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[Tools] Support NumPy 1.24 (#14728)

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* [Tools] Avoid use of NumPy deprecated types

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Revert "[Tools] Avoid use of NumPy deprecated types"

This reverts commit 21ffc167d1.

* Move to 1.24 Numpy

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
This commit is contained in:
Roman Kazantsev 2022-12-20 11:51:34 +04:00 committed by GitHub
parent d300abf743
commit 0b2f3347f6
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GPG Key ID: 4AEE18F83AFDEB23
107 changed files with 282 additions and 282 deletions
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12
src/bindings/python/tests/test_graph/test_create_op.py
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@ -2203,7 +2203,7 @@ def test_interpolate_opset10(dtype, expected_shape, shape_calculation_mode):
def test_is_finite_opset10():
input_shape = [1, 2, 3, 4]
input_node = ov.parameter(input_shape, np.float, name="InputData")
input_node = ov.parameter(input_shape, float, name="InputData")
node = ov_opset10.is_finite(input_node)
assert node.get_type_name() == "IsFinite"
@ -2214,7 +2214,7 @@ def test_is_finite_opset10():
def test_is_inf_opset10_default():
input_shape = [2, 2, 2, 2]
input_node = ov.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ov.parameter(input_shape, dtype=float, name="InputData")
node = ov_opset10.is_inf(input_node)
assert node.get_type_name() == "IsInf"
@ -2228,7 +2228,7 @@ def test_is_inf_opset10_default():
def test_is_inf_opset10_custom_attribute():
input_shape = [2, 2, 2]
input_node = ov.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ov.parameter(input_shape, dtype=float, name="InputData")
attributes = {
"detect_positive": False,
}
@ -2245,7 +2245,7 @@ def test_is_inf_opset10_custom_attribute():
def test_is_inf_opset10_custom_all_attributes():
input_shape = [2, 2, 2]
input_node = ov.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ov.parameter(input_shape, dtype=float, name="InputData")
attributes = {
"detect_negative": False,
"detect_positive": True,
@ -2263,7 +2263,7 @@ def test_is_inf_opset10_custom_all_attributes():
def test_is_nan_opset10():
input_shape = [1, 2, 3, 4]
input_node = ov.parameter(input_shape, np.float, name="InputData")
input_node = ov.parameter(input_shape, float, name="InputData")
node = ov_opset10.is_nan(input_node)
assert node.get_type_name() == "IsNaN"
@ -2274,7 +2274,7 @@ def test_is_nan_opset10():
def test_unique_opset10():
input_shape = [1, 2, 3, 4]
input_node = ov.parameter(input_shape, np.float, name="input_data")
input_node = ov.parameter(input_shape, float, name="input_data")
axis = ov.constant([1], np.int32, [1])
node = ov_opset10.unique(input_node, axis, False, "i32")

2
src/bindings/python/tests_compatibility/test_ngraph/test_basic.py
View File

@ -175,7 +175,7 @@ def test_convert_to_uint(destination_type, expected_type):
def test_constant_get_data_bool():
input_data = np.array([True, False, False, True])
node = ng.constant(input_data, dtype=np.bool)
node = ng.constant(input_data, dtype=bool)
retrieved_data = node.get_data()
assert np.allclose(input_data, retrieved_data)

32
src/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
View File

@ -259,7 +259,7 @@ def test_deformable_psroi_pooling(dtype):
([2, 3, 5, 6], [7, 4], [7], 2, 2, 1, 1.0, "avg", "asymmetric", [7, 3, 2, 2]),
([10, 3, 5, 5], [7, 4], [7], 3, 4, 1, 1.0, "avg", "half_pixel_for_nn", [7, 3, 3, 4]),
([10, 3, 5, 5], [3, 4], [3], 3, 4, 1, 1.0, "avg", "half_pixel", [3, 3, 3, 4]),
([10, 3, 5, 5], [3, 4], [3], 3, 4, 1, np.float(1), "avg", "half_pixel", [3, 3, 3, 4]),
([10, 3, 5, 5], [3, 4], [3], 3, 4, 1, float(1), "avg", "half_pixel", [3, 3, 3, 4]),
],
)
def test_roi_align(data_shape, rois, batch_indices, pooled_h, pooled_w, sampling_ratio, spatial_scale, mode, aligned_mode, expected_shape):
@ -832,7 +832,7 @@ def test_loop():
TensorIteratorConcatOutputDesc,
)
condition = ng.constant(True, dtype=np.bool)
condition = ng.constant(True, dtype=bool)
trip_count = ng.constant(16, dtype=np.int32)
# Body parameters
body_timestep = ng.parameter([], np.int32, "timestep")
@ -855,7 +855,7 @@ def test_loop():
initial_cma = ng.constant(np.zeros([2, 2], dtype=np.float32), dtype=np.float32)
iter_cnt = ng.range(zero, np.int32(16), np.int32(1))
ti_inputs = [iter_cnt, data, initial_cma, one]
body_const_condition = ng.constant(True, dtype=np.bool)
body_const_condition = ng.constant(True, dtype=bool)
graph_body = GraphBody([body_timestep, body_data_in, body_prev_cma, body_const_one],
[curr_cma, cma_hist, body_const_condition])
@ -1882,11 +1882,11 @@ def test_multiclass_nms():
0.0, -0.1, 1.0, 0.9, 0.0, 10.0, 1.0, 11.0,
0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0], dtype="float32")
boxes_data = boxes_data.reshape([1, 6, 4])
box = ng.constant(boxes_data, dtype=np.float)
box = ng.constant(boxes_data, dtype=float)
scores_data = np.array([0.9, 0.75, 0.6, 0.95, 0.5, 0.3,
0.95, 0.75, 0.6, 0.80, 0.5, 0.3], dtype="float32")
scores_data = scores_data.reshape([1, 2, 6])
score = ng.constant(scores_data, dtype=np.float)
score = ng.constant(scores_data, dtype=float)
nms_node = ng.multiclass_nms(box, score, None, output_type="i32", nms_top_k=3,
iou_threshold=0.5, score_threshold=0.0, sort_result_type="classid",
@ -1907,13 +1907,13 @@ def test_multiclass_nms():
[9.66, 3.36, 18.57, 13.26]],
[[6.50, 7.00, 13.33, 17.63],
[0.73, 5.34, 19.97, 19.97]]]).astype("float32")
box = ng.constant(boxes_data, dtype=np.float)
box = ng.constant(boxes_data, dtype=float)
scores_data = np.array([[0.34, 0.66],
[0.45, 0.61],
[0.39, 0.59]]).astype("float32")
score = ng.constant(scores_data, dtype=np.float)
score = ng.constant(scores_data, dtype=float)
rois_num_data = np.array([3]).astype("int32")
roisnum = ng.constant(rois_num_data, dtype=np.int)
roisnum = ng.constant(rois_num_data, dtype=int)
nms_node = ng.multiclass_nms(box, score, roisnum, output_type="i32", nms_top_k=3,
iou_threshold=0.5, score_threshold=0.0, sort_result_type="classid",
nms_eta=1.0)
@ -1933,11 +1933,11 @@ def test_matrix_nms():
0.0, -0.1, 1.0, 0.9, 0.0, 10.0, 1.0, 11.0,
0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0], dtype="float32")
boxes_data = boxes_data.reshape([1, 6, 4])
box = ng.constant(boxes_data, dtype=np.float)
box = ng.constant(boxes_data, dtype=float)
scores_data = np.array([0.9, 0.75, 0.6, 0.95, 0.5, 0.3,
0.95, 0.75, 0.6, 0.80, 0.5, 0.3], dtype="float32")
scores_data = scores_data.reshape([1, 2, 6])
score = ng.constant(scores_data, dtype=np.float)
score = ng.constant(scores_data, dtype=float)
nms_node = ng.matrix_nms(box, score, output_type="i32", nms_top_k=3,
score_threshold=0.0, sort_result_type="score", background_class=0,
@ -2268,7 +2268,7 @@ def test_interpolate_opset10(dtype, expected_shape, shape_calculation_mode):
def test_is_finite_opset10():
input_shape = [1, 2, 3, 4]
input_node = ng.parameter(input_shape, np.float, name="InputData")
input_node = ng.parameter(input_shape, float, name="InputData")
node = ng_opset10.is_finite(input_node)
assert node.get_type_name() == "IsFinite"
@ -2278,7 +2278,7 @@ def test_is_finite_opset10():
def test_is_inf_opset10_default():
input_shape = [2, 2, 2, 2]
input_node = ng.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ng.parameter(input_shape, dtype=float, name="InputData")
node = ng_opset10.is_inf(input_node)
assert node.get_type_name() == "IsInf"
@ -2292,7 +2292,7 @@ def test_is_inf_opset10_default():
def test_is_inf_opset10_custom_attribute():
input_shape = [2, 2, 2]
input_node = ng.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ng.parameter(input_shape, dtype=float, name="InputData")
attributes = {
"detect_positive": False,
}
@ -2309,7 +2309,7 @@ def test_is_inf_opset10_custom_attribute():
def test_is_inf_opset10_custom_all_attributes():
input_shape = [2, 2, 2]
input_node = ng.parameter(input_shape, dtype=np.float, name="InputData")
input_node = ng.parameter(input_shape, dtype=float, name="InputData")
attributes = {
"detect_negative": False,
"detect_positive": True,
@ -2327,7 +2327,7 @@ def test_is_inf_opset10_custom_all_attributes():
def test_is_nan_opset10():
input_shape = [1, 2, 3, 4]
input_node = ng.parameter(input_shape, np.float, name="InputData")
input_node = ng.parameter(input_shape, float, name="InputData")
node = ng_opset10.is_nan(input_node)
assert node.get_type_name() == "IsNaN"
@ -2338,7 +2338,7 @@ def test_is_nan_opset10():
def test_unique_opset10():
input_shape = [1, 2, 3, 4]
input_node = ng.parameter(input_shape, np.float, name="input_data")
input_node = ng.parameter(input_shape, float, name="input_data")
axis = ng.constant([1], np.int32, [1])
node = ng_opset10.unique(input_node, axis, False, "i32")

