import functools
import warnings
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch.optim import Optimizer
import torchbearer
from torchbearer import State
from torchbearer.metrics import MetricList
from torchbearer.callbacks import Callback, CallbackList, Tqdm, AggregatePredictions
class MockOptimizer(Optimizer):
"""The Mock Optimizer will be used inplace of an optimizer in the event that none is passed to the Trial class.
"""
def __init__(self):
super(MockOptimizer, self).__init__([torch.zeros(1)], [])
def add_param_group(self, param_group):
pass # Do Nothing
def load_state_dict(self, state_dict):
pass # Do Nothing
def state_dict(self):
return {} # Return Empty
def step(self, closure=None):
pass # Do Nothing
def zero_grad(self):
pass # Do Nothing
[docs]class CallbackListInjection(CallbackList):
"""This class allows for an callback to be injected into a callback list, without masking the methods available for
mutating the list. In this way, callbacks (such as printers) can be injected seamlessly into the methods of the
trial class.
:param callback: The callback to inject
:param callback_list: The underlying callback list
:type callback_list: CallbackList
"""
def __init__(self, callback, callback_list):
super(CallbackListInjection, self).__init__([])
self.callback = callback
self.callback_list = callback_list
[docs] def state_dict(self):
return self.callback_list.state_dict()
[docs] def load_state_dict(self, state_dict):
self.callback_list.load_state_dict(state_dict)
return self
def __iter__(self):
return self.callback_list.__iter__()
def __copy__(self):
return self.callback_list.copy()
[docs] def copy(self):
return self.__copy__()
[docs] def append(self, callback_list):
self.callback_list.append(callback_list)
def _for_list(self, function):
function(self.callback) # Call injected callback BEFORE the callback list
function(self.callback_list)
[docs]def inject_printer(validation_label_letter='v'):
"""The inject printer decorator is used to inject the appropriate printer callback, according to the verbosity level.
:param validation_label_letter: The validation label letter to use
:return: A decorator
"""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
import inspect
verbose = kwargs['verbose'] if 'verbose' in kwargs else inspect.signature(func).parameters['verbose'].default # Populate default value
verbose = self.verbose if verbose == -1 else verbose
printer = get_printer(verbose=verbose, validation_label_letter=validation_label_letter)
callback_list_old = self.state[torchbearer.CALLBACK_LIST]
self.state[torchbearer.CALLBACK_LIST] = CallbackListInjection(printer, callback_list_old)
res = func(self, *args, **kwargs)
self.state[torchbearer.CALLBACK_LIST] = callback_list_old
return res
return wrapper
return decorator
[docs]def get_printer(verbose, validation_label_letter):
if verbose >= 2:
printer = Tqdm(validation_label_letter=validation_label_letter)
elif verbose >= 1:
printer = Tqdm(validation_label_letter=validation_label_letter, on_epoch=True)
else:
printer = Callback()
return printer
[docs]def deep_to(batch, device, dtype):
""" Static method to call :func:`to` on tensors or tuples. All items in tuple will have :func:`deep_to` called
:param batch: The mini-batch which requires a :func:`to` call
:type batch: tuple, list, torch.Tensor
:param device: The desired device of the batch
:type device: torch.device
:param dtype: The desired datatype of the batch
:type dtype: torch.dtype
:return: The moved or casted batch
:rtype: tuple, list, torch.Tensor
"""
is_tuple = isinstance(batch, tuple)
if isinstance(batch, list) or isinstance(batch, tuple):
batch = list(batch)
for i in range(len(batch)):
batch[i] = deep_to(batch[i], device, dtype)
batch = tuple(batch) if is_tuple else batch
elif isinstance(batch, dict):
for key in batch:
batch[key] = deep_to(batch[key], device, dtype)
else:
if batch.dtype.is_floating_point:
batch = batch.to(device, dtype)
else:
batch = batch.to(device)
return batch
[docs]def load_batch_standard(state):
""" Load a standard (input data, target) tuple mini-batch from iterator into state
:param state: The current state dict of the :class:`Trial`.
:type state: dict[str,any]
"""
state[torchbearer.X], state[torchbearer.Y_TRUE] = deep_to(next(state[torchbearer.ITERATOR]),
state[torchbearer.DEVICE],
state[torchbearer.DATA_TYPE])
[docs]def load_batch_none(state):
""" Load a none (none, none) tuple mini-batch into state
:param state: The current state dict of the :class:`Trial`.
