mirror of https://github.com/Jittor/Jittor
98 lines
4.8 KiB
Python
98 lines
4.8 KiB
Python
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# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Wenyang Zhou <576825820@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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# All Rights Reserved.
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import unittest
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import jittor as jt
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import numpy as np
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import jittor.nn as jnn
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skip_this_test = False
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try:
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jt.dirty_fix_pytorch_runtime_error()
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import torch
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import torch.nn as tnn
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import torchvision
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except:
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torch = None
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tnn = None
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torchvision = None
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skip_this_test = True
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def check_equal(res1, res2, eps=1e-5):
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assert np.allclose(res1.detach().numpy(), res2.numpy(), eps)
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestPad(unittest.TestCase):
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def test_repeat(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).repeat(1,2,3,4), jt.array(arr).repeat(1,2,3,4))
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check_equal(torch.Tensor(arr).repeat(4,2,3,4), jt.array(arr).repeat(4,2,3,4))
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print('pass repeat test ...')
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def test_chunk(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).chunk(2,0)[0], jt.array(arr).chunk(2,0)[0])
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check_equal(torch.Tensor(arr).chunk(2,0)[1], jt.array(arr).chunk(2,0)[1])
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print('pass chunk test ...')
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def test_stack(self):
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arr1 = np.random.randn(16,3,224,224)
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arr2 = np.random.randn(16,3,224,224)
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check_equal(torch.stack([torch.Tensor(arr1), torch.Tensor(arr2)], 0), jt.stack([jt.array(arr1), jt.array(arr2)], 0))
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print('pass stack test ...')
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def test_flip(self):
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arr = np.random.randn(16,3,224,224)
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check_equal(torch.Tensor(arr).flip(0), jt.array(arr).flip(0))
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check_equal(torch.Tensor(arr).flip(1), jt.array(arr).flip(1))
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check_equal(torch.Tensor(arr).flip(2), jt.array(arr).flip(2))
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check_equal(torch.Tensor(arr).flip(3), jt.array(arr).flip(3))
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print('pass flip test ...')
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def test_cross(self):
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arr1 = np.random.randn(16,3,224,224,3)
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arr2 = np.random.randn(16,3,224,224,3)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=1), jt.array(arr1).cross(jt.array(arr2), dim=1), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=-4), jt.array(arr1).cross(jt.array(arr2), dim=-4), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=-1), jt.array(arr1).cross(jt.array(arr2), dim=-1), 1e-1)
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check_equal(torch.Tensor(arr1).cross(torch.Tensor(arr2), dim=4), jt.array(arr1).cross(jt.array(arr2), dim=4), 1e-1)
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print('pass cross test ...')
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def test_normalize(self):
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arr = np.random.randn(16,3,224,224,3)
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check_equal(tnn.functional.normalize(torch.Tensor(arr)), jt.normalize(jt.array(arr)))
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=0), jt.normalize(jt.array(arr), dim=0), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=1), jt.normalize(jt.array(arr), dim=1), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=-1), jt.normalize(jt.array(arr), dim=-1), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=2), jt.normalize(jt.array(arr), dim=2), 1e-1)
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check_equal(tnn.functional.normalize(torch.Tensor(arr), dim=3), jt.normalize(jt.array(arr), dim=3), 1e-1)
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print('pass normalize test ...')
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def test_make_grid(self):
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arr = np.random.randn(16,3,10,10)
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr)), jt.make_grid(jt.array(arr)))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=2), jt.make_grid(jt.array(arr), nrow=2))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3), jt.make_grid(jt.array(arr), nrow=3))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, padding=4), jt.make_grid(jt.array(arr), nrow=3, padding=4))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, padding=4, pad_value=-1), jt.make_grid(jt.array(arr), nrow=3, padding=4, pad_value=-1))
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check_equal(torchvision.utils.make_grid(torch.Tensor(arr), nrow=3, normalize=True, padding=4, pad_value=-1), jt.make_grid(jt.array(arr), nrow=3, normalize=True, padding=4, pad_value=-1))
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print('pass make_grid test ...')
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def test_unbind(self):
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arr = np.random.randn(2,3,4)
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for dim in range(len(arr.shape)):
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t_res = torch.unbind(torch.Tensor(arr), dim=dim)
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j_res = jt.unbind(jt.array(arr), dim=dim)
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for idx in range(len(t_res)):
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assert np.allclose(t_res[idx].numpy(), j_res[idx].numpy())
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print('pass unbind test ...')
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if __name__ == "__main__":
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unittest.main() |