Hello @ptrblck,
I’m stuck at this stage
end_point = 'Logits'
        **self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7],**
**                                     stride=(1, 1, 1))**
        self.dropout = nn.Dropout(dropout_keep_prob)
        self.logits = Unit3D(in_channels=384+384+128+128, output_channels=self._num_classes,
                             kernel_shape=[1, 1, 1],
                             padding=0,
                             activation_fn=None,
                             use_batch_norm=False,
                             use_bias=True,
                             name='logits')

        self.build()

As it is done in the Keras version of the code, I would like to flatten the output of the average pooling (avgpool3d), and then direct it to a dense layer to do a classification.
I don’t want to use the dropout and the Unit3D. I intend to remove these two last steps.
I tried to apply a flatten using pytorch, but it says to me that the output of avgpool3d can’t be flattened.