import sys
if sys.version_info[0] < 3:
import inspect
def get_default(fcn, arg):
a = inspect.getargspec(fcn)
return dict(zip(a.args[-len(a.defaults):], a.defaults))[arg]
else:
from inspect import signature
[docs] def get_default(fcn, arg):
return signature(fcn).parameters[arg].default
import functools
import warnings
import itertools
import torch
from torch.utils.data import DataLoader, TensorDataset
from torch.optim import Optimizer
import torchbearer
from torchbearer import cite
from torchbearer import State
from torchbearer.metrics import MetricList
from torchbearer.callbacks import Callback, CallbackList, Tqdm, AggregatePredictions
from torchbearer.bases import base_closure
bibtex = """
@article{2018torchbearer,
title={Torchbearer: A Model Fitting Library for PyTorch},
author={Harris, Ethan and Painter, Matthew and Hare, Jonathon},
journal={arXiv preprint arXiv:1809.03363},
year={2018}
}
"""
[docs]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)], [])
[docs] def add_param_group(self, param_group):
pass # Do Nothing
[docs] def load_state_dict(self, state_dict):
pass # Do Nothing
[docs] def state_dict(self):
return {} # Return Empty
[docs] def step(self, closure=None):
if closure is not None:
closure()
[docs] 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.
Args:
callback (Callback): The :class:`.Callback` to inject
callback_list (CallbackList): The underlying :class:`.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.
Args:
validation_label_letter (str): The validation label letter to use
Returns:
A decorator
"""
def decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
verbose = kwargs['verbose'] if 'verbose' in kwargs else get_default(func, 'verbose') # 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
Args:
batch (tuple / list / torch.Tensor): The mini-batch which requires a :func:`to` call
device (torch.device): The desired device of the batch
dtype (torch.dtype): The desired datatype of the batch
Returns:
tuple / list / torch.Tensor: The moved or casted batch
"""
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_infinite(loader):
""" Wraps a batch loader and refreshes the iterator once it has been completed.
Args:
loader: batch loader to wrap
"""
def call(state):
try:
loader(state)
except StopIteration:
state[torchbearer.ITERATOR] = iter(state[torchbearer.GENERATOR])
loader(state)
return call
[docs]def load_batch_standard(state):
""" Load a standard (input data, target) tuple mini-batch from iterator into state
Args:
state (dict): The current state dict of the :class:`Trial`.
"""
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
Args:
state (dict): The current state dict of the :class:`Trial`.
"""
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
Args:
state (dict): The current state dict of the :class:`Trial`.
"""
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
Args:
batch_loader (func): The batch loader to execute
"""
def __init__(self, batch_loader):
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]
Args:
data_key (StateKey): :class:`.StateKey` for the data to inject
predict (bool): If true, the prediction batch loader is used, if false the standard data loader is used
Returns:
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
if generator is not None and steps is not None:
over_steps = steps > len(generator)
inf_steps = steps == -1
inf_train_loader = key == torchbearer.TRAIN_DATA and self.state[torchbearer.INF_TRAIN_LOADING]
if over_steps or inf_steps or inf_train_loader: # Want iterator to refresh at end
if steps == -1: warnings.warn("Trial is set to run indefinitely. "
"Make sure you have some method to terminate safely.")
loader = load_batch_infinite(loader)
if inf_train_loader and not hasattr(generator, 'inf'): # First run and want iterator to not refresh each epoch but on end
generator.inf = True
generator.tb_iter = iter(generator)
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
Args:
callback (Callback): :class:`.Callback` to be injected
Returns:
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 update_device_and_dtype(state, *args, **kwargs):
"""Function get data type and device values from the args / kwargs and update state.
Args:
state (State): The :class:`.State` to update
args: Arguments to the :func:`Trial.to` function
kwargs: Keyword arguments to the :func:`Trial.to` function
Returns:
device, dtype pair
"""
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]@cite(bibtex)
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.
