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 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.global_variables_initializer()) # 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(cf.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 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() saver = tf.train.Saver(max_to_keep=3) sess = tf.Session(config=util.get_config()) # make a subdir of the root dir for eval data eval_dir = os.path.join(cf.log_root, "eval") # this is where checkpoints of best models are saved bestmodel_save_path = os.path.join(eval_dir, 'bestmodel') 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 cf.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 __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() 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) if cf.single_pass: # Make a descriptive decode directory name # this is something of the form "ckpt-123456" ckpt_name = "ckpt-" + ckpt_path.split('-')[-1] self._decode_dir = os.path.join(cf.log_root, get_decode_dir_name(ckpt_name)) if os.path.exists(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(cf.log_root, "decode") # Make the decode dir if necessary if not os.path.exists(self._decode_dir): os.mkdir(self._decode_dir) if cf.single_pass: # Make the dirs to contain output written in the correct format for pyrouge self._rouge_ref_dir = os.path.join(self._decode_dir, "reference") if not os.path.exists(self._rouge_ref_dir): os.mkdir(self._rouge_ref_dir) self._rouge_dec_dir = os.path.join(self._decode_dir, "decoded") if not os.path.exists(self._rouge_dec_dir): os.mkdir(self._rouge_dec_dir)
def decode(self): """ Decode examples until data is exhausted (if cf.single_pass) and return, or decode indefinitely, loading latest checkpoint at regular intervals """ t0 = time.time() start_time = t0 counter = 0 while True: # 1 example repeated across batch batch = self._batcher.next_batch() if batch is None: # finished decoding dataset in single_pass mode assert cf.single_pass, "Dataset exhausted, but we are not in single_pass mode" tf.logging.info( "Decoder has finished reading dataset for single_pass, using %d seconds.", time.time() - start_time) tf.logging.info( "Output has been saved in %s and %s. Now starting ROUGE eval...", self._rouge_ref_dir, self._rouge_dec_dir) #todo: need to update for rouge # results_dict = rouge_eval(self._rouge_ref_dir, # self._rouge_dec_dir) # rouge_log(results_dict, self._decode_dir) return original_context = batch.original_contexts[0] # string original_query = batch.original_querys[0] original_summarization = batch.original_summarizations[0] # string #original_abstract_sents = batch.original_abstracts_sents[ # 0] # list of strings context_withunks = data.show_art_oovs(original_context, self._vocab) abstract_withunks = data.show_abs_oovs( original_summarization, self._vocab, (batch.art_oovs[0] if cf.pointer_gen else None)) # string # Run beam search to get best Hypothesis best_hyp = beam_search.run_beam_search(self._sess, self._model, self._vocab, batch) # export_path = os.path.join(cf.export_dir,str(cf.export_version)) # 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 cf.pointer_gen else None)) # Remove the [STOP] token from decoded_words, if necessary try: # index of the (first) [STOP] symbol fst_stop_idx = decoded_words.index(data.MARK_EOS) decoded_words = decoded_words[:fst_stop_idx] except ValueError: decoded_words = decoded_words decoded_output = ''.join(decoded_words) # single string if cf.single_pass: #todo: need to check # write ref summary and decoded summary to file, to eval with pyrouge later self.write_result(original_context, original_summarization, decoded_words, counter) # self.write_for_eval(original_summarization, output_ids, # counter) counter += 1 # this is how many examples we've decoded else: # log output to screen print_results(context_withunks, abstract_withunks, decoded_output) # write info to .json file for visualization tool self.write_for_attnvis(context_withunks, abstract_withunks, decoded_words, best_hyp.attn_dists) # 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()