:type state: dict[str,any]
"""
state[torchbearer.X], state[torchbearer.Y_TRUE] = None, None
[docs]def load_batch_predict(state):
""" Load a prediction (input data, target) or (input data) mini-batch from iterator into state
:param state: The current state dict of the :class:`Trial`.
:type state: dict[str,any]
"""
data = deep_to(next(state[torchbearer.ITERATOR]), state[torchbearer.DEVICE], state[torchbearer.DATA_TYPE])
if isinstance(data, list) or isinstance(data, tuple):
state[torchbearer.X], state[torchbearer.Y_TRUE] = data
else:
state[torchbearer.X] = data
[docs]class Sampler:
"""
Sampler wraps a batch loader function and executes it when :meth:`Sampler.sample` is called
:param batch_loader: The batch loader to execute
:type batch_loader: function
"""
def __init__(self, batch_loader):
super().__init__()
self.batch_loader = batch_loader
[docs] def sample(self, state):
self.batch_loader(state)
[docs]def inject_sampler(data_key, predict=False):
""" Decorator to inject a :class:`Sampler` into state[torchbearer.SAMPLER] along with the specified \
generator into state[torchbearer.GENERATOR] and number of steps into state[torchbearer.STEPS]
:param data_key: Key for the data to inject
:type data_key: StateKey
:param predict: If true, the prediction batch loader is used, if false the standard data loader is used
:type predict: bool
:return: the decorator
"""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
key = kwargs['data_key'] if 'data_key' in kwargs else data_key # Populate default value
generator, steps = self.state[key] if self.state[key] is not None else (None, None)
if generator is None:
loader = load_batch_none
elif predict:
loader = load_batch_predict
else:
loader = load_batch_standard
self.state[torchbearer.DATA] = key
self.state[torchbearer.SAMPLER] = Sampler(loader)
self.state[torchbearer.GENERATOR] = generator
self.state[torchbearer.STEPS] = steps
res = func(self, *args, **kwargs)
return res
return wrapper
return decorator
[docs]def inject_callback(callback):
""" Decorator to inject a callback into the callback list and remove the callback after the decorated function has executed
:param callback: Callback to be injected
:type callback: Callback
:return: the decorator
"""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
callback_list_old = self.state[torchbearer.CALLBACK_LIST]
self.state[torchbearer.CALLBACK_LIST] = CallbackListInjection(callback, callback_list_old)
res = func(self, *args, **kwargs)
self.state[torchbearer.CALLBACK_LIST] = callback_list_old
return res
return wrapper
return decorator
[docs]def fluent(func):
"""Decorator for class methods which forces return of self.
"""
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
func(self, *args, **kwargs)
return self
return wrapper
[docs]def update_device_and_dtype(state, *args, **kwargs):
"""Function get data type and device values from the args / kwargs and update state.
:param state: The dict to update
:type state: State
:param args: Arguments to the :func:`Trial.to` function
:param kwargs: Keyword arguments to the :func:`Trial.to` function
:return: device, dtype pair
:rtype: tuple
"""
for key, _ in kwargs.items():
if key == str(torchbearer.DATA_TYPE):
state[torchbearer.DATA_TYPE] = kwargs['dtype']
elif str(torchbearer.DEVICE) in kwargs:
state[torchbearer.DEVICE] = kwargs['device']
for arg in args:
if isinstance(arg, torch.dtype):
state[torchbearer.DATA_TYPE] = arg
else:
state[torchbearer.DEVICE] = arg
return state
[docs]class Trial(object):
""" The trial class contains all of the required hyper-parameters for model running in torchbearer and presents an
API for model fitting, evaluating and predicting.
