torch_model.py

import numpy as np
import torch
try:
    import torch.utils.tensorboard
    _has_tensorboard = True
except:
    _has_tensorboard = False
import time
import logging
import os
import datetime

from deepchem.data import Dataset, NumpyDataset
from deepchem.metrics import Metric
from deepchem.models.losses import Loss
from deepchem.models.models import Model
from deepchem.models.optimizers import Adam, Optimizer, LearningRateSchedule
from deepchem.trans import Transformer, undo_transforms
from deepchem.utils.evaluate import GeneratorEvaluator

from collections.abc import Sequence as SequenceCollection
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
from deepchem.utils.typing import LossFn, OneOrMany
from deepchem.models.wandblogger import WandbLogger

try:
    import wandb
    wandb.ensure_configured()
    if wandb.api.api_key is None:
        _has_wandb = False
        wandb.termwarn(
            "W&B installed but not logged in.  Run `wandb login` or set the WANDB_API_KEY env variable."
        )
    else:
        _has_wandb = True
except (ImportError, AttributeError):
    _has_wandb = False

logger = logging.getLogger(__name__)


class TorchModel(Model):
    """This is a DeepChem model implemented by a PyTorch model.

    Here is a simple example of code that uses TorchModel to train
    a PyTorch model on a DeepChem dataset.

    >>> import torch
    >>> import deepchem as dc
    >>> import numpy as np
    >>> X, y = np.random.random((10, 100)), np.random.random((10, 1))
    >>> dataset = dc.data.NumpyDataset(X=X, y=y)
    >>> pytorch_model = torch.nn.Sequential(
    ...   torch.nn.Linear(100, 1000),
    ...   torch.nn.Tanh(),
    ...   torch.nn.Linear(1000, 1))
    >>> model = dc.models.TorchModel(pytorch_model, loss=dc.models.losses.L2Loss())
    >>> loss = model.fit(dataset, nb_epoch=5)

    The loss function for a model can be defined in two different
    ways.  For models that have only a single output and use a
    standard loss function, you can simply provide a
    dc.models.losses.Loss object.  This defines the loss for each
    sample or sample/task pair.  The result is automatically
    multiplied by the weights and averaged over the batch.

    For more complicated cases, you can instead provide a function
    that directly computes the total loss.  It must be of the form
    f(outputs, labels, weights), taking the list of outputs from
    the model, the expected values, and any weight matrices.  It
    should return a scalar equal to the value of the loss function
    for the batch.  No additional processing is done to the
    result; it is up to you to do any weighting, averaging, adding
    of penalty terms, etc.

    You can optionally provide an output_types argument, which
    describes how to interpret the model's outputs.  This should
    be a list of strings, one for each output. You can use an
    arbitrary output_type for a output, but some output_types are
    special and will undergo extra processing:

    - 'prediction': This is a normal output, and will be returned by predict().
        If output types are not specified, all outputs are assumed
        to be of this type.

    - 'loss': This output will be used in place of the normal
        outputs for computing the loss function.  For example,
        models that output probability distributions usually do it
        by computing unbounded numbers (the logits), then passing
        them through a softmax function to turn them into
        probabilities.  When computing the cross entropy, it is more
        numerically stable to use the logits directly rather than
        the probabilities.  You can do this by having the model
        produce both probabilities and logits as outputs, then
        specifying output_types=['prediction', 'loss'].  When
        predict() is called, only the first output (the
        probabilities) will be returned.  But during training, it is
        the second output (the logits) that will be passed to the
        loss function.

    - 'variance': This output is used for estimating the
        uncertainty in another output.  To create a model that can
        estimate uncertainty, there must be the same number of
        'prediction' and 'variance' outputs.  Each variance output
        must have the same shape as the corresponding prediction
        output, and each element is an estimate of the variance in
        the corresponding prediction.  Also be aware that if a model
        supports uncertainty, it MUST use dropout on every layer,
        and dropout most be enabled during uncertainty prediction.
        Otherwise, the uncertainties it computes will be inaccurate.

    - other: Arbitrary output_types can be used to extract outputs
        produced by the model, but will have no additional
        processing performed.
    """

    def __init__(self,
                 model: torch.nn.Module,
                 loss: Union[Loss, LossFn],
                 output_types: Optional[List[str]] = None,
                 batch_size: int = 100,
                 model_dir: Optional[str] = None,
                 learning_rate: Union[float, LearningRateSchedule] = 0.001,
                 optimizer: Optional[Optimizer] = None,
                 tensorboard: bool = False,
                 wandb: bool = False,
                 log_frequency: int = 100,
                 device: Optional[torch.device] = None,
                 regularization_loss: Optional[Callable] = None,
                 wandb_logger: Optional[WandbLogger] = None,
                 **kwargs) -> None:
        """Create a new TorchModel.

