def conv2d(input,
filter,
strides,
padding,
dilation_rate=None,
data_format='NHWC',
name=None,
**kwargs):
"""Compute 2D convolution according to the given 4D ``input`` and ``filter``.
For **NHWC** format, filter should be as ``[filter_height, filter_width, in_channels, out_channels]``.
For **NCHW** format, filter should be as ``[out_channels, in_channels, filter_height, filter_width]``.
Parameters
----------
input : Tensor
The input tensor.
filter : Tensor
The filter tensor.
strides : list of int
The strides with length 4.
padding : str
The padding algorithm. ``VALID`` or ``SAME``.
dilation_rate : list of int or None
The dilation rates with with length 4.
data_format : str
The data format. ``NHWC`` or ``NCHW``.
name : str
The optional name for this operator.
Returns
-------
Tensor
The output tensor.
"""
if filter.shape is None:
raise ValueError('filter must have a valid shape.')
else:
if len(filter.shape) != 4:
raise ValueError('filter must be a 4D Tensor.')
if len(strides) != 4:
raise ValueError('strides must be a list with length 4.')
if dilation_rate is not None:
if len(dilation_rate) != 4:
raise ValueError(' dilation_rate must be a list with length 4.')
if data_format == 'NHWC':
output = ops.Conv2d(
[input, filter],
num_output=filter.shape[3],
kernel_size=filter.shape[0:2],
stride=strides[1:3],
dilation=dilation_rate[1:3] if dilation_rate is not None else 1,
padding=padding,
data_format=data_format)
return output
elif data_format == 'NCHW':
output = ops.Conv2d(
[input, filter],
num_output=filter.shape[0],
kernel_size=filter.shape[2:4],
stride=strides[2:4],
dilation=dilation_rate[2:4] if dilation_rate is not None else 1,
padding=padding,
data_format=data_format)
return output
else:
raise ValueError('Unknown data format: {}'.format(data_format))
def Setup(self, bottom):
super(ConvolutionLayer, self).Setup(bottom)
return ops.Conv2d(bottom + [blob['data'] for blob in self._blobs], **self._param)
def LayerSetup(self, bottom):
inputs = [bottom] + [blob['data'] for blob in self._blobs]
return _ops.Conv2d(inputs, **self.arguments)