本文整理汇总了Python中chainer.serializers.load_npz方法的典型用法代码示例。如果您正苦于以下问题:Python serializers.load_npz方法的具体用法?Python serializers.load_npz怎么用?Python serializers.load_npz使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.serializers
的用法示例。
在下文中一共展示了serializers.load_npz方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test(self, cgp, model_file, comp_graph='comp_graph.dot', batchsize=256):
chainer.cuda.get_device(0).use() # Make a specified GPU current
model = CGP2CNN(cgp, self.n_class)
print('\tLoad model from', model_file)
serializers.load_npz(model_file, model)
model.to_gpu(0)
test_accuracy, test_loss = self.__test(model, batchsize)
print('\tparamNum={}'.format(model.param_num))
print('\ttest mean loss={}, test accuracy={}'.format(test_loss / self.test_data_num, test_accuracy / self.test_data_num))
if comp_graph is not None:
with open(comp_graph, 'w') as o:
g = computational_graph.build_computational_graph((model.loss,))
o.write(g.dump())
del g
print('\tCNN graph generated ({}).'.format(comp_graph))
return test_accuracy, test_loss
示例2: predict
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def predict(limit):
_limit = limit if limit > 0 else 5
td = TrainingData(LABEL_FILE, img_root=IMAGES_ROOT, mean_image_file=MEAN_IMAGE_FILE, image_property=IMAGE_PROP)
label_def = LabelingMachine.read_label_def(LABEL_DEF_FILE)
model = alex.Alex(len(label_def))
serializers.load_npz(MODEL_FILE, model)
i = 0
for arr, im in td.generate():
x = np.ndarray((1,) + arr.shape, arr.dtype)
x[0] = arr
x = chainer.Variable(np.asarray(x), volatile="on")
y = model.predict(x)
p = np.argmax(y.data)
print("predict {0}, actual {1}".format(label_def[p], label_def[im.label]))
im.image.show()
i += 1
if i >= _limit:
break
示例3: load_model_flexible
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def load_model_flexible(filename_list, encdec):
mode = "normal"
if isinstance(filename_list, tuple) or isinstance(filename_list, list):
if len(filename_list) == 1:
filename_list = filename_list[0]
else:
mode = "average"
if mode == "normal":
log.info("loading model parameters from %s", filename_list)
try:
serializers.load_npz(filename_list, encdec)
except KeyError:
log.info("not model format, trying snapshot format")
with np.load(filename_list) as fseri:
dicseri = serializers.NpzDeserializer(fseri, path="updater/model:main/")
dicseri.load(encdec)
else:
assert mode == "average"
log.info("loading averaged model parameters from %r", filename_list)
dseri = NpzDeserializerAverage([np.load(filename) for filename in filename_list])
dseri.load(encdec)
示例4: test_resumed_trigger
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test_resumed_trigger(self):
trainer = testing.get_trainer_with_mock_updater(
stop_trigger=None, iter_per_epoch=self.iter_per_epoch)
with tempfile.NamedTemporaryFile(delete=False) as f:
trigger = training.triggers.ManualScheduleTrigger(*self.schedule)
for expected, finished in zip(self.expected[:self.resume],
self.finished[:self.resume]):
trainer.updater.update()
self.assertEqual(trigger(trainer), expected)
self.assertEqual(trigger.finished, finished)
serializers.save_npz(f.name, trigger)
trigger = training.triggers.ManualScheduleTrigger(*self.schedule)
serializers.load_npz(f.name, trigger)
for expected, finished in zip(self.expected[self.resume:],
self.finished[self.resume:]):
trainer.updater.update()
self.assertEqual(trigger(trainer), expected)
self.assertEqual(trigger.finished, finished)
示例5: test_resumed_trigger_backward_compat
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test_resumed_trigger_backward_compat(self):
trainer = testing.get_trainer_with_mock_updater(
stop_trigger=None, iter_per_epoch=self.iter_per_epoch)
with tempfile.NamedTemporaryFile(delete=False) as f:
trigger = training.triggers.ManualScheduleTrigger(*self.schedule)
for expected, finished in zip(self.expected[:self.resume],
self.finished[:self.resume]):
trainer.updater.update()
self.assertEqual(trigger(trainer), expected)
self.assertEqual(trigger.finished, finished)
# old version does not save anything
np.savez(f, dummy=0)
trigger = training.triggers.ManualScheduleTrigger(*self.schedule)
with testing.assert_warns(UserWarning):
serializers.load_npz(f.name, trigger)
for expected, finished in zip(self.expected[self.resume:],
self.finished[self.resume:]):
trainer.updater.update()
self.assertEqual(trigger(trainer), expected)
self.assertEqual(trigger.finished, finished)
示例6: test_resumed_trigger_sparse_call
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test_resumed_trigger_sparse_call(self):
trainer = testing.get_trainer_with_mock_updater(
stop_trigger=None, iter_per_epoch=self.iter_per_epoch)
accumulated = False
with tempfile.NamedTemporaryFile(delete=False) as f:
trigger = training.triggers.IntervalTrigger(*self.interval)
for expected in self.expected[:self.resume]:
trainer.updater.update()
accumulated = accumulated or expected
if random.randrange(2):
self.assertEqual(trigger(trainer), accumulated)
accumulated = False
serializers.save_npz(f.name, trigger)
trigger = training.triggers.IntervalTrigger(*self.interval)
serializers.load_npz(f.name, trigger)
for expected in self.expected[self.resume:]:
trainer.updater.update()
accumulated = accumulated or expected
if random.randrange(2):
self.assertEqual(trigger(trainer), accumulated)
accumulated = False
示例7: test_resumed_trigger
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test_resumed_trigger(self):