        Parameters
        ----------
        model: torch.nn.Module
            the PyTorch model implementing the calculation
        loss: dc.models.losses.Loss or function
            a Loss or function defining how to compute the training loss for each
            batch, as described above
        output_types: list of strings, optional (default None)
            the type of each output from the model, as described above
        batch_size: int, optional (default 100)
            default batch size for training and evaluating
        model_dir: str, optional (default None)
            the directory on disk where the model will be stored.  If this is None,
            a temporary directory is created.
        learning_rate: float or LearningRateSchedule, optional (default 0.001)
            the learning rate to use for fitting.  If optimizer is specified, this is
            ignored.
        optimizer: Optimizer, optional (default None)
            the optimizer to use for fitting.  If this is specified, learning_rate is
            ignored.
        tensorboard: bool, optional (default False)
            whether to log progress to TensorBoard during training
        wandb: bool, optional (default False)
            whether to log progress to Weights & Biases during training
        log_frequency: int, optional (default 100)
            The frequency at which to log data. Data is logged using
            `logging` by default. If `tensorboard` is set, data is also
            logged to TensorBoard. If `wandb` is set, data is also logged
            to Weights & Biases. Logging happens at global steps. Roughly,
            a global step corresponds to one batch of training. If you'd
            like a printout every 10 batch steps, you'd set
            `log_frequency=10` for example.
        device: torch.device, optional (default None)
            the device on which to run computations.  If None, a device is
            chosen automatically.
        regularization_loss: Callable, optional
            a function that takes no arguments, and returns an extra contribution to add
            to the loss function
        wandb_logger: WandbLogger
            the Weights & Biases logger object used to log data and metrics
        """
        super(TorchModel, self).__init__(model=model,
                                         model_dir=model_dir,
                                         **kwargs)
        self.loss = loss  # not used
        self.learning_rate = learning_rate
        self.output_types = output_types  # not used
        if isinstance(loss, Loss):
            self._loss_fn: LossFn = _StandardLoss(self, loss)
        else:
            self._loss_fn = loss
        self.batch_size = batch_size
        if optimizer is None:
            self.optimizer: Optimizer = Adam(learning_rate=learning_rate)
        else:
            self.optimizer = optimizer
        self.tensorboard = tensorboard
        self.regularization_loss = regularization_loss

        # Select a device.

        if device is None:
            if torch.cuda.is_available():
                device = torch.device('cuda')
            elif torch.backends.mps.is_available():
                device = torch.device('mps')
            else:
                device = torch.device('cpu')
        self.device = device
        self.model = model.to(device)

        # W&B logging
        if wandb:
            logger.warning(
                "`wandb` argument is deprecated. Please use `wandb_logger` instead. "
                "This argument will be removed in a future release of DeepChem."
            )
        if wandb and not _has_wandb:
            logger.warning(
                "You set wandb to True but W&B is not installed. To use wandb logging, "
                "run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface."
            )
        self.wandb = wandb and _has_wandb

        self.wandb_logger = wandb_logger
        # If `wandb=True` and no logger is provided, initialize default logger
        if self.wandb and (self.wandb_logger is None):
            self.wandb_logger = WandbLogger()

        # Setup and initialize W&B logging
        if (self.wandb_logger
                is not None) and (not self.wandb_logger.initialized):
            self.wandb_logger.setup()

        # Update config with KerasModel params
        wandb_logger_config = dict(loss=loss,
                                   output_types=output_types,
                                   batch_size=batch_size,
                                   model_dir=model_dir,
                                   learning_rate=learning_rate,
                                   optimizer=optimizer,
                                   tensorboard=tensorboard,
                                   log_frequency=log_frequency,
                                   regularization_loss=regularization_loss)
        wandb_logger_config.update(**kwargs)

        if self.wandb_logger is not None:
            self.wandb_logger.update_config(wandb_logger_config)

        self.log_frequency = log_frequency
        if self.tensorboard and not _has_tensorboard:
            raise ImportError(
                "This class requires tensorboard to be installed.")
        if self.tensorboard:
            self._summary_writer = torch.utils.tensorboard.SummaryWriter(
                self.model_dir)
        if output_types is None:
            self._prediction_outputs = None
            self._loss_outputs = None
            self._variance_outputs = None
            self._other_outputs = None
        else:
            self._prediction_outputs = []
            self._loss_outputs = []
            self._variance_outputs = []
            self._other_outputs = []
            for i, type in enumerate(output_types):
                if type == 'prediction':
                    self._prediction_outputs.append(i)
                elif type == 'loss':
                    self._loss_outputs.append(i)
                elif type == 'variance':
                    self._variance_outputs.append(i)
                else:
                    self._other_outputs.append(i)
            if len(self._loss_outputs) == 0:
                self._loss_outputs = self._prediction_outputs
        self._built = False
        self._output_functions: Dict[Any, Any] = {}
        self._optimizer_for_vars: Dict[Any, Any] = {}

    def _ensure_built(self) -> None:
        """The first time this is called, create internal data structures."""
        if self._built:
            return
        self._built = True
        self._global_step = 0
        self._pytorch_optimizer = self.optimizer._create_pytorch_optimizer(
            self.model.parameters())
        if isinstance(self.optimizer.learning_rate, LearningRateSchedule):
            self._lr_schedule = self.optimizer.learning_rate._create_pytorch_schedule(
                self._pytorch_optimizer)
        else:
            self._lr_schedule = None

    def fit(self,
            dataset: Dataset,
            nb_epoch: int = 10,
            max_checkpoints_to_keep: int = 5,
            checkpoint_interval: int = 1000,
            deterministic: bool = False,
            restore: bool = False,
            variables: Optional[List[torch.nn.Parameter]] = None,
            loss: Optional[LossFn] = None,
            callbacks: Union[Callable, List[Callable]] = [],
            all_losses: Optional[List[float]] = None) -> float:
        """Train this model on a dataset.

