def finalize_evaluation(self, results_per_batch, training_step=None): total_word_lev = 0.0 total_word_count = 0.0 for word_lev, word_count in results_per_batch: total_word_lev += word_lev total_word_count += word_count total_wer = 1.0 * total_word_lev / total_word_count deco_print("Validation WER: {:.4f}".format(total_wer), offset=4) return {"Eval WER": total_wer}
def after_run(self, run_context, run_values): results, step = run_values.results self._iter_count = step if not results: return self._timer.update_last_triggered_step(self._iter_count - 1) if self._model.steps_in_epoch is None: deco_print("Global step {}:".format(step), end=" ") else: deco_print("Epoch {}, global step {}:".format( step // self._model.steps_in_epoch, step), end=" ") loss = results[0] deco_print("Train loss: {:.4f}".format(loss), offset=4) tm = (time.time() - self._last_time) / self._every_steps m, s = divmod(tm, 60) h, m = divmod(m, 60) deco_print("time per step = {}:{:02}:{:.3f}".format(int(h), int(m), s), start=" ") self._last_time = time.time()
def _build_forward_pass_graph(self, input_tensors, gpu_id = 0): """ This function connects encoder, decoder and loss together. As an input for encoder it will specify source tensors ( as returned from the data layer). As an input for decoder it will specify target tensors as well as all output returned from encoder. As an input for loss it will specify target tensors and all output returned from decoder. Inputs input_tensors(dict): "source_tensors" "target_tensors" (train or eval) Returns tuple: tuple containing loss tensor as returned from loss.compute_loss() and list of output tensors, which is taken from decoder.decode()["outputs"] """ if not isinstance(input_tensors, dict) or "source_tensors" not in input_tensors: raise ValueError("input tensors should be a dict containing 'source_tensors' key") if not isinstance(input_tensors["source_tensors"], list): raise ValueError("source_tensors should be a list") source_tensors = input_tensors["source_tensors"] if self.mode == "train" or self.mode == "eval": if "target_tensors" not in input_tensors: raise ValueError("Input tensors should contain 'target_tensors' key") if not isinstance(input_tensors["target_tensors"], list): raise ValueError("target_tensors should be a list") target_tensors = input_tensors["target_tensors"] with tf.variable_scope("ForwardPass"): encoder_input = {"source_tensors": source_tensors} encoder_output = self.encoder.encode(input_dict = encoder_input) decoder_input = {"encoder_output": encoder_output} if self.mode == "train" or self.mode == "eval": decoder_input["target_tensors"] = target_tensors decoder_output = self.decoder.decode(input_dict = decoder_input) model_outputs = decoder_output.get("outputs", None) if self.mode == "train" or self.mode == "eval": with tf.variable_scope("Loss"): loss_input_dict = { "decoder_output": decoder_output, "target_tensors": target_tensors} loss = self.loss_computator.compute_loss(loss_input_dict) else: deco_print("Inference mode, Loss part of graph isn't build") loss = None return loss, model_outputs
def _build_forward_pass_graph(self, input_tensors, gpu_id=0): if not isinstance(input_tensors, dict): raise ValueError( "Input tensors should be dict containing 'source_tensors' key") if not isinstance(input_tensors["source_tensors"], list): raise ValueError("source tensors should be a list") source_tensors = input_tensors["source_tensors"] if self.mode == "train" or self.mode == "eval": if "target_tensors" not in input_tensors: raise ValueError( "Input tensors should contain 'target_tensors' key in train and eval mode" ) if not isinstance(input_tensors["target_tensors"], list): raise ValueError("target_tensors should be a list") target_tensors = input_tensors["target_tensors"] with tf.variable_scope("ForwardPass"): """ 