Dear All,
I am wondering how can I write a Relay pass that tiles conv2d by the output channels (data partitioning) in Relay graph level? For example, let us assume that I have some relay program like below, and I want to able to traverse the relay graph that contains this conv2d, and able to re-write it to one below **with two conv2d**? ``` kernel_size = 3 # N, C, H, W input_shape = (1, 4, 10, 10) # OIHW out_channels = 6 weight_shape = (**out_channels**, 4, kernel_size, kernel_size) conv2d = relay.nn.conv2d( input, weight, channels = **out_channels **, kernel_size = 3 ) ``` Translate this into ``` conv2d_1 = relay.nn.conv2d( input, weight, channels = **out_channels/2 **, kernel_size = 3 ) conv2d_2 = relay.nn.conv2d( input, weight, channels = **out_channels/2 **, kernel_size = 3 ) conv2d_final = tvm.relay.concatenate (conv2d_1, conv2d_2 ) ``` --- [Visit Topic](https://discuss.tvm.ai/t/relay-level-tiling-of-conv2d-or-any-operator/6621/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.ai/email/unsubscribe/7af26b012424c3213cc80f49e2ae4fee054f4566e0d340450cc865d7218a03fb).