        Parameters
        ----------
        dataset: Dataset
            the Dataset to train on
        nb_epoch: int
            the number of epochs to train for
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
        checkpoint_interval: int
            the frequency at which to write checkpoints, measured in training steps.
            Set this to 0 to disable automatic checkpointing.
        deterministic: bool
            if True, the samples are processed in order.  If False, a different random
            order is used for each epoch.
        restore: bool
            if True, restore the model from the most recent checkpoint and continue training
            from there.  If False, retrain the model from scratch.
        variables: list of torch.nn.Parameter
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        all_losses: Optional[List[float]], optional (default None)
            If specified, all logged losses are appended into this list. Note that
            you can call `fit()` repeatedly with the same list and losses will
            continue to be appended.

        Returns
        -------
        The average loss over the most recent checkpoint interval
        """
        return self.fit_generator(
            self.default_generator(dataset,
                                   epochs=nb_epoch,
                                   deterministic=deterministic),
            max_checkpoints_to_keep, checkpoint_interval, restore, variables,
            loss, callbacks, all_losses)

    def fit_generator(self,
                      generator: Iterable[Tuple[Any, Any, Any]],
                      max_checkpoints_to_keep: int = 5,
                      checkpoint_interval: int = 1000,
                      restore: bool = False,
                      variables: Optional[Union[List[torch.nn.Parameter],
                                                torch.nn.ParameterList]] = None,
                      loss: Optional[LossFn] = None,
                      callbacks: Union[Callable, List[Callable]] = [],
                      all_losses: Optional[List[float]] = None) -> float:
        """Train this model on data from a generator.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
        checkpoint_interval: int
            the frequency at which to write checkpoints, measured in training steps.
            Set this to 0 to disable automatic checkpointing.
        restore: bool
            if True, restore the model from the most recent checkpoint and continue training
            from there.  If False, retrain the model from scratch.
        variables: list of torch.nn.Parameter or torch.nn.ParameterList
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
            ParameterList can be used like a regular Python list, but Tensors that are
            `Parameter` are properly registered, and will be visible by all `Module` methods.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        all_losses: Optional[List[float]], optional (default None)
            If specified, all logged losses are appended into this list. Note that
            you can call `fit()` repeatedly with the same list and losses will
            continue to be appended.

        Returns
        -------
        The average loss over the most recent checkpoint interval
        """
        if not isinstance(callbacks, SequenceCollection):
            callbacks = [callbacks]
        self._ensure_built()
        self.model.train()
        avg_loss = 0.0
        last_avg_loss = 0.0
        averaged_batches = 0
        if loss is None:
            loss = self._loss_fn
        if variables is None:
            optimizer = self._pytorch_optimizer
            lr_schedule = self._lr_schedule
        else:
            var_key = tuple(variables)
            if var_key in self._optimizer_for_vars:
                optimizer, lr_schedule = self._optimizer_for_vars[var_key]
            else:
                optimizer = self.optimizer._create_pytorch_optimizer(variables)
                if isinstance(self.optimizer.learning_rate,
                              LearningRateSchedule):
                    lr_schedule = self.optimizer.learning_rate._create_pytorch_schedule(
                        optimizer)
                else:
                    lr_schedule = None
                self._optimizer_for_vars[var_key] = (optimizer, lr_schedule)
        time1 = time.time()

        # Main training loop.

        for batch in generator:
            if restore:
                self.restore()
                restore = False
            inputs: OneOrMany[torch.Tensor]
            inputs, labels, weights = self._prepare_batch(batch)

            # Execute the loss function, accumulating the gradients.

            if isinstance(inputs, list) and len(inputs) == 1:
                inputs = inputs[0]

            optimizer.zero_grad()
            outputs = self.model(inputs)
            if isinstance(outputs, torch.Tensor):
                outputs = [outputs]
            if self._loss_outputs is not None:
                outputs = [outputs[i] for i in self._loss_outputs]
            batch_loss = loss(outputs, labels, weights)
            batch_loss.backward()
            optimizer.step()
            if lr_schedule is not None:
                lr_schedule.step()
            self._global_step += 1
            current_step = self._global_step

            avg_loss += batch_loss

            # Report progress and write checkpoints.
            averaged_batches += 1
            should_log = (current_step % self.log_frequency == 0)
            if should_log:
                avg_loss = float(avg_loss) / averaged_batches
                logger.info('Ending global_step %d: Average loss %g' %
                            (current_step, avg_loss))
                if all_losses is not None:
                    all_losses.append(avg_loss)
                # Capture the last avg_loss in case of return since we're resetting to 0 now
                last_avg_loss = avg_loss
                avg_loss = 0.0
                averaged_batches = 0

            if checkpoint_interval > 0 and current_step % checkpoint_interval == checkpoint_interval - 1:
                self.save_checkpoint(max_checkpoints_to_keep)
            for c in callbacks:
                c(self, current_step)
            if self.tensorboard and should_log:
                self._log_scalar_to_tensorboard('loss', batch_loss,
                                                current_step)
            if (self.wandb_logger is not None) and should_log:
                all_data = dict({'train/loss': batch_loss})
                self.wandb_logger.log_data(all_data, step=current_step)

