def restore_best_model(): """Load bestmodel file from eval directory, add variables for adagrad, and save to train directory""" tf.logging.info("Restoring bestmodel for training...") # Initialize all vars in the model sess = tf.Session(config=util.get_config()) print("Initializing all variables...") sess.run(tf.initialize_all_variables()) # Restore the best model from eval dir saver = tf.train.Saver( [v for v in tf.all_variables() if "Adagrad" not in v.name]) print("Restoring all non-adagrad variables from best model in eval dir...") curr_ckpt = util.load_ckpt(saver, sess, "eval") print("Restored %s." % curr_ckpt) # Save this model to train dir and quit new_model_name = curr_ckpt.split("/")[-1].replace("bestmodel", "model") new_fname = os.path.join(FLAGS.log_root, "train", new_model_name) print("Saving model to %s..." % (new_fname)) new_saver = tf.train.Saver( ) # this saver saves all variables that now exist, including Adagrad variables new_saver.save(sess, new_fname) print("Saved.") exit()
def convert_to_coverage_model(): """Load non-coverage checkpoint, add initialized extra variables for coverage, and save as new checkpoint""" tf.logging.info("converting non-coverage model to coverage model..") # initialize an entire coverage model from scratch sess = tf.Session(config=util.get_config()) print("initializing everything...") sess.run(tf.global_variables_initializer()) # load all non-coverage weights from checkpoint saver = tf.train.Saver([ v for v in tf.global_variables() if "coverage" not in v.name and "Adagrad" not in v.name ]) print("restoring non-coverage variables...") curr_ckpt = util.load_ckpt(saver, sess) print("restored.") # save this model and quit new_fname = curr_ckpt + '_cov_init' print("saving model to %s..." % (new_fname)) new_saver = tf.train.Saver( ) # this one will save all variables that now exist new_saver.save(sess, new_fname) print("saved.") exit()
def __init__(self, model, batcher, vocab): """Initialize decoder. Args: model: a Seq2SeqAttentionModel object. batcher: a Batcher object. vocab: Vocabulary object """ self._model = model self._model.build_graph() self._batcher = batcher self._vocab = vocab self._saver = tf.train.Saver( ) # we use this to load checkpoints for decoding self._sess = tf.Session(config=util.get_config()) # Load an initial checkpoint to use for decoding ckpt_path = util.load_ckpt(self._saver, self._sess) self._decode_dir = FLAGS.outputs_dir #if FLAGS.single_pass: # # Make a descriptive decode directory name # ckpt_name = "ckpt-" + ckpt_path.split('-')[-1] # this is something of the form "ckpt-123456" # self._decode_dir = os.path.join(FLAGS.log_root, get_decode_dir_name(ckpt_name)) # if os.path.exists(self._decode_dir): # shutil.rmtree(self._decode_dir) # #raise Exception("single_pass decode directory %s should not already exist" % self._decode_dir) #else: # Generic decode dir name # #self._decode_dir = os.path.join(FLAGS.log_root, "decode") # self._decode_dir = os.path.join(FLAGS.outputs_dir, get_decode_dir_name(ckpt_name)) # Make the decode dir if necessary if not os.path.exists(self._decode_dir): os.mkdir(self._decode_dir)
def run_eval(model, batcher, vocab): """Repeatedly runs eval iterations, logging to screen and writing summaries. Saves the model with the best loss seen so far.""" model.build_graph() # build the graph saver = tf.train.Saver( max_to_keep=3) # we will keep 3 best checkpoints at a time sess = tf.Session(config=util.get_config()) eval_dir = os.path.join( FLAGS.log_root, "eval") # make a subdir of the root dir for eval data bestmodel_save_path = os.path.join( eval_dir, 'bestmodel') # this is where checkpoints of best models are saved summary_writer = tf.summary.FileWriter(eval_dir) running_avg_loss = 0 # the eval job keeps a smoother, running average loss to tell it when to implement early stopping best_loss = None # will hold the best loss achieved so far while True: _ = util.load_ckpt(saver, sess) # load a new checkpoint batch = batcher.next_batch() # get the next batch # run eval on the batch t0 = time.time() results = model.run_eval_step(sess, batch) t1 = time.time() tf.logging.info('seconds for batch: %.2f', t1 - t0) # print the loss and coverage loss to screen loss = results['loss'] tf.logging.info('loss: %f', loss) if FLAGS.coverage: coverage_loss = results['coverage_loss'] tf.logging.info("coverage_loss: %f", coverage_loss) # add summaries summaries = results['summaries'] train_step = results['global_step'] summary_writer.add_summary(summaries, train_step) # calculate running avg loss running_avg_loss = calc_running_avg_loss(np.asscalar(loss), running_avg_loss, summary_writer, train_step) # If running_avg_loss is best so far, save this checkpoint (early stopping). # These checkpoints will appear as bestmodel-<iteration_number> in the eval dir if best_loss is None or running_avg_loss < best_loss: tf.logging.info( 'Found new best model with %.3f running_avg_loss. Saving to %s', running_avg_loss, bestmodel_save_path) saver.save(sess, bestmodel_save_path, global_step=train_step, latest_filename='checkpoint_best') best_loss = running_avg_loss # flush the summary writer every so often if train_step % 100 == 0: summary_writer.flush()
def decode(self): """Decode examples until data is exhausted (if FLAGS.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals""" t0 = time.time() counter = 0 tars = [] preds = [] while True: batch = self._batcher.next_batch( ) # 1 example repeated across batch if batch is None: # finished decoding dataset in single_pass mode assert FLAGS.single_pass, "Dataset exhausted, but we are not in single_pass mode" self.write_for_bleu(tars, preds) tf.logging.info( "Decoder has finished reading dataset for single_pass.") #tf.logging.info("Output has been saved in %s and %s. Now starting ROUGE eval...", self._rouge_ref_dir, self._rouge_dec_dir) #results_dict = rouge_eval(self._rouge_ref_dir, self._rouge_dec_dir) #rouge_log(results_dict, self._decode_dir) return original_article = batch.original_queses[0] # string original_abstract = batch.original_reses[0] # string article_withunks = data.show_art_oovs(original_article, self._vocab) # string abstract_withunks = data.show_abs_oovs( original_abstract, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # string if counter % 100 == 0: tf.logging.info('Decoding No.{} instance.'.format(counter + 1)) # Run beam search to get best Hypothesis best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch) # Extract the output ids from the hypothesis and convert back to words output_ids = [int(t) for t in best_hyp.tokens[1:]] decoded_words = data.outputids2words( output_ids, self._vocab, (batch.art_oovs[0] if FLAGS.pointer_gen else None)) # Remove the [STOP] token from decoded_words, if necessary try: fst_stop_idx = decoded_words.index( config.END_TOKEN) # index of the (first) [STOP] symbol decoded_words = decoded_words[:fst_stop_idx] except ValueError: decoded_words = decoded_words decoded_output = ' '.join(decoded_words) # single string if FLAGS.single_pass: #self.write_for_rouge(original_abstract, decoded_words, counter) # write ref summary and decoded summary to file, to eval with pyrouge later counter += 1 # this is how many examples we've decoded tars.append(original_abstract) preds.append(decoded_words) else: print_results(article_withunks, abstract_withunks, decoded_output) # log output to screen self.write_for_attnvis( article_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists, best_hyp.p_gens ) # write info to .json file for visualization tool # Check if SECS_UNTIL_NEW_CKPT has elapsed; if so return so we can load a new checkpoint t1 = time.time() if t1 - t0 > SECS_UNTIL_NEW_CKPT: tf.logging.info( 'We\'ve been decoding with same checkpoint for %i seconds. Time to load new checkpoint', t1 - t0) _ = util.load_ckpt(self._saver, self._sess) t0 = time.time()