def train():
Dt1_train_dir = "/media/common-ns/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01/Dt1/CV01_Dt1_(Gallery&Probe)"
train1 = load_GEI(path_dir=Dt1_train_dir, mode=True)
Dt2_train_dir = "/media/common-ns/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01/Dt1/CV01_Dt2_(Gallery&Probe)"
train2 = load_GEI(path_dir=Dt2_train_dir, mode=True)
model = Multi_modal_GEINet()
model.to_gpu()
Dt1_train_iter = iterators.SerialIterator(train1,
batch_size=2,
shuffle=False)
Dt2_train_iter = iterators.SerialIterator(train2,
batch_size=2,
shuffle=False)
optimizer = chainer.optimizers.MomentumSGD(lr=0.02, momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.01))
# updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1})
updater = Multi_modal_Updater(model,
Dt1_train_iter,
Dt2_train_iter,
optimizer,
device=0)
epoch = 6250
trainer = training.Trainer(
updater, (epoch, 'epoch'),
out='/home/wutong/Setoguchi/chainer_files/result')
# trainer.extend(extensions.Evaluator(test_iter, model, device=0))
trainer.extend(extensions.ExponentialShift(attr='lr', rate=0.56234),
trigger=(1250, 'epoch'))
trainer.extend(
extensions.LogReport(log_name='SFDEI_log', trigger=(50, "epoch")))
trainer.extend(extensions.snapshot(), trigger=(1250, 'epoch'))
trainer.extend(extensions.snapshot_object(
target=model, filename='model_snapshot_{.updater.epoch}'),
trigger=(1250, 'epoch'))
trainer.extend(extensions.PrintReport(['epoch', 'accuracy', 'loss']))
# 'validation/main/accuracy']),
# trigger=(1, "epoch"))
trainer.extend(
extensions.dump_graph(root_name="loss", out_name="multi_modal.dot"))
trainer.extend(extensions.PlotReport(["loss"]), trigger=(20, 'epoch'))
trainer.extend(extensions.ProgressBar())
# Run the trainer
trainer.run()
def recognition(model_name):
# 識別用のデータをダウンロード
train1, train_labels1 = load_GEI(
"/home/common/setoguchi/chainer_files/dataset/SFDEI/CV02(Gallery)_2nd",
mode=False)
train2, train_labels2 = load_GEI(
"/home/common/setoguchi/chainer_files/dataset/SFDEI/CV02_Dt2(Gallery)",
mode=False)
test1, test_labels1 = load_GEI(
"/home/common/setoguchi/chainer_files/dataset/SFDEI/CV02(Probe)_2nd",
mode=False)
test2, test_labels2 = load_GEI(
"/home/common/setoguchi/chainer_files/dataset/SFDEI/CV02_Dt2(Probe)",
mode=False)
# extract features
model = Multi_modal_GEINet()
serializers.load_npz(model_name, obj=model)
train_features = extract_features(model, train1, train2)
test_features = extract_features(model, test1, test2)
neigh = KNeighborsClassifier(n_neighbors=1)
neigh.fit(train_features, train_labels1)
correct = 0.0
for i, item in enumerate(test_features):
predit = neigh.predict([item])[0]
print "label:%d, predict:%d" % (test_labels1[i], predit)
if predit == test_labels1[i]:
correct = correct + 1
# else:
# f.write("label:%d, predict:%d" %(true_label[i],predit)+"\n")
acc = correct / len(test_features)
print acc
def train(train_dir):
train1 = load_GEI(path_dir=train_dir, mode=True)
model = L.Classifier(GEINet())
model.to_gpu()
Dt1_train_iter = iterators.SerialIterator(train1,
batch_size=239,
shuffle=False)
optimizer = chainer.optimizers.MomentumSGD(lr=0.02, momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.01))
# updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1})
updater = training.StandardUpdater(model,
Dt1_train_iter,
optimizer,
device=0)
epoch = 6250
trainer = training.Trainer(
updater, (epoch, 'epoch'),
out='/home/wutong/Setoguchi/chainer_files/result')
# trainer.extend(extensions.Evaluator(test_iter, model, device=0))
trainer.extend(extensions.ExponentialShift(attr='lr', rate=0.56234),
trigger=(1250, 'epoch'))
trainer.extend(
extensions.LogReport(log_name='SFDEI_log', trigger=(50, "epoch")))
trainer.extend(extensions.snapshot(), trigger=(1250, 'epoch'))
trainer.extend(extensions.snapshot_object(
target=model, filename='model_snapshot_{.updater.epoch}'),
trigger=(1250, 'epoch'))