        # Report final results.
        if averaged_batches > 0:
            avg_loss = float(avg_loss) / averaged_batches
            logger.info('Ending global_step %d: Average loss %g' %
                        (current_step, avg_loss))
            if all_losses is not None:
                all_losses.append(avg_loss)
            last_avg_loss = avg_loss

        if checkpoint_interval > 0:
            self.save_checkpoint(max_checkpoints_to_keep)

        time2 = time.time()
        logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
        return last_avg_loss

    def fit_on_batch(self,
                     X: Sequence,
                     y: Sequence,
                     w: Sequence,
                     variables: Optional[List[torch.nn.Parameter]] = None,
                     loss: Optional[LossFn] = None,
                     callbacks: Union[Callable, List[Callable]] = [],
                     checkpoint: bool = True,
                     max_checkpoints_to_keep: int = 5) -> float:
        """Perform a single step of training.

        Parameters
        ----------
        X: ndarray
            the inputs for the batch
        y: ndarray
            the labels for the batch
        w: ndarray
            the weights for the batch
        variables: list of torch.nn.Parameter
            the variables to train.  If None (the default), all trainable variables in
            the model are used.
        loss: function
            a function of the form f(outputs, labels, weights) that computes the loss
            for each batch.  If None (the default), the model's standard loss function
            is used.
        callbacks: function or list of functions
            one or more functions of the form f(model, step) that will be invoked after
            every step.  This can be used to perform validation, logging, etc.
        checkpoint: bool
            if true, save a checkpoint after performing the training step
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.

        Returns
        -------
        the loss on the batch
        """
        self._ensure_built()
        dataset = NumpyDataset(X, y, w)
        return self.fit(dataset,
                        nb_epoch=1,
                        max_checkpoints_to_keep=max_checkpoints_to_keep,
                        checkpoint_interval=self._global_step +
                        2 if checkpoint else 0,
                        variables=variables,
                        loss=loss,
                        callbacks=callbacks)

    def _predict(self, generator: Iterable[Tuple[Any, Any, Any]],
                 transformers: List[Transformer], uncertainty: bool,
                 other_output_types: Optional[OneOrMany[str]]):
        """
        Predict outputs for data provided by a generator.

        This is the private implementation of prediction.  Do not
        call it directly.  Instead call one of the public prediction
        methods.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        uncertainty: bool
            specifies whether this is being called as part of estimating uncertainty.
            If True, it sets the training flag so that dropout will be enabled, and
            returns the values of the uncertainty outputs.
        other_output_types: list, optional
            Provides a list of other output_types (strings) to predict from model.
        Returns:
            a NumPy array of the model produces a single output, or a list of arrays
            if it produces multiple outputs
        """
        results: Optional[List[List[np.ndarray]]] = None
        variances: Optional[List[List[np.ndarray]]] = None
        if uncertainty and (other_output_types is not None):
            raise ValueError(
                'This model cannot compute uncertainties and other output types simultaneously. Please invoke one at a time.'
            )
        if uncertainty:
            if self._variance_outputs is None or len(
                    self._variance_outputs) == 0:
                raise ValueError('This model cannot compute uncertainties')
            if len(self._variance_outputs) != len(self._prediction_outputs):
                raise ValueError(
                    'The number of variances must exactly match the number of outputs'
                )
        if other_output_types:
            if self._other_outputs is None or len(self._other_outputs) == 0:
                raise ValueError(
                    'This model cannot compute other outputs since no other output_types were specified.'
                )
        self._ensure_built()
        self.model.eval()
        for batch in generator:
            inputs, labels, weights = batch
            inputs, _, _ = self._prepare_batch((inputs, None, None))

            # Invoke the model.
            if isinstance(inputs, list) and len(inputs) == 1:
                inputs = inputs[0]
            output_values = self.model(inputs)
            if isinstance(output_values, torch.Tensor):
                output_values = [output_values]
            output_values = [t.detach().cpu().numpy() for t in output_values]

