N-dimensional convolution for inputs with NCHW storage order.
(
model,
blob_in,
blob_out,
dim_in,
dim_out,
kernel,
weight_init=None,
bias_init=None,
WeightInitializer=None,
BiasInitializer=None,
group=1,
transform_inputs=None,
order="NCHW",
**kwargs
)
| 141 | |
| 142 | |
| 143 | def conv_nd( |
| 144 | model, |
| 145 | blob_in, |
| 146 | blob_out, |
| 147 | dim_in, |
| 148 | dim_out, |
| 149 | kernel, |
| 150 | weight_init=None, |
| 151 | bias_init=None, |
| 152 | WeightInitializer=None, |
| 153 | BiasInitializer=None, |
| 154 | group=1, |
| 155 | transform_inputs=None, |
| 156 | order="NCHW", |
| 157 | **kwargs |
| 158 | ): |
| 159 | """N-dimensional convolution for inputs with NCHW storage order. |
| 160 | """ |
| 161 | assert order == "NCHW", "ConvNd only supported for NCHW storage." |
| 162 | return _ConvBase(model, True, blob_in, blob_out, dim_in, dim_out, kernel, |
| 163 | weight_init, bias_init, WeightInitializer, BiasInitializer, |
| 164 | group, transform_inputs, order=order, **kwargs) |
| 165 | |
| 166 | |
| 167 | def conv( |
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