12
src/bindings/python/tests_compatibility/test_ngraph/test_if.py
View File

@ -11,7 +11,7 @@ from ngraph.utils.tensor_iterator_types import (
def create_simple_if_with_two_outputs(condition_val):
condition = ng.constant(condition_val, dtype=np.bool)
condition = ng.constant(condition_val, dtype=bool)
# then_body
X_t = ng.parameter([], np.float32, "X")
@ -51,7 +51,7 @@ def create_simple_if_with_two_outputs(condition_val):
def create_diff_if_with_two_outputs(condition_val):
condition = ng.constant(condition_val, dtype=np.bool)
condition = ng.constant(condition_val, dtype=bool)
# then_body
X_t = ng.parameter([2], np.float32, "X")
@ -83,7 +83,7 @@ def create_diff_if_with_two_outputs(condition_val):
def simple_if(condition_val):
condition = ng.constant(condition_val, dtype=np.bool)
condition = ng.constant(condition_val, dtype=bool)
# then_body
X_t = ng.parameter([2], np.float32, "X")
Y_t = ng.parameter([2], np.float32, "Y")
@ -112,17 +112,17 @@ def simple_if(condition_val):
def simple_if_without_parameters(condition_val):
condition = ng.constant(condition_val, dtype=np.bool)
condition = ng.constant(condition_val, dtype=bool)
# then_body
then_constant = ng.constant(0.7, dtype=np.float)
then_constant = ng.constant(0.7, dtype=float)
then_body_res_1 = ng.result(then_constant)
then_body = GraphBody([], [then_body_res_1])
then_body_inputs = []
then_body_outputs = [TensorIteratorBodyOutputDesc(0, 0)]
# else_body
else_const = ng.constant(9.0, dtype=np.float)
else_const = ng.constant(9.0, dtype=float)
else_body_res_1 = ng.result(else_const)
else_body = GraphBody([], [else_body_res_1])
else_body_inputs = []

2
src/bindings/python/tests_compatibility/test_ngraph/test_ops.py
View File

@ -532,7 +532,7 @@ def test_select():
runtime = get_runtime()
computation = runtime.computation(function, *parameter_list)
result = computation(
np.array([[True, False]], dtype=np.bool),
np.array([[True, False]], dtype=bool),
np.array([[5, 6]], dtype=np.float32),
np.array([[7, 8]], dtype=np.float32),
)[0]

8
src/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py
View File

@ -76,8 +76,8 @@ def test_binary_op(ng_api_helper, expected_type):
)
def test_binary_logical_op(ng_api_helper):
shape = [2, 2]
parameter_a = ng.parameter(shape, name="A", dtype=np.bool)
parameter_b = ng.parameter(shape, name="B", dtype=np.bool)
parameter_a = ng.parameter(shape, name="A", dtype=bool)
parameter_b = ng.parameter(shape, name="B", dtype=bool)
model = ng_api_helper(parameter_a, parameter_b)
@ -91,10 +91,10 @@ def test_binary_logical_op(ng_api_helper):
[ng.logical_and, ng.logical_or, ng.logical_xor],
)
def test_binary_logical_op_with_scalar(ng_api_helper):
value_b = np.array([[False, True], [False, True]], dtype=np.bool)
value_b = np.array([[False, True], [False, True]], dtype=bool)
shape = [2, 2]
parameter_a = ng.parameter(shape, name="A", dtype=np.bool)
parameter_a = ng.parameter(shape, name="A", dtype=bool)
model = ng_api_helper(parameter_a, value_b)
assert model.get_output_size() == 1

2
src/bindings/python/tests_compatibility/test_ngraph/test_reduction.py
View File

@ -52,7 +52,7 @@ def test_reduction_ops(ng_api_helper, numpy_function, reduction_axes):
def test_reduction_logical_ops(ng_api_helper, numpy_function, reduction_axes):
shape = [2, 4, 3, 2]
np.random.seed(133391)
input_data = np.random.randn(*shape).astype(np.bool)
input_data = np.random.randn(*shape).astype(bool)
expected = numpy_function(input_data, axis=tuple(reduction_axes))
result = run_op_node([input_data], ng_api_helper, reduction_axes)

6
src/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py
View File

@ -11,9 +11,9 @@ from tests_compatibility.test_onnx.utils import run_node
@pytest.mark.parametrize(
"onnx_op, numpy_func, data_type",
[
pytest.param("And", np.logical_and, np.bool),
pytest.param("Or", np.logical_or, np.bool),
pytest.param("Xor", np.logical_xor, np.bool),
pytest.param("And", np.logical_and, bool),
pytest.param("Or", np.logical_or, bool),
pytest.param("Xor", np.logical_xor, bool),
pytest.param("Equal", np.equal, np.int32),
pytest.param("Greater", np.greater, np.int32),
pytest.param("Less", np.less, np.int32),

8
src/frontends/paddle/tests/test_models/gen_scripts/generate_prior_box.py
View File

@ -59,7 +59,7 @@ if __name__ == "__main__":
'clip': True,
'steps': np.array([1.25, 1.25]).astype('float32').tolist(),
'offset': 0.5,
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=np.float).flatten(),
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=float).flatten(),
'min_max_aspect_ratios_order': False
}
@ -72,7 +72,7 @@ if __name__ == "__main__":
'clip': True,
'steps': np.array([1.25, 1.25]).astype('float32').tolist(),
'offset': 0.5,
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=np.float).flatten(),
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=float).flatten(),
'min_max_aspect_ratios_order': False
}
@ -85,7 +85,7 @@ if __name__ == "__main__":
'clip': False,
'steps': np.array([1.25, 1.25]).astype('float32').tolist(),
'offset': 0.5,
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=np.float).flatten(),
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=float).flatten(),
'min_max_aspect_ratios_order': False
}
@ -98,7 +98,7 @@ if __name__ == "__main__":
'clip': True,
'steps': np.array([1.25, 1.25]).astype('float32').tolist(),
'offset': 0.5,
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=np.float).flatten(),
'variance': np.array([0.1, 0.1, 0.2, 0.2], dtype=float).flatten(),
'min_max_aspect_ratios_order': True
}

2
tests/layer_tests/onnx_tests/test_abs.py
View File

@ -90,7 +90,7 @@ class TestAbs(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

12
tests/layer_tests/onnx_tests/test_add_sub_mul_div.py
View File

@ -30,12 +30,12 @@ class TestOperations(OnnxRuntimeLayerTest):
min_val = 1 if op == 'Div' else -127
if shape2:
const = np.random.randint(min_val, 127, shape2).astype(np.float)
const = np.random.randint(min_val, 127, shape2).astype(float)
else:
const = np.random.randint(min_val, 127, 1).astype(np.float)
const = np.random.randint(min_val, 127, 1).astype(float)
# TODO: add check when MO remove redundant layer (as Add/Sub if const = 0 or Mul/Div if const = 1)
if const in [0, 1]:
const = np.array([2], dtype=np.float)
const = np.array([2], dtype=float)
node_const_def = helper.make_node(
'Constant',
@ -103,12 +103,12 @@ class TestOperations(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape1)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const1 = np.random.randint(-127, 127, shape1).astype(np.float)
const1 = np.random.randint(-127, 127, shape1).astype(float)
min_val = 1 if op == 'Div' else -127
if shape2:
const2 = np.random.randint(min_val, 127, shape2).astype(np.float)
const2 = np.random.randint(min_val, 127, shape2).astype(float)
else:
const2 = np.random.randint(min_val, 127, 1).astype(np.float)
const2 = np.random.randint(min_val, 127, 1).astype(float)
node_const1_def = helper.make_node(
'Constant',

8
tests/layer_tests/onnx_tests/test_and.py
View File

@ -12,7 +12,7 @@ from unit_tests.utils.graph import build_graph
class TestAnd(OnnxRuntimeLayerTest):
def _prepare_input(self, inputs_dict):
for input in inputs_dict.keys():
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
return inputs_dict
def create_net(self, shape1, shape2, ir_version):
@ -90,7 +90,7 @@ class TestAnd(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1)
const = np.random.randint(0, 2, shape2).astype(np.bool)
const = np.random.randint(0, 2, shape2).astype(bool)
node_const_def = helper.make_node(
'Constant',
@ -167,8 +167,8 @@ class TestAnd(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape)
const1 = np.random.randint(0, 2, shape1).astype(np.bool)
const2 = np.random.randint(0, 2, shape2).astype(np.bool)
const1 = np.random.randint(0, 2, shape1).astype(bool)
const2 = np.random.randint(0, 2, shape2).astype(bool)
node_const1_def = helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_ceil.py
View File

@ -92,7 +92,7 @@ class TestCeil(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randn(*shape).astype(np.float)
constant = np.random.randn(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_concat.py
View File

@ -33,7 +33,7 @@ class TestConcat(OnnxRuntimeLayerTest):
concat_output_shape[concat_axis] *= 2
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_shape)

4
tests/layer_tests/onnx_tests/test_conv.py
View File

@ -36,8 +36,8 @@ class TestConv(OnnxRuntimeLayerTest):
_pads = np.array(pads).reshape([2, -1])
kernel_extent = np.array(dilations) * (np.array(weights_shape[2:]) - 1) + 1
spatial_val_wo_stride = shape[2:] + np.add(_pads[0, :], _pads[1, :]) - kernel_extent
output_shape[2:] = (spatial_val_wo_stride.astype(np.float) / strides + 1).astype(np.int64)
output_shape = output_shape.astype(np.int).tolist()
output_shape[2:] = (spatial_val_wo_stride.astype(float) / strides + 1).astype(np.int64)
output_shape = output_shape.astype(int).tolist()
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)