            # Apply tranformers and record results.
            if uncertainty:
                var = [output_values[i] for i in self._variance_outputs]
                if variances is None:
                    variances = [var]
                else:
                    for i, t in enumerate(var):
                        variances[i].append(t)
            access_values = []
            if other_output_types:
                access_values += self._other_outputs
            elif self._prediction_outputs is not None:
                access_values += self._prediction_outputs

            if len(access_values) > 0:
                output_values = [output_values[i] for i in access_values]

            if len(transformers) > 0:
                if len(output_values) > 1:
                    raise ValueError(
                        "predict() does not support Transformers for models with multiple outputs."
                    )
                elif len(output_values) == 1:
                    output_values = [
                        undo_transforms(output_values[0], transformers)
                    ]
            if results is None:
                results = [[] for i in range(len(output_values))]
            for i, t in enumerate(output_values):
                results[i].append(t)

        # Concatenate arrays to create the final results.
        final_results = []
        final_variances = []
        if results is not None:
            for r in results:
                final_results.append(np.concatenate(r, axis=0))
        if uncertainty and variances is not None:
            for v in variances:
                final_variances.append(np.concatenate(v, axis=0))
            return zip(final_results, final_variances)
        if len(final_results) == 1:
            return final_results[0]
        else:
            return final_results

    def predict_on_generator(
            self,
            generator: Iterable[Tuple[Any, Any, Any]],
            transformers: List[Transformer] = [],
            output_types: Optional[OneOrMany[str]] = None
    ) -> OneOrMany[np.ndarray]:
        """
        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        output_types: String or list of Strings
            If specified, all outputs of this type will be retrieved
            from the model. If output_types is specified, outputs must
            be None.
        Returns:
            a NumPy array of the model produces a single output, or a list of arrays
            if it produces multiple outputs
        """
        return self._predict(generator, transformers, False, output_types)

    def predict_on_batch(
            self,
            X: np.typing.ArrayLike,
            transformers: List[Transformer] = []) -> OneOrMany[np.ndarray]:
        """Generates predictions for input samples, processing samples in a batch.

        Parameters
        ----------
        X: ndarray
            the input data, as a Numpy array.
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.

        Returns
        -------
        a NumPy array of the model produces a single output, or a list of arrays
        if it produces multiple outputs
        """
        dataset = NumpyDataset(X=X, y=None)
        return self.predict(dataset, transformers)

    def predict_uncertainty_on_batch(
            self,
            X: Sequence,
            masks: int = 50) -> OneOrMany[Tuple[np.ndarray, np.ndarray]]:
        """
        Predict the model's outputs, along with the uncertainty in each one.

        The uncertainty is computed as described in https://arxiv.org/abs/1703.04977.
        It involves repeating the prediction many times with different dropout masks.
        The prediction is computed as the average over all the predictions.  The
        uncertainty includes both the variation among the predicted values (epistemic
        uncertainty) and the model's own estimates for how well it fits the data
        (aleatoric uncertainty).  Not all models support uncertainty prediction.

        Parameters
        ----------
        X: ndarray
            the input data, as a Numpy array.
        masks: int
            the number of dropout masks to average over

        Returns
        -------
        for each output, a tuple (y_pred, y_std) where y_pred is the predicted
        value of the output, and each element of y_std estimates the standard
        deviation of the corresponding element of y_pred
        """
        dataset = NumpyDataset(X=X, y=None)
        return self.predict_uncertainty(dataset, masks)

    def predict(
            self,
            dataset: Dataset,
            transformers: List[Transformer] = [],
            output_types: Optional[List[str]] = None) -> OneOrMany[np.ndarray]:
        """
        Uses self to make predictions on provided Dataset object.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        output_types: String or list of Strings
            If specified, all outputs of this type will be retrieved
            from the model. If output_types is specified, outputs must
            be None.

        Returns
        -------
        a NumPy array of the model produces a single output, or a list of arrays
        if it produces multiple outputs
        """
        generator = self.default_generator(dataset,
                                           mode='predict',
                                           pad_batches=False)
        return self.predict_on_generator(generator,
                                         transformers=transformers,
                                         output_types=output_types)

    def predict_embedding(self, dataset: Dataset) -> OneOrMany[np.ndarray]:
        """
        Predicts embeddings created by underlying model if any exist.
        An embedding must be specified to have `output_type` of
        `'embedding'` in the model definition.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on

        Returns
        -------
        a NumPy array of the embeddings model produces, or a list
        of arrays if it produces multiple embeddings
        """
        generator = self.default_generator(dataset,
                                           mode='predict',
                                           pad_batches=False)
        return self._predict(generator, [], False, ['embedding'])

    def predict_uncertainty(
            self,
            dataset: Dataset,
            masks: int = 50) -> OneOrMany[Tuple[np.ndarray, np.ndarray]]:
        """
        Predict the model's outputs, along with the uncertainty in each one.

        The uncertainty is computed as described in https://arxiv.org/abs/1703.04977.
        It involves repeating the prediction many times with different dropout masks.
        The prediction is computed as the average over all the predictions.  The
        uncertainty includes both the variation among the predicted values (epistemic
        uncertainty) and the model's own estimates for how well it fits the data
        (aleatoric uncertainty).  Not all models support uncertainty prediction.