这里的self.encoder是DeepSpeech2Encoder类的实例 self.decoder是FullyConnectedCTCDecoder类的实例 """ encoder_input = {"source_tensors": source_tensors} encoder_output = self.encoder.encode(input_dict=encoder_input) decoder_input = {"encoder_output": encoder_output} if self.mode == "train" or self.mode == "eval": decoder_input["target_tensors"] = target_tensors decoder_output = self.decoder.decode(input_dict=decoder_input) model_outputs = decoder_output.get("outputs", None) if self.mode == "train" or self.mode == "eval": with tf.variable_scope("Loss"): loss_input_dict = { "decoder_output": decoder_output, "target_tensors": target_tensors } loss = self.loss_computator.compute_loss(loss_input_dict) else: deco_print("Inference Mode. Loss part of graph isn't built.") loss = None return loss, model_outputs
def main(): """ Parse args and create config e.g. python3 run.py --mode=train --config_file=config/ds2_small_1gpu.py """ import sys args, base_config, base_model, config_module = get_base_config(sys.argv[1:]) # load_model: model directory load_model = base_config.get('load_model', None) restore_best_checkpoint = base_config.get('restore_best_checkpoint', False) base_ckpt_dir = check_base_model_logdir(load_model, args, restore_best_checkpoint) base_config['load_model'] = base_ckpt_dir checkpoint = check_logdir(args, base_config, restore_best_checkpoint) if args.enable_logs: old_stdout, old_stderr, stdout_log, stderr_log = create_logdir(args, base_config) base_config["logdir"] = os.path.join(base_config["logdir"], 'logs') if args.mode == "train": if checkpoint is None: if base_ckpt_dir: deco_print("Starting training from the base model") else: deco_print("Starting training from scratch") else: deco_print("Resroring checkpoint from {}".format(checkpoint)) elif args.mode == "eval" or args.mode == "infer": deco_print("Loading model from {}".format(checkpoint)) # Create model and train/eval with tf.Graph().as_default(): model = create_model(args, base_config, config_module, base_model, checkpoint) print(model) # sys.exit(0) if args.mode == "train": train(model, eval_model = None, debug_port = None) elif args.mode == "eval": evaluate(model, checkpoint) elif args.mode == "infer": infer(model, checkpoint, args.infer_output_file) if args.enable_logs: sys.stdout = old_stdout sys,stderr = old_stderr stdout_log.close() stderr_log.close()
def maybe_print_logs(self, input_values, output_values, training_step): y, len_y = input_values["target_tensors"] decoded_sequence = output_values y_one_sample = y[0] len_y_one_sample = len_y[0] decoded_sequence_one_batch = decoded_sequence[0] if self.is_bpe: dec_list = sparse_tensor_to_chars_bpe( decoded_sequence_one_batch)[0] true_text = self.get_data_layer().sp.DecodeIds( y_one_sample[:len_y_one_sample].tolist()) pred_text = self.get_data_layer().sp.DecodeIds(dec_list) else: true_text = "".join( map(self.get_data_layer().params["idx2char"].get, y_one_sample[:len_y_one_sample])) pred_text = "".join( self.tensor_to_chars(decoded_sequence_one_batch, self.get_data_layer().params["idx2char"], **self.tensor_to_char_params)[0]) sample_wer = levenshtein(true_text.split(), pred_text.split()) / len( true_text.split()) self.autoaregressive = self.get_data_layer().params.get( "autoaregressive", False) self.plot_attention = False deco_print("Sample WER: {:.4f}".format(sample_wer), offset=4) deco_print("Sample target: " + true_text, offset=4) deco_print("Sample prediction: " + pred_text, offset=4) return {"Sample WER": sample_wer}