2
tests/layer_tests/onnx_tests/test_conv_transpose.py
View File

@ -28,7 +28,7 @@ class TestConvTranspose(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, input_shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
weights = np.random.randn(*kernel_shape).astype(np.float)
weights = np.random.randn(*kernel_shape).astype(float)
node_weights_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_cumsum.py
View File

@ -142,7 +142,7 @@ class TestCumSum(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randn(*shape).astype(np.float)
constant = np.random.randn(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

54
tests/layer_tests/onnx_tests/test_dequantize_linear.py
View File

@ -139,55 +139,55 @@ class TestDequantizeLinear(OnnxRuntimeLayerTest):
return onnx_net, ref_net
test_data = [
dict(shape=[8], y_scale=np.array(2, dtype=np.float),
dict(shape=[8], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(128, dtype=np.uint8)),
dict(shape=[8], y_scale=np.array(2, dtype=np.float),
dict(shape=[8], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(1, dtype=np.int8)),
dict(shape=[2, 4], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(128, dtype=np.uint8)),
dict(shape=[2, 4], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(1, dtype=np.int8)),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(128, dtype=np.uint8)),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(1, dtype=np.int8)),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(128, dtype=np.uint8)),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(1, dtype=np.int8)),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(128, dtype=np.uint8)),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=np.float),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=float),
y_zero_point=np.array(1, dtype=np.int8)),
]
test_data_def_zerop = [
dict(shape=[8], y_scale=np.array(2, dtype=np.float)),
dict(shape=[2, 4], y_scale=np.array(2, dtype=np.float)),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=np.float)),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=np.float)),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=np.float)),
dict(shape=[8], y_scale=np.array(2, dtype=float)),
dict(shape=[2, 4], y_scale=np.array(2, dtype=float)),
dict(shape=[2, 4, 6], y_scale=np.array(2, dtype=float)),
dict(shape=[2, 4, 6, 8], y_scale=np.array(2, dtype=float)),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array(2, dtype=float)),
]
test_data_axis = [
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float), axis=1),
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float), axis=1),
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([128, 128, 128, 128], dtype=np.uint8), axis=1),
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([1, 1, 1, 1], dtype=np.int8), axis=1),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float), axis=1),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float), axis=1),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([128, 128, 128, 128], dtype=np.uint8), axis=1),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([1, 1, 1, 1], dtype=np.int8), axis=1),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float), axis=1),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float), axis=1),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([128, 128, 128, 128], dtype=np.uint8), axis=1),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6, 8], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([1, 1, 1, 1], dtype=np.int8), axis=1),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float), axis=1),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float), axis=1),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([128, 128, 128, 128], dtype=np.uint8), axis=1),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=np.float),
dict(shape=[2, 4, 6, 8, 10], y_scale=np.array([2, 2.5, 3, 2.3], dtype=float),
y_zero_point=np.array([1, 1, 1, 1], dtype=np.int8), axis=1),
]

2
tests/layer_tests/onnx_tests/test_dropout.py
View File

@ -77,7 +77,7 @@ class TestDropout(OnnxRuntimeLayerTest):
from onnx import helper
from onnx import TensorProto
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
concat_axis = 0
output_shape = shape.copy()

2
tests/layer_tests/onnx_tests/test_elu.py
View File

@ -94,7 +94,7 @@ class TestElu(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_flatten.py
View File

@ -84,7 +84,7 @@ class TestFlatten(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_floor.py
View File

@ -92,7 +92,7 @@ class TestFloor(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randn(*shape).astype(np.float)
constant = np.random.randn(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_gather.py
View File

@ -127,7 +127,7 @@ class TestGather(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, input_shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

10
tests/layer_tests/onnx_tests/test_gemm.py
View File

@ -37,15 +37,15 @@ class TestGemm(OnnxRuntimeLayerTest):
extended_shape2 = np.concatenate([np.ones(max_len - len(shapeB)), shapeB], axis=0)
output_shape = np.concatenate(
[np.maximum(*[extended_shape1[0:-2], extended_shape2[0:-2]]), [shapeA[-2], shapeB[-1]]],
axis=0).astype(np.int).tolist()
axis=0).astype(int).tolist()
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shapeA)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
_shapeB = shapeB.copy()
if trans_b:
_shapeB.reverse()
const1 = np.random.ranf(_shapeB).astype(np.float)
const2 = np.random.ranf(shapeC).astype(np.float)
const1 = np.random.ranf(_shapeB).astype(float)
const2 = np.random.ranf(shapeC).astype(float)
nodes = list()
node_const1_def = onnx.helper.make_node(
@ -157,12 +157,12 @@ class TestGemm(OnnxRuntimeLayerTest):
extended_shape2 = np.concatenate([np.ones(max_len - len(shapeB)), shapeB], axis=0)
output_shape = np.concatenate(
[np.maximum(*[extended_shape1[0:-2], extended_shape2[0:-2]]), [shapeA[-2], shapeB[-1]]],
axis=0).astype(np.int).tolist()
axis=0).astype(int).tolist()
input1 = helper.make_tensor_value_info('input1', TensorProto.FLOAT, _shapeA)
input2 = helper.make_tensor_value_info('input2', TensorProto.FLOAT, _shapeB)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const = np.random.ranf(shapeC).astype(np.float)
const = np.random.ranf(shapeC).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_hard_sigmoid.py
View File

@ -118,7 +118,7 @@ class TestHardSigmoid(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_identity.py
View File

@ -89,7 +89,7 @@ class TestIdentity(OnnxRuntimeLayerTest):
from onnx import helper
from onnx import TensorProto
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
concat_axis = 0
output_shape = shape.copy()

6
tests/layer_tests/onnx_tests/test_image_scaler.py
View File

@ -27,7 +27,7 @@ class TestImageScaler(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
bias = np.random.randint(-10, 10, shape[1]).astype(np.float)
bias = np.random.randint(-10, 10, shape[1]).astype(float)
node_def = onnx.helper.make_node(
'ImageScaler',
@ -79,8 +79,8 @@ class TestImageScaler(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randint(-127, 127, shape).astype(np.float)
bias = np.random.randint(-10, 10, shape[1]).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
bias = np.random.randint(-10, 10, shape[1]).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

4
tests/layer_tests/onnx_tests/test_instance_normalization.py
View File

@ -25,8 +25,8 @@ class TestInstanceNormalization(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
scale_const = np.random.randn(shape[1]).astype(np.float)
bias_const = np.random.randn(shape[1]).astype(np.float)
scale_const = np.random.randn(shape[1]).astype(float)
bias_const = np.random.randn(shape[1]).astype(float)
node_scale_def = helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_leaky_relu.py
View File

@ -98,7 +98,7 @@ class TestLeakyRelu(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_log.py
View File

@ -94,7 +94,7 @@ class TestLog(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.rand(*shape).astype(np.float) * 255 + 0.5
constant = np.random.rand(*shape).astype(float) * 255 + 0.5
node_const_def = onnx.helper.make_node(
'Constant',

8
tests/layer_tests/onnx_tests/test_loop.py
View File

@ -42,7 +42,7 @@ class TestLoop(OnnxRuntimeLayerTest):
assert len(input_nodes) == len(input_names)
assert len(output_nodes) == len(output_names)
other_inputs_count = len(input_nodes) - 2
one_value = np.ones(input_shape, dtype=np.float)
one_value = np.ones(input_shape, dtype=float)
one = TestLoop.create_const('one_' + graph_name, TensorProto.FLOAT, one_value)
one_int = TestLoop.create_const('one_int_' + graph_name, TensorProto.INT64, np.ones([1]))
@ -108,7 +108,7 @@ class TestLoop(OnnxRuntimeLayerTest):
cond_out_1 = helper.make_tensor_value_info('cond_out_1', TensorProto.BOOL, [1])
m_1_value = np.array([10], dtype=np.int64)
cond_value = np.array([True], np.bool)
cond_value = np.array([True], bool)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
cond = self.create_const('cond', TensorProto.BOOL, cond_value)
@ -179,8 +179,8 @@ class TestLoop(OnnxRuntimeLayerTest):
m_1_value = np.array([10], dtype=np.int64)
m_2_value = np.array([5], dtype=np.int64)
cond_value = np.array([True], np.bool)
one_value = np.ones(input_shape, dtype=np.float)
cond_value = np.array([True], bool)
one_value = np.ones(input_shape, dtype=float)
M_1 = self.create_const('M_1', TensorProto.INT64, m_1_value)
M_2 = self.create_const('M_2', TensorProto.INT64, m_2_value)

4
tests/layer_tests/onnx_tests/test_matmul.py
View File

@ -33,7 +33,7 @@ class TestMatMul(OnnxRuntimeLayerTest):
extended_shape2 = np.concatenate([np.ones(max_len - len(shape2)), shape2], axis=0)
output_shape = np.concatenate(
[np.maximum(*[extended_shape1[0:-2], extended_shape2[0:-2]]), [shape1[-2], shape2[-1]]],
axis=0).astype(np.int).tolist()
axis=0).astype(int).tolist()
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape1)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
@ -107,7 +107,7 @@ class TestMatMul(OnnxRuntimeLayerTest):
extended_shape2 = np.concatenate([np.ones(max_len - len(shape2)), shape2], axis=0)
output_shape = np.concatenate(
[np.maximum(*[extended_shape1[0:-2], extended_shape2[0:-2]]), [shape1[-2], shape2[-1]]],
axis=0).astype(np.int).tolist()
axis=0).astype(int).tolist()
input1 = helper.make_tensor_value_info('input1', TensorProto.FLOAT, shape1)
input2 = helper.make_tensor_value_info('input2', TensorProto.FLOAT, shape2)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)

