reformat document

Author: itsmystyle

src/train.py

@@ -1,65 +1,36 @@
-import argparse
-import logging
 import os
-import pdb
-import pickle
 import sys
-import traceback
 import json
+import logging
+import traceback
+
+import pickle
+import argparse
+
+from metrics import Accuracy
 from callbacks import ModelCheckpoint, MetricsLogger
-from metrics import *
-from lr_finder import LRFinder
 
 
 def main(args):
  config_path = os.path.join(args.model_dir, 'config.json')
  with open(config_path) as f:
  config = json.load(f)
- 
+
  logging.info('loading word dictionary...')
  with open(config['words_dict'], 'rb') as f:
  words_dict = pickle.load(f)
 
  logging.info('loading train data...')
  with open(config['train'], 'rb') as f:
  train = pickle.load(f)
- 
+
  logging.info('loading validation data...')
  with open(config['model_parameters']['valid'], 'rb') as f:
  valid = pickle.load(f)
  config['model_parameters']['valid'] = valid
- 
+
  if args.lr_finder:
-# logging.info('creating model!')
- 
-# vocab_size = len(words_dict)
-# hidden_size = config['model_parameters']['hidden_size']
-# embedding_size = config['model_parameters']['embedding_size']
-# use_same_embedding = config['model_parameters']['use_same_embedding']
- 
-# encoder = EncoderRNN(vocab_size, hidden_size, embedding_size)
-# decoder = DecoderRNN(hidden_size, vocab_size, embedding_size, use_same_embedding)
- 
-# criterion = nn.CrossEntropyLoss()
- 
-# if config['model_parameters']['optimizer'] == 'Adam':
-# optimizer = torch.optim.Adam(list(encoder.parameters()) + list(decoder.parameters()), 
-# lr=1e-7, 
-# weight_decay=1e-2)
-# elif config['model_parameters']['optimizer'] == 'SGD':
-# optimizer = torch.optim.SGD(list(encoder.parameters()) + list(decoder.parameters()), 
-# lr=1e-7, 
-# momentum=0.9, 
-# weight_decay=1e-2)
- 
-# lr_finder = LRFinder(model, optimizer, criterion, device="cuda")
-# lr_finder.range_test(trainloader, end_lr=100, num_iter=100, step_mode="exp")
- 
-# # use cuda
-# self.encoder = self.encoder.to(self.device)
-# self.decoder = self.decoder.to(self.device)
  pass
- 
  else:
  if config['arch'] == 'Predictor':
  from predictor import Predictor