:param model: The base pytorch model
:type model: torch.nn.Module
:param optimizer: The optimizer used for pytorch model weight updates
:type optimizer: torch.optim.Optimizer
:param criterion: The final loss criterion that provides a loss value to the optimizer
:type criterion: function or None
:param metrics: The list of :class:`torchbearer.Metric <.Metric>` instances to process during fitting
:type metrics: list
:param callbacks: The list of :class:`torchbearer.Callback <.Callback>` instances to call during fitting
:type callbacks: list
:param pass_state: If True, the torchbearer state will be passed to the model during fitting
:type pass_state: bool
:param verbose: Global verbosity .If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress
:type verbose: int
"""
def __init__(self, model, optimizer=None, criterion=None, metrics=[], callbacks=[], pass_state=False, verbose=2):
if criterion is None:
def criterion(_, __):
return torch.zeros(1, device=self.state[torchbearer.DEVICE], dtype=self.state[torchbearer.DATA_TYPE], requires_grad=True)
self.pass_state = pass_state
self.verbose = verbose
self.state = State()
self.state.update({
torchbearer.MODEL: model,
torchbearer.CRITERION: criterion,
torchbearer.OPTIMIZER: optimizer if optimizer is not None else MockOptimizer(),
torchbearer.METRIC_LIST: MetricList(metrics),
torchbearer.CALLBACK_LIST: CallbackList(callbacks),
torchbearer.DEVICE: 'cpu',
torchbearer.DATA_TYPE: torch.float32,
torchbearer.SELF: self,
torchbearer.HISTORY: [],
torchbearer.BACKWARD_ARGS: {},
torchbearer.TRAIN_GENERATOR: None,
torchbearer.VALIDATION_GENERATOR: None,
torchbearer.TEST_GENERATOR: None,
torchbearer.TRAIN_STEPS: None,
torchbearer.VALIDATION_STEPS: None,
torchbearer.TEST_STEPS: None,
torchbearer.TRAIN_DATA: None,
torchbearer.VALIDATION_DATA: None,
torchbearer.TEST_DATA: None,
})
def __str__(self):
def state_string(name, state_key):
import math
N = (50-len(name))/2
return "-"*math.floor(N) + " " + name.upper()+ " " + "-"*math.ceil(N) + "\n" + str(self.state[state_key]) + "\n\n"
optim_str = state_string('Optimzer', torchbearer.OPTIMIZER)
crit_str = state_string("Criterion", torchbearer.CRITERION)
metrics_str = state_string("Metrics", torchbearer.METRIC_LIST)
callbacks_str = state_string("Callbacks", torchbearer.CALLBACK_LIST)
model_str = state_string("Model", torchbearer.MODEL)
return optim_str + crit_str + metrics_str + callbacks_str + model_str
def __repr__(self):
return str(self)
[docs] @fluent
def for_train_steps(self, steps):
"""Run this trial for the given number of training steps. Note that the generator will output (None, None) if it
has not been set. Useful for differentiable programming. Returns self so that methods can be chained for
convenience.
:param steps: The number of training steps per epoch to run
:type steps: int
:return: self
:rtype: Trial
"""
if not isinstance(steps, int):
warnings.warn("Number of training steps is not an int, casting to int")
steps = int(steps)
generator = self.state[torchbearer.TRAIN_GENERATOR]
if generator is not None and steps > len(generator):
warnings.warn("Number of training steps exceeds number of data items, limiting to number of items")
steps = len(generator)
self.state[torchbearer.TRAIN_STEPS] = steps
self.state[torchbearer.TRAIN_DATA] = (self.state[torchbearer.TRAIN_GENERATOR], self.state[torchbearer.TRAIN_STEPS])
[docs] @fluent
def with_train_generator(self, generator, steps=None):
"""Use this trial with the given train generator. Returns self so that methods can be chained for convenience.
:param generator: The train data generator to use during calls to :meth:`.run`
:type generator: DataLoader
:param steps: The number of steps per epoch to take when using this generator
:type steps: int
:return: self
:rtype: Trial
"""
self.state[torchbearer.TRAIN_GENERATOR] = generator
steps = len(generator) if steps is None else steps
self.for_train_steps(steps)
[docs] @fluent
def with_train_data(self, x, y, batch_size=1, shuffle=True, num_workers=1, steps=None):
"""Use this trial with the given train data. Returns self so that methods can be chained for convenience.
:param x: The train x data to use during calls to :meth:`.run`
:type x: torch.Tensor
:param y: The train labels to use during calls to :meth:`.run`
:type y: torch.Tensor
:param batch_size: The size of each batch to sample from the data
:type batch_size: int
:param shuffle: If True, then data will be shuffled each epoch
:type shuffle: bool
:param num_workers: Number of worker threads to use in the data loader
:type num_workers: int
:param steps: The number of steps per epoch to take when using this data
:type steps: int
:return: self
:rtype: Trial
"""
dataset = TensorDataset(x, y)
dataloader = DataLoader(dataset, batch_size, shuffle=shuffle, num_workers=num_workers)
self.with_train_generator(dataloader, steps=steps)
[docs] @fluent
def for_val_steps(self, steps):
"""Run this trial for the given number of validation steps. Note that the generator will output (None, None) if
it has not been set. Useful for differentiable programming. Returns self so that methods can be chained for
convenience.
