def predict_generator(self,
generator,
verbose=2,
steps=None,
pass_state=False):
"""Perform a prediction loop on given data generator to predict labels
:param generator: The prediction data generator (usually a pytorch DataLoader)
:type generator: DataLoader
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, Else: display no progress
:type verbose: int
:param steps: The number of evaluation mini-batches to run
:type steps: int
:param pass_state: If True the state dictionary is passed to the torch model forward method, if False only the input data is passed
:type pass_state: bool
:return: Tensor of final predicted labels
:rtype: torch.Tensor
"""
state = {
torchbearer.EPOCH: 0,
torchbearer.MAX_EPOCHS: 1,
torchbearer.STOP_TRAINING: False,
torchbearer.VALIDATION_GENERATOR: generator
}
state.update(self.main_state)
_callbacks = Model._add_printer([AggregatePredictions()],
verbose,
validation_label_letter='p')
self._test_loop(state, CallbackList(_callbacks), pass_state,
self._load_batch_predict, steps)
return state[torchbearer.FINAL_PREDICTIONS]
def evaluate_generator(self, generator, verbose=2, steps=None, pass_state=False):
""" Perform an evaluation loop on given data generator to evaluate metrics
:param generator: The evaluation data generator (usually a pytorch DataLoader)
:type generator: DataLoader
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, Else: display no progress
:type verbose: int
:param steps: The number of evaluation mini-batches to run
:type steps: int
:param pass_state: If True the state dictionary is passed to the torch model forward method, if False only the input data is passed
:type pass_state: bool
:return: The dictionary containing final metrics
:rtype: dict[str,any]
"""
state = {torchbearer.EPOCH: 0, torchbearer.MAX_EPOCHS: 1, torchbearer.STOP_TRAINING: False, torchbearer.VALIDATION_GENERATOR: generator}
state.update(self.main_state)
_callbacks = Model._add_printer([], verbose, validation_label_letter='e')
if state[torchbearer.VALIDATION_GENERATOR] is None:
batch_loader = self._load_batch_none
else:
batch_loader = self._load_batch_standard
self._test_loop(state, CallbackList(_callbacks), pass_state, batch_loader, steps)
return state[torchbearer.METRICS]
def evaluate_generator(self,
generator,
verbose=1,
steps=None,
pass_state=False):
""" Perform an evaluation loop on given data generator to evaluate metrics
:param generator: The evaluation data generator (usually a pytorch DataLoader)
:type generator: DataLoader
:param verbose: If 1 use tqdm progress frontend, else display no training progress
:type verbose: int
:param steps: The number of evaluation mini-batches to run
:type steps: int
:param pass_state: If True the state dictionary is passed to the torch model forward method, if False only the input data is passed
:type pass_state: bool
:return: The dictionary containing final metrics
:rtype: dict[str,any]
"""
state = {
torchbearer.EPOCH: 0,
torchbearer.MAX_EPOCHS: 1,
torchbearer.STOP_TRAINING: False,
torchbearer.VALIDATION_GENERATOR: generator
}
state.update(self.main_state)
_callbacks = []
if verbose == 1:
_callbacks.append(Tqdm('e'))
self._test_loop(state, CallbackList(_callbacks), pass_state,
self._load_batch_standard, steps)
return state[torchbearer.METRICS]
def fit_generator(self,
generator,
train_steps=None,
epochs=1,
verbose=2,
callbacks=[],
validation_generator=None,
validation_steps=None,
initial_epoch=0,
pass_state=False):
""" Perform fitting of a model to given data generator
:param generator: The training data generator (usually a pytorch DataLoader)
:type generator: DataLoader
:param train_steps: The number of training mini-batches to run per epoch
:type train_steps: int
:param epochs: The number of training epochs to be run (each sample from the dataset is viewed exactly once)
:type epochs: int