4
tests/layer_tests/onnx_tests/test_not.py
View File

@ -12,7 +12,7 @@ from unit_tests.utils.graph import build_graph
class TestNot(OnnxRuntimeLayerTest):
def _prepare_input(self, inputs_dict):
for input in inputs_dict.keys():
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
return inputs_dict
def create_net(self, shape, ir_version):
@ -96,7 +96,7 @@ class TestNot(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape)
constant = np.random.randint(0, 2, shape).astype(np.bool)
constant = np.random.randint(0, 2, shape).astype(bool)
node_const_def = onnx.helper.make_node(
'Constant',

8
tests/layer_tests/onnx_tests/test_or.py
View File

@ -12,7 +12,7 @@ from unit_tests.utils.graph import build_graph
class TestOr(OnnxRuntimeLayerTest):
def _prepare_input(self, inputs_dict):
for input in inputs_dict.keys():
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
return inputs_dict
def create_net(self, shape1, shape2, ir_version):
@ -90,7 +90,7 @@ class TestOr(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1)
const = np.random.randint(0, 2, shape2).astype(np.bool)
const = np.random.randint(0, 2, shape2).astype(bool)
node_const_def = helper.make_node(
'Constant',
@ -167,8 +167,8 @@ class TestOr(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape)
const1 = np.random.randint(0, 2, shape1).astype(np.bool)
const2 = np.random.randint(0, 2, shape2).astype(np.bool)
const1 = np.random.randint(0, 2, shape1).astype(bool)
const2 = np.random.randint(0, 2, shape2).astype(bool)
node_const1_def = helper.make_node(
'Constant',

12
tests/layer_tests/onnx_tests/test_pooling.py
View File

@ -10,7 +10,7 @@ from unit_tests.utils.graph import build_graph
def float_array(x):
return np.array(x, dtype=np.float)
return np.array(x, dtype=float)
class TestPooling(OnnxRuntimeLayerTest):
@ -36,7 +36,7 @@ class TestPooling(OnnxRuntimeLayerTest):
if auto_pad is not None:
node_args['auto_pad'] = auto_pad
if auto_pad == 'VALID':
pads = np.zeros(len(shape[2:]) * 2, dtype=np.int)
pads = np.zeros(len(shape[2:]) * 2, dtype=int)
else:
auto_pad = 'NOTSET'
if count_include_pad is not None:
@ -60,7 +60,7 @@ class TestPooling(OnnxRuntimeLayerTest):
node_args['ceil_mode'] = 1
if auto_pad in ['SAME_UPPER', 'SAME_LOWER']:
out_spacial_shape = np.ceil(np.array(shape[2:], dtype=np.float) / strides)
out_spacial_shape = np.ceil(np.array(shape[2:], dtype=float) / strides)
else:
rounding = np.ceil if ceil else np.floor
out_spacial_shape = rounding(
@ -69,14 +69,14 @@ class TestPooling(OnnxRuntimeLayerTest):
out_shape = np.array(shape)
out_shape[2:] = out_spacial_shape
out_shape = out_shape.astype(np.int).tolist()
out_shape = out_shape.astype(int).tolist()
concat_axis = 0
out_concat_shape = out_shape.copy()
out_concat_shape[concat_axis] *= 2
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, out_concat_shape)
constant = np.random.randint(-127, 127, out_shape).astype(np.float)
constant = np.random.randint(-127, 127, out_shape).astype(float)
node_def = onnx.helper.make_node(
op,
@ -183,7 +183,7 @@ class TestPooling(OnnxRuntimeLayerTest):
out_shape = np.ones(len(shape))
out_shape[:2] = np.array(shape)[:2]
out_shape = out_shape.astype(np.int).tolist()
out_shape = out_shape.astype(int).tolist()
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, out_shape)

2
tests/layer_tests/onnx_tests/test_reciprocal.py
View File

@ -88,7 +88,7 @@ class TestReciprocal(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const = np.random.randint(1, 256, shape).astype(np.float)
const = np.random.randint(1, 256, shape).astype(float)
node_const_def = helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_reduce_lp.py
View File

@ -122,7 +122,7 @@ class TestReduceL1L2(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
constant = np.random.randn(*shape).astype(np.float)
constant = np.random.randn(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_relu.py
View File

@ -93,7 +93,7 @@ class TestRelu(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_reshape.py
View File

@ -116,7 +116,7 @@ class TestReshape(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

8
tests/layer_tests/onnx_tests/test_resize.py
View File

@ -36,8 +36,8 @@ class TestResize(OnnxRuntimeLayerTest):
onnx_scales = scales
if scales is None:
onnx_scales = np.array(output_shape).astype(np.float) / np.array(input_shape).astype(
np.float)
onnx_scales = np.array(output_shape).astype(float) / np.array(input_shape).astype(
float)
scales_node = onnx.helper.make_node(
'Constant',
inputs=[],
@ -114,14 +114,14 @@ class TestResize(OnnxRuntimeLayerTest):
if sizes is not None and scales is not None:
shape_calculation_mode = 'sizes'
sizes_value = int64_array(sizes)
scales_value = np.array(scales).astype(np.float)
scales_value = np.array(scales).astype(float)
elif sizes is not None and scales is None:
shape_calculation_mode = 'sizes'
sizes_value = int64_array(sizes)
scales_value = sizes_value / input_shape_as_array
else:
shape_calculation_mode = 'scales'
scales_value = np.array(scales).astype(np.float)
scales_value = np.array(scales).astype(float)
sizes_value = np.floor(input_shape_as_array * scales_value + 1e-5).astype(np.int64)
if precision == 'FP16':

2
tests/layer_tests/onnx_tests/test_scale.py
View File

@ -76,7 +76,7 @@ class TestScale(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_sigmoid.py
View File

@ -99,7 +99,7 @@ class TestSigmoid(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_sign.py
View File

@ -92,7 +92,7 @@ class TestSign(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.randn(*shape).astype(np.float)
constant = np.random.randn(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_slice.py
View File

@ -152,7 +152,7 @@ class TestSlice(OnnxRuntimeLayerTest):
from onnx import TensorProto
# calculate output shape
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
slice_idx = [None] * len(shape)
for i, axis in enumerate(axes):

2
tests/layer_tests/onnx_tests/test_softsign.py
View File

@ -93,7 +93,7 @@ class TestSoftsign(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
const_number = np.prod(shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_split_concat.py
View File

@ -191,7 +191,7 @@ class TestSplitConcat(OnnxRuntimeLayerTest):
concat_output_shape[concat_axis] *= 2
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, input_shape)
outputs, split = [], []

2
tests/layer_tests/onnx_tests/test_sqrt.py
View File

@ -100,7 +100,7 @@ class TestSqrt(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.rand(*shape).astype(np.float) * 255
constant = np.random.rand(*shape).astype(float) * 255
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_squeeze.py
View File

@ -79,7 +79,7 @@ class TestSqueeze(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, input_shape)
node_const_def = onnx.helper.make_node(

4
tests/layer_tests/onnx_tests/test_sum.py
View File

@ -37,7 +37,7 @@ class TestSum(OnnxRuntimeLayerTest):
nodes = list()
consts = list()
for i, shape in enumerate(const_shapes):
const = np.random.randint(-127, 127, shape).astype(np.float)
const = np.random.randint(-127, 127, shape).astype(float)
const_name = 'const{}'.format(i + 1)
nodes.append(helper.make_node(
'Constant',
@ -111,7 +111,7 @@ class TestSum(OnnxRuntimeLayerTest):
input_names = list()
consts = list()
for i, shape in enumerate(const_shapes):
const = np.random.randint(-127, 127, shape).astype(np.float)
const = np.random.randint(-127, 127, shape).astype(float)
const_name = 'const{}'.format(i + 1)
nodes.append(helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_topk.py
View File

@ -34,7 +34,7 @@ class TestTopK(OnnxRuntimeLayerTest):
indices = helper.make_tensor_value_info('cindices', TensorProto.INT64, output_shape)
const1 = np.ones(output_shape).astype(np.int64)
const2 = np.ones(output_shape).astype(np.float)
const2 = np.ones(output_shape).astype(float)
nodes = list()
inputs = ['input']

2
tests/layer_tests/onnx_tests/test_transpose.py
View File

@ -80,7 +80,7 @@ class TestTranspose(OnnxRuntimeLayerTest):
from onnx import helper
from onnx import TensorProto
constant = np.random.randint(-127, 127, shape).astype(np.float)
constant = np.random.randint(-127, 127, shape).astype(float)
constant_transposed = np.transpose(constant, perm)
concat_axis = 0

2
tests/layer_tests/onnx_tests/test_trigonometry.py
View File

@ -85,7 +85,7 @@ class TestTrigonomery(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
constant = np.random.rand(*shape).astype(np.float)
constant = np.random.rand(*shape).astype(float)
node_const_def = onnx.helper.make_node(
'Constant',

2
tests/layer_tests/onnx_tests/test_unsqueeze.py
View File

@ -79,7 +79,7 @@ class TestUnsqueeze(OnnxRuntimeLayerTest):
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
const_number = np.prod(input_shape)
constant = np.random.randint(-127, 127, const_number).astype(np.float)
constant = np.random.randint(-127, 127, const_number).astype(float)
constant = np.reshape(constant, input_shape)
node_const_def = onnx.helper.make_node(