:param steps: The number of validation steps per epoch to run
:type steps: int
:return: self
:rtype: Trial
"""
if not isinstance(steps, int):
warnings.warn("Number of validation steps is not an int, casting to int")
steps = int(steps)
generator = self.state[torchbearer.VALIDATION_GENERATOR]
if generator is not None and steps > len(generator):
warnings.warn("Number of validation steps exceeds number of data items, limiting to number of items")
steps = len(generator)
self.state[torchbearer.VALIDATION_STEPS] = steps
self.state[torchbearer.VALIDATION_DATA] = (self.state[torchbearer.VALIDATION_GENERATOR], self.state[torchbearer.VALIDATION_STEPS])
[docs] @fluent
def with_val_generator(self, generator, steps=None):
"""Use this trial with the given validation generator. Returns self so that methods can be chained for
convenience.
:param generator: The validation data generator to use during calls to :meth:`.run` and :meth:`.evaluate`
:type generator: DataLoader
:param steps: The number of steps per epoch to take when using this generator
:type steps: int
:return: self
:rtype: Trial
"""
self.state[torchbearer.VALIDATION_GENERATOR] = generator
steps = len(generator) if steps is None else steps
self.for_val_steps(steps)
[docs] @fluent
def with_val_data(self, x, y, batch_size=1, shuffle=True, num_workers=1, steps=None):
"""Use this trial with the given validation data. Returns self so that methods can be chained for convenience.
:param x: The validation x data to use during calls to :meth:`.run` and :meth:`.evaluate`
:type x: torch.Tensor
:param y: The validation labels to use during calls to :meth:`.run` and :meth:`.evaluate`
:type y: torch.Tensor
:param batch_size: The size of each batch to sample from the data
:type batch_size: int
:param shuffle: If True, then data will be shuffled each epoch
:type shuffle: bool
:param num_workers: Number of worker threads to use in the data loader
:type num_workers: int
:param steps: The number of steps per epoch to take when using this data
:type steps: int
:return: self
:rtype: Trial
"""
dataset = TensorDataset(x, y)
dataloader = DataLoader(dataset, batch_size, shuffle=shuffle, num_workers=num_workers)
self.with_val_generator(dataloader, steps=steps)
[docs] @fluent
def for_test_steps(self, steps):
"""Run this trial for the given number of test steps. Note that the generator will output (None, None) if
it has not been set. Useful for differentiable programming. Returns self so that methods can be chained for
convenience.
:param steps: The number of test steps per epoch to run (when using :meth:`.predict`)
:type steps: int
:return: self
:rtype: Trial
"""
if not isinstance(steps, int):
warnings.warn("Number of test steps is not an int, casting to int")
steps = int(steps)
generator = self.state[torchbearer.TEST_GENERATOR]
if generator is not None and steps > len(generator):
warnings.warn("Number of test steps exceeds number of data items, limiting to number of items")
steps = len(generator)
self.state[torchbearer.TEST_STEPS] = steps
self.state[torchbearer.TEST_DATA] = (self.state[torchbearer.TEST_GENERATOR], self.state[torchbearer.TEST_STEPS])
[docs] @fluent
def with_test_generator(self, generator, steps=None):
"""Use this trial with the given test generator. Returns self so that methods can be chained for convenience.
:param generator: The test data generator to use during calls to :meth:`.predict`
:type generator: DataLoader
:param steps: The number of steps per epoch to take when using this generator
:type steps: int
:return: self
:rtype: Trial
"""
self.state[torchbearer.TEST_GENERATOR] = generator
steps = len(generator) if steps is None else steps
self.for_test_steps(steps)
[docs] @fluent
def with_test_data(self, x, batch_size=1, num_workers=1, steps=None):
"""Use this trial with the given test data. Returns self so that methods can be chained for convenience.
:param x: The test x data to use during calls to :meth:`.predict`
:type x: torch.Tensor
:param batch_size: The size of each batch to sample from the data
:type batch_size: int
:param num_workers: Number of worker threads to use in the data loader
:type num_workers: int
:param steps: The number of steps per epoch to take when using this data
:type steps: int
:return: self
:rtype: Trial
"""
dataset = TensorDataset(x)
dataloader = DataLoader(dataset, batch_size, num_workers=num_workers)
self.with_test_generator(dataloader, steps=steps)
[docs] @fluent
def for_steps(self, train_steps=None, val_steps=None, test_steps=None):
"""Use this trial for the given number of train, val and test steps. Returns self so that methods can be chained
for convenience.
