################################################
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)
model.load_state_dict(state_dict)
elif model_name == 'FT':
model = FT(class_n, loss_fn, word_matrix, **modelParams)
model.load_state_dict(state_dict)
elif model_name == 'Random':
model = FT(class_n, loss_fn, word_matrix, **modelParams)
train_task = ImpEpisodeTask(k, qk, n, N, train_dataset,
cuda=True, expand=expand,
parallel=modelParams['data_parallel_devices'])
val_task = ImpEpisodeTask(k, qk, n, N, val_dataset,
cuda=True, expand=expand,
parallel=modelParams['data_parallel_devices'])
else:
train_task = ProtoEpisodeTask(k, qk, n, N, train_dataset,
cuda=True, expand=expand,
parallel=modelParams['data_parallel_devices'])
val_task = ProtoEpisodeTask(k, qk, n, N, val_dataset,
cuda=True, expand=expand,
parallel=modelParams['data_parallel_devices'])
stat = TrainStatManager(model_save_path=train_path_manager.Model(),
stat_save_path=train_path_manager.Doc(),
train_report_iter=ValCycle,
criteria=criteria)
if RecordGradient:
types.append('line')
titles.append('gradient')
xlabels.append('iterations')
ylabels.append('gradient norm')
legends.append(['Encoder Gradient'])
if UseVisdom:
plot = VisdomPlot(env_title='train monitoring',
types=types,
titles=titles,
test_task = ProtoEpisodeTask(k,
qk,
n,
N,
test_dataset,
cuda=True,
expand=expand)
stat = TestStatManager()
################################################
#----------------------模型定义和初始化------------------
################################################
printState('init model...')
state_dict = t.load(test_path_manager.Model())
if model_type in ADAPTED_MODELS:
word_matrix = state_dict['Learner.Embedding.weight']
else:
word_matrix = state_dict['Embedding.weight']
loss = t.nn.NLLLoss().cuda() if loss_func == 'nll' else t.nn.MSELoss().cuda()
if model_type == 'FEAT':
model = FEAT(pretrained_matrix=word_matrix, **modelParams)
else:
raise ValueError('不支持的模型类型:', model_type)
model.load_state_dict(state_dict)
model = model.cuda()
qk,
n,
N,
test_dataset,
cuda=True,
expand=expand)
stat = TestStatManager()
################################################
#----------------------模型定义和初始化------------------
################################################
printState('init model...')
if not MODEL_RANDOM_STATE:
state_dict = t.load(test_path_manager.Model(type=cfg.loadBest()))
if model_type in ADAPTED_MODELS:
word_matrix = state_dict['Learner.Embedding.weight']
else:
word_matrix = state_dict['Embedding.weight']
else:
word_matrix = t.Tensor(
np.load(test_path_manager.WordEmbedMatrix(), allow_pickle=True))
print("loading done...")
loss = t.nn.NLLLoss().cuda() if loss_func == 'nll' else t.nn.MSELoss().cuda()
if model_type == 'ProtoNet':
model = ProtoNet(pretrained_matrix=word_matrix, **modelParams)
elif model_type == 'InductionNet':
model = InductionNet(pretrained_matrix=word_matrix, **modelParams)