relay.Expr

  1. def test_print_expr():
  2.     data = relay.var("data", shape=(1, 1, 5, 5))
  3.     weight = relay.var("weight")
  4.     conv = relay.nn.conv2d(data, weight,
  5.                            kernel_size=(3, 3),
  6.                            padding=(1, 1),
  7.                            channels=1)
  8.     z = relay.nn.relu(data=conv)
  9.     print(z)

打印出来是这样的

  1. v0.0.4
  2. free_var %data: Tensor[(1, 1, 5, 5), float32]
  3. free_var %weight
  4. %0 = nn.conv2d(%data, %weight, padding=[1, 1], channels=1, kernel_size=[3, 3]);
  5. nn.relu(%0)

relay.Function

  1. def test_print_function():
  2.     data = relay.var("data", shape=(1, 1, 5, 5))
  3.     weight = relay.var("weight")
  4.     conv = relay.nn.conv2d(data, weight,
  5.                            kernel_size=(3, 3),
  6.                            padding=(1, 1),
  7.                            channels=1)
  8.     z = relay.nn.relu(data=conv)
  9.     #print(z)
  10.     f = relay.Function([data,weight],z)
  11.     print(f)

打印的结果是这样的

  1. v0.0.4
  2. fn (%data: Tensor[(1, 1, 5, 5), float32], %weight) {
  3.   %0 = nn.conv2d(%data, %weight, padding=[1, 1], channels=1, kernel_size=[3, 3]);
  4.   nn.relu(%0)
  5. }

relay.Module

  1. def test_print_function():
  2.     data = relay.var("data", shape=(1, 1, 5, 5))
  3.     weight = relay.var("weight")
  4.     conv = relay.nn.conv2d(data, weight,
  5.                            kernel_size=(3, 3),
  6.                            padding=(1, 1),
  7.                            channels=1)
  8.     z = relay.nn.relu(data=conv)
  9.     # print(z)
  10.     # f = relay.Function([data,weight],z)
  11.     # print(f)
  12.     mod = relay.Module.from_expr(z)
  13.     print(mod.astext())

打印的结果是这样的

  1. v0.0.4
  2. def @main(%data: Tensor[(1, 1, 5, 5), float32], %weight: Tensor[(1, 1, 3, 3), float32]) -> Tensor[(1, 1, 5, 5), float32] {
  3.   %0 = nn.conv2d(%data, %weight, padding=[1, 1], channels=1, kernel_size=[3, 3]) /* ty=Tensor[(1, 1, 5, 5), float32] */;
  4.   nn.relu(%0) /* ty=Tensor[(1, 1, 5, 5), float32] */
  5. }