Source code for das.train_tune

"""Code for tuning the hyperparameters of a network."""
# write custom Tuner that generates datasets and overlap based nb_hist and kernel params
# see:
import time
import logging
import flammkuchen as fl
import numpy as np
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard
from tensorflow import keras
import keras_tuner as kt
import yaml
import os
import sys
from typing import List, Optional, Tuple, Dict, Any
from . import data, models, utils, predict, io, evaluate, tracking, data_hash

logger = logging.getLogger(__name__)

try:  # fixes cuDNN error when using LSTM layer
    import tensorflow as tf

    physical_devices = tf.config.list_physical_devices("GPU")
    if physical_devices:
        tf.config.experimental.set_memory_growth(physical_devices[0], enable=True)
except Exception as e:

[docs]class TunableModel(kt.HyperModel): def __init__(self, params, tune_config=None): self.params = params.copy() self.tune_config = tune_config if self.tune_config is None: self.tune_config = { "nb_filters": [4, 8, 16, 32, 64], "kernel_size": [4, 8, 16, 32, 64], "learning_rate": [0.01, 0.001, 0.0001], "nb_hist": [128, 256, 512, 1024, 2048], "nb_conv": [1, 2, 3, 4, 6, 8], }
[docs] def build(self, hp): for name, values in self.tune_config.items(): hp.Choice(name, values=values) self.params.update(hp.values) model = models.model_dict[self.params["model_name"]](**self.params) return model
[docs]class DasTuner(kt.Tuner): def __init__(self, params, *args, tracker=None, **kwargs): self.params = params.copy() self.tracker = tracker super().__init__(*args, **kwargs)
[docs] def run_trial( self, trial, train_x, train_y, val_x, val_y, epochs=10, steps_per_epoch=None, verbose=1, class_weight=None, callbacks=None, ): try: if callbacks is None: callbacks = [] self.params.update(trial.hyperparameters.values) self.current_trial = trial # these need updating based on current hyperparameters self.params["stride"] = self.params["nb_hist"] if self.params["ignore_boundaries"]: # this does not completely avoid boundary effects but should minimize them sufficiently self.params["data_padding"] = int(np.ceil(self.params["kernel_size"] * self.params["nb_conv"])) self.params["stride"] = self.params["stride"] - 2 * self.params["data_padding"] data_gen = data.AudioSequence( train_x, train_y, shuffle=True, nb_repeats=1, last_sample=train_x.shape[0] - 2 * self.params["nb_hist"], **self.params, ) val_gen = data.AudioSequence(val_x, val_y, shuffle=False, **self.params)"Data:")"training: {data_gen}")"validation: {val_gen}")"Hyperparameters:") if steps_per_epoch is None: steps_per_epoch = min(len(data_gen), 1000) if self.tracker is not None: self.tracker.reinit(self.params) model = model.params = self.params # attach params to model so we can save them in ModelParamsCheckpoint fit_hist = data_gen, validation_data=val_gen, epochs=epochs, steps_per_epoch=steps_per_epoch, callbacks=callbacks, verbose=verbose, ) return fit_hist except KeyboardInterrupt: logger.exception("Interrupted") sys.exit(0) except: logger.exception("Something went wrong. Will try to continue.") self.current_trial.status = "INVALID"
[docs]def train( *, data_dir: str, x_suffix: str = "", y_suffix: str = "", save_dir: str = "./", save_prefix: Optional[str] = None, save_name: Optional[str] = None, model_name: str = "tcn", nb_filters: int = 16, kernel_size: int = 16, nb_conv: int = 3, use_separable: List[bool] = False, nb_hist: int = 1024, ignore_boundaries: bool = True, batch_norm: bool = True, nb_pre_conv: int = 0, pre_nb_dft: int = 64, pre_kernel_size: int = 3, pre_nb_filters: int = 16, upsample: bool = True, dilations: Optional[List[int]] = None, nb_lstm_units: int = 0, verbose: int = 2, batch_size: int = 32, nb_epoch: int = 400, learning_rate: Optional[float] = None, reduce_lr: bool = False, reduce_lr_patience: int = 5, fraction_data: Optional[float] = None, seed: Optional[int] = None, batch_level_subsampling: bool = False, augmentations: Optional[str] = None, tensorboard: bool = False, wandb_api_token: Optional[str] = None, wandb_project: Optional[str] = None, wandb_entity: Optional[str] = None, log_messages: bool = False, nb_stacks: int = 2, with_y_hist: bool = True, balance: bool = False, version_data: bool = True, tune_config: Optional[str] = None, nb_tune_trials: int = 1_000, _qt_progress: bool = False, ) -> Tuple[keras.Model, Dict[str, Any]]: """Tune the hyperparameters of a DAS network. Args: data_dir (str): Path to the directory or file with the dataset for training. Accepts npy-dirs (recommended), h5 files or zarr files. See documentation for how the dataset should be organized. x_suffix (str): Select dataset used for training in the data_dir by suffix (`y_` + X_SUFFIX). Defaults to '' (will use the standard data 'x') y_suffix (str): Select dataset used as a training target in the data_dir by suffix (`y_` + Y_SUFFIX). Song-type specific targets can be created with a training dataset, Defaults to '' (will use the standard target 'y') save_dir (str): Directory to save training outputs. The path of output files will constructed from the SAVE_DIR, an optional SAVE_PREFIX, and the time stamp of the start of training. Defaults to the current directory ('./'). save_prefix (Optional[str]): Prepend to timestamp. Name of files created will be start with SAVE_DIR/SAVE_PREFIX + "_" + TIMESTAMP or with SAVE_DIR/TIMESTAMP if SAVE_PREFIX is empty. Defaults to '' (empty). save_name (Optional[str]): Append to prefix. Name of files created will be start with SAVE_DIR/SAVE_PREFIX + "_" + SAVE_NAME or with SAVE_DIR/SAVE_NAME if SAVE_PREFIX is empty. Defaults to TIMESTAMP. model_name (str): Network architecture to use. Use `tcn` (TCN) or `tcn_stft` (TCN with STFT frontend). See das.models for a description of all models. Defaults to `tcn`. nb_filters (int): Number of filters per layer. Defaults to 16. kernel_size (int): Duration of the filters (=kernels) in samples. Defaults to 16. nb_conv (int): Number of TCN blocks in the network. Defaults to 3. use_separable (List[bool]): Specify which TCN blocks should use separable convolutions. Provide as a space-separated sequence of "False" or "True. For instance: "True False False" will set the first block in a three-block (as given by nb_conv) network to use separable convolutions. Defaults to False (no block uses separable convolutions). nb_hist (int): Number of samples processed at once by the network (a.k.a chunk duration). Defaults to 1024 samples. ignore_boundaries (bool): Minimize edge effects by discarding predictions at the edges of chunks. Defaults to True. batch_norm (bool): Batch normalize. Defaults to True. nb_pre_conv (int): Adds fronted with downsampling. The downsampling factor is `2**nb_pre_conv`. The type of frontend depends on the model: if model is `tcn`: adds a frontend of N conv blocks (conv-relu-batchnorm-maxpool2) to the TCN. if model is `tcn_tcn`: adds a frontend of N TCN blocks to the TCN. if model is `tcn_stft`: adds a trainable STFT frontend. Defaults to 0 (no frontend, no downsampling). pre_nb_dft (int): Duration of filters (in samples) for the STFT frontend. Number of filters is pre_nb_dft // 2 + 1. Defaults to 64. pre_nb_filters (int): Number of filters per layer in the pre-processing TCN. Defaults to 16. pre_kernel_size (int): Duration of filters (=kernels) in samples in the pre-processing TCN. Defaults to 3. upsample (bool): whether or not to restore the model output to the input samplerate. Should generally be True during training and evaluation but my speed up inference. Defaults to True. dilations (List[int]): List of dilation rate, defaults to [1, 2, 4, 8, 16] (5 layer with 2x dilation per TCN block) nb_lstm_units (int): If >0, adds LSTM with `nb_lstm_units` LSTM units to the output of the stack of TCN blocks. Defaults to 0 (no LSTM layer). verbose (int): Verbosity of training output (0 - no output during training, 1 - progress bar, 2 - one line per epoch). Defaults to 2. batch_size (int): Batch size Defaults to 32. nb_epoch (int): Maximal number of training epochs. Training will stop early if validation loss did not decrease in the last 20 epochs. Defaults to 400. learning_rate (Optional[float]): Learning rate of the model. Defaults should work in most cases. Values typically range between 0.1 and 0.00001. If None, uses model specific defaults: `tcn` 0.0001, `tcn_stft` and `tcn_tcn` 0.0005. Defaults to None. reduce_lr (bool): Reduce learning rate when the validation loss plateaus. Defaults to False. reduce_lr_patience (int): Number of epochs w/o a reduction in