2
tests/layer_tests/onnx_tests/test_where.py
View File

@ -12,7 +12,7 @@ from unit_tests.utils.graph import build_graph
class TestWhere(OnnxRuntimeLayerTest):
def _prepare_input(self, inputs_dict):
for input in inputs_dict.keys():
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
return inputs_dict
def create_net(self, condition_shape, shape_than, else_shape, ir_version):

8
tests/layer_tests/onnx_tests/test_xor.py
View File

@ -12,7 +12,7 @@ from unit_tests.utils.graph import build_graph
class TestXor(OnnxRuntimeLayerTest):
def _prepare_input(self, inputs_dict):
for input in inputs_dict.keys():
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 2, inputs_dict[input]).astype(bool)
return inputs_dict
def create_net(self, shape1, shape2, ir_version):
@ -90,7 +90,7 @@ class TestXor(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, shape1)
const = np.random.randint(0, 2, shape2).astype(np.bool)
const = np.random.randint(0, 2, shape2).astype(bool)
node_const_def = helper.make_node(
'Constant',
@ -167,8 +167,8 @@ class TestXor(OnnxRuntimeLayerTest):
input = helper.make_tensor_value_info('input', TensorProto.BOOL, shape1)
output = helper.make_tensor_value_info('output', TensorProto.BOOL, output_shape)
const1 = np.random.randint(0, 2, shape1).astype(np.bool)
const2 = np.random.randint(0, 2, shape2).astype(np.bool)
const1 = np.random.randint(0, 2, shape1).astype(bool)
const2 = np.random.randint(0, 2, shape2).astype(bool)
node_const1_def = helper.make_node(
'Constant',

2
tests/layer_tests/tensorflow_tests/test_tf_BinaryOps.py
View File

@ -23,7 +23,7 @@ def generate_input(op_type, size):
upper = 16
if op_type in logical_type:
return np.random.randint(0, 1, size).astype(np.bool)
return np.random.randint(0, 1, size).astype(bool)
elif op_type in narrow_borders:
return np.random.uniform(lower, upper, size).astype(np.float32)
else:

2
tests/layer_tests/tensorflow_tests/test_tf_UnaryOps.py
View File

@ -39,7 +39,7 @@ class TestUnaryOps(CommonTFLayerTest):
for input in inputs_dict.keys():
if self.current_op_type in logical_type:
inputs_dict[input] = np.random.randint(0, 1, inputs_dict[input]).astype(np.bool)
inputs_dict[input] = np.random.randint(0, 1, inputs_dict[input]).astype(bool)
else:
inputs_dict[input] = np.random.uniform(lower, upper, inputs_dict[input]).astype(
np.float32)

2
tools/benchmark_tool/openvino/tools/benchmark/utils/inputs_filling.py
View File

@ -260,7 +260,7 @@ def get_image_info_tensors(image_sizes, layer):
def fill_tensors_with_random(layer):
dtype = get_dtype(layer.element_type)
rand_min, rand_max = (0, 1) if dtype == np.bool else (np.iinfo(np.uint8).min, np.iinfo(np.uint8).max)
rand_min, rand_max = (0, 1) if dtype == bool else (np.iinfo(np.uint8).min, np.iinfo(np.uint8).max)
# np.random.uniform excludes high: add 1 to have it generated
if np.dtype(dtype).kind in ['i', 'u', 'b']:
rand_max += 1

2
tools/benchmark_tool/openvino/tools/benchmark/utils/utils.py
View File

@ -528,7 +528,7 @@ def parse_scale_or_mean(parameter_string, input_info):
if matches:
for match in matches:
input_name, value = match
f_value = np.array(value.split(",")).astype(np.float)
f_value = np.array(value.split(",")).astype(float)
if input_name != '':
return_value[input_name] = f_value
else:

2
tools/mo/openvino/tools/mo/analysis/boolean_input.py
View File

@ -10,7 +10,7 @@ from openvino.tools.mo.utils.model_analysis import AnalyzeAction
class TrainingPhaseAnalysis(AnalyzeAction):
def analyze(self, graph: Graph):
nodes = graph.get_op_nodes(op='Parameter', data_type=np.bool)
nodes = graph.get_op_nodes(op='Parameter', data_type=bool)
names = ""
params = ""
if not nodes:

4
tools/mo/openvino/tools/mo/front/freeze_placeholder_value.py
View File

@ -46,10 +46,10 @@ class FreezePlaceholderValue(FrontReplacementSubgraph):
data_type = SUPPORTED_DATA_TYPES[graph.graph['cmd_params'].data_type][0]
string_value = graph.graph['freeze_placeholder'][name]
try:
if data_type != np.bool:
if data_type != bool:
value = mo_array(string_value, dtype=data_type)
# TODO: investigate why boolean type is allowed only for TensorFlow
elif data_type == np.bool and graph.graph['fw'] == 'tf':
elif data_type == bool and graph.graph['fw'] == 'tf':
from openvino.tools.mo.front.tf.common import tf_data_type_cast
if isinstance(string_value, list):
casted_list = list()

2
tools/mo/openvino/tools/mo/front/kaldi/extractors/lstm_nonlinearity_ext.py
View File

@ -22,7 +22,7 @@ class LSTMNonlinearityFrontExtractor(FrontExtractorOp):
ifo_x_weights, ifo_x_weights_shape = read_binary_matrix(pb)
try:
use_dropout = collect_until_token_and_read(pb, b'<UseDropout>', np.bool)
use_dropout = collect_until_token_and_read(pb, b'<UseDropout>', bool)
except Error:
# layer have not UseDropout attribute, so setup it to False
use_dropout = False

2
tools/mo/openvino/tools/mo/front/kaldi/loader/loader.py
View File

@ -324,7 +324,7 @@ def read_node(file_descr, graph, component_layer_map, layer_node_map):
if tokens[0] == b'input-node':
in_name = s[s.find(b'name=') + len(b'name='):].split(b' ')[0]
in_name = str(in_name).strip('b').replace('\'', "")
in_shape = mo_array([1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]], dtype=np.int)
in_shape = mo_array([1, s[s.find(b'dim=') + len(b'dim='):].split(b' ')[0]], dtype=int)
if in_name not in layer_node_map:
graph.add_node(in_name, name=in_name, kind='op', op='Parameter', parameters=None, shape=in_shape)

4
tools/mo/openvino/tools/mo/front/kaldi/loader/utils.py
View File

@ -258,7 +258,7 @@ def read_token_value(file_desc: io.BufferedReader, token: bytes = b'', value_typ
getters = {
np.uint32: read_binary_integer32_token,
np.uint64: read_binary_integer64_token,
np.bool: read_binary_bool_token
bool: read_binary_bool_token
}
current_token = collect_until_whitespace(file_desc)
if token != b'' and token != current_token:
@ -314,7 +314,7 @@ def collect_until_token_and_read(file_desc: io.BufferedReader, token, value_type
getters = {
np.uint32: read_binary_integer32_token,
np.uint64: read_binary_integer64_token,
np.bool: read_binary_bool_token,
bool: read_binary_bool_token,
np.string_: read_string
}
collect_until_token(file_desc, token)

2
tools/mo/openvino/tools/mo/front/kaldi/restrictedattentioncomponent_replacer.py
View File

@ -177,7 +177,7 @@ class RestrictedAttentionComponentReplacer(FrontReplacementPattern):
split_2_node.out_port(0).connect(einsum_1_node.in_port(1))
mul_node = create_op_with_const_inputs(graph, Mul, {1: mo_array(key_scale, dtype=np.float)},
mul_node = create_op_with_const_inputs(graph, Mul, {1: mo_array(key_scale, dtype=float)},
{'name': self.in_name + '/Mul'})
reshape_helper_1_node.out_port(0).connect(mul_node.in_port(0))

2
tools/mo/openvino/tools/mo/front/onnx/LoopNormalize.py
View File

@ -39,7 +39,7 @@ class ONNXLoopNormalize(FrontReplacementSubgraph):
# connect "execution condition" input if it is not connected with default value True
if not loop_node.is_in_port_connected(1):
loop_node.add_input_port(1, skip_if_exist=True)
Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': mo_array(True, dtype=np.bool)}).\
Const(loop_node.graph, {'name': loop_name + '/execution_cond', 'value': mo_array(True, dtype=bool)}).\
create_node().out_port(0).connect(loop_node.in_port(1))
# scan output need Unsqueeze over axis 0

2
tools/mo/openvino/tools/mo/front/onnx/extractors/utils.py
View File

@ -56,7 +56,7 @@ def get_onnx_opset_version(node: Node):
def get_onnx_datatype_as_numpy(value):
datatype_to_numpy = {
1: np.float32,
9: np.bool,
9: bool,
11: np.double,
10: np.float16,
5: np.int16,

4
tools/mo/openvino/tools/mo/front/onnx/group_norm_ext.py
View File

@ -16,7 +16,7 @@ class ExperimentalDetectronGroupNorm(FrontExtractorOp):
@classmethod
def extract(cls, node):
attrs = {
'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=float),
'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
}
GroupNorm.update_node_stat(node, attrs)
@ -30,7 +30,7 @@ class GroupNormExtractor(FrontExtractorOp):
@classmethod
def extract(cls, node):
attrs = {
'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=np.float),
'eps': mo_array(onnx_attr(node, 'eps', 'f', default=1e-6), dtype=float),
'num_groups': int64_array(onnx_attr(node, 'num_groups', 'i', default=1)),
}
GroupNorm.update_node_stat(node, attrs)

