本文整理汇总了Python中chainer.serializers.load_hdf5方法的典型用法代码示例。如果您正苦于以下问题:Python serializers.load_hdf5方法的具体用法?Python serializers.load_hdf5怎么用?Python serializers.load_hdf5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_hdf5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: get_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def get_model(args):
model_fn = os.path.basename(args.model)
model = imp.load_source(model_fn.split('.')[0], args.model).model
if 'result_dir' in args:
dst = '%s/%s' % (args.result_dir, model_fn)
if not os.path.exists(dst):
shutil.copy(args.model, dst)
dst = '%s/%s' % (args.result_dir, os.path.basename(__file__))
if not os.path.exists(dst):
shutil.copy(__file__, dst)
# load model
if args.resume_model is not None:
serializers.load_hdf5(args.resume_model, model)
# prepare model
if args.gpu >= 0:
model.to_gpu()
return model
示例2: test
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def test(model, test_data, vocab, inv_vocab, modelfile_to_load, params):
print('Testing ...')
print('Beam size: {}'.format(params.beam_size))
print('print output to file:', out_test_filename)
serializers.load_hdf5(modelfile_to_load, model)
batch_test = utils_seq2seq.gen_batch_test(test_data, args.feature, 1, vocab, xp)
output_file = open(out_test_filename, mode='w')
for vid_batch, caption_batch, id_batch in batch_test:
output = predict(model, params, vocab, inv_vocab, vid_batch,
batch_size=1, beam_size=params.beam_size)
print('%s %s' % (id_batch[0], output))
output_file.write(id_batch[0] + '\t' + output + '\n')
output_file.close()
utils_coco.convert(out_test_filename, eval_test_filename)
eval_coco.eval_coco(args.cocotest, eval_test_filename)
示例3: test_batch
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def test_batch(model, test_data, vocab, inv_vocab, modelfile_to_load):
print('Testing (beam size = 1)...')
print('print output to file: {}'.format(out_test_filename))
serializers.load_hdf5(modelfile_to_load, model)
batch_test = \
utils_seq2seq.gen_batch_test(test_data, args.feature, params.batch_size_val, vocab, xp)
caption_out = []
output_file = open(out_test_filename, mode='w')
for vid_batch_test, caption_batch_test, id_batch_test in batch_test:
output_test = forward(model, params, vocab, inv_vocab,
vid_batch_test, caption_batch_test,
'test-on-train', args.batchsizeval)
for ii in range(args.batchsizeval):
caption_out.append({'image_id': id_batch_test[ii],
'caption': output_test[ii]})
print('%s %s' % (id_batch_test[ii], output_test[ii]))
output_file.write(id_batch_test[ii] + '\t' + output_test[ii] + '\n')
output_file.close()
with open(eval_test_filename, mode='w') as f:
json.dump(caption_out, f)
eval_coco.eval_coco(args.cocotest, eval_test_filename)
示例4: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def __init__(self):
# hyper parameters
weight_file = "./yolov2_darknet.model"
self.n_classes = 80
self.n_boxes = 5
self.detection_thresh = 0.5
self.iou_thresh = 0.5
self.labels = ["person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"]
anchors = [[0.738768, 0.874946], [2.42204, 2.65704], [4.30971, 7.04493], [10.246, 4.59428], [12.6868, 11.8741]]
# load model
print("loading coco model...")
yolov2 = YOLOv2(n_classes=self.n_classes, n_boxes=self.n_boxes)
serializers.load_hdf5(weight_file, yolov2) # load saved model
model = YOLOv2Predictor(yolov2)
model.init_anchor(anchors)
model.predictor.train = False
model.predictor.finetune = False
self.model = model
示例5: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def __init__(self):
# hyper parameters
weight_file = "./backup/yolov2_final_cpu.model"
self.n_classes = 10
self.n_boxes = 5
self.detection_thresh = 0.3
self.iou_thresh = 0.3
self.label_file = "./data/label.txt"
with open(self.label_file, "r") as f:
self.labels = f.read().strip().split("\n")
# load model
print("loading animal model...")
yolov2 = YOLOv2(n_classes=self.n_classes, n_boxes=self.n_boxes)
model = YOLOv2Predictor(yolov2)
serializers.load_hdf5(weight_file, model) # load saved model
model.predictor.train = False
model.predictor.finetune = False
self.model = model
示例6: load_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def load_model(self):
model_fn = os.path.basename(self.args.model)
self.model = imp.load_source(
model_fn.split('.')[0], self.args.model).model
self.model.train = False
serializers.load_hdf5(self.args.param, self.model)
if self.args.gpu >= 0:
self.model.to_gpu()
示例7: get_model_optimizer
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def get_model_optimizer(args):
model = get_model(args)
if 'opt' in args:
# prepare optimizer
if args.opt == 'MomentumSGD':
optimizer = optimizers.MomentumSGD(lr=args.lr, momentum=0.9)
elif args.opt == 'Adam':
optimizer = optimizers.Adam(alpha=args.alpha)
elif args.opt == 'AdaGrad':
optimizer = optimizers.AdaGrad(lr=args.lr)
else:
raise Exception('No optimizer is selected')
optimizer.setup(model)
if args.opt == 'MomentumSGD':
optimizer.add_hook(
chainer.optimizer.WeightDecay(args.weight_decay))
if args.resume_opt is not None:
serializers.load_hdf5(args.resume_opt, optimizer)
args.epoch_offset = int(
re.search('epoch-([0-9]+)', args.resume_opt).groups()[0])
return model, optimizer
else:
print('No optimizer generated.')
return model
示例8: load_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def load_model(self, model_filename):
"""Load a network model form a file
"""
serializers.load_hdf5(model_filename, self.model)
copy_param.copy_param(target_link=self.model,
source_link=self.shared_model)
opt_filename = model_filename + '.opt'
if os.path.exists(opt_filename):
print('WARNING: {0} was not found, so loaded only a model'.format(
opt_filename))
serializers.load_hdf5(model_filename + '.opt', self.optimizer)
示例9: save_and_load_hdf5
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def save_and_load_hdf5(src, dst):
"""Saves ``src`` to an HDF5 file and loads it to ``dst``.
