9bedafb560
* [PT FE]: support aten::erf and aten::adaptive_avg_pool1d * align adaptive avg pools for different sizes * refactor adaptive max pool
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
# Copyright (C) 2018-2023 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 TestErf(PytorchLayerTest):
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def _prepare_input(self, input_dtype, out=False):
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import numpy as np
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x = np.linspace(-3, 3).astype(input_dtype)
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if not out:
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return (x, )
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return (x, np.zeros_like(x).astype(input_dtype))
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def create_model(self, mode="", input_dtype="float32"):
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import torch
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dtypes = {
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"float32": torch.float32,
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"float64": torch.float64,
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"int32": torch.int32
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}
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dtype = dtypes[input_dtype]
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class aten_erf(torch.nn.Module):
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def __init__(self, mode, dtype):
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super(aten_erf, self).__init__()
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self.dtype = dtype
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if mode == "out":
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self.forward = self.forward_out
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elif mode == "inplace":
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self.forward = self.forward_inplace
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def forward(self, x):
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return torch.special.erf(x.to(self.dtype))
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def forward_out(self, x, y):
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return torch.special.erf(x.to(self.dtype), out=y), y
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def forward_inplace(self, x):
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x = x.to(self.dtype)
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return x.erf_(), x
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ref_net = None
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return aten_erf(mode, dtype), ref_net, "aten::erf" if mode != "inplace" else "aten::erf_"
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("mode,input_dtype", [
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("", "float32"), ("", "float64"), ("", "int32"),
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("out", "float32"), ("out", "float64"),
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("inplace", "float32"), ("inplace", "float64")])
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def test_erf(self, mode, input_dtype, ie_device, precision, ir_version):
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self._test(*self.create_model(mode, input_dtype), ie_device, precision, ir_version,
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kwargs_to_prepare_input={"input_dtype": input_dtype, "out": mode == "out"} ) |