:param train_steps: The number of training steps per epoch to run
:type train_steps: int, optional
:param val_steps: The number of validation steps per epoch to run
:type val_steps: int, optional
:param test_steps: The number of test steps per epoch to run (when using :meth:`.predict`)
:type test_steps: int, optional
:return: self
:rtype: Trial
"""
if train_steps is not None:
self.for_train_steps(train_steps)
if val_steps is not None:
self.for_val_steps(val_steps)
if test_steps is not None:
self.for_test_steps(test_steps)
[docs] @fluent
def with_generators(self, train_generator=None, val_generator=None, test_generator=None, train_steps=None, val_steps=None, test_steps=None):
"""Use this trial with the given generators. Returns self so that methods can be chained for convenience.
:param train_generator: The training data generator to use during calls to :meth:`.run`
:type train_generator: DataLoader
:param val_generator: The validation data generator to use during calls to :meth:`.run` and :meth:`.evaluate`
:type val_generator: DataLoader
:param test_generator: The testing data generator to use during calls to :meth:`.predict`
:type test_generator: DataLoader
:param train_steps: The number of steps per epoch to take when using the training generator
:type train_steps: int
:param val_steps: The number of steps per epoch to take when using the validation generator
:type val_steps: int
:param test_steps: The number of steps per epoch to take when using the testing generator
:type test_steps: int
:return: self
:rtype: Trial
"""
if train_generator is not None:
self.with_train_generator(train_generator, train_steps)
if val_generator is not None:
self.with_val_generator(val_generator, val_steps)
if test_generator is not None:
self.with_test_generator(test_generator, test_steps)
[docs] @inject_printer()
def run(self, epochs=1, verbose=-1):
r"""Run this trial for the given number of epochs, starting from the last trained epoch.
Args:
epochs (int, optional): The number of epochs to run for
verbose (int, optional): If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training
progress, If -1: Automatic
State Requirements:
- :attr:`torchbearer.state.MODEL`: Model should be callable and not none, set on Trial init
Returns:
list: The model history (list of tuple of steps summary and epoch metric dicts)
"""
state = State()
state.update({
torchbearer.MAX_EPOCHS: epochs,
torchbearer.STOP_TRAINING: False,
})
state.update(self.state) # TODO: Swap this for something which makes `self.state` still mutable
if state[torchbearer.MODEL] is None or not callable(state[torchbearer.MODEL]):
warnings.warn('The Model is None or not callable which may cause issues if not deliberate')
state[torchbearer.MODEL] = lambda *args, **kwargs: None
if state[torchbearer.TRAIN_GENERATOR] is not None \
or state[torchbearer.TRAIN_STEPS] is not None \
or state[torchbearer.VALIDATION_GENERATOR] is not None \
or state[torchbearer.VALIDATION_STEPS] is not None:
state[torchbearer.CALLBACK_LIST].on_start(state)
for state[torchbearer.EPOCH] in range(len(state[torchbearer.HISTORY]), state[torchbearer.MAX_EPOCHS]):
state[torchbearer.CALLBACK_LIST].on_start_epoch(state)
final_metrics = self._fit_pass(state)[torchbearer.METRICS]
if state[torchbearer.STOP_TRAINING]:
break
final_metrics.update(self._validation_pass(state))
state[torchbearer.METRICS] = final_metrics
state[torchbearer.CALLBACK_LIST].on_end_epoch(state)
steps_summary = (state[torchbearer.TRAIN_STEPS], state[torchbearer.VALIDATION_STEPS])
self.state[torchbearer.HISTORY].append((steps_summary, state[torchbearer.METRICS]))
state[torchbearer.CALLBACK_LIST].on_checkpoint(state)
if state[torchbearer.STOP_TRAINING]:
break
state[torchbearer.CALLBACK_LIST].on_end(state)
return self.state[torchbearer.HISTORY]
@inject_sampler(torchbearer.TRAIN_DATA)
def _fit_pass(self, state):
state.update(self.state) # TODO: Hack to make injection work, should be removed if `self.state` is mutable
self.train()
state[torchbearer.ITERATOR] = iter(state[torchbearer.GENERATOR]) if state[torchbearer.GENERATOR] is not None else None # TODO: Inject this?