:param verbose: If 2: use tqdm on batch, If 1: use tqdm on epoch, Else: display no training progress
:type verbose: int
:param callbacks: The list of torchbearer callbacks to be called during training and validation
:type callbacks: list
:param validation_generator: The validation data generator (usually a pytorch DataLoader)
:type validation_generator: DataLoader
:param validation_steps: The number of validation mini-batches to run per epoch
:type validation_steps: int
:param initial_epoch: The integer value representing the first epoch - useful for continuing training after a number of epochs
:type initial_epoch: int
:param pass_state: If True the state dictionary is passed to the torch model forward method, if False only the input data is passed
:type pass_state: bool
:return: The final state context dictionary
:rtype: dict[str,any]
"""
callbacks = Model._add_printer(callbacks, verbose)
_callbacks = CallbackList(callbacks)
# Get train and validation steps
if validation_steps is None and validation_generator is not None:
validation_steps = len(validation_generator)
if train_steps is None:
train_steps = len(generator)
if generator is not None and train_steps > len(generator):
train_steps = len(generator)
if not isinstance(train_steps, int):
train_steps = int(train_steps)
warnings.warn(
"Number of training steps is not an int, converting to int")
if not isinstance(epochs, int):
if isinstance(epochs, float):
epochs = int(epochs)
warnings.warn("Number of epochs is a float, converting to int")
else:
warnings.warn(
"Number of epochs is neither float nor int, setting to 0")
epochs = 0
# Init state
state = {
torchbearer.MAX_EPOCHS: epochs,
torchbearer.TRAIN_STEPS: train_steps,
torchbearer.BATCH: 0,
torchbearer.GENERATOR: generator,
torchbearer.STOP_TRAINING: False
}
state.update(self.main_state)
state[torchbearer.CALLBACK_LIST] = state[
torchbearer.CALLBACK_LIST].copy()
state[torchbearer.CALLBACK_LIST].append(_callbacks)
state[torchbearer.CALLBACK_LIST].on_start(state)
for state[torchbearer.EPOCH] in range(initial_epoch, epochs):
state[torchbearer.CALLBACK_LIST].on_start_epoch(state)
if state[torchbearer.GENERATOR] is not None:
state[torchbearer.TRAIN_ITERATOR] = iter(
state[torchbearer.GENERATOR])
self.train()
state[torchbearer.CALLBACK_LIST].on_start_training(state)
state[torchbearer.METRIC_LIST].reset(state)
state[torchbearer.METRICS] = {}
for state[torchbearer.BATCH] in range(
0, state[torchbearer.TRAIN_STEPS]):
# Extract batch
if state[torchbearer.
GENERATOR] is None: # TODO: Replace with flag check
self._load_batch_none('train', state)
else:
self._load_batch_standard('train', state)
state[torchbearer.CALLBACK_LIST].on_sample(state)
# Zero grads
state[torchbearer.OPTIMIZER].zero_grad()
# Forward pass
if 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)
state[torchbearer.METRICS] = state[
torchbearer.METRIC_LIST].process(state)
# Backwards pass
state[torchbearer.LOSS].backward()
state[torchbearer.CALLBACK_LIST].on_backward(state)
# Update parameters
state[torchbearer.OPTIMIZER].step()
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))
final_metrics = state[torchbearer.METRICS]
state[torchbearer.CALLBACK_LIST].on_end_training(state)
# Validate
if validation_generator is not None or validation_steps is not None:
state[torchbearer.VALIDATION_GENERATOR] = validation_generator
state[torchbearer.VALIDATION_STEPS] = validation_steps
self.eval()
self._validate(state, state[torchbearer.CALLBACK_LIST],
pass_state)
final_metrics.update(state[torchbearer.METRICS])
state[torchbearer.METRICS] = final_metrics
state[torchbearer.CALLBACK_LIST].on_end_epoch(state)
if state[torchbearer.STOP_TRAINING]:
break
state[torchbearer.CALLBACK_LIST].on_end(state)
return state
def fit_generator(self, generator, train_steps=None, epochs=1, verbose=1, callbacks=[],
validation_generator=None, validation_steps=None, initial_epoch=0, pass_state=False):
