assert shape[0].ndim == 0
            assert shape[1].ndim == 0
            assert shape[2].ndim == 0
        else:
            height_width_depth = []

        broadcastable = [topgrad.type.broadcastable[1], img.type.broadcastable[1],
                         False, False, False]
        return Apply(self, [img, topgrad] + height_width_depth, [CudaNdarrayType(broadcastable)()])

def make_node(self, kern, topgrad, shape=None):
        kern = as_cuda_ndarray_variable(kern)
        topgrad = as_cuda_ndarray_variable(topgrad)
        if kern.type.ndim != 5:
            raise TypeError('kern must be 5D tensor')
        if topgrad.type.ndim != 5:
            raise TypeError('topgrad must be 5D tensor')
        if self.subsample != (1, 1, 1) and shape is None:
            raise ValueError('shape must be given if subsample != (1, 1, 1)')
        height_width_depth = [shape[0], shape[1], shape[2]] if self.subsample != (1, 1, 1) else []
        if height_width_depth:
            assert shape[0].ndim == 0
            assert shape[1].ndim == 0
            assert shape[2].ndim == 0

        broadcastable = [topgrad.type.broadcastable[0], kern.type.broadcastable[1],
                         False, False, False]
        return Apply(self, [kern, topgrad] + height_width_depth, [CudaNdarrayType(broadcastable)()])

def make_node(self, V, W, b, d):
        """

        Parameters
        ----------
        V
            Visible unit, input.
        W
            Weights, filter.
        b
            Bias.
        d
            Strides when moving the filter over the input.

        """
        V_ = as_cuda_ndarray_variable(V)
        W_ = as_cuda_ndarray_variable(W)
        b_ = as_cuda_ndarray_variable(b)
        d_ = T.as_tensor_variable(d)
        broad = (V_.broadcastable[0], W_.broadcastable[0], False, False, False)
        return theano.Apply(self, inputs=[V_, W_, b_, d_],
                            outputs=[CudaNdarrayType(dtype=V_.dtype,
                                                     broadcastable=broad)()])

def make_node(self, V, d, WShape, dCdH):
        """

        Parameters
        ----------
        V
            Visible.
        d
            Strides.
        WShape
            Shapes of the weights -> shape of this op output.
        dCdH
            Other input with what V will be convolved.

        """
        V_ = as_cuda_ndarray_variable(V)
        d_ = T.as_tensor_variable(d)
        WShape_ = T.as_tensor_variable(WShape)
        dCdH_ = as_cuda_ndarray_variable(dCdH)
        broad = (False,) * 5
        return theano.Apply(self, inputs=[V_, d_, WShape_, dCdH_],
                            outputs=[CudaNdarrayType(dtype=V_.dtype,
                                                     broadcastable=broad)()])

def make_node(self, s_x_):
        if s_x_.type.dtype != 'float32':
            raise ValueError('Only float32 is allowed')
        ctx_name = infer_context_name(s_x_)
        s_x = as_gpuarray_variable(s_x_, ctx_name)
        return th.Apply(self, [s_x], [s_x.type(), s_x.type()])

def make_node(self, s_x_):
        if s_x_.type.dtype != 'float32':
            raise ValueError('Only float32 is allowed')
        ctx_name = infer_context_name(s_x_)
        s_x = as_gpuarray_variable(s_x_, ctx_name)
        return th.Apply(self, [s_x], [s_x.type()])

def make_node(self, input_):
        input_ = tensor.as_tensor_variable(input_)
        output_type = tensor.TensorType(
            input_.dtype, input_.broadcastable[:-1])
        return theano.Apply(self, [input_], [output_type()])

def make_node(self, input_):
        input_ = tensor.as_tensor_variable(input_)
        output_type = tensor.TensorType(
            input_.dtype, input_.broadcastable[:-1])
        return theano.Apply(self, [input_], [output_type()])

def make_node(self, input_):
        defs_type = tensor.TensorType('int64', [False, False])
        def_mask_type = tensor.TensorType('float32', [False, False])
        # both type happened to be the same, but this is just a coincidence
        def_map_type = defs_type
        return theano.Apply(
            self, [input_], [defs_type(), def_mask_type(), def_map_type()])

def make_node(self, x):
        x = as_gpuarray_variable(x, self.context_name)

        x_arg = pygpu.elemwise.arg('x', 'float32', read=True)
        c_arg = pygpu.elemwise.arg('c', 'float32', read=True, write=True)
        self.my_op = pygpu.elemwise.GpuElemwise(get_context(self.context_name), "c = " + str(self.a) + " * x + " + str(self.b), [x_arg, c_arg], convert_f16=True)

        return Apply(self, [x], [x.type()])