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train_imagenet.py
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train_imagenet.py
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#!/usr/bin/env python
from __future__ import print_function
import argparse
import multiprocessing
import random
import sys
import numpy as np
import chainer
import chainer.cuda
from chainer import training
from chainer.training import extensions
import chainermn
if chainer.__version__.startswith('1.'):
import models_v1.alex as alex
import models_v1.googlenet as googlenet
import models_v1.googlenetbn as googlenetbn
import models_v1.nin as nin
import models_v1.resnet50 as resnet50
else:
import models_v2.alex as alex
import models_v2.googlenet as googlenet
import models_v2.googlenetbn as googlenetbn
import models_v2.nin as nin
import models_v2.resnet50 as resnet50
# Check Python version if it supports multiprocessing.set_start_method,
# which was introduced in Python 3.4
major, minor, _, _, _ = sys.version_info
if major <= 2 or (major == 3 and minor < 4):
sys.stderr.write("Error: ImageNet example uses "
"chainer.iterators.MultiprocessIterator, "
"which works only with Python >= 3.4. \n"
"For more details, see "
"http://chainermn.readthedocs.io/en/master/"
"tutorial/tips_faqs.html#using-multiprocessiterator\n")
exit(-1)
class PreprocessedDataset(chainer.dataset.DatasetMixin):
def __init__(self, path, root, mean, crop_size, random=True):
self.base = chainer.datasets.LabeledImageDataset(path, root)
self.mean = mean.astype('f')
self.crop_size = crop_size
self.random = random
def __len__(self):
return len(self.base)
def get_example(self, i):
# It reads the i-th image/label pair and return a preprocessed image.
# It applies following preprocesses:
# - Cropping (random or center rectangular)
# - Random flip
# - Scaling to [0, 1] value
crop_size = self.crop_size
image, label = self.base[i]
_, h, w = image.shape
if self.random:
# Randomly crop a region and flip the image
top = random.randint(0, h - crop_size - 1)
left = random.randint(0, w - crop_size - 1)
if random.randint(0, 1):
image = image[:, :, ::-1]
else:
# Crop the center
top = (h - crop_size) // 2
left = (w - crop_size) // 2
bottom = top + crop_size
right = left + crop_size
image = image[:, top:bottom, left:right]
image -= self.mean[:, top:bottom, left:right]
image *= (1.0 / 255.0) # Scale to [0, 1]
return image, label
# chainermn.create_multi_node_evaluator can be also used with user customized
# evaluator classes that inherit chainer.training.extensions.Evaluator.
class TestModeEvaluator(extensions.Evaluator):
def evaluate(self):
model = self.get_target('main')
model.train = False
ret = super(TestModeEvaluator, self).evaluate()
model.train = True
return ret
def main():
# Check if GPU is available
# (ImageNet example does not support CPU execution)
if not chainer.cuda.available:
raise RuntimeError("ImageNet requires GPU support.")
archs = {
'alex': alex.Alex,
'googlenet': googlenet.GoogLeNet,
'googlenetbn': googlenetbn.GoogLeNetBN,
'nin': nin.NIN,
'resnet50': resnet50.ResNet50,
}
parser = argparse.ArgumentParser(
description='Learning convnet from ILSVRC2012 dataset')
parser.add_argument('train', help='Path to training image-label list file')
parser.add_argument('val', help='Path to validation image-label list file')
parser.add_argument('--arch', '-a', choices=archs.keys(), default='nin',
help='Convnet architecture')
parser.add_argument('--batchsize', '-B', type=int, default=32,
help='Learning minibatch size')
parser.add_argument('--epoch', '-E', type=int, default=10,
help='Number of epochs to train')
parser.add_argument('--initmodel',
help='Initialize the model from given file')
parser.add_argument('--loaderjob', '-j', type=int,
help='Number of parallel data loading processes')
parser.add_argument('--mean', '-m', default='mean.npy',
help='Mean file (computed by compute_mean.py)')
parser.add_argument('--resume', '-r', default='',
help='Initialize the trainer from given file')
parser.add_argument('--out', '-o', default='result',
help='Output directory')
parser.add_argument('--root', '-R', default='.',
help='Root directory path of image files')
parser.add_argument('--val_batchsize', '-b', type=int, default=250,
help='Validation minibatch size')
parser.add_argument('--test', action='store_true')
parser.add_argument('--communicator', default='hierarchical')
parser.set_defaults(test=False)
args = parser.parse_args()
# Prepare ChainerMN communicator.
comm = chainermn.create_communicator(args.communicator)
device = comm.intra_rank
if comm.mpi_comm.rank == 0:
print('==========================================')
print('Num process (COMM_WORLD): {}'.format(comm.size))
print('Using {} communicator'.format(args.communicator))
print('Using {} arch'.format(args.arch))
print('Num Minibatch-size: {}'.format(args.batchsize))
print('Num epoch: {}'.format(args.epoch))
print('==========================================')
model = archs[args.arch]()
if args.initmodel:
print('Load model from', args.initmodel)
chainer.serializers.load_npz(args.initmodel, model)
chainer.cuda.get_device(device).use() # Make the GPU current
model.to_gpu()
# Split and distribute the dataset. Only worker 0 loads the whole dataset.
# Datasets of worker 0 are evenly split and distributed to all workers.
mean = np.load(args.mean)
if comm.rank == 0:
train = PreprocessedDataset(args.train, args.root, mean, model.insize)
val = PreprocessedDataset(
args.val, args.root, mean, model.insize, False)
else:
train = None
val = None
train = chainermn.scatter_dataset(train, comm, shuffle=True)
val = chainermn.scatter_dataset(val, comm)
# We need to change the start method of multiprocessing module if we are
# using InfiniBand and MultiprocessIterator. This is because processes
# often crash when calling fork if they are using Infiniband.
# (c.f., https://www.open-mpi.org/faq/?category=tuning#fork-warning )
multiprocessing.set_start_method('forkserver')
train_iter = chainer.iterators.MultiprocessIterator(
train, args.batchsize, n_processes=args.loaderjob)
val_iter = chainer.iterators.MultiprocessIterator(
val, args.val_batchsize, repeat=False, n_processes=args.loaderjob)
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(
chainer.optimizers.MomentumSGD(lr=0.01, momentum=0.9), comm)
optimizer.setup(model)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), args.out)
val_interval = (10, 'iteration') if args.test else (1, 'epoch')
log_interval = (10, 'iteration') if args.test else (1, 'epoch')
# Create a multi node evaluator from an evaluator.
evaluator = TestModeEvaluator(val_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator, trigger=val_interval)
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0:
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.observe_lr(), trigger=log_interval)
trainer.extend(extensions.PrintReport([
'epoch', 'iteration', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'lr'
]), trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
trainer.run()
if __name__ == '__main__':
main()