""" Perform fitting of a model to given data generator
:param generator: The training data generator (usually a pytorch DataLoader)
:type generator: DataLoader
:param train_steps: The number of training mini-batches to run per epoch
:type train_steps: int
:param epochs: The number of training epochs to be run (each sample from the dataset is viewed exactly once)
:type epochs: int
:param verbose: If 1 use tqdm progress frontend, else display no training progress
:type verbose: int
:param callbacks: The list of torchbearer callbacks to be called during training and validation
:type callbacks: list
:param validation_generator: The validation data generator (usually a pytorch DataLoader)
:type validation_generator: DataLoader
:param validation_steps: The number of validation mini-batches to run per epoch
:type validation_steps: int
:param initial_epoch: The integer value representing the first epoch - useful for continuing training after a number of epochs
:type initial_epoch: int
:param pass_state: If True the state dictionary is passed to the torch model forward method, if False only the input data is passed
:type pass_state: bool
:return: The final state context dictionary
:rtype: dict[str,any]
"""
if verbose == 1:
callbacks = [Tqdm()] + callbacks
_callbacks = CallbackList(callbacks)
# Get train and validation steps
if validation_steps is None and validation_generator is not None:
validation_steps = len(validation_generator)
if train_steps is None or train_steps > len(generator):
train_steps = len(generator)
if not isinstance(train_steps, int):
train_steps = int(train_steps)
warnings.warn("Number of training steps is not an int, converting to int")
if not isinstance(epochs, int):
if isinstance(epochs, float):
epochs = int(epochs)
warnings.warn("Number of epochs is a float, converting to int")
else:
warnings.warn("Number of epochs is neither float nor int, setting to 0")
epochs = 0
# Init state
state = {
torchbearer.MAX_EPOCHS: epochs,
torchbearer.TRAIN_STEPS: train_steps,
torchbearer.BATCH: 0,
torchbearer.GENERATOR: generator,
torchbearer.STOP_TRAINING: False
}
state.update(self.main_state)
_callbacks.on_start(state)
for state[torchbearer.EPOCH] in range(initial_epoch, epochs):
_callbacks.on_start_epoch(state)
state[torchbearer.TRAIN_ITERATOR] = iter(state[torchbearer.GENERATOR])
self.train()
_callbacks.on_start_training(state)
state[torchbearer.METRIC_LIST].reset(state)
state[torchbearer.METRICS] = {}
for state[torchbearer.BATCH] in range(0, state[torchbearer.TRAIN_STEPS]):
# Extract batch
self._load_batch_standard('train', state)
_callbacks.on_sample(state)
# Zero grads
state[torchbearer.OPTIMIZER].zero_grad()
# Forward pass
if 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])
_callbacks.on_forward(state)
# Loss Calculation
state[torchbearer.LOSS] = state[torchbearer.CRITERION](state[torchbearer.Y_PRED], state[torchbearer.Y_TRUE])
_callbacks.on_criterion(state)
state[torchbearer.METRICS] = state[torchbearer.METRIC_LIST].process(state)
# Backwards pass
state[torchbearer.LOSS].backward()
_callbacks.on_backward(state)
# Update parameters
state[torchbearer.OPTIMIZER].step()
_callbacks.on_step_training(state)
if state[torchbearer.STOP_TRAINING]:
break
state[torchbearer.METRICS].update(state[torchbearer.METRIC_LIST].process_final(state))
final_metrics = state[torchbearer.METRICS]
_callbacks.on_end_training(state)
# Validate
if validation_generator is not None:
state[torchbearer.VALIDATION_GENERATOR] = validation_generator
state[torchbearer.VALIDATION_STEPS] = validation_steps
self.eval()
self._validate(state, _callbacks, pass_state)
final_metrics.update(state[torchbearer.METRICS])
state[torchbearer.METRICS] = final_metrics
_callbacks.on_end_epoch(state)
if state[torchbearer.STOP_TRAINING]:
break
_callbacks.on_end(state)
return state