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My model def bunched_infer(inp):
out = model.forward(inp[:,inp.shape[1]//2:,...], inp[:, :inp.shape[1]//2,...])
return out
inp1 = data1['image']
inp2 = data2['image']
stack_inp = torch.cat([inp1, inp2], axis = 1)
pred1= sliding_window_inference(stack_inp, roi_size=(16, 16, 16), sw_batch_size=1, predictor=bunched_infer, overlap=0.50, mode='gaussian') This answer works with the original query I had #4318 (reply in thread) |
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Hi, @a-parida12
Could you please show your sample code about using this function in multiple inputs and single output scenarios
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