else:
model_save_path = sys.argv[1]
# config logger
logger = logging.getLogger('__main__')
util.init_logger()
logger.info("Evaluation of trained model on MultiNLI. Model ({})".format(
model_save_path))
# init supervisor
spv = Supervisor(config)
# load trained embedding
spv.load_trained_emb()
# load trained model
spv.load_checkpoint(f_path=model_save_path)
eval_results = []
# Evaluate on each genre of MultiSNLI
for genre in MultiGenre:
logger.info("##### GENRE: {} #####".format(genre.upper()))
spv.load_data(TaskName.MNLI, genre)
spv.get_dataloader()
acc, _ = spv.eval_model(spv.loaders[LoaderType.VAL])
eval_results.append({'Genre': genre, "valAcc": acc})
# logger.info("(model){} (genre){} (val_acc){}".format(spv.config['model_name'], genre, acc))
print("\n", pd.DataFrame.from_records(eval_results), "\n")
logger.info(
"Model from {}\nEvaluation on MultiNLI is done!\n".format(model_save_path))
from constants import DefaultConfig as config, LoaderType
from data_loader import LABEL_IDX
from supervisor import Supervisor
import util
if len(sys.argv) < 2:
print("=== ! ===\nNo input for trained model path!")
model_save_path = 'results_1540947201/checkpoints/encRNNhid50lea0.01.tar'
print("Using model ({}) for illustration...".format(model_save_path))
else:
model_save_path = sys.argv[1]
# init supervisor
spv = Supervisor(config)
spv.load_trained_emb()
spv.load_data()
spv.get_dataloader()
# ============= Load checkpoint ============
# load trained model
spv.load_checkpoint(f_path='./results_1540933016/checkpoints/demo_rnn_hidd200_drop0.2_if_eTrue.tar')
acc, loss = spv.eval_model(spv.loaders[LoaderType.VAL])
print("Load model evaluated on val_loader: (valAcc){} (valLoss){}".format(acc, loss))
# ============= Sample Analysis ============
# Find at least 3 samples from correct and
# incorrect classification respectively
spv.model.eval()
corr_count = 0
incorr_count = 0
for prem, hypo, p_len, h_len, labels in spv.loaders[LoaderType.VAL]:
