BATCH_SIZE = 256
GEN_EMBEDDING_DIM = 256
GEN_HIDDEN_DIM = 256
if __name__ == '__main__':
evaluator = Evaluator(vocab_size=VOCAB_SIZE,
min_seq_len=MIN_SEQ_LEN,
max_seq_len=MAX_SEQ_LEN,
batch_size=BATCH_SIZE)
result = {}
for i in range(0, 32):
gen = Generator(evaluator.sos_id,
evaluator.eou_id,
VOCAB_SIZE,
GEN_HIDDEN_DIM,
GEN_EMBEDDING_DIM,
MAX_SEQ_LEN,
teacher_forcing_ratio=0)
model_path = '../generator_checkpoint' + str(i) + '.pth.tar'
data = torch.load(model_path, map_location='cpu')
gen.load_state_dict(data['state_dict'])
gen.decoder = TopKDecoder(gen.decoder, 5)
gen.to(DEVICE)
print('Evaluating ' + model_path)
result[i] = evaluator.evaluate_embeddings(gen)
print('Result')
print(result)
