cleanup convdw torch
@@ -32,13 +32,9 @@ def convdw(x,grad_output,dw,stride,groups):
grad_output = grad_output.reshape(C.bs, C.groups, C.rcout, C.oy, C.ox).repeat(1, 1, C.cin, 1, 1)
grad_output = grad_output.reshape(C.bs * C.groups * C.rcout * C.cin, 1, C.oy, C.ox)
x = x.reshape(1, C.bs * C.groups * C.cin, C.iy, C.ix)
- #print(input.shape, grad_output.shape)
grad_weight = torch.conv2d(x, grad_output, dilation=stride, groups=C.bs*C.groups*C.cin)
- grad_weight = grad_weight.reshape(C.bs, grad_weight.shape[1] // C.bs, *grad_weight.shape[2:]).sum(dim=0)
- grad_weight = grad_weight.view(C.groups, C.cin, C.rcout, *grad_weight.shape[1:]).transpose(2, 1)
- # narrow removes excess for strided
- dw[:] = grad_weight.contiguous().view(C.groups*C.rcout, C.cin, *grad_weight.shape[3:]).narrow(
- 2, 0, dw.shape[2]).narrow(3, 0, dw.shape[3])
+ grad_weight = grad_weight.reshape(C.bs, C.groups, C.cin, C.rcout, *grad_weight.shape[2:]).sum(dim=0).transpose(2, 1)
+ dw[:] = grad_weight.reshape(C.groups*C.rcout, C.cin, *grad_weight.shape[3:])[:, :, :dw.shape[2], :dw.shape[3]]
return dw
def processing_op(op,x,w,ret,stride,groups):