Args:
model (torch.nn.Module): The base pytorch model
optimizer (torch.optim.Optimizer): The optimizer used for pytorch model weight updates
criterion (func / None): The final loss criterion that provides a loss value to the optimizer
metrics (list): The list of :class:`torchbearer.Metric <.Metric>` instances to process during fitting
callbacks (list): The list of :class:`torchbearer.Callback <.Callback>` instances to call during fitting
verbose (int): Global verbosity .If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training
progress
"""
def __init__(self, model, optimizer=None, criterion=None, metrics=[], callbacks=[], 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.verbose = verbose
self.closure = base_closure(torchbearer.X, torchbearer.MODEL, torchbearer.Y_PRED, torchbearer.Y_TRUE, torchbearer.CRITERION, torchbearer.LOSS, torchbearer.OPTIMIZER)
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,
torchbearer.INF_TRAIN_LOADING: False,
})
self.state[torchbearer.CALLBACK_LIST].on_init(self.state)
def __str__(self):
def state_string(name, state_key):
import math
N = (50-len(name))/2
res = "-" * int(math.floor(N)) + " " + name.upper() + " " + "-" * int(math.ceil(N))
res = res + "-" if len(res) < 52 else res
return res + "\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] 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. If steps is larger than dataset size then loader will be refreshed like if it was a new epoch. If
steps is -1 then loader will be refreshed until stopped by STOP_TRAINING flag or similar.
Args:
steps (int): The number of training steps per epoch to run.
Returns:
Trial: self
"""
if not isinstance(steps, int):
warnings.warn("Number of training steps is not an int, casting to int")
steps = int(steps)
self.state[torchbearer.TRAIN_STEPS] = steps
self.state[torchbearer.TRAIN_DATA] = (self.state[torchbearer.TRAIN_GENERATOR], self.state[torchbearer.TRAIN_STEPS])
return self
[docs] 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.
Args:
generator: The train data generator to use during calls to :meth:`.run`
steps (int): The number of steps per epoch to take when using this generator.
Returns:
Trial: self
"""
self.state[torchbearer.TRAIN_GENERATOR] = generator
steps = self.state[torchbearer.TRAIN_STEPS] if steps is None else steps
steps = len(generator) if steps is None else steps
self.for_train_steps(steps)
return self
[docs] 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.
Args:
x (torch.Tensor): The train x data to use during calls to :meth:`.run`
y (torch.Tensor): The train labels to use during calls to :meth:`.run`
batch_size (int): The size of each batch to sample from the data
shuffle (bool): If True, then data will be shuffled each epoch
num_workers (int): Number of worker threads to use in the data loader
steps (int): The number of steps per epoch to take when using this data
Returns:
Trial: self
"""
dataset = TensorDataset(x, y)
dataloader = DataLoader(dataset, batch_size, shuffle=shuffle, num_workers=num_workers)
self.with_train_generator(dataloader, steps=steps)
return self
[docs] 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. If steps larger than dataset size then loader will be refreshed like if it was a new epoch. If
steps -1 then loader will be refreshed until stopped by STOP_TRAINING flag or similar.
Args:
steps (int): The number of validation steps per epoch to run
Returns:
Trial: self
"""
if not isinstance(steps, int):
warnings.warn("Number of validation steps is not an int, casting to int")
steps = int(steps)
self.state[torchbearer.VALIDATION_STEPS] = steps
self.state[torchbearer.VALIDATION_DATA] = (self.state[torchbearer.VALIDATION_GENERATOR], self.state[torchbearer.VALIDATION_STEPS])
return self
[docs] 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.
Args:
generator: The validation data generator to use during calls to :meth:`.run` and :meth:`.evaluate`
steps (int): The number of steps per epoch to take when using this generator
Returns:
Trial: self
"""
self.state[torchbearer.VALIDATION_GENERATOR] = generator
steps = self.state[torchbearer.VALIDATION_STEPS] if steps is None else steps
steps = len(generator) if steps is None else steps
self.for_val_steps(steps)
return self
[docs] 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.