validation loss after which to trigger a reduction in learning rate. Defaults to 5 epochs. fraction_data (Optional[float]): Fraction of training and validation data to use. Defaults to 1.0. seed (Optional[int]): Random seed to reproducibly select fractions of the data. Defaults to None (no seed). batch_level_subsampling (bool): Select fraction of data for training from random subset of shuffled batches. If False, select a continuous chunk of the recording. Defaults to False. tensorboard (bool): Write tensorboard logs to save_dir. Defaults to False. wandb_api_token (Optional[str]): API token for logging to wandb. Defaults to None (no logging to wandb). wandb_project (Optional[str]): Project to log to for wandb. Defaults to None (no logging to wandb). wandb_entity (Optional[str]): Entity (user or team) to log to for wandb. Defaults to None (no logging to wandb). log_messages (bool): Sets terminal logging level to INFO. Defaults to False (will follow existing settings). nb_stacks (int): Unused if model name is `tcn`, `tcn_tcn`, or `tcn_stft`. Defaults to 2. with_y_hist (bool): Unused if model name is `tcn`, `tcn_tcn`, or `tcn_stft`. Defaults to True. balance (bool): Balance data. Weights class-wise errors by the inverse of the class frequencies. Defaults to False. version_data (bool): Save MD5 hash of the data_dir to log and params.yaml. Defaults to True (set to False for large datasets since it can be slow). tune_config (Optional[str]): Yaml file with key:value pairs defining the search space for tuning. Keys are parameter names, values are lists of possible parameter values. nb_tune_trials (int): Number of model variants to test during hyper parameter tuning. Defaults to 1_000. Returns model (keras.Model) params (Dict[str, Any]) """ if log_messages: logging.basicConfig(level=logging.INFO) # FIXME THIS IS NOT GREAT: sample_weight_mode = None data_padding = 0 if with_y_hist: # regression return_sequences = True stride = nb_hist y_offset = 0 sample_weight_mode = "temporal" if ignore_boundaries: data_padding = int( np.ceil(kernel_size * nb_conv) ) # this does not completely avoid boundary effects but should minimize them sufficiently stride = stride - 2 * data_padding else: # classification return_sequences = False stride = 1 # should take every sample, since sampling rates of both x and y are now the same y_offset = int(round(nb_hist / 2)) if stride <= 0: raise ValueError( "Stride <=0 - needs to be >0. Possible solutions: reduce kernel_size, increase nb_hist parameters, uncheck ignore_boundaries" ) if not upsample: output_stride = int(2**nb_pre_conv) else: output_stride = 1 # since we upsample output to original sampling rate. w/o upsampling: `output_stride = int(2**nb_pre_conv)` since each pre-conv layer does 2x max pooling if save_prefix is None: save_prefix = "" if len(save_prefix): save_prefix = save_prefix + "_" params = locals() del params["_qt_progress"] if tune_config is not None: with open(tune_config, "r") as stream: try: tune_config = yaml.safe_load(stream) except yaml.YAMLError as exc: logger.exception(exc) if stride <= 0: raise ValueError( "Stride <=0 - needs to be >0. Possible solutions: reduce kernel_size, increase nb_hist parameters, uncheck ignore_boundaries" ) # remove learning rate param if not set so the value from the model def is used if params["learning_rate"] is None: del params["learning_rate"] if "_multi" in model_name: params["unpack_channels"] = True"Loading data from {data_dir}.") d = io.load(data_dir, x_suffix=x_suffix, y_suffix=y_suffix) # FIXME throw error if no `val` in `d`` if "val" not in d: logging.error("No validation data found.") raise ValueError("No validation data found.") params.update(d.attrs) # add metadata from data.attrs to params for saving if version_data: params["data_hash"] = data_hash.hash_data(data_dir)"Version of the data:")" MD5 hash of {data_dir} is")" {params['data_hash']}") if fraction_data is not None: if fraction_data > 1.0: # seconds f"{fraction_data} seconds corresponds to {fraction_data / (d['train']['x'].shape[0] / d.attrs['samplerate_x_Hz']):1.4f} of the training data." ) fraction_data = np.min((fraction_data / (d["train"]["x"].shape[0] / d.attrs["samplerate_x_Hz"]), 1.0)) elif fraction_data < 1.0:"Using {fraction_data:1.4f} of the training and