Python API for If operation (#7934)
* add if in python_api
* add test for if python api
* fix code style
* fix typr of output_desc
* move to ne api
* Revert "add if in python_api"
This reverts commit fca6e5a449.
* Revert compatibility if_op
* fix codestyle
* add new tests, disable test where bug is appear
* fix codestyle
* fix test
* rewrite interface if python_api
* fix dict_attribute_visitor.cpp
* fix codestyle
* fix codestyle
* update dict_attribute_visitor.cpp
* add comapti
* add compatibilty tests
* fix if_op description, and paths
* add compatible test
* fix comp opset
* fix opset 8 whitespace
* fix codestyle
* fix tests
* delete old tests
* Revert fixes in test_reduction.py, test_node_factory.py
* Revert fixes in test_reduction.py, test_node_factory.py
* fix tuple
* fix tuple
* fix tuple
* fix tuple
* fix test path
* fix test path
This commit is contained in:
Eugeny Volosenkov
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GitHub
GitHub
parent
caf1f22f63
commit
81b003e688
175
src/bindings/python/tests/test_ngraph/test_if.py
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175
src/bindings/python/tests/test_ngraph/test_if.py
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import numpy as np
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import openvino.runtime.opset8 as ov
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import pytest
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from openvino.runtime.utils.tensor_iterator_types import (
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GraphBody,
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TensorIteratorInvariantInputDesc,
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TensorIteratorBodyOutputDesc,
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)
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from tests.runtime import get_runtime
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def create_simple_if_with_two_outputs(condition_val):
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condition = ov.constant(condition_val, dtype=np.bool)
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# then_body
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X_t = ov.parameter([], np.float32, "X")
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Y_t = ov.parameter([], np.float32, "Y")
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Z_t = ov.parameter([], np.float32, "Z")
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add_t = ov.add(X_t, Y_t)
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mul_t = ov.multiply(Y_t, Z_t)
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then_body_res_1 = ov.result(add_t)
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then_body_res_2 = ov.result(mul_t)
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then_body = GraphBody([X_t, Y_t, Z_t], [then_body_res_1, then_body_res_2])
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then_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(2, 1),
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TensorIteratorInvariantInputDesc(3, 2)]
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then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0), TensorIteratorBodyOutputDesc(1, 1)]
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# else_body
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X_e = ov.parameter([], np.float32, "X")
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Z_e = ov.parameter([], np.float32, "Z")
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W_e = ov.parameter([], np.float32, "W")
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add_e = ov.add(X_e, W_e)
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pow_e = ov.power(W_e, Z_e)
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else_body_res_1 = ov.result(add_e)
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else_body_res_2 = ov.result(pow_e)
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else_body = GraphBody([X_e, Z_e, W_e], [else_body_res_1, else_body_res_2])
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else_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(3, 1),
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TensorIteratorInvariantInputDesc(4, 2)]
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else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0), TensorIteratorBodyOutputDesc(1, 1)]
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X = ov.constant(15.0, dtype=np.float32)
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Y = ov.constant(-5.0, dtype=np.float32)
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Z = ov.constant(4.0, dtype=np.float32)
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W = ov.constant(2.0, dtype=np.float32)
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if_node = ov.if_op(condition, [X, Y, Z, W], (then_body, else_body), (then_body_inputs, else_body_inputs),
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(then_body_outputs, else_body_outputs))
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return if_node
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def create_diff_if_with_two_outputs(condition_val):
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condition = ov.constant(condition_val, dtype=np.bool)
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# then_body
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X_t = ov.parameter([2], np.float32, "X")
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Y_t = ov.parameter([2], np.float32, "Y")
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mmul_t = ov.matmul(X_t, Y_t, False, False)
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mul_t = ov.multiply(Y_t, X_t)
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then_body_res_1 = ov.result(mmul_t)
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then_body_res_2 = ov.result(mul_t)
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then_body = GraphBody([X_t, Y_t], [then_body_res_1, then_body_res_2])
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then_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(2, 1)]
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then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0), TensorIteratorBodyOutputDesc(1, 1)]
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# else_body
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X_e = ov.parameter([2], np.float32, "X")
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Z_e = ov.parameter([], np.float32, "Z")
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mul_e = ov.multiply(X_e, Z_e)
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else_body_res_1 = ov.result(Z_e)
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else_body_res_2 = ov.result(mul_e)
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else_body = GraphBody([X_e, Z_e], [else_body_res_1, else_body_res_2])
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else_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(3, 1)]
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else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0), TensorIteratorBodyOutputDesc(1, 1)]
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X = ov.constant([3, 4], dtype=np.float32)
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Y = ov.constant([2, 1], dtype=np.float32)
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Z = ov.constant(4.0, dtype=np.float32)