Args:
x (torch.Tensor): The validation x data to use during calls to :meth:`.run` and :meth:`.evaluate`
y (torch.Tensor): The validation labels to use during calls to :meth:`.run` and :meth:`.evaluate`
batch_size (int): The size of each batch to sample from the data
shuffle (bool): If True, then data will be shuffled each epoch
num_workers (int): Number of worker threads to use in the data loader
steps (int): The number of steps per epoch to take when using this data
Returns:
Trial: self
"""
dataset = TensorDataset(x, y)
dataloader = DataLoader(dataset, batch_size, shuffle=shuffle, num_workers=num_workers)
self.with_val_generator(dataloader, steps=steps)
return self
[docs] 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. If steps larger than dataset size then loader will be refreshed like if it was a new epoch. If
steps -1 then loader will be refreshed until stopped by STOP_TRAINING flag or similar.
Args:
steps (int): The number of test steps per epoch to run (when using :meth:`.predict`)
Returns:
Trial: self
"""
if not isinstance(steps, int):
warnings.warn("Number of test steps is not an int, casting to int")
steps = int(steps)
self.state[torchbearer.TEST_STEPS] = steps
self.state[torchbearer.TEST_DATA] = (self.state[torchbearer.TEST_GENERATOR], self.state[torchbearer.TEST_STEPS])
return self
[docs] 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.
Args:
generator: The test data generator to use during calls to :meth:`.predict`
steps (int): The number of steps per epoch to take when using this generator
Returns:
Trial: self
"""
self.state[torchbearer.TEST_GENERATOR] = generator
steps = self.state[torchbearer.TEST_STEPS] if steps is None else steps
steps = len(generator) if steps is None else steps
self.for_test_steps(steps)
return self
[docs] 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.
Args:
x (torch.Tensor): The test x data to use during calls to :meth:`.predict`
batch_size (int): The size of each batch to sample from the data
num_workers (int): Number of worker threads to use in the data loader
steps (int): The number of steps per epoch to take when using this data
Returns:
Trial: self
"""
dataset = TensorDataset(x)
dataloader = DataLoader(dataset, batch_size, num_workers=num_workers)
self.with_test_generator(dataloader, steps=steps)
return self
[docs] 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. If steps larger than dataset size then loader will be refreshed like if it was a new epoch. If
steps -1 then loader will be refreshed until stopped by STOP_TRAINING flag or similar.
Args:
train_steps (int): The number of training steps per epoch to run
val_steps (int): The number of validation steps per epoch to run
test_steps (int): The number of test steps per epoch to run (when using :meth:`.predict`)
Returns:
Trial: self
"""
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)
return self
[docs] 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.
Args:
train_generator: The training data generator to use during calls to :meth:`.run`
val_generator: The validation data generator to use during calls to :meth:`.run` and :meth:`.evaluate`
test_generator: The testing data generator to use during calls to :meth:`.predict`
train_steps (int): The number of steps per epoch to take when using the training generator
val_steps (int): The number of steps per epoch to take when using the validation generator
test_steps (int): The number of steps per epoch to take when using the testing generator
Returns:
Trial: self
"""
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)
return self
[docs] def for_inf_train_steps(self):
"""Use this trial with an infinite number of training steps (until stopped via STOP_TRAINING flag or similar).
Returns self so that methods can be chained for convenience.
Returns:
Trial: self
"""
self.for_train_steps(-1)
return self
[docs] def for_inf_val_steps(self):
"""Use this trial with an infinite number of validation steps (until stopped via STOP_TRAINING flag or similar).
Returns self so that methods can be chained for convenience.
Returns:
Trial: self
"""
self.for_val_steps(-1)
return self
[docs] def for_inf_test_steps(self):
"""Use this trial with an infinite number of test steps (until stopped via STOP_TRAINING flag or similar).
Returns self so that methods can be chained for convenience.
Returns:
Trial: self
"""
self.for_test_steps(-1)
return self
[docs] def for_inf_steps(self, train=True, val=True, test=True):
"""Use this trail with infinite steps. Returns self so that methods can be chained for convenience.
Args:
train (bool): Use an infinite number of training steps
val (bool): Use an infinite number of validation steps
test (bool): Use an infinite number of test steps
Returns:
Trial: self
"""
if train: self.for_inf_train_steps()
if val: self.for_inf_val_steps()
if test: self.for_inf_test_steps()
return self
[docs] def with_inf_train_loader(self):
"""Use this trial with a training iterator that refreshes when it finishes instead of each epoch.
