def _create(self):
model = utils.get_pretrained_model(self.base_model,
include_top=False,
input_shape=self.input_shape)
interm = model.output
interm = Flatten()(interm)
interm = Dropout(0.5)(interm)
interm = Dense(self.fc_layer_size, activation='relu')(interm)
interm = Dropout(0.5)(interm)
output = Dense(len(self.classes), activation='softmax')(interm)
self.model = Model(inputs=model.input, outputs=output)
def load_weights_from_top_model(self, top_model_weights_path=None):
if top_model_weights_path is None:
top_model_weights_path = config.get_top_model_weights_path(
self.base_model)
pretrained_model = utils.get_pretrained_model(
self.base_model, include_top=False, input_shape=self.input_shape)
top_model = TopModel(base_model=self.base_model,
fc_layer_size=self.fc_layer_size,
classes=self.classes)
top_model.load_weights(top_model_weights_path)
model = Model(inputs=pretrained_model.input,
outputs=top_model.model(pretrained_model.output))
self.model = model
def _create(self):
pretrained_model = utils.get_pretrained_model(
self.base_model,
include_top=False,
input_shape=utils.get_input_shape(self.base_model))
self.pretrained_model = pretrained_model
input_shape = [int(ele) for ele in pretrained_model.output.shape[1:]]
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dropout(0.5))
model.add(Dense(self.fc_layer_size, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(self.output_dim, activation='softmax'))
self.model = model
def main():
parser = ArgumentParser()
#任务配置
parser.add_argument('-device', default=0, type=int)
parser.add_argument('-output_name', default='', type=str)
parser.add_argument('-saved_model_path', default='', type=str) #如果是k fold合并模型进行预测，只需设置为对应k_fold模型对应的output path
parser.add_argument('-type', default='train', type=str)
parser.add_argument('-k_fold', default=-1, type=int) #如果是-1则说明不采用k折，否则说明采用k折的第几折
parser.add_argument('-merge_classification', default='avg', type=str) # 个数预测：vote则采用投票法，avg则是平均概率
parser.add_argument('-merge_with_bert_sort', default='yes', type=str) # 是否融合之前bert模型计算的相似度
parser.add_argument('-k_fold_cache', default='no', type=str) #是否使用之前k_fold的cache
parser.add_argument('-generate_candidates', default='', type=str) # 是否融合之前bert模型计算的相似度
parser.add_argument('-seed', default=123456, type=int) # 随机数种子
parser.add_argument('-cls_position', default='zero', type=str) # 添加的两个cls的position是否使用0
parser.add_argument('-pretrained_model_path', default='/home/liangming/nas/lm_params/chinese_L-12_H-768_A-12/', type=str) # bert参数地址
#训练参数
parser.add_argument('-train_batch_size', default=64, type=int)
parser.add_argument('-val_batch_size', default=256, type=int)
parser.add_argument('-lr', default=2e-5, type=float)
parser.add_argument('-epoch_num', default=20, type=int)
parser.add_argument('-max_len', default=64, type=int)
parser.add_argument('-dropout', default=0.3, type=float)
parser.add_argument('-print_loss_step', default=2, type=int)
parser.add_argument('-hit_list', default=[2, 5, 7, 10], type=list)
args = parser.parse_args()
# assert args.train_batch_size % args.neg_num == 0, print('batch size应该是neg_num的整数倍')
#定义时间格式
DATE_FORMAT = "%Y-%m-%d-%H:%M:%S"
#定义输出文件夹，如果不存在则创建,
if args.output_name == '':
output_path = os.path.join('./output/rerank_keywords_output', time.strftime(DATE_FORMAT,time.localtime(time.time())))
