mirror of https://github.com/Jittor/Jittor
53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Haoyang Peng <2247838039@qq.com>
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# Guoye Yang <498731903@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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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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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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from torch.nn import functional as F
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except:
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torch = None
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skip_this_test = True
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestBicubicInterpolate(unittest.TestCase):
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# this is for testing bicubic interpolate
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def test_bicubic(self):
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for _ in range(20):
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try:
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tn = np.random.randn(1,1,5,5).astype('float32')
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ja = jt.array(tn)
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ta = torch.autograd.Variable(torch.from_numpy(tn),requires_grad=True)
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# test upsample
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ju = jt.nn.interpolate(ja,scale_factor=2,mode='bicubic')
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tu = F.interpolate(ta,scale_factor=2,mode='bicubic')
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assert np.allclose(ju.data,tu.detach().numpy(),rtol=1e-03,atol=1e-06)
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gju = jt.grad(ju,ja)
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gtu = torch.autograd.grad(tu,ta,torch.ones_like(tu),retain_graph=True)[0]
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assert np.allclose(gju.data,gtu.detach().numpy(),rtol=1e-03,atol=1e-06)
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# test align
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je = jt.nn.interpolate(ja,scale_factor=2,mode='bicubic',align_corners=True)
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te = F.interpolate(ta,scale_factor=2,mode='bicubic',align_corners=True)
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assert np.allclose(je.data,te.detach().numpy(),rtol=1e-03,atol=1e-06)
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gje = jt.grad(je,ja)
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gte = torch.autograd.grad(te,ta,torch.ones_like(tu),retain_graph=True)[0]
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assert np.allclose(gje.data,gte.detach().numpy(),rtol=1e-03,atol=1e-06)
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except AssertionError:
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print(ju,tu)
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print(je,te)
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if __name__ == "__main__":
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unittest.main() |