def evaluate_config():
# args = parser.parse_args()
data_dir = '/home/deepracer/DeepRacer/AI_Samurai/Esun_AML/NER_BERT_pytorch/temp'
bert_model_dir = '/home/deepracer/DeepRacer/AI_Samurai/Esun_AML/NER_BERT_pytorch/bert-base-chinese-pytorch'
model_dir = '/home/deepracer/DeepRacer/AI_Samurai/Esun_AML/NER_BERT_pytorch/experiments/base_model'
seed = 23 # help="random seed for initialization"
restore_file = 'best' # help="name of the file in `model_dir` containing weights to load"
multi_gpu = False # action='store_true', help="Whether to use multiple GPUs if available"
fp16 = False # action='store_true', help="Whether to use 16-bit float precision instead of 32-bit"
# Load the parameters from json file
json_path = os.path.join(model_dir, 'params.json')
assert os.path.isfile(
json_path), "No json configuration file found at {}".format(json_path)
params = Params(json_path)
# Use GPUs if available
params.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
params.n_gpu = torch.cuda.device_count()
params.multi_gpu = multi_gpu
# Set the random seed for reproducible experiments
random.seed(seed)
torch.manual_seed(seed)
if params.n_gpu > 0:
torch.cuda.manual_seed_all(seed) # set random seed for all GPUs
params.seed = seed
# Set the logger
set_logger(os.path.join(model_dir, 'evaluate.log'))
# Create the input data pipeline
logging.info("Loading the dataset...")
# Initialize the DataLoader
data_loader = DataLoader(data_dir, bert_model_dir, params, token_pad_idx=0)
logging.info("- done.")
# Define the model
config_path = os.path.join(bert_model_dir, 'bert_config.json')
config = BertConfig.from_json_file(config_path)
model = BertForTokenClassification(config, num_labels=len(params.tag2idx))
model.to(params.device)
# Reload weights from the saved file
load_checkpoint(os.path.join(model_dir, restore_file + '.pth.tar'), model)
if fp16:
model.half()
if params.n_gpu > 1 and multi_gpu:
model = torch.nn.DataParallel(model)
return data_loader, model, params
# Initialize the DataLoader
data_loader = DataLoader(args.data_dir, args.bert_model_dir, params, token_pad_idx=0)
# Load data
test_data = data_loader.load_data('test')
# Specify the test set size
params.test_size = test_data['size']
params.eval_steps = params.test_size // params.batch_size
test_data_iterator = data_loader.data_iterator(test_data, shuffle=False)
logging.info("- done.")
# Define the model
config_path = os.path.join(args.bert_model_dir, 'bert_config.json')
config = BertConfig.from_json_file(config_path)
model = BertForTokenClassification(config, num_labels=len(params.tag2idx))
model.to(params.device)
# Reload weights from the saved file
utils.load_checkpoint(os.path.join(args.model_dir, args.restore_file + '.pth.tar'), model)
if args.fp16:
model.half()
if params.n_gpu > 1 and args.multi_gpu:
model = torch.nn.DataParallel(model)
logging.info("Starting evaluation...")
test_metrics = evaluate(model, test_data_iterator, params, mark='Test', verbose=True)