mirror of https://github.com/Jittor/Jittor
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
# ***************************************************************
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# Copyright (c) 2020 Jittor. Authors:
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# Guoye Yang <498731903@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 jittor as jt
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from jittor import nn, Module
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from jittor.models import vgg
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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 .test_reorder_tuner import simple_parser
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from .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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model_test = os.environ.get("model_test", "") == "1"
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skip_model_test = not model_test
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class MnistNet(Module):
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def __init__(self):
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self.model = vgg.vgg16_bn()
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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_model_test, "skip_this_test, model_test != 1")
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class TestVGGClass(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 = 32
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self.weight_decay = 0.0001
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self.momentum = 0.9
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self.learning_rate = 0.01
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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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# 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_vgg(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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SGD = nn.SGD(mnist_net.parameters(), self.learning_rate, self.momentum, self.weight_decay)
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for batch_idx, (data, target) in enumerate(self.train_loader):
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output = mnist_net(data)
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loss = nn.cross_entropy_loss(output, target)
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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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SGD.step(loss)
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def callback(loss, output, target, batch_idx):
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# print train info
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pred = np.argmax(output, axis=1)
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acc = np.sum(target==pred)/self.batch_size
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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}'
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.format(0, batch_idx, 100,1. * batch_idx, loss[0], acc))
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jt.fetch([loss, output, target], callback, batch_idx)
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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_idx:
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assert len(log_conv)==38 and len(log_matmul)==12, (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 < 11e9, mem_used
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assert jt.core.number_of_lived_vars() < 3500
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if (np.mean(loss_list[-50:])<0.2):
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break
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assert np.mean(loss_list[-50:])<0.2
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if __name__ == "__main__":
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unittest.main()
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