chenfei
5 years ago
1 changed files with 24 additions and 7 deletions
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+24 -7mindspore/nn/layer/conv.py
+ 24
- 7
mindspore/nn/layer/conv.py
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| @@ -21,7 +21,7 @@ from mindspore.ops.primitive import constexpr | |||
| from mindspore.common.parameter import Parameter | |||
| from mindspore.common.initializer import initializer | |||
| from mindspore.common.tensor import Tensor | |||
| from mindspore._checkparam import Rel | |||
| from mindspore._checkparam import ParamValidator as validator, Rel | |||
| from mindspore._checkparam import Validator | |||
| from mindspore._checkparam import check_bool, twice, check_int_positive | |||
| from mindspore._extends import cell_attr_register | |||
| @@ -29,10 +29,12 @@ from ..cell import Cell | |||
| __all__ = ['Conv2d', 'Conv2dTranspose', 'DepthwiseConv2d', 'Conv1d', 'Conv1dTranspose'] | |||
| class _Conv(Cell): | |||
| """ | |||
| Applies a N-D convolution over an input signal composed of several input planes. | |||
| """ | |||
| def __init__(self, | |||
| in_channels, | |||
| out_channels, | |||
| @@ -68,15 +70,16 @@ class _Conv(Cell): | |||
| self.group = check_int_positive(group) | |||
| self.has_bias = has_bias | |||
| if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \ | |||
| kernel_size[0] < 1 or kernel_size[1] < 1: | |||
| isinstance(kernel_size[0], bool) or isinstance(kernel_size[1], bool) or \ | |||
| kernel_size[0] < 1 or kernel_size[1] < 1: | |||
| raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed " | |||
| + str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.") | |||
| if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or \ | |||
| isinstance(stride[0], bool) or isinstance(stride[1], bool) or stride[0] < 1 or stride[1] < 1: | |||
| isinstance(stride[0], bool) or isinstance(stride[1], bool) or stride[0] < 1 or stride[1] < 1: | |||
| raise ValueError("Attr 'stride' of 'Conv2D' Op passed " | |||
| + str(self.stride) + ", should be a int or tuple and equal to or greater than 1.") | |||
| if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \ | |||
| dilation[0] < 1 or dilation[1] < 1: | |||
| isinstance(dilation[0], bool) or isinstance(dilation[1], bool) or dilation[0] < 1 or dilation[1] < 1: | |||
| raise ValueError("Attr 'dilation' of 'Conv2D' Op passed " | |||
| + str(self.dilation) + ", should equal to or greater than 1.") | |||
| if in_channels % group != 0: | |||
| @@ -192,6 +195,7 @@ class Conv2d(_Conv): | |||
| >>> net(input).shape | |||
| (1, 240, 1024, 640) | |||
| """ | |||
| @cell_attr_register | |||
| def __init__(self, | |||
| in_channels, | |||
| @@ -263,6 +267,7 @@ def _check_input_3d(input_shape): | |||
| if len(input_shape) != 3: | |||
| raise ValueError(f"Input should be 3d, but got shape {input_shape}") | |||
| class Conv1d(_Conv): | |||
| r""" | |||
| 1D convolution layer. | |||
| @@ -343,6 +348,7 @@ class Conv1d(_Conv): | |||
| >>> net(input).shape | |||
| (1, 240, 640) | |||
| """ | |||
| @cell_attr_register | |||
| def __init__(self, | |||
| in_channels, | |||
| @@ -497,6 +503,7 @@ class Conv2dTranspose(_Conv): | |||
| >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32) | |||
| >>> net(input) | |||
| """ | |||
| def __init__(self, | |||
| in_channels, | |||
| out_channels, | |||
| @@ -661,6 +668,7 @@ class Conv1dTranspose(_Conv): | |||
| >>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32) | |||
| >>> net(input) | |||
| """ | |||
| def __init__(self, | |||
| in_channels, | |||
| out_channels, | |||
| @@ -808,7 +816,8 @@ class DepthwiseConv2d(Cell): | |||
| filter and :math:`out_{j}` corresponds to the :math:`j`-th channel of the output. :math:`W_{ij}` is a slice | |||
| of kernel and it has shape :math:`(\text{ks_h}, \text{ks_w})`, where :math:`\text{ks_h}` and | |||
| :math:`\text{ks_w}` are the height and width of the convolution kernel. The full kernel has shape | |||
| :math:`(C_{out}, C_{in}, \text{ks_h}, \text{ks_w})` to split the input in the channel dimension. | |||
| :math:`(C_{out}, C_{in} // \text{group}, \text{ks_h}, \text{ks_w})`, where group is the group number | |||
| to split the input in the channel dimension. | |||
| If the 'pad_mode' is set to be "valid", the output height and width will be | |||
| :math:`\left \lfloor{1 + \frac{H_{in} + 2 \times \text{padding} - \text{ks_h} - | |||
| @@ -851,6 +860,8 @@ class DepthwiseConv2d(Cell): | |||
| be :math:`k - 1` pixels skipped for each sampling location. Its value should | |||
| be greater than or equal to 1 and bounded by the height and width of the | |||
| input. Default: 1. | |||
| group (int): Split filter into groups, `in_ channels` and `out_channels` should be | |||
| divisible by the number of groups. Default: 1. | |||
| has_bias (bool): Specifies whether the layer uses a bias vector. Default: False. | |||
| weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the convolution kernel. | |||
| It can be a Tensor, a string, an Initializer or a number. When a string is specified, | |||
| @@ -874,6 +885,7 @@ class DepthwiseConv2d(Cell): | |||
| >>> net(input).shape | |||
| (1, 240, 1024, 640) | |||
| """ | |||
| def __init__(self, | |||
| in_channels, | |||
| out_channels, | |||
| @@ -882,6 +894,7 @@ class DepthwiseConv2d(Cell): | |||
| pad_mode='same', | |||
| padding=0, | |||
| dilation=1, | |||
| group=1, | |||
| has_bias=False, | |||
| weight_init='normal', | |||
| bias_init='zeros'): | |||
| @@ -891,9 +904,13 @@ class DepthwiseConv2d(Cell): | |||
| self.dilation = twice(dilation) | |||
| self.in_channels = check_int_positive(in_channels) | |||
| self.out_channels = check_int_positive(out_channels) | |||
| validator.check_integer('group', group, in_channels, Rel.EQ) | |||
| validator.check_integer('group', group, out_channels, Rel.EQ) | |||
| validator.check_integer('group', group, 1, Rel.GE) | |||
| self.pad_mode = pad_mode | |||
| self.padding = padding | |||
| self.dilation = dilation | |||
| self.group = group | |||
| self.has_bias = has_bias | |||
| self.weight_init = weight_init | |||
| self.bias_init = bias_init | |||
| @@ -921,10 +938,10 @@ class DepthwiseConv2d(Cell): | |||
| def extend_repr(self): | |||
| s = 'input_channels={}, output_channels={}, kernel_size={}, stride={}, ' \ | |||
| 'pad_mode={}, padding={}, dilation={}' \ | |||
| 'pad_mode={}, padding={}, dilation={}, group={}, ' \ | |||
| 'has_bias={}, weight_init={}, bias_init={}'.format( | |||
| self.in_channels, self.out_channels, self.kernel_size, self.stride, | |||
| self.pad_mode, self.padding, self.dilation, | |||
| self.pad_mode, self.padding, self.dilation, self.group, | |||
| self.has_bias, self.weight_init, self.bias_init) | |||
| if self.has_bias: | |||