110 lines
4.1 KiB
Python
110 lines
4.1 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 TestExpand(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 3).astype(np.float32),)
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def create_model(self, dim, op_type="expand"):
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import torch
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class aten_expand(torch.nn.Module):
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def __init__(self, dims, op_type="expand"):
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super(aten_expand, self).__init__()
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self.dims = dims
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if op_type == "broadcast_to":
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self.forward = self.forward_broadcast
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def forward(self, x):
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return x.expand(self.dims)
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def forward_broadcast(self, x):
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return x.broadcast_to(self.dims)
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ref_net = None
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return aten_expand(dim, op_type), ref_net, f"aten::{op_type}"
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@pytest.mark.parametrize("dims", [(4, 3), (-1, -1), (1, 2, 3), (1, 2, 2, 3)])
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@pytest.mark.parametrize("op_type", ["expand", "broadcast_to"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_expand(self, dims, op_type, ie_device, precision, ir_version):
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self._test(*self.create_model(dims, op_type), ie_device, precision, ir_version)
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class TestExpandList(PytorchLayerTest):
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def _prepare_input(self, broadcast_shape):
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import numpy as np
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return (np.random.randn(1, 3).astype(np.float32), np.random.randn(*broadcast_shape).astype(np.float32))
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def create_model(self, op_type="expand"):
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import torch
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class aten_expand(torch.nn.Module):
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def __init__(self, op_type="expand"):
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super(aten_expand, self).__init__()
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if op_type == "broadcast_to":
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self.forward = self.forward_broadcast
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def forward(self, x, y):
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y_shape = y.shape
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return x.expand([y_shape[0], y_shape[1]])
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def forward_broadcast(self, x, y):
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y_shape = y.shape
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return x.broadcast_to([y_shape[0], y_shape[1]])
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ref_net = None
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return aten_expand(op_type), ref_net, [f"aten::{op_type}", "prim::ListConstruct"]
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@pytest.mark.parametrize("dims", [(3, 3), (2, 3), (1, 3), [4, 3]])
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@pytest.mark.parametrize("op_type", ["expand", "broadcast_to"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_expand(self, dims, op_type, ie_device, precision, ir_version):
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self._test(*self.create_model(op_type), ie_device, precision, ir_version, kwargs_to_prepare_input={"broadcast_shape": dims})
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class TestExpandAs(PytorchLayerTest):
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def _prepare_input(self, input_shape, broadcast_shape):
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import numpy as np
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return (np.random.randn(*input_shape).astype(np.float32), np.random.randn(*broadcast_shape).astype(np.float32),)
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def create_model(self):
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import torch
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class aten_expand_as(torch.nn.Module):
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def __init__(self):
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super(aten_expand_as, self).__init__()
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def forward(self, x, y):
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return x.expand_as(y)
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ref_net = None
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return aten_expand_as(), ref_net, "aten::expand_as"
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@pytest.mark.parametrize("kwargs_to_prepare_input", [
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{'input_shape': [1, 2], "broadcast_shape": [1, 2]},
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{'input_shape': [1, 2], "broadcast_shape": [1, 4, 2]},
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{'input_shape': [1, 2], "broadcast_shape": [2, 2]},
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{'input_shape': [1, 2], "broadcast_shape": [2, 2, 2]},
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{'input_shape': [1, 2], "broadcast_shape": [1, 4, 2]},
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{'input_shape': [1, 2, 3], "broadcast_shape": [1, 2, 3]},
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{'input_shape': [1, 2, 3], "broadcast_shape": [1, 4, 2, 3]},
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{'input_shape': [1, 2, 3, 4], "broadcast_shape": [1, 2, 3, 4]},
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{'input_shape': [1, 2, 3, 4], "broadcast_shape": [1, 4, 2, 3, 4]},
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_expand(self, ie_device, precision, ir_version, kwargs_to_prepare_input):
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self._test(*self.create_model(), ie_device, precision,
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ir_version, kwargs_to_prepare_input=kwargs_to_prepare_input)
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