validation data.") if fraction_data is not None and not batch_level_subsampling and fraction_data != 1.0: # train on a subset min_nb_samples = nb_hist * (batch_size + 2) # ensure the generator contains at least one full batch first_sample_train, last_sample_train = data.sub_range( d["train"]["x"].shape[0], fraction_data, min_nb_samples, seed=seed ) first_sample_val, last_sample_val = data.sub_range(d["val"]["x"].shape[0], fraction_data, min_nb_samples, seed=seed) else: first_sample_train, last_sample_train = 0, None first_sample_val, last_sample_val = 0, None # TODO clarify nb_channels, nb_freq semantics - always [nb_samples,..., nb_channels] - nb_freq is ill-defined for 2D data params.update( { "nb_freq": d["train"]["x"].shape[1], "nb_channels": d["train"]["x"].shape[-1], "nb_classes": len(params["class_names"]), "first_sample_train": first_sample_train, "last_sample_train": last_sample_train, "first_sample_val": first_sample_val, "last_sample_val": last_sample_val, } )"Parameters:")"Preparing data") if fraction_data is not None and batch_level_subsampling: # train on a subset np.random.seed(seed) shuffle_subset = fraction_data else: shuffle_subset = None params["class_weights"] = None if balance:"Balancing classes:")" Computing class weights.") params["class_weights"] = data.compute_class_weights(d["train"]["y"][first_sample_train:last_sample_train])" {params['class_weights']}")"Building network") os.makedirs(os.path.abspath(save_dir), exist_ok=True) if save_name is None: save_name = time.strftime("%Y%m%d_%H%M%S") save_name = "{0}/{1}{2}".format(save_dir, save_prefix, save_name) params["save_name"] = save_name"Will save to {save_name}.") # setup callbacks callbacks = [EarlyStopping(monitor="val_loss", patience=15)] if reduce_lr: callbacks.append(ReduceLROnPlateau(patience=reduce_lr_patience, verbose=1)) if _qt_progress: callbacks.append(utils.QtProgressCallback(nb_epoch, _qt_progress)) if tensorboard: callbacks.append(TensorBoard(log_dir=save_name)) if wandb_api_token and wandb_project: # could also get those from env vars! del params["wandb_api_token"] wandb = tracking.Wandb(wandb_project, wandb_api_token, wandb_entity, params) if wandb: callbacks.append(wandb.callback()) else: wandb = None tuner = DasTuner( params=params, oracle=kt.oracles.BayesianOptimization( objective=kt.Objective("val_loss", "min"), max_trials=nb_tune_trials, ), hypermodel=TunableModel(params, tune_config), overwrite=False, directory=save_dir, project_name=os.path.basename(save_name), tracker=wandb, ) utils.save_params(params, save_name) # TRAIN NETWORK"Start hyperparameter tuning") train_x=d["train"]["x"], train_y=d["train"]["y"], val_x=d["val"]["x"], val_y=d["val"]["y"], epochs=nb_epoch, verbose=verbose, callbacks=callbacks, class_weight=params["class_weights"], ) # update params with best hp best_hp = tuner.get_best_hyperparameters()[0].values params.update(best_hp)" Saving best params and model to {params['save_name']}.") utils.save_params(params, params["save_name"]) model = models.model_dict[params["model_name"]](**params)["save_name"] + "_model.h5") # TEST if "test" not in d or len(d["test"]["x"]) < nb_hist:" No test data - skipping final evaluation step.") else:" Re-loading last best model from {params['save_name']}.")" Predicting") x_test, y_test, y_pred = evaluate.evaluate_probabilities(x=d["test"]["x"], y=d["test"]["y"], model=model, params=params) labels_test = predict.labels_from_probabilities(y_test) labels_pred = predict.labels_from_probabilities(y_pred)" Evaluating") conf_mat, report = evaluate.evaluate_segments(labels_test, labels_pred, params["class_names"], report_as_dict=True) params["conf_mat"] = conf_mat.tolist() params["report"] = report' Updating params file "{save_name}_params.yaml" with the test results.') utils.save_params(params, save_name) if wandb_api_token and wandb_project: # could also get those from env vars! wandb.log_test_results(report) save_filename = "{0}_results.h5".format(save_name)" Saving to " + save_filename + ".") ddd = { "fit_hist": [], "confusion_matrix": conf_mat, "classification_report": report, "x_test": x_test, "y_test": y_test, "y_pred": y_pred, "labels_test": labels_test, "labels_pred": np.array(labels_pred), "params": params, }, ddd)"DONE.") return model, params, tuner