else:
output_path = os.path.join('./output/rerank_keywords_output', args.output_name)
# if os.path.exists(output_path):
# raise Exception('the output path {} already exists'.format(output_path))
if not os.path.exists(output_path):
os.makedirs(output_path)
#配置tensorboard
tensor_board_log_path = os.path.join(output_path, 'tensor_board_log{}'.format('' if args.k_fold == -1 else args.k_fold))
writer = SummaryWriter(tensor_board_log_path)
#定义log参数
logger = Logger(output_path,'main{}'.format('' if args.k_fold == -1 else args.k_fold)).logger
#设置seed
logger.info('set seed to {}'.format(args.seed))
set_seed(args)
#打印args
print_args(args, logger)
#读取数据
logger.info('#' * 20 + 'loading data and model' + '#' * 20)
data_path = os.path.join(project_path, 'candidates')
# data_path = os.path.join(project_path, 'tf_idf_candidates')
train_list, val_list, test_list, code_to_name, name_to_code, standard_name_list = read_rerank_data(data_path, logger, args)
#load model
# pretrained_model_path = '/home/liangming/nas/lm_params/chinese_L-12_H-768_A-12/'
pretrained_model_path = args.pretrained_model_path
bert_config, bert_tokenizer, bert_model = get_pretrained_model(pretrained_model_path, logger)
#获取dataset
logger.info('create dataloader')
train_dataset = RerankKeywordDataset(train_list, bert_tokenizer, args, logger)
train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=False, collate_fn=train_dataset.collate_fn)
val_dataset = RerankKeywordDataset(val_list, bert_tokenizer, args, logger)
val_dataloader = DataLoader(val_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=val_dataset.collate_fn)
test_dataset = RerankKeywordDataset(test_list, bert_tokenizer, args, logger)
test_dataloader = DataLoader(test_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=test_dataset.collate_fn)
#创建model
logger.info('create model')
model = BertKeywordsClassification(bert_model, bert_config, args)
model = model.to(args.device)
#配置optimizer和scheduler
t_total = len(train_dataloader) * args.epoch_num
optimizer, _ = get_optimizer_and_scheduler(model, t_total, args.lr, 0)
if args.type == 'train':
train(model, train_dataloader, val_dataloader, test_dataloader, optimizer, writer, args, logger, output_path, standard_name_list)
elif args.type == 'evaluate':
if args.saved_model_path == '':
raise Exception('saved model path不能为空')
# 非k折模型
if args.k_fold == -1:
logger.info('loading saved model')
checkpoint = torch.load(args.saved_model_path, map_location='cpu')
model.load_state_dict(checkpoint)
model = model.to(args.device)
# #生成icd标准词的最新embedding
evaluate(model, test_dataloader, args, logger, writer, standard_name_list, is_test=True)
else:
evaluate_k_fold(model, test_dataloader, args, logger, writer, standard_name_list)
'val': DataLoader(data['val'], batch_size=batch_size, shuffle=True),
'test': DataLoader(data['test'], batch_size=batch_size, shuffle=True)
}
trainiter = iter(dataloaders['train'])
features, labels = next(trainiter)
print("class numbers: ", len(data['train'].classes))
n_classes = len(data['train'].classes)
# This should return the same as the pretrained model with the custom classifier. In the case of resnet, we replace the `fc` layers with the same classifier.
