* enabled convolution & activation fusion

* a few more optimizations:
+ optimized the common case when the indices of max pooling layer are not used. in this case we use the more efficient branch that computes just maximums over the aperture.
+ optimized the convolution + activation fusion when the activation is relu, which is another common case
+ convolution can now be fused with batch norm. It's the zero-cost fusion. If the batch norm is followed by relu, all three (conv + batchnorm + relu) are fused together. this modification seriously improved ENet performance

* hopefully fixed warnings on Windows