        Parameters
        ----------
        dataset: dc.data.Dataset
            Dataset to make prediction on
        masks: int
            the number of dropout masks to average over

        Returns
        -------
        for each output, a tuple (y_pred, y_std) where y_pred is the predicted
        value of the output, and each element of y_std estimates the standard
        deviation of the corresponding element of y_pred
        """
        sum_pred: List[np.ndarray] = []
        sum_sq_pred: List[np.ndarray] = []
        sum_var: List[np.ndarray] = []
        for i in range(masks):
            generator = self.default_generator(dataset,
                                               mode='uncertainty',
                                               pad_batches=False)
            results = self._predict(generator, [], True, None)
            if len(sum_pred) == 0:
                for p, v in results:
                    sum_pred.append(p)
                    sum_sq_pred.append(p * p)
                    sum_var.append(v)
            else:
                for j, (p, v) in enumerate(results):
                    sum_pred[j] += p
                    sum_sq_pred[j] += p * p
                    sum_var[j] += v
        output = []
        std = []
        for i in range(len(sum_pred)):
            p = sum_pred[i] / masks
            output.append(p)
            std.append(
                np.sqrt(sum_sq_pred[i] / masks - p * p + sum_var[i] / masks))
        if len(output) == 1:
            return (output[0], std[0])
        else:
            return list(zip(output, std))

    def evaluate_generator(self,
                           generator: Iterable[Tuple[Any, Any, Any]],
                           metrics: List[Metric],
                           transformers: List[Transformer] = [],
                           per_task_metrics: bool = False):
        """Evaluate the performance of this model on the data produced by a generator.

        Parameters
        ----------
        generator: generator
            this should generate batches, each represented as a tuple of the form
            (inputs, labels, weights).
        metric: list of deepchem.metrics.Metric
            Evaluation metric
        transformers: list of dc.trans.Transformers
            Transformers that the input data has been transformed by.  The output
            is passed through these transformers to undo the transformations.
        per_task_metrics: bool
            If True, return per-task scores.

        Returns
        -------
        dict
            Maps tasks to scores under metric.
        """
        evaluator = GeneratorEvaluator(self, generator, transformers)
        return evaluator.compute_model_performance(metrics, per_task_metrics)

    def compute_saliency(self, X: np.ndarray) -> OneOrMany[np.ndarray]:
        """Compute the saliency map for an input sample.

        This computes the Jacobian matrix with the derivative of each output element
        with respect to each input element.  More precisely,

        - If this model has a single output, it returns a matrix of shape
            (output_shape, input_shape) with the derivatives.
        - If this model has multiple outputs, it returns a list of matrices, one
            for each output.

        This method cannot be used on models that take multiple inputs.

        Parameters
        ----------
        X: ndarray
            the input data for a single sample

        Returns
        -------
        the Jacobian matrix, or a list of matrices
        """
        input_shape = X.shape
        X = np.reshape(X, [1] + list(X.shape))
        self._ensure_built()
        X_batch, _, _ = self._prepare_batch(([X], None, None))

        # Compute the gradients.

        X_tensor = X_batch[0]
        X_tensor.requires_grad_(True)
        outputs = self.model(X_tensor)
        if isinstance(outputs, torch.Tensor):
            outputs = [outputs]
        final_result = []
        for output in outputs:
            output_shape = tuple(output.shape[1:])
            output = output.reshape([-1])
            result = []
            grad_output = torch.zeros(output.shape[0], device=self.device)
            for i in range(output.shape[0]):
                grad_output.zero_()
                grad_output[i] = 1
                output.backward(grad_output, retain_graph=True)
                assert isinstance(X_tensor.grad, torch.Tensor)
                result.append(X_tensor.grad.clone())
                X_tensor.grad.zero_()
            final_result.append(
                torch.reshape(torch.stack(result),
                              output_shape + input_shape).cpu().numpy())
        if len(final_result) == 1:
            return final_result[0]
        return final_result

    def _prepare_batch(
        self, batch: Tuple[Any, Any, Any]
    ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]:
        inputs, labels, weights = batch
        inputs = [
            x.astype(np.float32) if x.dtype == np.float64 else x for x in inputs
        ]
        input_tensors = [torch.as_tensor(x, device=self.device) for x in inputs]
        if labels is not None:
            labels = [
                x.astype(np.float32) if x.dtype == np.float64 else x
                for x in labels
            ]
            label_tensors = [
                torch.as_tensor(x, device=self.device) for x in labels
            ]
        else:
            label_tensors = []
        if weights is not None:
            weights = [
                x.astype(np.float32) if x.dtype == np.float64 else x
                for x in weights
            ]
            weight_tensors = [
                torch.as_tensor(x, device=self.device) for x in weights
            ]
        else:
            weight_tensors = []

        return (input_tensors, label_tensors, weight_tensors)

    def default_generator(
            self,
            dataset: Dataset,
            epochs: int = 1,
            mode: str = 'fit',
            deterministic: bool = True,
            pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
        """Create a generator that iterates batches for a dataset.

        Subclasses may override this method to customize how model inputs are
        generated from the data.