state[torchbearer.METRIC_LIST].reset(state)
state[torchbearer.METRICS] = {}
state[torchbearer.CALLBACK_LIST].on_start_training(state)
for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
state[torchbearer.SAMPLER].sample(state)
state[torchbearer.CALLBACK_LIST].on_sample(state)
def closure():
# Zero grads
state[torchbearer.OPTIMIZER].zero_grad()
# Forward Pass
if self.pass_state:
state[torchbearer.Y_PRED] = state[torchbearer.MODEL](state[torchbearer.X], state=state)
else:
state[torchbearer.Y_PRED] = state[torchbearer.MODEL](state[torchbearer.X])
state[torchbearer.CALLBACK_LIST].on_forward(state)
# Loss Calculation
state[torchbearer.LOSS] = state[torchbearer.CRITERION](state[torchbearer.Y_PRED], state[torchbearer.Y_TRUE])
state[torchbearer.CALLBACK_LIST].on_criterion(state)
# Backwards pass
state[torchbearer.LOSS].backward(**state[torchbearer.BACKWARD_ARGS])
state[torchbearer.CALLBACK_LIST].on_backward(state)
# Update parameters
state[torchbearer.OPTIMIZER].step(closure)
state[torchbearer.METRICS] = state[torchbearer.METRIC_LIST].process(state)
state[torchbearer.CALLBACK_LIST].on_step_training(state)
if state[torchbearer.STOP_TRAINING]:
break
state[torchbearer.METRICS].update(state[torchbearer.METRIC_LIST].process_final(state))
state[torchbearer.CALLBACK_LIST].on_end_training(state)
return state
def _test_pass(self, state):
with torch.no_grad():
state[torchbearer.ITERATOR] = iter(state[torchbearer.GENERATOR]) if state[torchbearer.GENERATOR] is not None else None # TODO: Inject this?
state[torchbearer.METRIC_LIST].reset(state)
state[torchbearer.METRICS] = {}
state[torchbearer.CALLBACK_LIST].on_start_validation(state)
for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
state[torchbearer.SAMPLER].sample(state)
state[torchbearer.CALLBACK_LIST].on_sample_validation(state)
# Forward Pass
if self.pass_state:
state[torchbearer.Y_PRED] = state[torchbearer.MODEL](state[torchbearer.X], state=state)
else:
state[torchbearer.Y_PRED] = state[torchbearer.MODEL](state[torchbearer.X])
state[torchbearer.CALLBACK_LIST].on_forward_validation(state)
# Loss and metrics
if torchbearer.Y_TRUE in state:
state[torchbearer.LOSS] = state[torchbearer.CRITERION](state[torchbearer.Y_PRED],
state[torchbearer.Y_TRUE])
state[torchbearer.CALLBACK_LIST].on_criterion_validation(state)
state[torchbearer.METRICS] = state[torchbearer.METRIC_LIST].process(state)
state[torchbearer.CALLBACK_LIST].on_step_validation(state)
if state[torchbearer.STOP_TRAINING]:
break
if torchbearer.Y_TRUE in state:
state[torchbearer.METRICS].update(state[torchbearer.METRIC_LIST].process_final(state))
state[torchbearer.CALLBACK_LIST].on_end_validation(state)
return state
@inject_sampler(torchbearer.VALIDATION_DATA)
def _validation_pass(self, state):
state.update(self.state) # TODO: Hack to make injection work, should be removed if `self.state` is mutable
if state[torchbearer.VALIDATION_GENERATOR] is not None or state[torchbearer.VALIDATION_STEPS] is not None:
self.eval()
self._test_pass(state)
return state[torchbearer.METRICS]
[docs] @inject_sampler(torchbearer.VALIDATION_DATA)
@inject_printer(validation_label_letter='e')
def evaluate(self, verbose=-1, data_key=None): # Note: kwargs appear unused but are inspected in inject_sampler
"""Evaluate this trial on the validation data.
