8d0381b0fe
* [PT FE] Implement custom op for types alignment * Fix code style * Fix inplace ops * Fix layer tests * Remove no longer needed change * Fix ovc tests * Fix fe tests
96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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class TestTranspose(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(2, 3, 4, 5).astype(np.float32),)
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def create_model(self, dim0, dim1, op_type):
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class swapaxes(torch.nn.Module):
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def __init__(self, dim0, dim1):
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super().__init__()
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self.dim0 = dim0
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self.dim1 = dim1
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return torch.swapaxes(x, self.dim0, self.dim1)
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class aten_transpose(torch.nn.Module):
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def __init__(self, dim0, dim1, op_type):
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super(aten_transpose, self).__init__()
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self.dim0 = dim0
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self.dim1 = dim1
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op_types = {"transpose": self.forward_transpose, "swapaxes": self.forward_swapaxes}
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self.swapaxes = swapaxes(dim0, dim1)
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self.forward = op_types.get(op_type)
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def forward_transpose(self, x):
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return torch.transpose(x, self.dim0, self.dim1)
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def forward_swapaxes(self, x: torch.Tensor) -> torch.Tensor:
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# To reproduce aten::swapaxes in graph, swapaxes need to be in separate graph and tracing need to be used.
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return self.swapaxes(x)
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ref_net = None
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return aten_transpose(dim0, dim1, op_type), ref_net, f"aten::{op_type}"
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@pytest.mark.parametrize("dim0", [0, 1, 2, 3, -1, -2, -3, -4])
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@pytest.mark.parametrize("dim1", [0, 1, 2, 3, -1, -2, -3, -4])
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@pytest.mark.parametrize("op_type", ["transpose", "swapaxes"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_transpose(self, dim0, dim1, op_type, ie_device, precision, ir_version):
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self._test(*self.create_model(dim0, dim1, op_type), ie_device, precision, ir_version, trace_model=True)
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class TestTSmall(PytorchLayerTest):
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def _prepare_input(self, num_dims=2, input_dtype="float32"):
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shape = (2, 3)
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if num_dims == 0:
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return (np.array(num_dims).astype(input_dtype),)
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return (np.random.randn(*shape[:num_dims]).astype(input_dtype),)
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def create_model(self, mode):
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class aten_transpose(torch.nn.Module):
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def __init__(self, mode):
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super(aten_transpose, self).__init__()
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if mode == "inplace":
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self.forward = self.forward_inplace
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elif mode == "numpy":
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self.forward = self.forward_numpy_t
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def forward(self, x):
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return x.t(), x
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def forward_inplace(self, x):
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return x.t_(), x
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def forward_numpy_t(self, x):
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return x.T, x
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ref_net = None
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return aten_transpose(mode), ref_net, "aten::t_" if mode == "inplace" else ("aten::numpy_T" if mode == "numpy" else "aten::t")
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@pytest.mark.parametrize("num_dims", [0, 1, 2])
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@pytest.mark.parametrize("input_dtype", ["float32", "int32"])
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@pytest.mark.parametrize("mode", [None, "inplace", "numpy"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_t_small(self, num_dims, input_dtype, mode, ie_device, precision, ir_version):
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self._test(
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*self.create_model(mode),
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ie_device,
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precision,
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ir_version,
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kwargs_to_prepare_input={"num_dims": num_dims, "input_dtype": input_dtype},
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use_convert_model=True,
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)
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