#
# The `torchsummary` library has a helpful function called `summary` which summarizes our model.
model = get_pretrained_model('resnet50', train_on_gpu, n_classes, multi_gpu)
# if multi_gpu:
# summary(
# model.module,
# input_size=(3, 224, 224),
# batch_size=batch_size,
# device='cuda')
# else:
# summary(
# model, input_size=(3, 224, 224), batch_size=batch_size, device='cuda')
model.class_to_idx = data['train'].class_to_idx
model.idx_to_class = {
idx: class_
for class_, idx in model.class_to_idx.items()
}
ovule_loader = DataLoader(ovule_dset,
batch_size=cfg.general.trainBatchSize,
shuffle=False,
pin_memory=True)
tomato_loader = DataLoader(tomato_dset,
batch_size=cfg.general.testBatchSize,
shuffle=False,
pin_memory=True)
print('----START TRAINING----' * 4)
# accs = {}
# utils.get_pretrained_model()
# model = FsModel()
model = utils.get_pretrained_model()
# model.load_state_dict(torch.load(os.path.join(cfg.general.checkpointDir, cfg.model.loadFromName)), strict=True)
for param in model.parameters():
param.requires_grad = True
# if cfg.model.freezeVgg:
# for param in model.features.parameters():
# param.requires_grad = False
criterion = HypercubeDistLoss(
weights=torch.tensor(cfg.general.lossWeights, device=device))
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
model, val_acc_history, best_acc = utils.getFeaturesAndCompare(
model, ovule_loader, tomato_loader, criterion, optimizer)
def get_bottleneck_features(model=None,
source='path',
container_path=None,
tensor=None,
classes=None,
save=False,
filename=None,
verbose=False):
"""Extract bottleneck features
Parameters
----------
model: string
pre-trained model name, being one of
'inception_v3',
'mobilenet',
'resnet50',
'resnet101',
'resnet152',
'vgg16',
'vgg19',
'xception'
source: string
where to extract bottleneck features, either 'path' or 'tensor'
container_path: string
if `source='path'`, `container_path` specifies the folder path that
contains images of all the classes. If `None`, container_path will be
set to 'path_to_the_module/data/train'
tensor: numpy array/string
if `source='tensor'`, `tensor` specifies the tensor from which
bottleneck features are extracted or the path to the saved tensor file
classes: tuple/list
a tuple/list of classes for prediction
save: boolen
whether to save the extracted bottleneck features or not
filename: string
if `save=True`, specifies the name of the file in which the bottleneck
features are saved
verbose: boolean
verbosity mode
"""
assert source in {'path', 'tensor'}
if source == 'path':
tensors = get_x_from_path(model=model,
container_path=container_path,
classes=classes,
save=False,
verbose=verbose)
else:
assert isinstance(tensor, (str, np.ndarray))
if isinstance(tensor, np.ndarray):
tensors = tensor
else:
assert os.path.exists(tensor)
tensors = utils.load_h5file(tensor)
input_shape = utils.get_input_shape(model)
pretrained_model = utils.get_pretrained_model(model,
include_top=False,
input_shape=input_shape)
bottleneck_features = pretrained_model.predict(tensors,
verbose=1 if verbose else 0)
if save:
assert filename is not None
filepath = os.path.join(config.precomputed_dir, filename)
utils.remove_file(filepath)
if verbose:
print('Started saving {}'.format(filename))
with h5py.File(filepath, 'w') as hf:
hf.create_dataset('data', data=bottleneck_features)
if verbose:
print('Finished saving {}'.format(filename))
else:
return bottleneck_features
def main():
parser = ArgumentParser()
#任务配置
parser.add_argument('-neg_sample', default='online', type=str)
parser.add_argument('-neg_num', default=4, type=int)
parser.add_argument('-device', default=0, type=int)
parser.add_argument('-output_name', default='', type=str)
parser.add_argument('-saved_model_path', default='', type=str) #如果是k fold合并模型进行预测，只需设置为对应k_fold模型对应的output path
parser.add_argument('-type', default='train', type=str)
parser.add_argument('-k_fold', default=-1, type=int) #如果是-1则说明不采用k折，否则说明采用k折的第几折
parser.add_argument('-merge_classification', default='vote', type=str) # 个数预测：vote则采用投票法，avg则是平均概率