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress, If -1: Automatic
:type verbose: int
:param data_key: Optional key for the data to evaluate on. Default: torchbearer.VALIDATION_DATA
:type data_key: StateKey
:return: The final metric values
:rtype: dict
"""
state = State()
state.update({
torchbearer.MAX_EPOCHS: 1,
torchbearer.EPOCH: 0,
torchbearer.STOP_TRAINING: False
})
state.update(self.state) # TODO: Hack to make injection work, should be removed if `self.state` is mutable
if state[torchbearer.GENERATOR] is not None or state[torchbearer.STEPS] is not None:
state[torchbearer.CALLBACK_LIST].on_start(state)
state[torchbearer.CALLBACK_LIST].on_start_epoch(state)
self.eval()
state = self._test_pass(state)
state[torchbearer.CALLBACK_LIST].on_end_epoch(state)
if len(self.state[torchbearer.HISTORY]) != 0:
self.state[torchbearer.HISTORY][-1][1].update(state[torchbearer.METRICS])
state[torchbearer.CALLBACK_LIST].on_end(state)
return state[torchbearer.METRICS]
return {}
[docs] @inject_callback(AggregatePredictions())
@inject_sampler(torchbearer.TEST_DATA, predict=True)
@inject_printer(validation_label_letter='p')
def predict(self, verbose=-1, data_key=None): # Note: kwargs appear unused but are inspected in inject_sampler
"""Determine predictions for this trial on the test data.
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress, If -1: Automatic
:type verbose: int
:param data_key: Optional key for the data to predict on. Default: torchbearer.TEST_DATA
:type data_key: StateKey
:return: Model outputs as a list
:rtype: list
"""
state = {
torchbearer.MAX_EPOCHS: 1,
torchbearer.EPOCH: 0,
torchbearer.STOP_TRAINING: False
}
state.update(self.state) # TODO: Hack to make injection work, should be removed if `self.state` is mutable
if state[torchbearer.GENERATOR] is not None or state[torchbearer.STEPS] is not None:
state[torchbearer.CALLBACK_LIST].on_start(state)
state[torchbearer.CALLBACK_LIST].on_start_epoch(state)
self.eval()
res = self._test_pass(state)[torchbearer.FINAL_PREDICTIONS]
state[torchbearer.CALLBACK_LIST].on_end_epoch(state)
state[torchbearer.CALLBACK_LIST].on_end(state)
return res
return []
[docs] @fluent
def replay(self, callbacks=[], verbose=2, one_batch=False): # TODO: Should we track if testing passes have happened?
""" Replay the fit passes stored in history with given callbacks, useful when reloading a saved Trial. Note that only progress and metric information is populated in state during a replay.
:param callbacks: List of callbacks to be run during the replay
:type callbacks: list
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress
:type verbose: int
:param one_batch: If True, only one batch per epoch is replayed. If False, all batches are replayed
:type one_batch: bool
:return: self
:rtype: Trial
"""
history = self.state[torchbearer.HISTORY]
callbacks.append(get_printer(verbose=verbose, validation_label_letter='v'))
callbacks = CallbackList(callbacks)
state = State()
state.update(self.state)
state[torchbearer.STOP_TRAINING] = False
state[torchbearer.MAX_EPOCHS] = len(history)
callbacks.on_start(state)
for i in range(len(history)):
state[torchbearer.EPOCH] = i
if not one_batch:
state[torchbearer.TRAIN_STEPS], state[torchbearer.VALIDATION_STEPS] = history[i][0]
else:
state[torchbearer.TRAIN_STEPS], state[torchbearer.VALIDATION_STEPS] = 1, 1
state[torchbearer.METRICS] = history[i][1]
self._replay_pass(state, callbacks)
callbacks.on_end(state)
@fluent
def _replay_pass(self, state, callback_list):
callback_list.on_start_epoch(state)
all_metrics = state[torchbearer.METRICS]
# Training pass
state[torchbearer.STEPS] = state[torchbearer.TRAIN_STEPS]
state[torchbearer.METRICS] = {key: all_metrics[key] for key in all_metrics.keys() if "val_" not in key}
callback_list.on_start_training(state)
for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
callback_list.on_sample(state)
callback_list.on_forward(state)
callback_list.on_criterion(state)
callback_list.on_backward(state)
callback_list.on_step_training(state)
if state[torchbearer.STOP_TRAINING]:
break
callback_list.on_end_training(state)
# Validation pass
if not state[torchbearer.STOP_TRAINING]:
state[torchbearer.STEPS] = state[torchbearer.VALIDATION_STEPS]
state[torchbearer.METRICS] = {key: all_metrics[key] for key in all_metrics.keys() if "val_" in key}
callback_list.on_start_validation(state)
for state[torchbearer.BATCH] in range(state[torchbearer.STEPS]):
callback_list.on_sample_validation(state)
callback_list.on_forward_validation(state)
callback_list.on_criterion_validation(state)
callback_list.on_step_validation(state)
if state[torchbearer.STOP_TRAINING]:
break
callback_list.on_end_validation(state)
state[torchbearer.METRICS] = all_metrics
callback_list.on_end_epoch(state)
[docs] @fluent
def train(self):
"""Set model and metrics to training mode.
