def main(_):
config = flags.FLAGS
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
extractor = utils.Extractor()
extractor.Extract()
loader = utils.Loader(cut_len)
t_data = loader.load_trigger()
trigger = Trigger_Model(t_data, loader.maxlen, loader.wordemb, config.mode)
trigger.train_trigger()
def main(_):
config = flags.FLAGS
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu
extractor = utils.Extractor()
extractor.Extract()
loader = utils.Loader()
t_data = loader.load_trigger()
a_data = loader.load_argument()
trigger = DMCNN(t_data,a_data,loader.maxlen,loader.max_argument_len,loader.wordemb)
a_data_process = trigger.train_trigger()
argument = DMCNN(t_data,a_data_process,loader.maxlen,loader.max_argument_len,loader.wordemb,stage=config.mode,classify=config.classify)
argument.train_argument()
def main(args):
chainer.set_debug(True)
# Initialize the model to train
model = models.archs[args.arch]()
if args.finetune and hasattr(model, 'finetuned_model_path'):
utils.finetuning.load_param(model.finetuned_model_path, model,
args.ignore)
if args.initmodel:
print('Load model from', args.initmodel)
chainer.serializers.load_npz(args.initmodel, model)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use() # Make the GPU current
#cuda.cudnn_enabled = False
model.to_gpu()
nowt = datetime.datetime.today()
outputdir = os.path.join(args.out, args.arch, 'extract')
if args.initmodel is not None:
outputdir = os.path.dirname(args.initmodel)
# Load the datasets and mean file
mean = None
if hasattr(model, 'mean_value'):
mean = makeMeanImage(model.mean_value)
else:
mean = np.load(args.mean)
assert mean is not None
val = ppds.PreprocessedDataset(args.val, args.root, mean, model.insize,
False)
val_iter = chainer.iterators.SerialIterator(val,
args.val_batchsize,
repeat=False,
shuffle=False)
# Set up an optimizer
optimizer = chainer.optimizers.MomentumSGD()
optimizer.setup(model)
# Set up a trainer
updater = training.StandardUpdater(val_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (1, 'epoch'), outputdir)
#val_interval = (10 if args.test else int(len(train) / args.batchsize)), 'iteration'
val_interval = (1, 'iteration')
#snapshot_interval = (10, 'iteration') if args.test else (2, 'epoch')
#log_interval = (10, 'iteration')
# Copy the chain with shared parameters to flip 'train' flag only in test
eval_model = model.copy()
eval_model.train = False
val_extractor = utils.Extractor(val_iter, eval_model, device=args.gpu)
val_extractor.layer_rank = eval_model.layer_rank[args.layer]
val_extractor.layer_name = args.layer
val_extractor.operation = args.operation
val_extractor.save_features = args.savefeatures
val_extractor.top = args.top
if 'googlenet' in args.arch:
val_extractor.lastname = 'validation/main/loss3'
trainer.extend(val_extractor, trigger=val_interval)
#trainer.extend(extensions.PrintReport([
# 'epoch', 'iteration', 'main/loss', 'validation/main/loss',
# 'main/accuracy', 'validation/main/accuracy',
#]), trigger=log_interval)
#trainer.extend(extensions.ProgressBar(update_interval=10))
if args.resume:
chainer.serializers.load_npz(args.resume, trainer)
results = val_extractor(trainer)
results['outputdir'] = outputdir
return results
#!/usr/bin/env python
import logging
import utils
import json
# Set log level to info
logging.basicConfig(level=logging.INFO)
# Load dataset description file
dataset_description = json.load(open("./dataset.json"))
# Download resource
downloader = utils.Downloader(dataset_description, "../../data/raw")
downloader.download()
# Extract resources
extractor = utils.Extractor(dataset_description, "../../data/raw",
"../../data/processed")
extractor.extract()