2
tools/mo/openvino/tools/mo/front/onnx/slice_ext.py
View File

@ -25,7 +25,7 @@ class SliceFrontExtractor(FrontExtractorOp):
if len(starts) == 0 or len(ends) == 0:
raise Error("starts or/and ends are not specified for the node {}".format(node.name))
if len(axes) == 0:
axes = np.arange(len(starts), dtype=np.int)
axes = np.arange(len(starts), dtype=int)
attrs = {'axes': axes, 'starts': starts, 'ends': ends}
AttributedSlice.update_node_stat(node, attrs)

2
tools/mo/openvino/tools/mo/front/tf/MapFNTransformation.py
View File

@ -243,7 +243,7 @@ class MapFNOutputConcatenation(FrontReplacementSubgraph):
if 'purpose' in record and record['purpose'] == 'execution_condition':
exec_cond_layer_id = record['internal_layer_id']
exec_cond_node = Loop.get_body_node_by_internal_id(loop_node, exec_cond_layer_id)
const_true = Const(body_graph, {'value': mo_array(True, dtype=np.bool)}).create_node()
const_true = Const(body_graph, {'value': mo_array(True, dtype=bool)}).create_node()
exec_cond_node.in_port(0).get_connection().set_source(const_true.out_port(0))
# remove back edge

2
tools/mo/openvino/tools/mo/front/tf/WhileNormalize.py
View File

@ -34,7 +34,7 @@ class WhileNormalize(FrontReplacementSubgraph):
# connect execution condition port
exec_cond_node = Const(graph, {'name': loop_name + '/ExecutionConditionValue',
'value': mo_array(True, dtype=np.bool)}).create_node()
'value': mo_array(True, dtype=bool)}).create_node()
loop_node.in_port(1).get_connection().set_source(exec_cond_node.out_port(0))
loop_node.body.clean_up()

6
tools/mo/openvino/tools/mo/front/tf/common.py
View File

@ -7,7 +7,7 @@ from tensorflow.core.framework import types_pb2 as tf_types # pylint: disable=n
# Suppress false positive pylint warning about function with too many arguments
# pylint: disable=E1121
# mapping between TF data type and numpy data type and function to extract data from TF tensor
_tf_np_mapping = [('DT_BOOL', np.bool, lambda pb: pb.bool_val, lambda x: bool_cast(x)),
_tf_np_mapping = [('DT_BOOL', bool, lambda pb: pb.bool_val, lambda x: bool_cast(x)),
('DT_INT8', np.int8, lambda pb: pb.int_val, lambda x: np.int8(x)),
('DT_INT16', np.int16, lambda pb: pb.int_val, lambda x: np.int16(x)),
('DT_INT32', np.int32, lambda pb: pb.int_val, lambda x: np.int32(x)),
@ -19,7 +19,7 @@ _tf_np_mapping = [('DT_BOOL', np.bool, lambda pb: pb.bool_val, lambda x: bool_ca
('DT_HALF', np.float16, lambda pb: np.uint16(pb.half_val).view(np.float16), lambda x: np.float16(x)),
('DT_FLOAT', np.float32, lambda pb: pb.float_val, lambda x: np.float32(x)),
('DT_DOUBLE', np.double, lambda pb: pb.double_val, lambda x: np.double(x)),
('DT_STRING', np.str, lambda pb: pb.string_val, lambda x: np.str(x)),
('DT_STRING', str, lambda pb: pb.string_val, lambda x: str(x)),
]
tf_data_type_decode = {getattr(tf_types, tf_dt): (np_type, func) for tf_dt, np_type, func, _ in _tf_np_mapping if
@ -32,4 +32,4 @@ def bool_cast(x):
if isinstance(x, str):
return False if x.lower() in ['false', '0'] else True if x.lower() in ['true', '1'] else 'unknown_boolean_cast'
else:
return np.bool(x)
return bool(x)

2
tools/mo/openvino/tools/mo/front/tf/extractors/utils.py
View File

@ -63,7 +63,7 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor):
value = mo_array(np.frombuffer(pb_tensor.tensor_content, type_helper[0]))
else:
# load typed value
if type_helper[0] != np.str:
if type_helper[0] != str:
value = mo_array(type_helper[1](pb_tensor), dtype=type_helper[0])
else:
try:

2
tools/mo/openvino/tools/mo/middle/passes/convert_data_type.py
View File

@ -40,7 +40,7 @@ SUPPORTED_DATA_TYPES = {
'int8': (np.int8, 'I8', 'i8'),
'int32': (np.int32, 'I32', 'i32'),
'int64': (np.int64, 'I64', 'i64'),
'bool': (np.bool, 'BOOL', 'boolean'),
'bool': (bool, 'BOOL', 'boolean'),
'uint8': (np.uint8, 'U8', 'u8'),
'uint32': (np.uint32, 'U32', 'u32'),
'uint64': (np.uint64, 'U64', 'u64'),

2
tools/mo/openvino/tools/mo/middle/quantize_linear_resolver.py
View File

@ -99,7 +99,7 @@ class QuantizeLinearResolver(MiddleReplacementPattern):
format(quantize_node.soft_get('name', soft_get('id')))
if axis is not None and len(scale_y_shape) > 0 and scale_y_shape[0] > 1:
input_shape = fake_quantize.in_port(0).data.get_shape()
target_shape = np.ones(len(input_shape), np.int)
target_shape = np.ones(len(input_shape), int)
target_shape[axis] = input_shape[axis]
mul_low_reshape = create_op_with_const_inputs(graph, Reshape, {1: int64_array(target_shape)},
{'name': node_name + '/Reshape/Mul/Low'})

4
tools/mo/openvino/tools/mo/ops/ReduceOps.py
View File

@ -43,7 +43,7 @@ def reduce_helper(func: callable, x: np.array, axis: tuple, keepdims: bool):
if is_fully_defined(x):
return result
else:
return np.ma.masked_array(result, mask=np.ones(result.shape, dtype=np.bool))
return np.ma.masked_array(result, mask=np.ones(result.shape, dtype=bool))
def reduce_infer(node: Node):
@ -73,7 +73,7 @@ def reduce_infer(node: Node):
value = reduce_helper(reduce_map[node.op], in_value.copy(), axis=tuple(axis), keepdims=node.keep_dims)
node.out_port(0).data.set_value(value)
else:
used_dims = np.zeros(len(in_shape), dtype=np.bool)
used_dims = np.zeros(len(in_shape), dtype=bool)
output_shape = in_shape.copy()
for dim in axis:

2
tools/mo/openvino/tools/mo/ops/activation_ops.py
View File

@ -231,7 +231,7 @@ class LogicalNot(Activation):
@staticmethod
def type_infer(node: Node):
node.out_port(0).set_data_type(np.bool)
node.out_port(0).set_data_type(bool)
class Log(Activation):

2
tools/mo/openvino/tools/mo/ops/elementwise.py
View File

@ -115,7 +115,7 @@ class LogicalElementwise(Elementwise):
@staticmethod
def type_infer(node):
override_data_type_of_constant(node)
node.out_port(0).set_data_type(np.bool)
node.out_port(0).set_data_type(bool)
class Greater(LogicalElementwise):

2
tools/mo/openvino/tools/mo/ops/select.py
View File

@ -90,7 +90,7 @@ class Select(Op):
# one of the branches is None (which is not selected)
# if we use np.where for such cases then dtype of output_value will be object (non numeric type)
# and subsequent numpy operation on such tensors will fail
output_value = resulting_tensors[not np.bool(condition_value.item(0))]
output_value = resulting_tensors[not bool(condition_value.item(0))]
if output_value is None:
return
if broadcast_rule == 'numpy':

2
tools/mo/openvino/tools/mo/ops/sparse_segment_mean.py
View File

@ -81,7 +81,7 @@ class SparseSegmentMean(Op):
"Some value in indices tensor is out of range"
# infer
num_adds = np.zeros(num_segments, dtype=np.int)
num_adds = np.zeros(num_segments, dtype=int)
output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float32)
output_shape = output_value.shape
for i in range(len(segment_ids_value)):

2
tools/mo/openvino/tools/mo/ops/sparse_segment_sqrtn.py
View File

@ -81,7 +81,7 @@ class SparseSegmentSqrtN(Op):
"Some value in indices tensor is out of range"
# infer
num_adds = np.zeros(num_segments, dtype=np.int)
num_adds = np.zeros(num_segments, dtype=int)
output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float32)
output_shape = output_value.shape
for i in range(len(segment_ids_value)):

2
tools/mo/openvino/tools/mo/ops/unique.py
View File

@ -152,6 +152,6 @@ class Unique(Op):
# write result to output nodes
j = 0
for out_node_ind in node.out_nodes():
node.out_node(out_node_ind).value = mo_array(unique_output[j], dtype=np.float)
node.out_node(out_node_ind).value = mo_array(unique_output[j], dtype=float)
node.out_node(out_node_ind).shape = int64_array(node.out_node(out_node_ind).value.shape)
j += 1

2
tools/mo/openvino/tools/mo/utils/ir_engine/ir_engine.py
View File

@ -346,7 +346,7 @@ class IREngine(object):
'U1': (1, np.uint8),
'U4': (1, np.uint8),
'I4': (1, np.uint8),
'BOOL': (1, np.bool),
'BOOL': (1, bool),
'BIN': (1, np.uint8),
'U64': (8, np.uint64)
}

