def backward(ctx, grad_output):
grad_input = torch.zeros_like(ctx.saved_tensors[0])
saved = [grad_output] + list(
ctx.saved_tensors) + [ctx.kernel, ctx.stride] + [grad_input]
softpool_cuda.backward_1d(*saved)
# Gradient underflow
saved[-1][torch.isnan(saved[-1])] = 0
return saved[-1], None, None
def backward(ctx, grad_output):
# Create contiguous tensor (if tensor is not contiguous)
if (not grad_output.is_contiguous()):
x = grad_output.contiguous()
grad_input = torch.zeros_like(ctx.saved_tensors[0])
saved = [grad_output] + list(ctx.saved_tensors) + [ctx.kernel, ctx.stride] + [grad_input]
softpool_cuda.backward_1d(*saved)
# Gradient underflow
saved[-1][torch.isnan(saved[-1])] = 0
return saved[-1], None, None