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if_node = ov.if_op(condition, [X, Y, Z], (then_body, else_body), (then_body_inputs, else_body_inputs),
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(then_body_outputs, else_body_outputs))
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return if_node
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def simple_if(condition_val):
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condition = ov.constant(condition_val, dtype=np.bool)
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# then_body
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X_t = ov.parameter([2], np.float32, "X")
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Y_t = ov.parameter([2], np.float32, "Y")
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then_mul = ov.multiply(X_t, Y_t)
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then_body_res_1 = ov.result(then_mul)
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then_body = GraphBody([X_t, Y_t], [then_body_res_1])
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then_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(2, 1)]
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then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
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# else_body
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X_e = ov.parameter([2], np.float32, "X")
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Y_e = ov.parameter([2], np.float32, "Y")
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add_e = ov.add(X_e, Y_e)
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else_body_res_1 = ov.result(add_e)
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else_body = GraphBody([X_e, Y_e], [else_body_res_1])
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else_body_inputs = [TensorIteratorInvariantInputDesc(1, 0), TensorIteratorInvariantInputDesc(2, 1)]
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else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
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X = ov.constant([3, 4], dtype=np.float32)
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Y = ov.constant([2, 1], dtype=np.float32)
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if_node = ov.if_op(condition, [X, Y], (then_body, else_body), (then_body_inputs, else_body_inputs),
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(then_body_outputs, else_body_outputs))
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relu = ov.relu(if_node)
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return relu
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def simple_if_without_parameters(condition_val):
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condition = ov.constant(condition_val, dtype=np.bool)
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# then_body
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then_constant = ov.constant(0.7, dtype=np.float)
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then_body_res_1 = ov.result(then_constant)
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then_body = GraphBody([], [then_body_res_1])
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then_body_inputs = []
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then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
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# else_body
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else_const = ov.constant(9.0, dtype=np.float)
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else_body_res_1 = ov.result(else_const)
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else_body = GraphBody([], [else_body_res_1])
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else_body_inputs = []
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else_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
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if_node = ov.if_op(condition, [], (then_body, else_body), (then_body_inputs, else_body_inputs),
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(then_body_outputs, else_body_outputs))
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relu = ov.relu(if_node)
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return relu
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def check_results(results, expected_results):
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assert len(results) == len(expected_results)
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for id_result, res in enumerate(results):
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assert np.allclose(res, expected_results[id_result])
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def check_if(if_model, cond_val, exp_results):
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last_node = if_model(cond_val)
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runtime = get_runtime()
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computation = runtime.computation(last_node)
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results = computation()
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check_results(results, exp_results)
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# After deleting evalute method for if, constant folding stopped working.
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# As result bug with id 67255 began to appear
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@pytest.mark.xfail(reason="bug 67255")
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def test_if_with_two_outputs():
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check_if(create_simple_if_with_two_outputs, True,
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[np.array([10], dtype=np.float32), np.array([-20], dtype=np.float32)])
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check_if(create_simple_if_with_two_outputs, False,
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[np.array([17], dtype=np.float32), np.array([16], dtype=np.float32)])
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@pytest.mark.xfail(reason="bug 67255")
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def test_diff_if_with_two_outputs():
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check_if(create_diff_if_with_two_outputs, True,
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[np.array([10], dtype=np.float32), np.array([6, 4], dtype=np.float32)])
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check_if(create_diff_if_with_two_outputs, False,
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[np.array([4], dtype=np.float32), np.array([12, 16], dtype=np.float32)])
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def test_simple_if():
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check_if(simple_if, True, [np.array([6, 4], dtype=np.float32)])
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check_if(simple_if, False, [np.array([5, 5], dtype=np.float32)])
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def test_simple_if_without_body_parameters():
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check_if(simple_if_without_parameters, True, [np.array([0.7], dtype=np.float32)])
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check_if(simple_if_without_parameters, False, [np.array([9.0], dtype=np.float32)])
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