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#!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import torch
import ignite
import chainer_pytorch_migration as cpm
import chainer_pytorch_migration.ignite
import matplotlib
matplotlib.use('Agg')
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_in, 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
self.l1 = L.Linear(n_in, n_units) # n_in -> n_units
self.l2 = L.Linear(n_units, n_units) # n_units -> n_units
self.l3 = L.Linear(n_units, 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))
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(784, args.unit, 10))
model.to_device(device)
device.use()
torched_model = cpm.LinkAsTorchModel(model)
# Setup an optimizer
#optimizer = chainer.optimizers.Adam()
#optimizer.setup(model)
optimizer = torch.optim.Adam(torched_model.parameters())
# 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)
train_loader = torch.utils.data.DataLoader(train, shuffle=True, batch_size=args.batchsize, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test, shuffle=False, batch_size=args.batchsize, pin_memory=True)
# Set up a trainer
#updater = training.updaters.StandardUpdater(
# train_iter, optimizer, device=device)
#trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
torch_device = torch.device('cpu')
trainer = ignite.engine.create_supervised_trainer(
torched_model, optimizer, torch.nn.functional.nll_loss, device=torch_device)
# Evaluate the model with the test dataset for each epoch
#trainer.extend(extensions.Evaluator(test_iter, model, device=device),
# call_before_training=True)
evaluator = ignite.engine.create_supervised_evaluator(
torched_model,
metrics={
'accuracy': ignite.metrics.Accuracy(),
'loss': ignite.metrics.Loss(torch.nn.functional.nll_loss),
},
device=torch_device)
@trainer.on(ignite.engine.Events.EPOCH_COMPLETED)
def validation(engine):
evaluator.run(val_loader)
average_accuracy = evaluator.state.metrics['accuracy']
average_loss = evaluator.state.metrics['loss']
print(average_accuracy, average_loss)
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
# TODO(niboshi): Temporarily disabled for chainerx. Fix it.
#if device.xp is not chainerx:
# trainer.extend(extensions.DumpGraph('main/loss'))
# 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
#trainer.extend(
# extensions.PlotReport(['main/loss', 'validation/main/loss'],
# 'epoch', file_name='loss.png'),
# call_before_training=True)
#trainer.extend(
# extensions.PlotReport(
# ['main/accuracy', 'validation/main/accuracy'],
# 'epoch', file_name='accuracy.png'),
# call_before_training=True)
# 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']),
# call_before_training=True)
# Print a progress bar to stdout
#trainer.extend(extensions.ProgressBar())
#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
#trainer.run()
trainer.run(train_loader, max_epochs=args.epoch)
if __name__ == '__main__':
main()
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