trainer.extend(extensions.PrintReport(['epoch', 'accuracy', 'loss']))
# 'validation/main/accuracy']),
# trigger=(1, "epoch"))
trainer.extend(
extensions.dump_graph(root_name="loss", out_name="multi_modal.dot"))
trainer.extend(extensions.PlotReport(["loss"]), trigger=(20, 'epoch'))
trainer.extend(extensions.ProgressBar())
# Run the trainer
trainer.run()
def train(mode):
Dt1_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_(Gallery&Probe)_2nd"
train1 = load_GEI(path_dir=Dt1_train_dir, mode=True)
Dt2_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt2_(Gallery&Probe)"
train2 = load_GEI(path_dir=Dt2_train_dir, mode=True)
Dt3_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt3_(Gallery&Probe)"
train3 = load_GEI(path_dir=Dt3_train_dir, mode=True)
Dt4_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt4_(Gallery&Probe)"
train4 = load_GEI(path_dir=Dt4_train_dir, mode=True)
Dt5_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt5_(Gallery&Probe)"
train5 = load_GEI(path_dir=Dt5_train_dir, mode=True)
Dt6_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt6_(Gallery&Probe)"
train6 = load_GEI(path_dir=Dt6_train_dir, mode=True)
Dt7_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt7_(Gallery&Probe)"
train7 = load_GEI(path_dir=Dt7_train_dir, mode=True)
Dt8_train_dir = "/media/wutong/New Volume/reseach/Dataset/OU-ISIR_by_Setoguchi/Gallery/signed/128_3ch/CV01_Dt8_(Gallery&Probe)"
train8 = load_GEI(path_dir=Dt8_train_dir, mode=True)
model = Multi_modal_GEINet()
model.to_gpu()
# train_iter = iterators.MultiprocessIterator(train, batch_size=239)
Dt1_train_iter = iterators.SerialIterator(train1,
batch_size=239,
shuffle=False)
Dt2_train_iter = iterators.SerialIterator(train2,
batch_size=239,
shuffle=False)
Dt3_train_iter = iterators.SerialIterator(train3,
batch_size=239,
shuffle=False)
Dt4_train_iter = iterators.SerialIterator(train4,
batch_size=239,
shuffle=False)
Dt5_train_iter = iterators.SerialIterator(train5,
batch_size=239,
shuffle=False)
Dt6_train_iter = iterators.SerialIterator(train6,
batch_size=239,
shuffle=False)
Dt7_train_iter = iterators.SerialIterator(train7,
batch_size=239,
shuffle=False)
Dt8_train_iter = iterators.SerialIterator(train8,
batch_size=239,
shuffle=False)
# optimizer = chainer.optimizers.SGD(lr=0.02)
optimizer = chainer.optimizers.MomentumSGD(lr=0.02, momentum=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.01))
# updater = training.ParallelUpdater(train_iter, optimizer, devices={'main': 0, 'second': 1})
updater = Multi_modal_Updater(model,
Dt1_train_iter,
Dt2_train_iter,
Dt3_train_iter,
Dt4_train_iter,
Dt5_train_iter,
Dt6_train_iter,
Dt7_train_iter,
Dt8_train_iter,
optimizer,
device=0)
epoch = 6250
trainer = training.Trainer(
updater, (epoch, 'epoch'),
out='/home/wutong/Setoguchi/chainer_files/result')
# trainer.extend(extensions.Evaluator(test_iter, model, device=0))
trainer.extend(extensions.ExponentialShift(attr='lr', rate=0.56234),
trigger=(1250, 'epoch'))
trainer.extend(
extensions.LogReport(log_name='SFDEI_log', trigger=(20, "epoch")))
trainer.extend((extensions.snapshot_object(
model, filename='model_shapshot_{.update.epoch}')),
trigger=(1250, 'epoch'))
trainer.extend(extensions.snapshot(), trigger=(1250, 'epoch'))
trainer.extend(extensions.PrintReport(['epoch', 'accuracy', 'loss']))
trainer.extend(
extensions.dump_graph(root_name="loss", out_name="multi_modal_3.dot"))
trainer.extend(extensions.PlotReport(["loss"]), trigger=(50, 'epoch'))
trainer.extend(extensions.ProgressBar())
if mode == True:
# Run the trainer
trainer.run()
else:
serializers.load_npz(
"/home/wutong/Setoguchi/chainer_files/SFDEINet_multi_modal/SFDEINet_multi_modal_model",
trainer)
trainer.run()
serializers.save_npz(
"/home/wutong/Setoguchi/chainer_files/SFDEINet_multi_modal/SFDEINet_multi_modal_model",
trainer)
serializers.save_npz(
"/home/wutong/Setoguchi/chainer_files/SFDEINet_multi_modal/SFDEINet_multi_modal_model",
model)