If

Generates a conditional execution of network subgraphs. The true and false subgraphs aren’t explicitly used to define the operator but instead represent sets of input and output tensors.

See also

Working With Conditionals

Inputs

condition a tensor of type T1

inputs tensors of type T2

Outputs

outputs tensors of type T2

Data Types

T1: bool

T2: bool, int32, float16, float32, bfloat16

Shape Information

condition is a scalar (zero-dimensional tensor).

inputs the number of input tensors and their shapes can be different for each of the subgraphs.

outputs the number of output tensors and their shapes for each of the subgraphs must be the same.

Examples

If

condition = network.add_input(name="condition", shape=(), dtype=trt.bool)
true_inp = network.add_input(name="true_input", shape=(1, 1), dtype=trt.float32)
false_inp = network.add_input(name="false_input", shape=(1, 1), dtype=trt.float32)
conditional = network.add_if_conditional()
conditional.set_condition(condition)

true_sg = conditional.add_input(true_inp)
false_sg = conditional.add_input(false_inp)
output = conditional.add_output(true_sg.get_output(0), false_sg.get_output(0))
network.mark_output(output.get_output(0))

inputs[condition.name] = np.array([True])
inputs[true_inp.name] = np.array([5.0])
inputs[false_inp.name] = np.array([0.0])

outputs[output.get_output(0).name] = output.get_output(0).shape
expected[output.get_output(0).name] = np.array([5.0])

If with ElementWise Subgraphs

condition = network.add_input("condition", dtype=trt.bool, shape=())
in1 = network.add_input(name="input1", shape=(2, 2), dtype=trt.float32)
in2 = network.add_input(name="input2", shape=(1, 2), dtype=trt.float32)

true_elemwise = network.add_elementwise(in1, in2, op=trt.ElementWiseOperation.PROD)
false_elemwise = network.add_elementwise(in1, in2, op=trt.ElementWiseOperation.SUM)

conditional = network.add_if_conditional()
conditional.set_condition(condition)

conditional.add_input(in1)
conditional.add_input(in2)
output = conditional.add_output(true_elemwise.get_output(0), false_elemwise.get_output(0))
network.mark_output(output.get_output(0))

inputs[condition.name] = np.array([False])
inputs[in1.name] = np.array(
    [
        [5.0, 7.8],
        [-3.2, 4.6],
    ]
)
inputs[in2.name] = np.array(
    [
        [1.0, -1.0],
    ]
)

outputs[output.get_output(0).name] = output.get_output(0).shape
expected[output.get_output(0).name] = np.array([[6.0, 6.8], [-2.2, 3.6]])

C++ API

For more information about the C++ IConditionalLayer operator, refer to the C++ IConditionalLayer documentation.

Python API

For more information about the Python IConditionalLayer operator, refer to the Python IConditionalLayer documentation.