93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# Copyright (C) 2018-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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class TestRound(PytorchLayerTest):
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def _prepare_input(self, out=False, dtype="float32"):
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import numpy as np
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input = np.random.randn(1, 3, 224, 224).astype(dtype)
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if dtype == "float64":
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# fp64 can fail by accuracy, because pytorch rounds fp64 value and ov will round fp32 value.
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# To remove sporadic accuracy fails we will round the number to 6 decimal places.
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input = np.round(input, 6)
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if not out:
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return (input, )
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return (input, np.zeros_like(input))
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def create_model(self, out=False):
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import torch
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class aten_round(torch.nn.Module):
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def __init__(self, out):
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super(aten_round, self).__init__()
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if out:
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self.forward = self.forward_out
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def forward(self, x):
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return torch.round(x)
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def forward_out(self, x, y):
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return torch.round(x, out=y), y
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ref_net = None
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return aten_round(out), ref_net, "aten::round"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("out", [True, False])
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@pytest.mark.parametrize("dtype", ["float32", "float64", "int32", "int64"])
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def test_round(self, out, dtype, ie_device, precision, ir_version):
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if ie_device == "GPU" and dtype not in ["float32", "float64"]:
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pytest.xfail(reason="square is not supported on GPU for integer types")
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self._test(*self.create_model(out), ie_device, precision, ir_version, kwargs_to_prepare_input={"out": out, "dtype": dtype})
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class TestRoundScalar(PytorchLayerTest):
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def _prepare_input_int(self):
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import numpy as np
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return (np.array(np.random.randint(low=-5, high=5)), )
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def _prepare_input_float(self):
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import numpy as np
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return (np.array(np.random.uniform(low=-5, high=5)), )
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def create_model(self, input_type="float"):
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import torch
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class aten_round(torch.nn.Module):
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def __init__(self, input_type):
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super(aten_round, self).__init__()
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if input_type == "int":
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self.forward = self.forward_int
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else:
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self.forward = self.forward_float
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def forward_int(self, x:int):
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return torch.round(x)
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def forward_float(self, x:float):
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return torch.round(x)
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ref_net = None
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return aten_round(input_type), ref_net, "aten::round"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("input_type", ["int", "float"])
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def test_round(self, input_type, ie_device, precision, ir_version):
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if input_type == "int":
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if ie_device == "GPU":
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pytest.xfail(reason="round is not supported on GPU for integer types")
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self._prepare_input = self._prepare_input_int
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else:
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self._prepare_input = self._prepare_input_float
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self._test(*self.create_model(input_type), ie_device, precision, ir_version, trace_model=True)
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