mirror of https://github.com/Jittor/Jittor
136 lines
5.4 KiB
Python
136 lines
5.4 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Guowei Yang <471184555@qq.com>
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# Meng-Hao Guo <guomenghao1997@gmail.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 jittor as jt
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from jittor import nn, Module
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from jittor.models import resnet
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import numpy as np
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import sys, os
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import random
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import math
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import unittest
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from jittor.test.test_reorder_tuner import simple_parser
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from jittor.test.test_log import find_log_with_re
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from jittor.dataset.mnist import MNIST
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import jittor.transform as trans
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import time
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skip_this_test = False
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if os.name == 'nt':
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skip_this_test = True
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class MnistNet(Module):
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def __init__(self):
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self.model = resnet.Resnet18()
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self.layer = nn.Linear(1000,10)
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def execute(self, x):
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x = self.model(x)
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x = self.layer(x)
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return x
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@unittest.skipIf(skip_this_test, "skip_this_test")
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class TestResnet(unittest.TestCase):
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@classmethod
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def setUpClass(self):
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# hyper-parameters
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self.batch_size = int(os.environ.get("TEST_BATCH_SIZE", "100"))
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self.weight_decay = 0.0001
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self.momentum = 0.9
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self.learning_rate = 0.1
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# mnist dataset
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self.train_loader = MNIST(train=True, transform=trans.Resize(224)) \
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.set_attrs(batch_size=self.batch_size, shuffle=True)
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self.train_loader.num_workers = 4
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# setup random seed
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def setup_seed(self, seed):
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np.random.seed(seed)
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random.seed(seed)
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jt.seed(seed)
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@unittest.skipIf(not jt.has_cuda, "Cuda not found")
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@jt.flag_scope(use_cuda=1, use_stat_allocator=1)
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def test_resnet(self):
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self.setup_seed(1)
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loss_list=[]
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acc_list=[]
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mnist_net = MnistNet()
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global prev
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prev = time.time()
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SGD = nn.SGD(mnist_net.parameters(), self.learning_rate, self.momentum, self.weight_decay)
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self.train_loader.endless = True
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for data, target in self.train_loader:
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batch_id = self.train_loader.batch_id
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epoch_id = self.train_loader.epoch_id
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# train step
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# with jt.log_capture_scope(
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# log_silent=1,
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# log_v=1, log_vprefix="op.cc=100,exe=10",
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# ) as logs:
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output = mnist_net(data)
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loss = nn.cross_entropy_loss(output, target)
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SGD.step(loss)
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def callback(epoch_id, batch_id, loss, output, target):
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# print train info
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global prev
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pred = np.argmax(output, axis=1)
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acc = np.mean(target==pred)
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loss_list.append(loss[0])
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acc_list.append(acc)
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAcc: {:.6f} \tTime:{:.3f}'
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.format(epoch_id, batch_id, 600,1. * batch_id / 6.0, loss[0], acc, time.time()-prev))
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# prev = time.time()
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jt.fetch(epoch_id, batch_id, loss, output, target, callback)
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# log_conv = find_log_with_re(logs,
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# "Jit op key (not )?found: ((mkl)|(cudnn))_conv.*")
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# log_matmul = find_log_with_re(logs,
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# "Jit op key (not )?found: ((mkl)|(cublas))_matmul.*")
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# if batch_id > 2:
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# assert len(log_conv)==59 and len(log_matmul)==6, (len(log_conv), len(log_matmul))
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mem_used = jt.flags.stat_allocator_total_alloc_byte \
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-jt.flags.stat_allocator_total_free_byte
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# assert mem_used < 4e9, mem_used
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# TODO: why bigger?
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assert mem_used < 5.6e9, mem_used
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# example log:
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# Train Epoch: 0 [0/100 (0%)] Loss: 2.352903 Acc: 0.110000
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# Train Epoch: 0 [1/100 (1%)] Loss: 2.840830 Acc: 0.080000
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# Train Epoch: 0 [2/100 (2%)] Loss: 3.473594 Acc: 0.100000
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# Train Epoch: 0 [3/100 (3%)] Loss: 3.131615 Acc: 0.200000
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# Train Epoch: 0 [4/100 (4%)] Loss: 2.524094 Acc: 0.230000
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# Train Epoch: 0 [5/100 (5%)] Loss: 7.780025 Acc: 0.080000
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# Train Epoch: 0 [6/100 (6%)] Loss: 3.890721 Acc: 0.160000
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# Train Epoch: 0 [7/100 (7%)] Loss: 6.370137 Acc: 0.140000
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# Train Epoch: 0 [8/100 (8%)] Loss: 11.390827 Acc: 0.150000
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# Train Epoch: 0 [9/100 (9%)] Loss: 21.598564 Acc: 0.080000
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# Train Epoch: 0 [10/100 (10%)] Loss: 23.369165 Acc: 0.130000
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# Train Epoch: 0 [20/100 (20%)] Loss: 4.804510 Acc: 0.100000
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# Train Epoch: 0 [30/100 (30%)] Loss: 3.393924 Acc: 0.110000
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# Train Epoch: 0 [40/100 (40%)] Loss: 2.286762 Acc: 0.130000
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# Train Epoch: 0 [50/100 (50%)] Loss: 2.055014 Acc: 0.290000
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if jt.in_mpi:
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assert jt.core.number_of_lived_vars() < 8100, jt.core.number_of_lived_vars()
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else:
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assert jt.core.number_of_lived_vars() < 7000, jt.core.number_of_lived_vars()
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if self.train_loader.epoch_id >= 2:
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break
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jt.sync_all(True)
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assert np.mean(loss_list[-50:])<0.5
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assert np.mean(acc_list[-50:])>0.8
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if __name__ == "__main__":
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unittest.main()
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