def compile(self, force_var_reuse=False, checkpoint=None): """ Tensorflow graph is built here. """ if "initializer" not in self.params: initializer = None else: init_dict = self.params.get("initializer_params", {}) initializer = self.params["initializer"](**init_dict) losses = [] for gpu_cnt, gpu_id in enumerate(self._gpu_ids): """ 如果GPU>=2,启用reuse模式,即多个GPU上的图共用相同名称的变量 单个GPU的话共用没有意义,所以这里用gpu_cnt>0判断一下 """ with tf.device("/gpu:{}".format(gpu_id)), tf.variable_scope( name_or_scope=tf.get_variable_scope(), reuse=force_var_reuse or (gpu_cnt > 0), initializer=initializer, dtype=self.get_tf_dtype()): deco_print("Building graph on GPU:{}".format(gpu_id)) if self._interactive: self.get_data_layer( gpu_cnt).create_interactive_placeholders() else: self.get_data_layer(gpu_cnt).build_graph() input_tensors = self.get_data_layer(gpu_cnt).input_tensors """ _build_forward_pass_graph 在Speech2Text中实现 """ loss, self._outputs[gpu_cnt] = self._build_forward_pass_graph( input_tensors, gpu_id=gpu_cnt) if self._outputs[gpu_cnt] is not None and not isinstance( self._outputs[gpu_cnt], list): raise ValueError( "Decoder outputs have to be either None or list") if self._mode == "train" or self._mode == "eval": losses.append(loss) # end of for gpu_ind loop if self._mode == "train": self.loss = tf.reduce_mean(losses) if self._mode == "eval": self.eval_losses = losses try: self._num_objects_per_step = [ self._get_num_objects_per_step(worker_id) for worker_id in range(self.num_gpus) ] except NotImplementedError: pass if self._mode == "train": if "lr_policy" not in self.params: lr_policy = None else: lr_params = self.params.get("lr_policy_params", {}) func_params = signature(self.params["lr_policy"]).parameters if "decay_steps" in func_params and "decay_steps" not in lr_params: lr_params["decay_steps"] = self._last_step if "begin_decay_at" in func_params: if "warmup_steps" in func_params: lr_params["begin_decay_at"] = max( lr_params.get("begin_decay_at", 0), lr_prams.get("warmup_steps", 0)) lr_params["decay_steps"] -= lr_params.get( "begin_decay_at", 0) if "steps_per_epoch" in func_params and "steps_per_epoch" not in lr_params and "num_epochs" in self.params: lr_params["steps_per_epoch"] = self.steps_in_epoch lr_policy = lambda gs: self.params["lr_policy"](global_step=gs, **lr_params) if self.params.get("iter_size", 1) > 1: self.skip_update_ph = tf.placeholder(tf.bool) var_list = tf.trainable_variables() freeze_variables_regex = self.params.get("freeze_variables_regex", None) if freeze_variables_regex is not None: pattern = re.compile(freeze_variables_regex) var_list = [ var for var in tf.trainable_variables() if not pattern.match(var.name) ] self.train_op, _ = optimize_loss( loss=tf.cast(self.loss, tf.float32), dtype=self.params["dtype"], optimizer=self.params["optimizer"], optimizer_params=self.params["optimizer_params"], var_list=var_list, clip_gradients=self.params.get("max_grad_norm", None), learning_rate_decay_fn=lr_policy, summaries=self.params.get("summaries", None), larc_params=self.params.get("larc_params", None), loss_scaling=self.params.get("loss_scaling", 1.0), loss_scaling_params=self.params.get("loss_scaling_params", None), iter_size=self.params.get("iter_size", 1), skip_update_ph=self.skip_update_ph, model=self) tf.summary.scalar(name="train_loss", tensor=self.loss) if self.steps_in_epoch: tf.summary.scalar( name="epoch", tensor=tf.floor( tf.train.get_global_step() / tf.constant(self.steps_in_epoch, dtype=tf.int64))) if freeze_variables_regex is not None: deco_print("Complete list of variables:") for var in tf.trainable_variables(): deco_print("{}".format(var.name), offset=2) deco_print("Trainable variables:") total_params = 0 unknown_shapes = False for var in var_list: var_params = 1 deco_print("{}".format(var.name), offset=2) deco_print("shape: {}, {}".format(var.get_shape(), var.dtype), offset=2) if var.get_shape(): for dim in var.get_shape(): var_params *= dim.value total_params += var_params else: unknown_shapes = True if unknown_shapes: deco_print( "Encountered unknown variable shape, can't compute total number of parameters" ) else: deco_print( "Total trainable parameters: {}".format(total_params))