        Parameters
        ----------
        dataset: Dataset
            the data to iterate
        epochs: int
            the number of times to iterate over the full dataset
        mode: str
            allowed values are 'fit' (called during training), 'predict' (called
            during prediction), and 'uncertainty' (called during uncertainty
            prediction)
        deterministic: bool
            whether to iterate over the dataset in order, or randomly shuffle the
            data for each epoch
        pad_batches: bool
            whether to pad each batch up to this model's preferred batch size

        Returns
        -------
        a generator that iterates batches, each represented as a tuple of lists:
        ([inputs], [outputs], [weights])
        """
        for epoch in range(epochs):
            logger.info("Starting training for epoch %d at %s" %
                        (epoch, datetime.datetime.now().ctime()))
            for (X_b, y_b, w_b,
                 ids_b) in dataset.iterbatches(batch_size=self.batch_size,
                                               deterministic=deterministic,
                                               pad_batches=pad_batches):
                yield ([X_b], [y_b], [w_b])

    def save_checkpoint(self,
                        max_checkpoints_to_keep: int = 5,
                        model_dir: Optional[str] = None) -> None:
        """Save a checkpoint to disk.

        Usually you do not need to call this method, since fit() saves checkpoints
        automatically.  If you have disabled automatic checkpointing during fitting,
        this can be called to manually write checkpoints.

        Parameters
        ----------
        max_checkpoints_to_keep: int
            the maximum number of checkpoints to keep.  Older checkpoints are discarded.
            If set to zero, the function will simply return as no checkpoint is saved.
        model_dir: str, default None
            Model directory to save checkpoint to. If None, revert to self.model_dir
        """
        if max_checkpoints_to_keep == 0:
            return
        self._ensure_built()
        if model_dir is None:
            model_dir = self.model_dir
        if not os.path.exists(model_dir):
            os.makedirs(model_dir)

        # Save the checkpoint to a file.

        data = {
            'model_state_dict': self.model.state_dict(),
            'optimizer_state_dict': self._pytorch_optimizer.state_dict(),
            'global_step': self._global_step
        }
        temp_file = os.path.join(model_dir, 'temp_checkpoint.pt')
        torch.save(data, temp_file)

        # Rename and delete older files.

        paths = [
            os.path.join(model_dir, 'checkpoint%d.pt' % (i + 1))
            for i in range(max_checkpoints_to_keep)
        ]
        if os.path.exists(paths[-1]):
            os.remove(paths[-1])
        for i in reversed(range(max_checkpoints_to_keep - 1)):
            if os.path.exists(paths[i]):
                os.rename(paths[i], paths[i + 1])
        os.rename(temp_file, paths[0])

    def get_checkpoints(self, model_dir: Optional[str] = None):
        """Get a list of all available checkpoint files.

        Parameters
        ----------
        model_dir: str, default None
            Directory to get list of checkpoints from. Reverts to self.model_dir if None

        """
        if model_dir is None:
            model_dir = self.model_dir
        files = sorted(os.listdir(model_dir))
        files = [
            f for f in files if f.startswith('checkpoint') and f.endswith('.pt')
        ]
        return [os.path.join(model_dir, f) for f in files]

    def restore(self,
                checkpoint: Optional[str] = None,
                model_dir: Optional[str] = None,
                strict: Optional[bool] = True) -> None:
        """Reload the values of all variables from a checkpoint file.

        Parameters
        ----------
        checkpoint: str
            the path to the checkpoint file to load.  If this is None, the most recent
            checkpoint will be chosen automatically.  Call get_checkpoints() to get a
            list of all available checkpoints.
        model_dir: str, default None
            Directory to restore checkpoint from. If None, use self.model_dir.  If
            checkpoint is not None, this is ignored.
        strict: bool, default True
            Whether or not to strictly enforce that the keys in checkpoint match
            the keys returned by this model's get_variable_scope() method.
        """
        logger.info('Restoring model')
        self._ensure_built()
        if checkpoint is None:
            checkpoints = sorted(self.get_checkpoints(model_dir))
            if len(checkpoints) == 0:
                raise ValueError('No checkpoint found')
            checkpoint = checkpoints[0]
        data = torch.load(checkpoint, map_location=self.device)
        self.model.load_state_dict(data['model_state_dict'], strict=strict)
        self._pytorch_optimizer.load_state_dict(data['optimizer_state_dict'])
        self._global_step = data['global_step']

    def get_global_step(self) -> int:
        """Get the number of steps of fitting that have been performed."""
        return self._global_step

    def _log_scalar_to_tensorboard(self, name: str, value: Any, step: int):
        """Log a scalar value to Tensorboard."""
        self._summary_writer.add_scalar(name, value, step)

    def _create_assignment_map(self,
                               source_model: "TorchModel",
                               include_top: bool = True,
                               **kwargs) -> Dict[Any, Any]:
        """
        Creates a default assignment map between parameters of source and current model.
        This is used only when a custom assignment map is missing. This assumes the
        model is made of different layers followed by a dense layer for mapping to
        output tasks. include_top is used to control whether or not the final dense
        layer is used. The default assignment map is useful in cases where the type
        of task is different (classification vs regression) and/or number of tasks.

