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mnist.py
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mnist.py
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import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import numpy as np
import onnx
import onnx_chainer
# Network definition
class CNN(chainer.Chain):
def __init__(self):
super(CNN, self).__init__()
with self.init_scope():
self.cn1 = L.Convolution2D(3, 20, 5)
self.cn2 = L.Convolution2D(20, 50, 5)
self.fc1 = L.Linear(800, 500)
self.fc2 = L.Linear(500, 10)
def __call__(self, x):
h1 = F.max_pooling_2d(F.relu(self.cn1(x)), 2)
h2 = F.max_pooling_2d(F.relu(self.cn2(h1)), 2)
h3 = F.dropout(F.relu(self.fc1(h2)))
return self.fc2(h3)
def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--batchsize', '-b', type=int, default=100,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--frequency', '-f', type=int, default=-1,
help='Frequency of taking a snapshot')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
parser.add_argument('--noplot', dest='plot', action='store_false',
help='Disable PlotReport extension')
args = parser.parse_args(args=[])
print('GPU: {}'.format(args.gpu))
print('# unit: {}'.format(args.unit))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
# model = L.Classifier(MLP(args.unit, 10))
model = L.Classifier(CNN())
if args.gpu >= 0:
# Make a specified GPU current
chainer.backends.cuda.get_device_from_id(args.gpu).use()
model.to_gpu() # Copy the model to the GPU
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist(ndim=3, rgb_format=True)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
# Set up a trainer
updater = training.updaters.StandardUpdater(
train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu))
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
trainer.extend(extensions.dump_graph('main/loss'))
# Take a snapshot for each specified epoch
frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
trainer.extend(extensions.snapshot(), trigger=(frequency, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Save two plot images to the result dir
if args.plot and extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'))
trainer.extend(
extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
'epoch', file_name='accuracy.png'))
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
if args.gpu >= 0:
model.to_cpu() # Copy the model to the CPU
# Export the trained model as onnx format
# Don't forget to set train flag off!
chainer.config.train = False
pseudo_input = np.zeros((1, 3, 28, 28), dtype=np.float32)
onnx_chainer.export(model.predictor, pseudo_input, filename='mnist.onnx')
if __name__ == '__main__':
main()