parser.add_argument('-merge_sort', default='avg', type=str) # k折融合算法: vote则采用投票法，avg则是平均概率
parser.add_argument('-generate_candidates', default='no', type=str) # 在预测过程中，是否生成候选词并保存在output_name下，如果是no则不生成，如果不是no则是保存的名字
parser.add_argument('-k_fold_cache', default='no', type=str) # 在预测k_fold中，是否采用之前的cache，如果存在cache，会在output_name下存在三个list文件, 避免5个模型进行预测
parser.add_argument('-add_keywords', default='no', type=str) # 这里不添加关键词信息
parser.add_argument('-seed', default=123456, type=int) # 种子
parser.add_argument('-loss_type', default='union', type=str) # loss type: class就仅更新分类的参数；sim就仅更新triple loss；union就同时更新二者参数
parser.add_argument('-hidden_layers', default=12, type=int) # bert的层数
parser.add_argument('-pretrained_model_path', default='/home/liangming/nas/lm_params/chinese_L-12_H-768_A-12/', type=str) # bert参数地址
#训练参数
parser.add_argument('-train_batch_size', default=64, type=int)
parser.add_argument('-val_batch_size', default=256, type=int)
parser.add_argument('-lr', default=2e-5, type=float)
parser.add_argument('-epoch_num', default=20, type=int)
parser.add_argument('-max_len', default=64, type=int)
parser.add_argument('-margin', default=1, type=float)
parser.add_argument('-distance', default='eu', type=str)
parser.add_argument('-label_nums', default=3, type=int)
parser.add_argument('-dropout', default=0.3, type=float)
parser.add_argument('-pool', default='avg', type=str)
parser.add_argument('-print_loss_step', default=2, type=int)
parser.add_argument('-hit_list', default=[2, 5, 7, 10], type=list)
args = parser.parse_args()
assert args.train_batch_size % args.neg_num == 0, print('batch size应该是neg_num的整数倍')
#定义时间格式
DATE_FORMAT = "%Y-%m-%d-%H:%M:%S"
#定义输出文件夹，如果不存在则创建,
if args.output_name == '':
output_path = os.path.join('./output/mto_output', time.strftime(DATE_FORMAT,time.localtime(time.time())))
else:
output_path = os.path.join('./output/mto_output', args.output_name)
# if os.path.exists(output_path):
# raise Exception('the output path {} already exists'.format(output_path))
if not os.path.exists(output_path):
os.makedirs(output_path)
#配置tensorboard
tensor_board_log_path = os.path.join(output_path, 'tensor_board_log{}'.format('' if args.k_fold == -1 else args.k_fold))
writer = SummaryWriter(tensor_board_log_path)
#定义log参数
logger = Logger(output_path,'main{}'.format('' if args.k_fold == -1 else args.k_fold)).logger
#打印args
print_args(args, logger)
#设置seed
logger.info('set seed to {}'.format(args.seed))
set_seed(args)
#读取数据
logger.info('#' * 20 + 'loading data and model' + '#' * 20)
data_path = os.path.join(project_path, 'data')
train_list, val_list, test_list, code_to_name, name_to_code, standard_name_list = read_data(data_path, logger, args)
#load model
# pretrained_model_path = '/home/liangming/nas/lm_params/chinese_L-12_H-768_A-12/'
pretrained_model_path = args.pretrained_model_path
bert_config, bert_tokenizer, bert_model = get_pretrained_model(pretrained_model_path, logger, args)
#获取dataset
logger.info('create dataloader')
train_dataset = TripleDataset(train_list, standard_name_list, code_to_name, name_to_code, bert_tokenizer, args, logger)
train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=False, collate_fn=train_dataset.collate_fn)
val_dataset = TermDataset(val_list, bert_tokenizer, args, False)
val_dataloader = DataLoader(val_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=val_dataset.collate_fn)
test_dataset = TermDataset(test_list, bert_tokenizer, args, False)
test_dataloader = DataLoader(test_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=test_dataset.collate_fn)
icd_dataset = TermDataset(standard_name_list, bert_tokenizer, args, True)
icd_dataloader = DataLoader(icd_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=icd_dataset.collate_fn)
train_term_dataset = TermDataset(train_list, bert_tokenizer, args, False)
train_term_dataloader = DataLoader(train_term_dataset, batch_size=args.val_batch_size, shuffle=False, collate_fn=train_term_dataset.collate_fn)
#创建model
logger.info('create model')
model = SiameseClassificationModel(bert_model, bert_config, args)