This is a short cut of :func:`save_and_load` using HDF5 de/serializers.
Args:
src: An object to save.
dst: An object to load to.
"""
save_and_load(src, dst, 'tmp.h5',
serializers.save_hdf5, serializers.load_hdf5)
示例10: load
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def load(self):
filename = "conv.model"
if os.path.isfile(filename):
serializers.load_hdf5(filename, self.conv)
print "convolutional network loaded."
if self.fcl_eliminated is False:
filename = "fc.model"
if os.path.isfile(filename):
serializers.load_hdf5(filename, self.fc)
print "fully-connected network loaded."
示例11: load_inception_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def load_inception_model():
infile = "%s/../common/inception/inception_score.model"%os.path.dirname(__file__)
model = Inception()
serializers.load_hdf5(infile, model)
model.to_gpu()
return model
示例12: load_inception_model
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def load_inception_model():
infile = "%s/inception/inception_score.model"%os.path.dirname(__file__)
model = Inception()
serializers.load_hdf5(infile, model)
model.to_gpu()
return model
示例13: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def __init__(self,modelpath=None,mean='misc/ilsvrc_2012_mean.npy',labels='data/labels.txt',optimizer=None):
super(NetworkInNetwork,self).__init__('NetworkInNetwork',in_size=227)
self.func = deel.model.nin.NIN()
if modelpath is not None:
cs.load_hdf5("misc/"+modelpath,self.func)
self.graph_generated=None
xp = Deel.xp
#ImageNet.mean_image = pickle.load(open(mean, 'rb'))
ImageNet.mean_image = np.ndarray((3, 256, 256), dtype=xp.float32)
ImageNet.mean_image[0] = 104
ImageNet.mean_image[1] = 117
ImageNet.mean_image[2] = 123
ImageNet.in_size = self.func.insize
self.labels = np.loadtxt(labels, str, delimiter="\t")
self.t = ChainerTensor(Variable(Deel.xp.asarray([1.0])))
if Deel.gpu>=0:
self.func.to_gpu()
if optimizer is None:
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.func)
示例14: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def __init__(self,modelpath='bvlc_googlenet.caffemodel',
mean='ilsvrc_2012_mean.npy',
labels='misc/labels.txt',in_size=224):
super(GoogLeNet,self).__init__('GoogLeNet',in_size)
if os.path.splitext(modelpath)[1]==".caffemodel":
self.func = LoadCaffeModel(modelpath)
self.model = convert(self.func)
else:
self.func=None
self.model = chainermodel.GoogLeNet()
cs.load_hdf5(modelpath,self.model)
xp = Deel.xp
ImageNet.mean_image = np.ndarray((3, 256, 256), dtype=np.float32)
ImageNet.mean_image[0] = 103.939
ImageNet.mean_image[1] = 116.779
ImageNet.mean_image[2] = 123.68
ImageNet.in_size = in_size
#print type(ImageNet.mean_image)
self.labels = np.loadtxt(labels, str, delimiter="\t")
self.batchsize = 1
self.x_batch = xp.ndarray((self.batchsize, 3, self.in_size, self.in_size), dtype=np.float32)
if Deel.gpu >=0:
self.model = self.model.to_gpu(Deel.gpu)
self.optimizer = optimizers.MomentumSGD(lr=0.01,momentum=0.9)
#self.optimizer = optimizers.Adam()
#self.optimizer.setup(self.func)
self.optimizer.setup(self.model)
示例15: __init__
# 需要导入模块: from chainer import serializers [as 别名]
# 或者: from chainer.serializers import load_hdf5 [as 别名]
def __init__(self,modelpath=None,mean='misc/ilsvrc_2012_mean.npy',labels='data/labels.txt',optimizer=None):
super(RegionalNetworkInNetwork,self).__init__('RegionalNetworkInNetwork',in_size=227)
self.func = deel.model.rnin.RNIN()
if modelpath is not None:
cs.load_hdf5("misc/"+modelpath,self.func)
self.graph_generated=None
xp = Deel.xp
#ImageNet.mean_image = pickle.load(open(mean, 'rb'))
ImageNet.mean_image = np.ndarray((3, 256, 256), dtype=xp.float32)
ImageNet.mean_image[0] = 104
ImageNet.mean_image[1] = 117
ImageNet.mean_image[2] = 123
ImageNet.in_size = self.func.insize
self.labels = np.loadtxt(labels, str, delimiter="\t")
self.t = ChainerTensor(Variable(Deel.xp.asarray([1.0])))
if Deel.gpu>=0:
self.func.to_gpu()
if optimizer is None:
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.func)