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Last active May 7, 2020
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Migration Guide from Chainer to PyTorch
#!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from import extensions
import chainerx
import matplotlib
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
# the size of the inputs to each layer will be inferred
self.l1 = L.Linear(None, n_units) # n_in -> n_units
self.l2 = L.Linear(None, n_units) # n_units -> n_units
self.l3 = L.Linear(None, n_out) # n_units -> n_out
def forward(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
return self.l3(h2)
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('--device', '-d', type=str, default='-1',
help='Device specifier. Either ChainerX device '
'specifier or an integer. If non-negative integer, '
'CuPy arrays with specified device id are used. If '
'negative integer, NumPy arrays are used')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', type=str,
help='Resume the training from snapshot')
parser.add_argument('--autoload', action='store_true',
help='Automatically load trainer snapshots in case'
' of preemption or other temporary system failure')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
group = parser.add_argument_group('deprecated arguments')
group.add_argument('--gpu', '-g', dest='device',
type=int, nargs='?', const=0,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
device = chainer.get_device(args.device)
print('Device: {}'.format(device))
print('# unit: {}'.format(args.unit))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist()
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
# 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))
# Setup an optimizer
optimizer = chainer.optimizers.Adam()
# Set up a trainer
updater = training.updaters.StandardUpdater(
train_iter, optimizer, device=device)
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=device),
# Take a snapshot for each specified epoch
frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
# Take a snapshot each ``frequency`` epoch, delete old stale
# snapshots and automatically load from snapshot files if any
# files are already resident at result directory.
trainer.extend(extensions.snapshot(n_retains=1, autoload=args.autoload),
trigger=(frequency, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(), call_before_training=True)
# Save two plot images to the result dir
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'),
['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.
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']),
# Print a progress bar to stdout
if args.resume is not None:
# Resume from a snapshot (Note: this loaded model is to be
# overwritten by --autoload option, autoloading snapshots, if
# any snapshots exist in output directory)
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
if __name__ == '__main__':
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