6
tools/mo/openvino/tools/mo/utils/type_utils.py
View File

@ -9,7 +9,7 @@ from openvino.tools.mo.graph.graph import Node
from openvino.tools.mo.pipeline.common import convert_const_node_value_type
from openvino.tools.mo.utils.error import Error
np_map_cast = {np.bool: lambda x: bool_cast(x),
np_map_cast = {bool: lambda x: bool_cast(x),
np.int8: lambda x: np.int8(x),
np.int16: lambda x: np.int16(x),
np.int32: lambda x: np.int32(x),
@ -21,14 +21,14 @@ np_map_cast = {np.bool: lambda x: bool_cast(x),
np.float16: lambda x: np.float16(x),
np.float32: lambda x: np.float32(x),
np.double: lambda x: np.double(x),
np.str: lambda x: np.str(x)}
str: lambda x: str(x)}
def bool_cast(x):
if isinstance(x, str):
return False if x.lower() in ['false', '0'] else True if x.lower() in ['true', '1'] else 'unknown_boolean_cast'
else:
return np.bool(x)
return bool(x)
def override_data_type_of_constant(node: Node, lhs_idx: int = 0, rhs_idx: int = 1):

2
tools/mo/requirements_caffe.txt
View File

@ -1,6 +1,6 @@
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
protobuf>=3.18.1,<4.0.0
defusedxml>=0.7.1
requests>=2.25.1

2
tools/mo/requirements_kaldi.txt
View File

@ -1,6 +1,6 @@
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
defusedxml>=0.7.1
requests>=2.25.1
fastjsonschema~=2.15.1

2
tools/mo/requirements_mxnet.txt
View File

@ -2,7 +2,7 @@ mxnet~=1.2.0; sys_platform == 'win32'
mxnet>=1.7.0.post2,<=1.9.1; sys_platform != 'win32'
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
defusedxml>=0.7.1
urllib3>=1.26.4
requests>=2.25.1

2
tools/mo/requirements_onnx.txt
View File

@ -1,7 +1,7 @@
onnx>=1.8.1,<=1.12
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
defusedxml>=0.7.1
requests>=2.25.1
fastjsonschema~=2.15.1

2
tools/mo/requirements_tf.txt
View File

@ -1,4 +1,4 @@
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
tensorflow>=1.15.5,<=2.10.0
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"

2
tools/mo/requirements_tf2.txt
View File

@ -1,4 +1,4 @@
numpy>=1.16.6,<=1.23.4
numpy>=1.16.6,<=1.24.0
tensorflow>=2.5,<=2.10.0
networkx~=2.5; python_version <= "3.6"
networkx<=2.8.8; python_version > "3.6"

2
tools/mo/unit_tests/mo/front/kaldi/restrictedattentioncomponent_replacer_test.py
View File

@ -35,7 +35,7 @@ class RestrictedAttentionComponentReplacerTest(unittest.TestCase):
**regular_op('reshape_helper_1', {'type': 'Reshape'}),
**const('reshape_helper_1_shape', int64_array([10, 1])),
**regular_op('mul', {'type': 'Multiply'}),
**const('mul_scale', mo_array(0.5, dtype=np.float)),
**const('mul_scale', mo_array(0.5, dtype=float)),
**regular_op('add', {'type': 'Add'}),
**regular_op('softmax', {'type': 'SoftMax'}),
**regular_op('reshape_helper_3', {'type': 'Reshape'}),

8
tools/mo/unit_tests/mo/front/onnx/priorbox_clustered_ext_test.py
View File

@ -46,8 +46,8 @@ class TestPriorBoxClusteredExt(unittest.TestCase):
self.assertRaises(AttributeError, PriorBoxClusteredFrontExtractor.extract, None)
def test_priorbox_clustered_ext_ideal_numbers(self):
node = self._create_priorbox_clustered_node(width= np.array([2, 3], dtype=np.float),
height=np.array([4, 5], dtype=np.float),
node = self._create_priorbox_clustered_node(width= np.array([2, 3], dtype=float),
height=np.array([4, 5], dtype=float),
variance=np.array([0.2, 0.3, 0.2, 0.3]),
img_size=300, step=5.0, offset=0.6, flip=True)
@ -58,8 +58,8 @@ class TestPriorBoxClusteredExt(unittest.TestCase):
'type': 'PriorBoxClustered',
'clip': 0,
'flip': 1,
'width': np.array([2, 3], dtype=np.float),
'height': np.array([4, 5], dtype=np.float),
'width': np.array([2, 3], dtype=float),
'height': np.array([4, 5], dtype=float),
'variance': [0.2, 0.3, 0.2, 0.3],
'img_size': 300,
'img_h': 0,

4
tools/mo/unit_tests/mo/front/onnx/priorbox_ext_test.py
View File

@ -47,7 +47,7 @@ class TestPriorBoxExt(unittest.TestCase):
self.assertRaises(AttributeError, PriorBoxFrontExtractor.extract, None)
def test_priorbox_ext_ideal_numbers(self):
node = self._create_priorbox_node(aspect_ratio=np.array([2, 3], dtype=np.float),
node = self._create_priorbox_node(aspect_ratio=np.array([2, 3], dtype=float),
variance=np.array([0.2, 0.3, 0.2, 0.3]),
img_size=300, step=5.0, offset=0.6, flip=True)
@ -58,7 +58,7 @@ class TestPriorBoxExt(unittest.TestCase):
'type': 'PriorBox',
'clip': 0,
'flip': 1,
'aspect_ratio': np.array([2, 3], dtype=np.float),
'aspect_ratio': np.array([2, 3], dtype=float),
'variance': [0.2, 0.3, 0.2, 0.3],
'img_size': 300,
'img_h': 0,

4
tools/mo/unit_tests/mo/ops/If_test.py
View File

@ -25,8 +25,8 @@ from unit_tests.utils.graph import regular_op_with_empty_data, connect, result,
@generator
class TestIf(unittest.TestCase):
@generate(*[
(np.array([True], dtype=np.bool), shape_array([3]), shape_array([3])),
(np.array([False], dtype=np.bool), shape_array([3]), shape_array([2])),
(np.array([True], dtype=bool), shape_array([3]), shape_array([3])),
(np.array([False], dtype=bool), shape_array([3]), shape_array([2])),
(shape_array(dynamic_dimension_value), shape_array([3]), shape_array([dynamic_dimension_value])),
])
def test_simple_shape_inf(self, cond, output_port_0_shape, output_port_1_shape):

2
tools/mo/unit_tests/mo/ops/pad_test.py
View File

@ -107,7 +107,7 @@ class TestPadOps(unittest.TestCase):
nodes_with_edges_only=True,
)
out_shape = (1, 1, 5, 8)
mask = np.zeros(out_shape, dtype=np.bool)
mask = np.zeros(out_shape, dtype=bool)
mask[0][0][1][2] = True
ref_value = np.ma.masked_array(np.zeros(out_shape, dtype=np.int64), mask=mask, dtype=np.int64)
ref_value[0][0][1][3] = 3

