def symbolic(g, input, pad, value=0): paddings = prepare_onnx_paddings(len(input.type().sizes()), pad) return g.op("Pad", input, pads_i=paddings, mode_s="constant", value_f=value)
def symbolic(g, input, pad, value=0): paddings = prepare_onnx_paddings(len(input.type().sizes()), pad) return g.appendNode( g.create("Pad", [input]).is_("paddings", paddings).s_("mode", "constant").f_("value", value))
def replication_pad(g, input, padding): from torch.autograd._functions.utils import prepare_onnx_paddings mode = "edge" paddings = prepare_onnx_paddings(len(input.type().sizes()), padding) return g.op("Pad", input, pads_i=paddings, mode_s=mode)
def test_prepare_onnx_paddings(self): sizes = [2, 3, 4] pad = [1, 2, 3, 4] paddings = prepare_onnx_paddings(len(sizes), pad) self.assertEqual(paddings, [0, 3, 1, 0, 4, 2])
def constant_pad_nd(g, input, padding, value): from torch.autograd._functions.utils import prepare_onnx_paddings mode = "constant" paddings = prepare_onnx_paddings(len(input.type().sizes()), padding) return g.op("Pad", input, pads_i=paddings, mode_s=mode, value_f=value)
def test_prepare_onnx_paddings(self): sizes = [2, 3, 4] pad = [1, 2, 3, 4] paddings = prepare_onnx_paddings(len(sizes), pad) self.assertEqual(paddings, [0, 3, 1, 0, 4, 2])
def replication_pad(g, input, padding): from torch.autograd._functions.utils import prepare_onnx_paddings mode = "edge" paddings = prepare_onnx_paddings(len(input.type().sizes()), padding) return g.op("Pad", input, pads_i=paddings, mode_s=mode)
def replicationpad_symbolic(g, input, *params): mode = "edge" paddings = prepare_onnx_paddings(len(input.type().sizes()), params) return g.op("Pad", input, paddings_i=paddings, mode_s=mode)
def replicationpad_symbolic(g, input, *params): mode = "edge" paddings = prepare_onnx_paddings(len(input.type().sizes()), params) return g.op("Pad", input, pads_i=paddings, mode_s=mode)
def symbolic(g, input: Variable, padding: Union[int, Tuple[int]]): paddings = prepare_onnx_paddings(len(input.type().sizes()), pad) return g.op("Pad", input, pads_i=paddings, mode_s="reflect")
def symbolic(g, input, pad, value=0): paddings = prepare_onnx_paddings(len(input.type().sizes()), pad) return g.op("Pad", input, paddings_i=paddings, mode_s="constant", value_f=value)
def reflectionpad_symbolic(g, input, *params): mode = "reflect" paddings = prepare_onnx_paddings(input, params) return g.op("Pad", input, paddings_i=paddings, mode_s=mode)