trainer = testing.get_trainer_with_mock_updater(
stop_trigger=None, iter_per_epoch=self.iter_per_epoch)
with tempfile.NamedTemporaryFile(delete=False) as f:
trigger = training.triggers.OnceTrigger(self.call_on_resume)
for expected, finished in zip(self.resumed_expected[:self.resume],
self.resumed_finished[:self.resume]):
trainer.updater.update()
self.assertEqual(trigger.finished, finished)
self.assertEqual(trigger(trainer), expected)
serializers.save_npz(f.name, trigger)
trigger = training.triggers.OnceTrigger(self.call_on_resume)
serializers.load_npz(f.name, trigger)
for expected, finished in zip(self.resumed_expected[self.resume:],
self.resumed_finished[self.resume:]):
trainer.updater.update()
self.assertEqual(trigger.finished, finished)
self.assertEqual(trigger(trainer), expected)
示例8: test_resumed_trigger_backward_compat
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def test_resumed_trigger_backward_compat(self):
trainer = testing.get_trainer_with_mock_updater(
stop_trigger=None, iter_per_epoch=self.iter_per_epoch)
with tempfile.NamedTemporaryFile(delete=False) as f:
trigger = training.triggers.OnceTrigger(self.call_on_resume)
for expected, finished in zip(self.resumed_expected[:self.resume],
self.resumed_finished[:self.resume]):
trainer.updater.update()
self.assertEqual(trigger.finished, finished)
self.assertEqual(trigger(trainer), expected)
# old version does not save anything
np.savez(f, dummy=0)
trigger = training.triggers.OnceTrigger(self.call_on_resume)
with testing.assert_warns(UserWarning):
serializers.load_npz(f.name, trigger)
for expected, finished in zip(self.resumed_expected[self.resume:],
self.resumed_finished[self.resume:]):
trainer.updater.update()
self.assertEqual(trigger.finished, finished)
self.assertEqual(trigger(trainer), expected)
示例9: get_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def get_model(model_path, n_joints, result_dir, resume_model):
model_fn = os.path.basename(model_path)
model_name = model_fn.split('.')[0]
model = imp.load_source(model_name, model_path)
model = getattr(model, model_name)
# Initialize
model = model(n_joints)
# Copy files
dst = '{}/{}'.format(result_dir, model_fn)
if not os.path.exists(dst):
shutil.copy(model_path, dst)
# load model
if resume_model is not None:
serializers.load_npz(resume_model, model)
return model
示例10: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def __init__(self,modelpath='misc/VGG16_faster_rcnn_final.model',
mean=[102.9801, 115.9465, 122.7717],
in_size=224):
super(FasterRCNN,self).__init__('FasterRCNN',in_size)
self.func = FRCNN(Deel.gpu)
self.func.train=False
serializers.load_npz('misc/VGG16_faster_rcnn_final.model', self.func)
ImageNet.mean_image = np.ndarray((3, 256, 256), dtype=np.float32)
ImageNet.mean_image[0] = mean[0]
ImageNet.mean_image[1] = mean[1]
ImageNet.mean_image[2] = mean[2]
ImageNet.in_size = in_size
self.labels = CLASSES
self.batchsize = 1
xp = Deel.xp
self.x_batch = xp.ndarray((self.batchsize, 3, self.in_size, self.in_size), dtype=np.float32)
if Deel.gpu >=0:
self.func = self.func.to_gpu(Deel.gpu)
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.func)
示例11: load_npz_no_strict
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def load_npz_no_strict(filename, obj):
try:
serializers.load_npz(filename, obj)
except KeyError as e:
warnings.warn(repr(e))
with numpy.load(filename) as f:
d = serializers.NpzDeserializer(f, strict=False)
d.load(obj)
示例12: create_and_load_encdec_from_files
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def create_and_load_encdec_from_files(config_training_fn, trained_model):
log.info("loading model config from %s" % config_training_fn)
config_training = train_config.load_config_train(config_training_fn)
encdec, eos_idx, src_indexer, tgt_indexer = train.create_encdec_and_indexers_from_config_dict(config_training)
log.info("loading model from %s" % trained_model)
serializers.load_npz(trained_model, encdec)
return encdec, eos_idx, src_indexer, tgt_indexer
示例13: load_encdec_from_config
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def load_encdec_from_config(config_fn, model_fn):
config=json.load(open(config_fn))
ced = create_model(config)
charlist = json.load(open(config["indexer"], "r"))
chardict = dict((c,i) for i,c in enumerate(charlist))
serializers.load_npz(model_fn, ced)
return ced, charlist, chardict
示例14: generate
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def generate():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--gen', type=str, default=None)
parser.add_argument('--depth', '-d', type=int, default=0)
parser.add_argument('--out', '-o', type=str, default='img/')
parser.add_argument('--num', '-n', type=int, default=10)
args = parser.parse_args()
gen = network.Generator(depth=args.depth)
print('loading generator model from ' + args.gen)
serializers.load_npz(args.gen, gen)
if args.gpu >= 0:
cuda.get_device_from_id(0).use()
gen.to_gpu()
xp = gen.xp
z1 = gen.z(1)
z2 = gen.z(1)
for i in range(args.num):
print(i)
p = i / (args.num-1)
z = z1 * p + z2 * (1 - p)
x = gen(z, alpha=1.0)
x = chainer.cuda.to_cpu(x.data)
img = x[0].copy()
filename = os.path.join(args.out, 'gen_%04d.png'%i)
utils.save_image(img, filename)
示例15: model_fn
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_npz [as 别名]
def model_fn(model_dir):
model = L.Classifier(MLP(1000, 10))
serializers.load_npz(os.path.join(model_dir, "model.npz"), model)
return model.predictor