        Parameters
        ----------
        source_model: dc.models.TorchModel
            Source model to copy parameter values from.
        include_top: bool, default True
            if true, copies the last dense layer
        """
        assignment_map: Dict[Any, Any] = {}
        source_vars = list(source_model.model.parameters())
        dest_vars = list(self.model.parameters())

        if not include_top:
            source_vars = source_vars[:-2]
            dest_vars = dest_vars[:-2]

        for source_var, dest_var in zip(source_vars, dest_vars):
            assignment_map[source_var] = dest_var

        return assignment_map

    def _create_value_map(self, source_model: "TorchModel",
                          **kwargs) -> Dict[Any, Any]:
        """
        Creates a value map between parameters in the source model and their
        current values. This is used only when a custom value map is missing, and
        assumes the restore method has been called.

        Parameters
        ----------
        source_model: dc.models.TorchModel
            Source model to create value map from
        """
        value_map: Dict[Any, Any] = {}
        source_vars = list(source_model.model.parameters())

        for source_var in source_vars:
            value_map[source_var] = source_var.detach().cpu().numpy()

        return value_map

    def load_from_pretrained(self,
                             source_model: "TorchModel",
                             assignment_map: Optional[Dict[Any, Any]] = None,
                             value_map: Optional[Dict[Any, Any]] = None,
                             checkpoint: Optional[str] = None,
                             model_dir: Optional[str] = None,
                             include_top: bool = True,
                             inputs: Optional[Sequence[Any]] = None,
                             **kwargs) -> None:
        """Copies parameter values from a pretrained model. `source_model` can either
        be a pretrained model or a model with the same architecture. `value_map`
        is a parameter-value dictionary. If no `value_map` is provided, the parameter
        values are restored to the `source_model` from a checkpoint and a default
        `value_map` is created. `assignment_map` is a dictionary mapping parameters
        from the `source_model` to the current model. If no `assignment_map` is
        provided, one is made from scratch and assumes the model is composed of
        several different layers, with the final one being a dense layer. include_top
        is used to control whether or not the final dense layer is used. The default
        assignment map is useful in cases where the type of task is different
        (classification vs regression) and/or number of tasks in the setting.

        Parameters
        ----------
        source_model: dc.TorchModel, required
            source_model can either be the pretrained model or a dc.TorchModel with
            the same architecture as the pretrained model. It is used to restore from
            a checkpoint, if value_map is None and to create a default assignment map
            if assignment_map is None
        assignment_map: Dict, default None
            Dictionary mapping the source_model parameters and current model parameters
        value_map: Dict, default None
            Dictionary containing source_model trainable parameters mapped to numpy
            arrays. If value_map is None, the values are restored and a default
            parameter map is created using the restored values
        checkpoint: str, default None
            the path to the checkpoint file to load.  If this is None, the most recent
            checkpoint will be chosen automatically.  Call get_checkpoints() to get a
            list of all available checkpoints
        model_dir: str, default None
            Restore source model from custom model directory if needed
        include_top: bool, default True
            if True, copies the weights and bias associated with the final dense
            layer. Used only when assignment map is None
        inputs: List, input tensors for model
            if not None, then the weights are built for both the source and self.
        """
        if inputs is not None:
            # Ensure weights for both models are built.
            source_model.model(inputs)
            self.model(inputs)

        self._ensure_built()
        if value_map is None:
            logger.info(
                "No value map provided. Creating default value map from restored model."
            )
            source_model.restore(model_dir=model_dir, checkpoint=checkpoint)
            value_map = self._create_value_map(source_model=source_model)

        if assignment_map is None:
            logger.info(
                "No assignment map provided. Creating custom assignment map.")
            assignment_map = self._create_assignment_map(
                source_model=source_model, include_top=include_top)

        for source_var, dest_var in assignment_map.items():
            assert source_var.shape == dest_var.shape
            dest_var.data = torch.as_tensor(value_map[source_var],
                                            device=self.device)


class _StandardLoss(object):
    """The implements the loss function for models that use a dc.models.losses.Loss."""

    def __init__(self, model: TorchModel, loss: Loss) -> None:
        self.model = model
        self.loss = loss  # not used
        self.criterion = loss._create_pytorch_loss()

    def __call__(self, outputs: List, labels: List, weights: List) -> float:
        if len(outputs) != 1 or len(labels) != 1 or len(weights) != 1:
            raise ValueError(
                "Loss functions expects exactly one each of outputs, labels, and weights"
            )
        losses = self.criterion(outputs[0], labels[0])
        w = weights[0]
        if len(w.shape) < len(losses.shape):
            if isinstance(w, torch.Tensor):
                shape = tuple(w.shape)
            else:
                shape = w.shape
            shape = tuple(-1 if x is None else x for x in shape)
            w = w.reshape(shape + (1,) * (len(losses.shape) - len(w.shape)))

        loss = losses * w
        loss = loss.mean()
        if self.model.regularization_loss is not None:
            loss += self.model.regularization_loss()
        return loss