mirror of https://github.com/Jittor/Jittor
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
# ***************************************************************
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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.models as jtmodels
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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 torchvision.models as tcmodels
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from torch import nn
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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, "skip_this_test")
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class test_models(unittest.TestCase):
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@classmethod
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def setUpClass(self):
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self.models = [
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'inception_v3',
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'squeezenet1_0',
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'squeezenet1_1',
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'alexnet',
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'resnet18',
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'resnet34',
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'resnet50',
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'resnet101',
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'resnet152',
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'resnext50_32x4d',
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'resnext101_32x8d',
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'vgg11',
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'vgg11_bn',
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'vgg13',
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'vgg13_bn',
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'vgg16',
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'vgg16_bn',
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'vgg19',
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'vgg19_bn',
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'wide_resnet50_2',
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'wide_resnet101_2',
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'googlenet',
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'mobilenet_v2',
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'mnasnet0_5',
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'mnasnet0_75',
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'mnasnet1_0',
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'mnasnet1_3',
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'shufflenet_v2_x0_5',
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'shufflenet_v2_x1_0',
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'shufflenet_v2_x1_5',
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'shufflenet_v2_x2_0',
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]
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@unittest.skipIf(not jt.has_cuda, "Cuda not found")
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@jt.flag_scope(use_cuda=1)
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def test_models(self):
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def to_cuda(x):
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if jt.has_cuda:
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return x.cuda()
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return x
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threshold = 1e-2
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# Define numpy input image
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bs = 1
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test_img = np.random.random((bs,3,224,224)).astype('float32')
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# Define pytorch & jittor input image
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pytorch_test_img = to_cuda(torch.Tensor(test_img))
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jittor_test_img = jt.array(test_img)
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for test_model in self.models:
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if test_model == "inception_v3":
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test_img = np.random.random((bs,3,300,300)).astype('float32')
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pytorch_test_img = to_cuda(torch.Tensor(test_img))
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jittor_test_img = jt.array(test_img)
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# Define pytorch & jittor model
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pytorch_model = to_cuda(tcmodels.__dict__[test_model]())
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jittor_model = jtmodels.__dict__[test_model]()
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# Set eval to avoid dropout layer
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pytorch_model.eval()
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jittor_model.eval()
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# Jittor loads pytorch parameters to ensure forward alignment
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jittor_model.load_parameters(pytorch_model.state_dict())
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# Judge pytorch & jittor forward relative error. If the differece is lower than threshold, this test passes.
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pytorch_result = pytorch_model(pytorch_test_img)
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jittor_result = jittor_model(jittor_test_img)
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x = pytorch_result.detach().cpu().numpy() + 1
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y = jittor_result.data + 1
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relative_error = abs(x - y) / abs(y)
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diff = relative_error.mean()
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assert diff < threshold, f"[*] {test_model} forward fails..., Relative Error: {diff}"
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print(f"[*] {test_model} forword passes with Relative Error {diff}")
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print('all models pass test.')
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if __name__ == "__main__":
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unittest.main()
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