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train_minc2500.py
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train_minc2500.py
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#!/usr/bin/env python
"""Train convnet for MINC-2500 dataset.
Prerequisite: To run this example, put MINC-2500 dataset ("minc-2500" direcotry)
into this direcotry.
"""
from __future__ import print_function
import argparse
import random
import numpy as np
import chainer
from chainer import training
from chainer.training import extensions
from chainer import cuda
import models
import utils
import preprocessed_dataset as ppds
import datetime
import time
import os
import config
image_size = 362
optimizers = {'momentumsgd':chainer.optimizers.MomentumSGD,
'adagrad':chainer.optimizers.AdaGrad,
'adadelta':chainer.optimizers.AdaDelta,
'adam':chainer.optimizers.Adam}
def makeMeanImage(mean_value):
mean_image = np.ndarray((3, image_size, image_size), dtype=np.float32)
for i in range(3): mean_image[i] = mean_value[i]
return mean_image
def main(args):
# Initialize the model to train
model = models.archs[args.arch]()
if args.finetune and hasattr(model, 'finetuned_model_path'):
utils.finetuning.load_param(model.finetuned_model_path, model, args.ignore)
#model.finetune = True
if args.initmodel:
print('Load model from', args.initmodel)
chainer.serializers.load_npz(args.initmodel, model)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu()
nowt = datetime.datetime.today()
outputdir = args.out + '/' + args.arch + '/' + nowt.strftime("%Y%m%d-%H%M") + '_bs' + str(args.batchsize)
if args.test and args.initmodel is not None:
outputdir = os.path.dirname(args.initmodel)
# Load the datasets and mean file
mean = None
if hasattr(model, 'mean_value'):
mean = makeMeanImage(model.mean_value)
else:
mean = np.load(args.mean)
assert mean is not None
train = ppds.PreprocessedDataset(args.train, args.root, mean, model.insize)
val = ppds.PreprocessedDataset(args.val, args.root, mean, model.insize, False)
# These iterators load the images with subprocesses running in parallel to
# the training/validation.
train_iter = chainer.iterators.MultiprocessIterator(
train, args.batchsize, shuffle=False, n_processes=args.loaderjob)
#val_iter = chainer.iterators.MultiprocessIterator(
# val, args.val_batchsize, repeat=False, shuffle=False, n_processes=args.loaderjob)
val_iter = chainer.iterators.SerialIterator(
val, args.val_batchsize, repeat=False, shuffle=False)
# Set up an optimizer
optimizer = optimizers[args.opt]()
#if args.opt == 'momentumsgd':
if hasattr(optimizer, 'lr'):
optimizer.lr = args.baselr
if hasattr(optimizer, 'momentum'):
optimizer.momentum = args.momentum
optimizer.setup(model)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), outputdir)
#val_interval = (10 if args.test else int(len(train) / args.batchsize)), 'iteration'
val_interval = (10, 'iteration') if args.test else (1, 'epoch')
snapshot_interval = (10, 'iteration') if args.test else (4, 'epoch')
log_interval = (10 if args.test else 200), 'iteration'
# Copy the chain with shared parameters to flip 'train' flag only in test
eval_model = model.copy()
eval_model.train = False
if not args.test:
val_evaluator = extensions.Evaluator(val_iter, eval_model, device=args.gpu)
else:
val_evaluator = utils.EvaluatorPlus(val_iter, eval_model, device=args.gpu)
if 'googlenet' in args.arch:
val_evaluator.lastname = 'validation/main/loss3'
trainer.extend(val_evaluator, trigger=val_interval)
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.snapshot(), trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
model, 'model_iter_{.updater.iteration}'), trigger=(500, 'iteration'))
# Be careful to pass the interval directly to LogReport
# (it determines when to emit log rather than when to read observations)
trainer.extend(extensions.LogReport(trigger=log_interval))
trainer.extend(extensions.PrintReport([
'epoch', 'iteration', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy',
]), trigger=log_interval)
trainer.extend(extensions.ProgressBar(update_interval=10))
if args.opt == 'momentumsgd':
trainer.extend(extensions.ExponentialShift('lr', args.gamma),
trigger=(1, 'epoch'))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
if not args.test:
chainer.serializers.save_npz(outputdir + '/model0', model)
trainer.run()
chainer.serializers.save_npz(outputdir + '/model', model)
with open(outputdir + '/args.txt', 'w') as o:
print(args, file=o)
results = val_evaluator(trainer)
results['outputdir'] = outputdir
if args.test:
print(val_evaluator.confmat)
categories = utils.io.load_categories(args.categories)
confmat_csv_name = args.initmodel + '.csv'
confmat_fig_name = args.initmodel + '.eps'
utils.io.save_confmat_csv(confmat_csv_name, val_evaluator.confmat, categories)
utils.io.save_confmat_fig(confmat_fig_name, val_evaluator.confmat, categories,
mode="rate", saveFormat="eps")
return results
parser = argparse.ArgumentParser(
description='Learning convnet from MINC-2500 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('--categories', '-c', default=config.categories_path,
help='Path to category list file')
parser.add_argument('--arch', '-a', choices=models.archs.keys(), default='nin',
help='Convnet architecture')
parser.add_argument('--batchsize', '-B', type=int, default=32,
help='Learning minibatch size')
parser.add_argument('--baselr', default=0.001, type=float,
help='Base learning rate')
parser.add_argument('--gamma', default=0.7, type=float,
help='Base learning rate')
parser.add_argument('--momentum', default=0.9, type=float,
help='momentum')
parser.add_argument('--epoch', '-E', type=int, default=10,
help='Number of epochs to train')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--finetune', '-f', default=False, action='store_true',
help='do fine-tuning if this flag is set (default: False)')
parser.add_argument('--initmodel',
help='Initialize the model from given file')
parser.add_argument('--ignore', nargs='*', default=[],
help='Ignored layers in parameter copy')
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('--opt', choices=optimizers.keys(), default='momentumsgd',
help='optimizer')
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=20,
help='Validation minibatch size')
parser.add_argument('--test', action='store_true')
parser.set_defaults(test=False)
if __name__ == '__main__':
args = parser.parse_args()
val_result = main(args)
print("loss\taccuracy\n{0}\t{1}".format(
val_result['validation/main/loss'],
val_result['validation/main/accuracy']))