Ejemplo n.º 1
0
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 pad_mode='same',
                 padding=0,
                 dilation=1,
                 group=1,
                 has_bias=False,
                 weight_init='normal',
                 bias_init='zeros'):

        Validator.check_value_type("kernel_size", kernel_size, [int],
                                   self.cls_name)
        Validator.check_value_type("stride", stride, [int], self.cls_name)
        Validator.check_value_type("padding", padding, [int], self.cls_name)
        Validator.check_value_type("dilation", dilation, [int], self.cls_name)
        Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size',
                            self.cls_name)
        Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name)
        Validator.check_non_negative_int(padding, 'padding', self.cls_name)
        Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name)
        kernel_size = (1, kernel_size)
        stride = (1, stride)
        dilation = (1, dilation)
        get_shape = P.Shape()
        get_dtype = P.DType()
        if isinstance(weight_init, Tensor):
            weight_init_shape = get_shape(weight_init)
            Validator.check_equal_int(len(weight_init_shape), 3,
                                      'weight_init_shape', self.cls_name)
            weight_init_dtype = get_dtype(weight_init)
            weight_init_value = weight_init.asnumpy()
            weight_init_value = np.expand_dims(weight_init_value, 2)
            weight_init = Tensor(weight_init_value, weight_init_dtype)

        super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
                                     stride, pad_mode, padding, dilation,
                                     group, has_bias, weight_init, bias_init)
        self.padding = (0, 0, padding, padding)
        self.conv2d = P.Conv2D(out_channel=self.out_channels,
                               kernel_size=self.kernel_size,
                               mode=1,
                               pad_mode=self.pad_mode,
                               pad=self.padding,
                               stride=self.stride,
                               dilation=self.dilation,
                               group=self.group)
        self.bias_add = P.BiasAdd()
        if pad_mode not in ('valid', 'same', 'pad'):
            raise ValueError(
                'Attr \'pad_mode\' of \'Conv1d\' Op passed ' + str(pad_mode) +
                ', should be one of values in \'valid\', \'same\', \'pad\'.')
        self.expand_dims = P.ExpandDims()
        self.squeeze = P.Squeeze(2)
        self.shape = P.Shape()
Ejemplo n.º 2
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 def _init_depthwise_conv2d(self):
     """Initialize depthwise conv2d op"""
     if context.get_context("device_target") == "Ascend" and self.group > 1:
         self.dilation = self._dilation
         Validator.check_equal_int(self.group, self.in_channels, 'group')
         Validator.check_equal_int(self.group, self.out_channels, 'group')
         self.conv2d = P.DepthwiseConv2dNative(channel_multiplier=1,
                                               kernel_size=self.kernel_size,
                                               pad_mode=self.pad_mode,
                                               pad=self.padding,
                                               stride=self.stride,
                                               dilation=self.dilation)
         weight_shape = [1, self.in_channels, *self.kernel_size]
         if isinstance(self.weight_init, Tensor):
             self.weight_init = Tensor(self.weight_init.asnumpy().swapaxes(0, 1), self.weight_init.dtype)
         if isinstance(self.weight_init, Initializer):
             self.weight_init.shape = weight_shape
         self.weight = Parameter(initializer(self.weight_init, weight_shape), name='weight')
Ejemplo n.º 3
0
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 pad_mode='same',
                 padding=0,
                 dilation=1,
                 group=1,
                 has_bias=False,
                 weight_init='normal',
                 bias_init='zeros'):
        Validator.check_value_type("kernel_size", kernel_size, [int],
                                   self.cls_name)
        Validator.check_value_type("stride", stride, [int], self.cls_name)
        Validator.check_value_type("padding", padding, [int], self.cls_name)
        Validator.check_value_type("dilation", dilation, [int], self.cls_name)
        Validator.check_int(kernel_size, 1, Rel.GE, 'kernel_size',
                            self.cls_name)
        Validator.check_int(stride, 1, Rel.GE, 'stride', self.cls_name)
        Validator.check_non_negative_int(padding, 'padding', self.cls_name)
        Validator.check_int(dilation, 1, Rel.GE, 'dilation', self.cls_name)
        kernel_size = (1, kernel_size)
        stride = (1, stride)
        dilation = (1, dilation)
        get_shape = P.Shape()
        get_dtype = P.DType()
        if isinstance(weight_init, Tensor):
            weight_init_shape = get_shape(weight_init)
            Validator.check_equal_int(len(weight_init_shape), 3,
                                      'weight_init_shape', self.cls_name)
            weight_init_dtype = get_dtype(weight_init)
            weight_init_value = weight_init.asnumpy()
            weight_init_value = np.expand_dims(weight_init_value, 2)
            weight_init = Tensor(weight_init_value, weight_init_dtype)
        # out_channels and in_channels swap.
        # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
        # then Conv1dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
        super(Conv1dTranspose, self).__init__(in_channels,
                                              out_channels,
                                              kernel_size,
                                              stride,
                                              pad_mode,
                                              padding,
                                              dilation,
                                              group,
                                              has_bias,
                                              weight_init,
                                              bias_init,
                                              transposed=True)
        self.padding = (0, 0, padding, padding)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.shape = P.Shape()
        if pad_mode not in ('valid', 'same', 'pad'):
            raise ValueError(
                'Attr \'pad_mode\' of \'Conv1dTranspose\' Op passed ' +
                str(pad_mode) +
                ', should be one of values in \'valid\', \'same\', \'pad\'.')
        self.is_valid = self.pad_mode == 'valid'
        self.is_same = self.pad_mode == 'same'
        self.is_pad = self.pad_mode == 'pad'
        if Validator.check_bool(has_bias):
            self.bias = Parameter(initializer(bias_init, [out_channels]),
                                  name='bias')

        # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
        self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
                                                      kernel_size=kernel_size,
                                                      mode=1,
                                                      pad_mode=pad_mode,
                                                      pad=self.padding,
                                                      stride=stride,
                                                      dilation=dilation,
                                                      group=group)
        self.bias_add = P.BiasAdd()
        self.expand_dims = P.ExpandDims()
        self.squeeze = P.Squeeze(2)
Ejemplo n.º 4
0
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 pad_mode='same',
                 padding=0,
                 dilation=1,
                 group=1,
                 has_bias=False,
                 weight_init='normal',
                 bias_init='zeros'):
        kernel_size = twice(kernel_size)
        stride = twice(stride)
        dilation = twice(dilation)
        Validator.check_value_type('padding', padding, (int, tuple),
                                   self.cls_name)
        if isinstance(padding, tuple):
            Validator.check_equal_int(len(padding), 4, 'padding size',
                                      self.cls_name)
        # out_channels and in_channels swap.
        # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
        # then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
        super(Conv2dTranspose, self).__init__(in_channels,
                                              out_channels,
                                              kernel_size,
                                              stride,
                                              pad_mode,
                                              padding,
                                              dilation,
                                              group,
                                              has_bias,
                                              weight_init,
                                              bias_init,
                                              transposed=True)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.shape = P.Shape()
        if pad_mode not in ('valid', 'same', 'pad'):
            raise ValueError(
                'Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed ' +
                str(pad_mode) +
                ', should be one of values in \'valid\', \'same\', \'pad\'.')
        self.is_valid = self.pad_mode == 'valid'
        self.is_same = self.pad_mode == 'same'
        self.is_pad = self.pad_mode == 'pad'
        if Validator.check_bool(has_bias):
            self.bias = Parameter(initializer(bias_init, [out_channels]),
                                  name='bias')

        # cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
        self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
                                                      kernel_size=kernel_size,
                                                      mode=1,
                                                      pad_mode=pad_mode,
                                                      pad=padding,
                                                      stride=stride,
                                                      dilation=dilation,
                                                      group=group)
        self.bias_add = P.BiasAdd()
        if isinstance(self.padding, int):
            self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = (
                self.padding, ) * 4
        else:
            self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = self.padding
Ejemplo n.º 5
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def t(data):
    validator.check_equal_int(data.ndim, 2)
    return mnp.swapaxes(data, 1, 0)