:return: self
:rtype: Trial
"""
self.state[torchbearer.MODEL].train()
self.state[torchbearer.METRIC_LIST].train()
[docs] @fluent
def eval(self):
"""Set model and metrics to evaluation mode
:return: self
:rtype: Trial
"""
self.state[torchbearer.MODEL].eval()
if torchbearer.DATA in self.state:
self.state[torchbearer.METRIC_LIST].eval(data_key=self.state[torchbearer.DATA])
else:
self.state[torchbearer.METRIC_LIST].eval()
[docs] @fluent
def to(self, *args, **kwargs):
""" Moves and/or casts the parameters and buffers.
:param args: See: `torch.nn.Module.to <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.to>`_
:param kwargs: See: `torch.nn.Module.to <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.to>`_
:return: self
:rtype: Trial
"""
self.state[torchbearer.MODEL].to(*args, **kwargs)
for state in self.state[torchbearer.OPTIMIZER].state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(*args, **kwargs)
self.state = update_device_and_dtype(self.state, *args, **kwargs)
[docs] @fluent
def cuda(self, device=None):
""" Moves all model parameters and buffers to the GPU.
:param device: if specified, all parameters will be copied to that device
:type device: int, optional
:return: self
:rtype: Trial
"""
if device is None:
device = torch.cuda.current_device()
self.to('cuda:' + str(device))
[docs] @fluent
def cpu(self):
""" Moves all model parameters and buffers to the CPU.
:return: self
:rtype: Trial
"""
self.to('cpu')
[docs] def state_dict(self, **kwargs):
"""Get a dict containing the model and optimizer states, as well as the model history.
:param kwargs: See: `torch.nn.Module.state_dict <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.state_dict>`_
:return: A dict containing parameters and persistent buffers.
:rtype: dict
"""
state_dict = {
torchbearer.VERSION: torchbearer.__version__.replace('.dev', ''),
torchbearer.MODEL: self.state[torchbearer.MODEL].state_dict(**kwargs),
torchbearer.OPTIMIZER: self.state[torchbearer.OPTIMIZER].state_dict(),
torchbearer.HISTORY: self.state[torchbearer.HISTORY],
torchbearer.CALLBACK_LIST: self.state[torchbearer.CALLBACK_LIST].state_dict()
}
return state_dict
[docs] @fluent
def load_state_dict(self, state_dict, resume=True, **kwargs):
"""Resume this trial from the given state. Expects that this trial was constructed in the same way. Optionally,
just load the model state when resume=False.
:param state_dict: The state dict to reload
:type state_dict: dict
:param resume: If True, resume from the given state. Else, just load in the model weights.
:param kwargs: See: `torch.nn.Module.load_state_dict <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.load_state_dict>`_
:return: self
:rtype: Trial
"""
if resume and torchbearer.MODEL in state_dict: # torchbearer dict
if torchbearer.VERSION in state_dict and state_dict[torchbearer.VERSION] != torchbearer.__version__.replace('.dev', ''):
warnings.warn('This state dict was saved with a different torchbearer version, loading available keys. Consider setting resume=False')
if torchbearer.MODEL in state_dict:
self.state[torchbearer.MODEL].load_state_dict(state_dict[torchbearer.MODEL], **kwargs)
if torchbearer.OPTIMIZER in state_dict:
self.state[torchbearer.OPTIMIZER].load_state_dict(state_dict[torchbearer.OPTIMIZER])
if torchbearer.HISTORY in state_dict:
self.state[torchbearer.HISTORY] = state_dict[torchbearer.HISTORY]
if torchbearer.CALLBACK_LIST in state_dict:
self.state[torchbearer.CALLBACK_LIST].load_state_dict(state_dict[torchbearer.CALLBACK_LIST])
elif torchbearer.MODEL in state_dict:
self.state[torchbearer.MODEL].load_state_dict(state_dict[torchbearer.MODEL], **kwargs)
else: # something else
warnings.warn('Not a torchbearer state dict, passing to model')
self.state[torchbearer.MODEL].load_state_dict(state_dict, **kwargs)