model = model.to(args.device)
#配置optimizer和scheduler
t_total = len(train_dataloader) * args.epoch_num
optimizer, _ = get_optimizer_and_scheduler(model, t_total, args.lr, 0)
if args.type == 'train':
train(model, train_dataloader, val_dataloader, test_dataloader, icd_dataloader, train_term_dataloader, optimizer, writer, args, logger, \
standard_name_list, train_list, output_path, bert_tokenizer, name_to_code, code_to_name)
elif args.type == 'evaluate':
if args.saved_model_path == '':
raise Exception('saved model path不能为空')
# 非k折模型
if args.k_fold == -1:
logger.info('loading saved model')
checkpoint = torch.load(args.saved_model_path, map_location='cpu')
model.load_state_dict(checkpoint)
model = model.to(args.device)
# #生成icd标准词的最新embedding
logger.info('generate icd embedding')
icd_embedding = get_model_embedding(model, icd_dataloader, True)
start_time = time.time()
evaluate(model, test_dataloader, icd_embedding, args, logger, writer, standard_name_list, True)
end_time = time.time()
logger.info('total length is {}, predict time is {}, per mention time is {}'.format(len(test_list), end_time - start_time, (end_time - start_time) / len(test_list)))
else:
evaluate_k_fold(model, test_dataloader, icd_dataloader, args, logger, writer, standard_name_list)
def main():
# Training settings
parser = argparse.ArgumentParser(description='Embedding extraction module')
parser.add_argument('--net',
default='lenet5',
help='DNN name (default=lenet5)')
parser.add_argument('--root',
default='data',
help='rootpath (default=data)')
parser.add_argument('--dataset',
default='imagenet',
help='dataset (default=imagenet)')
parser.add_argument('--tensor_folder',
default='tensor_pub',
help='tensor_folder (default=tensor_pub)')
parser.add_argument('--layer-info',
default='layer_info',
help='layer-info (default=layer_info)')
parser.add_argument('--gpu-id',
default='1',
type=str,
help='id(s) for CUDA_VISIBLE_DEVICES')
parser.add_argument('-j',
'--workers',
default=8,
type=int,
metavar='N',
help='number of data loading workers (default: 8)')
parser.add_argument('-b',
'--batch-size',
default=1,
type=int,
metavar='N',
help='should be 1')
args = parser.parse_args()
use_cuda = True
# Define what device we are using
print("CUDA Available: ", torch.cuda.is_available())
root = args.root
dataset = args.dataset
net = args.net
tensor_folder = args.tensor_folder
layers, cols = utils.get_layer_info(root, dataset, net, args.layer_info)
print(dataset)
print(root, dataset, net)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
if dataset.startswith('imagenet'):
if net == 'resnet50':
model = utils.load_resnet50_model(True)
elif net == 'vgg16':
model = utils.load_vgg_model(pretrained=True, net=net)
else:
model = utils.load_resnet_model(pretrained=True)
sub_models = utils.load_imagenet_sub_models(
utils.get_model_root(root, dataset, net), layers, net, cols)
# sub_models = utils.load_resnet_sub_models(utils.get_model_root(root,
# dataset, net), layers, net)
test_loader = utils.load_imagenet_test(args.batch_size, args.workers)
anatomy(model, sub_models, test_loader, root, dataset, tensor_folder,
net, layers)
else: # cifar10, cifar100, mnist
device = torch.device("cuda" if (
use_cuda and torch.cuda.is_available()) else "cpu")
nclass = 10
if dataset == 'cifar100':
nclass = 100
model = utils.load_model(
net, device, utils.get_pretrained_model(root, dataset, net),
dataset)
weight_models = utils.load_weight_models(
net, device, utils.get_model_root(root, dataset, net), layers,
cols, nclass)
if dataset == 'mnist':
train_loader, test_loader = utils.load_mnist(
utils.get_root(root, dataset, 'data', net))
elif dataset == 'cifar10':
train_loader, test_loader = utils.load_cifar10(
utils.get_root(root, dataset, 'data', net))
elif dataset == 'cifar100':
train_loader, test_loader = utils.load_cifar100(
utils.get_root(root, dataset, 'data', net))
else: #default mnist
train_loader, test_loader = utils.load_mnist(
utils.get_root(root, dataset, 'data', net))
anatomy(model, weight_models, test_loader, root, dataset,
tensor_folder, net, layers)