84
tools/mo/unit_tests/mo/ops/select_test.py
View File

@ -60,16 +60,16 @@ class TestSelect(unittest.TestCase):
def test_1(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([5, 6], dtype=bool),
then_value=np.ones([5, 6], dtype=np.float),
else_value=np.zeros([5, 6], dtype=np.float),
out_value=np.ones([5, 6], dtype=np.float))
then_value=np.ones([5, 6], dtype=float),
else_value=np.zeros([5, 6], dtype=float),
out_value=np.ones([5, 6], dtype=float))
self.assertTrue(flag, msg)
def test_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
then_value=np.ones([15, 3, 5], dtype=np.float),
else_value=np.zeros([15, 1, 5], dtype=np.float),
out_value=np.ones([15, 3, 5], dtype=np.float))
then_value=np.ones([15, 3, 5], dtype=float),
else_value=np.zeros([15, 1, 5], dtype=float),
out_value=np.ones([15, 3, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_no_condition(self):
@ -95,44 +95,44 @@ class TestSelect(unittest.TestCase):
def test_select_infer_condition_true_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array([True], dtype=bool),
then_value=np.ones([15, 3, 5], dtype=np.float),
else_value=np.zeros([15, 1, 5], dtype=np.float),
out_value=np.ones([15, 3, 5], dtype=np.float))
then_value=np.ones([15, 3, 5], dtype=float),
else_value=np.zeros([15, 1, 5], dtype=float),
out_value=np.ones([15, 3, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_condition_true_then_and_else_are_scalars(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array([True], dtype=bool),
then_value=np.array(3, dtype=np.float),
else_value=np.array(1, dtype=np.float),
out_value=np.array([3], dtype=np.float))
then_value=np.array(3, dtype=float),
else_value=np.array(1, dtype=float),
out_value=np.array([3], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_condition_true_then_and_else_are_scalars_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array(True, dtype=bool),
then_value=np.array(3, dtype=np.float),
else_value=np.array(1, dtype=np.float),
out_value=np.array(3, dtype=np.float))
then_value=np.array(3, dtype=float),
else_value=np.array(1, dtype=float),
out_value=np.array(3, dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_condition_false_then_and_else_are_scalars(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
then_value=np.array(3, dtype=np.float),
else_value=np.array(1, dtype=np.float),
out_value=np.array([1], dtype=np.float))
then_value=np.array(3, dtype=float),
else_value=np.array(1, dtype=float),
out_value=np.array([1], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_condition_false_then_and_else_are_scalars_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array(False, dtype=bool),
then_value=np.array(3, dtype=np.float),
else_value=np.array(1, dtype=np.float),
out_value=np.array(1, dtype=np.float))
then_value=np.array(3, dtype=float),
else_value=np.array(1, dtype=float),
out_value=np.array(1, dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_condition_false_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.array([False], dtype=bool),
then_value=np.ones([15, 3, 5], dtype=np.float),
else_value=np.zeros([15, 1, 5], dtype=np.float),
out_value=np.zeros([15, 3, 5], dtype=np.float))
then_value=np.ones([15, 3, 5], dtype=float),
else_value=np.zeros([15, 1, 5], dtype=float),
out_value=np.zeros([15, 3, 5], dtype=float))
self.assertTrue(flag, msg)
# if one of the branches is None then np.where shouldn't be used to avoid object dtype in output
@ -142,57 +142,57 @@ class TestSelect(unittest.TestCase):
def test_select_infer_None_then_branch_1(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.zeros([15, 3, 5], dtype=bool),
then_value=None, then_shape=[15, 3, 5],
else_value=np.ones([15, 1, 5], dtype=np.float),
out_value=np.ones([15, 3, 5], dtype=np.float))
else_value=np.ones([15, 1, 5], dtype=float),
out_value=np.ones([15, 3, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_None_then_branch_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
then_value=None, then_shape=[15, 3, 5],
else_value=np.ones([15, 1, 5], dtype=np.float),
else_value=np.ones([15, 1, 5], dtype=float),
out_value=None)
self.assertTrue(flag, msg)
def test_select_infer_None_else_branch_1(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([15, 3, 5], dtype=bool),
then_value=np.ones([15, 1, 5], dtype=np.float),
then_value=np.ones([15, 1, 5], dtype=float),
else_value=None, else_shape=[15, 3, 5],
out_value=np.ones([15, 3, 5], dtype=np.float))
out_value=np.ones([15, 3, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_infer_None_else_branch_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.zeros([15, 3, 5], dtype=bool),
then_value=np.ones([15, 1, 5], dtype=np.float),
then_value=np.ones([15, 1, 5], dtype=float),
else_value=None, else_shape=[15, 3, 5],
out_value=None)
self.assertTrue(flag, msg)
def test_select_broadcast_1(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 5], dtype=bool),
then_value=np.ones([], dtype=np.float),
else_value=np.zeros([2, 3, 4, 5], dtype=np.float),
out_value=np.ones([2, 3, 4, 5], dtype=np.float))
then_value=np.ones([], dtype=float),
else_value=np.zeros([2, 3, 4, 5], dtype=float),
out_value=np.ones([2, 3, 4, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_broadcast_2(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 1], dtype=bool),
then_value= np.ones([1, 3, 1, 5], dtype=np.float),
else_value=np.zeros([2, 1, 1, 5], dtype=np.float),
out_value=np.ones([2, 3, 4, 5], dtype=np.float))
then_value= np.ones([1, 3, 1, 5], dtype=float),
else_value=np.zeros([2, 1, 1, 5], dtype=float),
out_value=np.ones([2, 3, 4, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_broadcast_3(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 1, 1], dtype=bool),
then_value= np.ones([2, 3, 4, 5], dtype=np.float),
else_value=np.zeros([2, 1, 1, 5], dtype=np.float),
out_value=np.ones([2, 3, 4, 5], dtype=np.float))
then_value= np.ones([2, 3, 4, 5], dtype=float),
else_value=np.zeros([2, 1, 1, 5], dtype=float),
out_value=np.ones([2, 3, 4, 5], dtype=float))
self.assertTrue(flag, msg)
def test_select_broadcast_4(self):
flag, msg = self.build_select_graph_and_infer(condition_value=np.ones([2, 3, 4, 5], dtype=bool),
then_value= np.ones([5], dtype=np.float),
else_value=np.zeros([2, 3, 4, 5], dtype=np.float),
out_value=np.ones([2, 3, 4, 5], dtype=np.float))
then_value= np.ones([5], dtype=float),
else_value=np.zeros([2, 3, 4, 5], dtype=float),
out_value=np.ones([2, 3, 4, 5], dtype=float))
self.assertTrue(flag, msg)
# when output shape is broadcasted from condition, then, and else shapes

8
tools/mo/unit_tests/mo/ops/sparse_segment_mean_test.py
View File

@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_mean_node', {'in': 0}),
('input_segment_ids', 'sparse_segment_mean_node', {'in': 2}),
('sparse_segment_mean_node', 'output_segments', {'out': 0})]
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1, 2, 2], dtype=np.float)}}
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1, 2, 2], dtype=float)}}
class TestSparseSegmentMean(unittest.TestCase):
def test_partial_infer(self):
@ -76,7 +76,7 @@ class TestSparseSegmentMean(unittest.TestCase):
# prepare reference results
ref_output_segments_shape = int64_array([3, 4])
ref_output_segments_value = np.array([[3, 4, 5, 6], [-1, -2, -3, -4], [2, 2, 2, 2]], dtype=np.float)
ref_output_segments_value = np.array([[3, 4, 5, 6], [-1, -2, -3, -4], [2, 2, 2, 2]], dtype=float)
# get resulted shapes
res_output_segments_shape = graph.node['output_segments']['shape']

8
tools/mo/unit_tests/mo/ops/sparse_segment_sqrtn_test.py
View File

@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_sqrtn_node', {'in': 0}),
('input_segment_ids', 'sparse_segment_sqrtn_node', {'in': 2}),
('sparse_segment_sqrtn_node', 'output_segments', {'out': 0})]
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=np.float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 0, 0, 2], dtype=np.float)}}
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 2, 1, 1, 2], dtype=float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 0, 0, 2], dtype=float)}}
class TestSparseSegmentSqrtN(unittest.TestCase):
def test_partial_infer(self):
@ -76,7 +76,7 @@ class TestSparseSegmentSqrtN(unittest.TestCase):
# prepare reference results
ref_output_segments_shape = int64_array([3, 4])
ref_output_segments_value = np.array([[2, 2, 2, 2], [0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float)
ref_output_segments_value = np.array([[2, 2, 2, 2], [0, 0, 0, 0], [5, 6, 7, 8]], dtype=float)
# get resulted shapes
res_output_segments_shape = graph.node['output_segments']['shape']

8
tools/mo/unit_tests/mo/ops/sparse_segment_sum_test.py
View File

@ -40,9 +40,9 @@ edges2 = [('input_data', 'sparse_segment_sum_node', {'in': 0}),
('input_segment_ids', 'sparse_segment_sum_node', {'in': 2}),
('sparse_segment_sum_node', 'output_segments', {'out': 0})]
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 1, 2], dtype=np.float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1], dtype=np.float)}}
inputs2 = {'input_data': {'shape': int64_array([3, 4]), 'value': np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=float)},
'input_indices': {'shape': int64_array([3]), 'value': np.array([0, 1, 2], dtype=float)},
'input_segment_ids': {'shape': int64_array([3]), 'value': np.array([0, 0, 1], dtype=float)}}
class TestSparseSegmentSum(unittest.TestCase):
def test_partial_infer(self):
@ -76,7 +76,7 @@ class TestSparseSegmentSum(unittest.TestCase):
# prepare reference results
ref_output_segments_shape = int64_array([2, 4])
ref_output_segments_value = np.array([[0, 0, 0, 0], [5, 6, 7, 8]], dtype=np.float)
ref_output_segments_value = np.array([[0, 0, 0, 0], [5, 6, 7, 8]], dtype=float)
# get resulted shapes
res_output_segments_shape = graph.node['output_segments']['shape']

16
tools/mo/unit_tests/mo/ops/unique_test.py
View File

@ -162,7 +162,7 @@ class TestUnique(unittest.TestCase):
('unique_node', 'output_indices', {'out': 1}),
('unique_node', 'output_counts', {'out': 2})]
inputs_ = {'input': {'shape': int64_array([10]),
'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float)},
'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=float)},
'unique_node': {
'sorted': 'false',
'return_inverse': 'true',
@ -175,11 +175,11 @@ class TestUnique(unittest.TestCase):
# prepare reference results
ref_output_uniques_shape = int64_array([5])
ref_output_uniques_value = np.array([8.0, 1.0, 2.0, 5.0, 0.0], dtype=np.float)
ref_output_uniques_value = np.array([8.0, 1.0, 2.0, 5.0, 0.0], dtype=float)
ref_output_indices_shape = int64_array([10])
ref_output_indices_value = np.array([0.0, 1.0, 2.0, 1.0, 0.0, 3.0, 1.0, 3.0, 4.0, 4.0], dtype=np.float)
ref_output_indices_value = np.array([0.0, 1.0, 2.0, 1.0, 0.0, 3.0, 1.0, 3.0, 4.0, 4.0], dtype=float)
ref_output_counts_shape = int64_array([5])
ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float)
ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=float)
# get resulted shapes
res_output_uniques_shape = graph.node['output_uniques']['shape']
@ -217,7 +217,7 @@ class TestUnique(unittest.TestCase):
('unique_node', 'output_indices', {'out': 1}),
('unique_node', 'output_counts', {'out': 2})]
inputs_ = {'input': {'shape': int64_array([10]),
'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=np.float)},
'value': np.array([8.0, 1.0, 2.0, 1.0, 8.0, 5.0, 1.0, 5.0, 0.0, 0.0], dtype=float)},
'unique_node': {
'sorted': 'true',
'return_inverse': 'true',
@ -230,11 +230,11 @@ class TestUnique(unittest.TestCase):
# prepare reference results
ref_output_uniques_shape = int64_array([5])
ref_output_uniques_value = np.array([0.0, 1.0, 2.0, 5.0, 8.0], dtype=np.float)
ref_output_uniques_value = np.array([0.0, 1.0, 2.0, 5.0, 8.0], dtype=float)
ref_output_indices_shape = int64_array([10])
ref_output_indices_value = np.array([4.0, 1.0, 2.0, 1.0, 4.0, 3.0, 1.0, 3.0, 0.0, 0.0], dtype=np.float)
ref_output_indices_value = np.array([4.0, 1.0, 2.0, 1.0, 4.0, 3.0, 1.0, 3.0, 0.0, 0.0], dtype=float)
ref_output_counts_shape = int64_array([5])
ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=np.float)
ref_output_counts_value = np.array([2.0, 3.0, 1.0, 2.0, 2.0], dtype=float)
# get resulted shapes
res_output_uniques_shape = graph.node['output_uniques']['shape']

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