Greetings,

What I want to do is rewriting the Keras code into PyTorch.

The only problem is how to implement Depthwise and Separable 2D convolution in PyTorch.

Here’s the original code from https://github.com/vlawhern/arl-eegmodels/blob/master/EEGModels.py

```
input1 = Input(shape = (1, Chans, Samples))
##################################################################
block1 = Conv2D(F1, (1, kernLength), padding = 'same',
input_shape = (1, Chans, Samples),
use_bias = False)(input1)
block1 = BatchNormalization(axis = 1)(block1)
block1 = DepthwiseConv2D((Chans, 1), use_bias = False,
depth_multiplier = D,
depthwise_constraint = max_norm(1.))(block1)
block1 = BatchNormalization(axis = 1)(block1)
block1 = Activation('elu')(block1)
block1 = AveragePooling2D((1, 4))(block1)
block1 = dropoutType(dropoutRate)(block1)
block2 = SeparableConv2D(F2, (1, 16),
use_bias = False, padding = 'same')(block1)
block2 = BatchNormalization(axis = 1)(block2)
block2 = Activation('elu')(block2)
block2 = AveragePooling2D((1, 8))(block2)
block2 = dropoutType(dropoutRate)(block2)
flatten = Flatten(name = 'flatten')(block2)
dense = Dense(nb_classes, name = 'dense',
kernel_constraint = max_norm(norm_rate))(flatten)
softmax = Activation('softmax', name = 'softmax')(dense)
return Model(inputs=input1, outputs=softmax)
```