This allows for setting training steps less than the size of the generator and model will still be trained on
all training samples if enough "epochs" are run.
Returns:
Trial: self:
"""
self.state[torchbearer.INF_TRAIN_LOADING] = True
return self
[docs] def with_closure(self, closure):
"""Use this trial with custom closure
Args:
closure (function): Function of state that defines the custom closure
Returns:
Trial: self:
"""
self.closure = closure
return self
[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]
@staticmethod
def _new_iter(generator):
if generator is None:
return None
if hasattr(generator, 'inf') and generator.inf: # Inf train loader deals with the iterator itself
return generator.tb_iter
else:
return iter(generator)
@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] = Trial._new_iter(state[torchbearer.GENERATOR])
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]) if state[torchbearer.STEPS] != -1 else itertools.count()):
state[torchbearer.SAMPLER].sample(state)
state[torchbearer.CALLBACK_LIST].on_sample(state)
# Update parameters
state[torchbearer.OPTIMIZER].step(lambda: self.closure(state))
state[torchbearer.METRICS] = state[torchbearer.METRIC_LIST].process(state.data)
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.data))
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
try:
state[torchbearer.Y_PRED] = state[torchbearer.MODEL](state[torchbearer.X], state=state)
except TypeError:
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:
# Loss Calculation
try:
state[torchbearer.LOSS] = state[torchbearer.CRITERION](state)
except TypeError:
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.data)
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.data))
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.
Args:
verbose (int): If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress, If -1: Automatic
data_key (StateKey): Optional :class:`.StateKey` for the data to evaluate on. Default: torchbearer.VALIDATION_DATA
Returns:
dict: The final metric values
"""
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.
Args:
verbose (int): If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress, If -1: Automatic
data_key (StateKey): Optional :class:`.StateKey` for the data to predict on. Default: torchbearer.TEST_DATA
Returns:
list: Model outputs as a 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] 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.
Args:
callbacks (list): List of callbacks to be run during the replay
verbose (int): If 2: use tqdm on batch, If 1: use tqdm on epoch, If 0: display no training progress
one_batch (bool): If True, only one batch per epoch is replayed. If False, all batches are replayed
Returns:
Trial: self
"""
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)
return self
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] if state[torchbearer.TRAIN_STEPS] is not None else 0
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] if state[torchbearer.VALIDATION_STEPS] is not None else 0
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)
return self
[docs] def train(self):
"""Set model and metrics to training mode.
Returns:
Trial: self
"""
self.state[torchbearer.MODEL].train()
self.state[torchbearer.METRIC_LIST].train()
return self
[docs] def eval(self):
"""Set model and metrics to evaluation mode
Returns:
Trial: self
"""
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()
return self
[docs] def to(self, *args, **kwargs):
""" Moves and/or casts the parameters and buffers.
Args:
args: See: `torch.nn.Module.to <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.to>`_
kwargs: See: `torch.nn.Module.to <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.to>`_
Returns:
Trial: self
"""
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)
return self
[docs] def cuda(self, device=None):
""" Moves all model parameters and buffers to the GPU.
Args:
device (int): if specified, all parameters will be copied to that device
Returns:
Trial: self
"""
if device is None:
device = torch.cuda.current_device()
self.to('cuda:' + str(device))
return self
[docs] def cpu(self):
""" Moves all model parameters and buffers to the CPU.
Returns:
Trial: self
"""
self.to('cpu')
return self
[docs] def state_dict(self, **kwargs):
"""Get a dict containing the model and optimizer states, as well as the model history.
Args:
kwargs: See: `torch.nn.Module.state_dict <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.state_dict>`_
Returns:
dict: A dict containing parameters and persistent buffers.
"""
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] 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.
Args:
state_dict (dict): The state dict to reload
resume (bool): If True, resume from the given state. Else, just load in the model weights.
kwargs: See: `torch.nn.Module.load_state_dict <https://pytorch.org/docs/stable/nn.html?highlight=#torch.nn.Module.load_state_dict>`_
Returns:
Trial: self
"""
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)
return self