remove unused get_opt from predict.py; revert unneeded change to train.py

Author: paulfitz

predict.py

@@ -33,7 +33,7 @@
 import argparse, os
 from sqlnet.dbengine import DBEngine
 from sqlova.utils.utils_wikisql import *
-from train import construct_hyper_param, get_models, get_opt
+from train import construct_hyper_param, get_models
 
 # This is a stripped down version of the test() method in train.py - identical, except:
 # - does not attempt to measure accuracy and indeed does not expect the data to be labelled.
@@ -107,7 +107,6 @@ def predict(data_loader, data_table, model, model_bert, bert_config, tokenizer,
 path_model = args.model_file
 args.no_pretraining = True # counterintuitive, but avoids loading unused models
 model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH, trained=True, path_model_bert=path_model_bert, path_model=path_model)
-opt, opt_bert = get_opt(model, model_bert, args)
 
 # Load data
 dev_data, dev_table = load_wikisql_data(args.data_path, mode=args.split, toy_model=args.toy_model, toy_size=args.toy_size, no_hs_tok=True)

train.py

@@ -127,8 +127,8 @@ def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):
 
  return model_bert, tokenizer, bert_config
 
-def get_opt(model, model_bert, args):
- if args.fine_tune:
+def get_opt(model, model_bert, fine_tune):
+ if fine_tune:
  opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
  lr=args.lr, weight_decay=0)
 
@@ -582,7 +582,7 @@ def print_result(epoch, acc, dname):
  # model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH, trained=True, path_model_bert=path_model_bert, path_model=path_model)
 
  ## 5. Get optimizers
- opt, opt_bert = get_opt(model, model_bert, args)
+ opt, opt_bert = get_opt(model, model_bert, args.fine_tune)
 
  ## 6. Train
  acc_lx_t_best = -1