path_man = PathManager(dataset=dataset_name,
d_type=dataset_subtype,
model_name=model_name_norm,
version=version)
################################################
#----------------------读取模型参数------------------
################################################
if model_name == 'SIMPLE':
model_cfg = TrainingConfigManager(path_man.Doc() + 'config.json')
else:
model_cfg = TrainingConfigManager('../run/runConfig.json')
modelParams = model_cfg.modelParams()
dataset = SeqFileDataset(path_man.FileData(), path_man.FileSeqLen(), N=N)
dataloader = DataLoader(dataset,
batch_size=N,
collate_fn=batchSequenceWithoutPad)
if model_name != 'Random':
state_dict = t.load(path_man.Model() + '_v%s.0' % version)
word_matrix = state_dict['Embedding.weight']
else:
word_matrix = t.Tensor(
np.load(path_man.WordEmbedMatrix(), allow_pickle=True))
loss_fn = t.nn.NLLLoss().cuda()
if model_name == 'SIMPLE':
model = SIMPLE(word_matrix, **modelParams)
data_path_man = PathManager(dataset=data_dataset_name,
d_type=dataset_subtype)
model_path_man = PathManager(dataset=model_dataset_name,
version=version,
model_name=model_name)
################################################
#----------------------读取模型参数------------------
################################################
model_cfg = TrainingConfigManager(model_path_man.Doc()+'config.json')
modelParams = model_cfg.modelParams()
dataset = SeqFileDataset(data_path_man.FileData(), data_path_man.FileSeqLen(), N=N)
state_dict = t.load(model_path_man.Model() + '_v%s.0' % version)
# state_dict = t.load(path_man.DatasetBase()+'models/ProtoNet_v105.0')
word_matrix = state_dict['Embedding.weight']
if model_name == 'IMP':
model = IMP(word_matrix,
**modelParams)
elif model_name == 'SIMPLE':
model = SIMPLE(word_matrix,
**modelParams)
elif model_name == 'HybridIMP':
model = HybridIMP(word_matrix,
**modelParams)
elif model_name == 'ProtoNet':
loss = t.nn.NLLLoss().cuda() \
if loss_func=='nll' else \
t.nn.MSELoss().cuda()
printState('init managers...')
train_path_manager = PathManager(dataset=data_folder,
d_type='train',
model_name=model_name,
version=version)
val_path_manager = PathManager(dataset=data_folder,
d_type='validate',
model_name=model_name,
version=version)
train_dataset = SeqFileDataset(train_path_manager.FileData(),
train_path_manager.FileSeqLen(),
N)
val_dataset = SeqFileDataset(val_path_manager.FileData(),
val_path_manager.FileSeqLen(),
N)
# train_dataset = ImageFileDataset(train_path_manager.FileData(), N, rd_crop_size=224)
# val_dataset = ImageFileDataset(val_path_manager.FileData(), N, rd_crop_size=224)
# train_task = MatrixProtoEpisodeTask(k ,qk, n, N,
# dataset=train_dataset,
# cuda=True,
# label_expand=expand,
# unsqueeze=False)
# val_task = MatrixProtoEpisodeTask(k ,qk, n, N,
# dataset=val_dataset,
d_type=USED_SUB_DATASET,
model_name=model_name,
version=version)
################################################
#----------------------读取模型参数------------------
################################################
model_cfg = TrainingConfigManager(test_path_manager.Doc() + 'config.json')
modelParams = model_cfg.modelParams()
LRDecayIters, LRDecayGamma, optimizer_type,\
weight_decay, loss_func, default_lr, lrs, taskBatchSize = model_cfg.trainingParams()
test_dataset = SeqFileDataset(test_path_manager.FileData(),
test_path_manager.FileSeqLen(), N)
expand = True if loss_func == 'mse' else False
if model_type in ADAPTED_MODELS:
test_task = AdaptEpisodeTask(k,
qk,
n,
N,
test_dataset,
cuda=True,
expand=expand)
else:
test_task = ProtoEpisodeTask(k,
qk,
# splitDatas(src=man.DatasetBase()+'train/',
# dest=man.DatasetBase()+'test/',
# ratio=30,
# mode='x',
# is_dir=True)
################################################################
# 制作基于下标的数据集
################################################################
for d_type in ['train', 'validate', 'test']:
manager = PathManager(dataset='virushare-20-3gram-tfidf', d_type=d_type)
makeDataFile(json_path=manager.Folder(),
w2idx_path=manager.WordIndexMap(),
seq_length_save_path=manager.FileSeqLen(),
data_save_path=manager.FileData(),
idx2cls_mapping_save_path=manager.FileIdx2Cls(),
num_per_class=20,
max_seq_len=700)
################################################################
# renameItemFolder('/home/asichurter/datasets/JSONs/LargePE-100-original/')
# 统计序列长度分布
################################################################
# apiStat('/home/asichurter/datasets/JSONs/HKS/all/',
# ratio_stairs=[50, 100, 200, 400, 500, 1000, 2000, 5000],
# dump_report_path=None,#'/home/asichurter/datasets/reports/HKS_3gram_tfidf_api_report.json',#None,#
# dump_apiset_path=None,#'/home/asichurter/datasets/reports/HKS_3gram_tfidf_api_set.json',#None
# class_dir=True)
################################################################