def _compute_validation_metrics(self) -> workload.Response:
self.context.experimental.reset_reducers()
# Set the behavior of certain layers (e.g., dropout) that are
# different between training and inference.
for model in self.context.models:
model.eval()
for callback in self.callbacks.values():
logging.warning(
"on_validation_step_start is now deprecated, please use on_validation_start instead"
)
callback.on_validation_step_start()
for callback in self.callbacks.values():
callback.on_validation_start()
num_inputs = 0
metrics = {} # type: Dict[str, Any]
if self._evaluate_batch_defined():
keys = None
batch_metrics = []
self.validation_loader = cast(torch.utils.data.DataLoader,
self.validation_loader)
check.gt(len(self.validation_loader), 0)
for batch in self.validation_loader:
batch = self.context.to_device(batch)
num_inputs += pytorch.data_length(batch)
vld_metrics = self.trial.evaluate_batch(batch=batch)
# Verify validation metric names are the same across batches.
if keys is None:
keys = vld_metrics.keys()
else:
check.eq(
keys,
vld_metrics.keys(),
"Validation metric names must match across all batches of data.",
)
check.is_instance(
vld_metrics,
dict,
"validation_metrics() must return a "
"dictionary of string names to Tensor "
"metrics",
)
# TODO: For performance perform -> cpu() only at the end of validation.
batch_metrics.append(
self._convert_metrics_to_numpy(vld_metrics))
if self.env.test_mode:
break
metrics = self._reduce_metrics(
batch_metrics=batch_metrics,
keys=keys,
metrics_reducers=self._prepare_metrics_reducers(keys=keys),
)
if self.hvd_config.use:
num_inputs *= hvd.size()
else:
check.true(self._evaluate_full_dataset_defined())
self.validation_loader = cast(torch.utils.data.DataLoader,
self.validation_loader)
if self.is_chief:
metrics = self.trial.evaluate_full_dataset(
data_loader=self.validation_loader)
check.is_instance(
metrics, dict,
f"eval() must return a dictionary, got {type(metrics)}.")
metrics = self._convert_metrics_to_numpy(metrics)
num_inputs = self.context.get_per_slot_batch_size() * len(
self.validation_loader)
metrics.update(
self._convert_metrics_to_numpy(
self.context.experimental.reduce_metrics(for_training=False)))
if self.hvd_config.use and any(
map(
lambda c: util.is_overridden(
c.on_validation_end, pytorch.
PyTorchCallback) or util.is_overridden(
c.on_validation_step_end, pytorch.PyTorchCallback),
self.callbacks.values(),
)):
logging.debug(
"Broadcasting metrics to all worker processes to execute a "
"validation step end callback")
metrics = hvd.broadcast_object(metrics, root_rank=0)
for callback in self.callbacks.values():
logging.warning(
"on_validation_step_end is now deprecated, please use on_validation_end instead"
)
callback.on_validation_step_end(metrics)
for callback in self.callbacks.values():
callback.on_validation_end(metrics)
if not self.is_chief:
return workload.Skipped()
return {"num_inputs": num_inputs, "validation_metrics": metrics}
def _compute_validation_metrics(self) -> workload.Response:
self.context.reset_reducers()
# Set the behavior of certain layers (e.g., dropout) that are
# different between training and inference.
for model in self.context.models:
model.eval()
step_start_time = time.time()
for callback in self.callbacks.values():
if util.is_overridden(callback.on_validation_step_start,
pytorch.PyTorchCallback):
logging.warning("on_validation_step_start is now deprecated, "
"please use on_validation_start instead")
callback.on_validation_step_start()
for callback in self.callbacks.values():
callback.on_validation_start()
num_inputs = 0
metrics = {} # type: Dict[str, Any]
if self._evaluate_batch_defined():
keys = None
batch_metrics = []
self.validation_loader = cast(torch.utils.data.DataLoader,
self.validation_loader)
check.gt(len(self.validation_loader), 0)
for callback in self.callbacks.values():
callback.on_validation_epoch_start()
for idx, batch in enumerate(self.validation_loader):
if self.context.experimental._auto_to_device:
batch = self.context.to_device(batch)
num_inputs += self.trial.get_batch_length(batch)
if has_param(self.trial.evaluate_batch, "batch_idx", 2):
vld_metrics = self.trial.evaluate_batch(batch=batch,
batch_idx=idx)
else:
vld_metrics = self.trial.evaluate_batch(
batch=batch) # type: ignore
# Verify validation metric names are the same across batches.
if keys is None:
keys = vld_metrics.keys()
else:
check.eq(
keys,
vld_metrics.keys(),
"Validation metric names must match across all batches of data.",
)
check.is_instance(
vld_metrics,
dict,
"validation_metrics() must return a "
"dictionary of string names to Tensor "
"metrics",
)
# TODO: For performance perform -> cpu() only at the end of validation.
batch_metrics.append(
pytorch._convert_metrics_to_numpy(vld_metrics))
if self.env.test_mode:
break
for callback in self.callbacks.values():
callback.on_validation_epoch_end(batch_metrics)
metrics = pytorch._reduce_metrics(
self.context.distributed,
batch_metrics=batch_metrics,
keys=keys,
metrics_reducers=pytorch._prepare_metrics_reducers(
self.trial.evaluation_reducer(), keys=keys),
)
# Gather a list of per-worker (num_inputs, num_batches) tuples.
input_counts = self.context.distributed.gather(
(num_inputs, idx + 1))
if self.context.distributed.rank == 0:
assert input_counts is not None
# Reshape and sum.
num_inputs, num_batches = [sum(n) for n in zip(*input_counts)]
else:
check.true(self._evaluate_full_dataset_defined())
self.validation_loader = cast(torch.utils.data.DataLoader,
self.validation_loader)
if self.is_chief:
metrics = self.trial.evaluate_full_dataset(
data_loader=self.validation_loader)
check.is_instance(
metrics, dict,
f"eval() must return a dictionary, got {type(metrics)}.")
metrics = pytorch._convert_metrics_to_numpy(metrics)
num_inputs = self.context.get_per_slot_batch_size() * len(
self.validation_loader)
metrics.update(
pytorch._convert_metrics_to_numpy(
self.context.reduce_metrics(for_training=False)))
if self.context.distributed.size > 1 and any(
map(
lambda c: util.is_overridden(
c.on_validation_end, pytorch.
PyTorchCallback) or util.is_overridden(
c.on_validation_step_end, pytorch.PyTorchCallback),
self.callbacks.values(),
)):
logging.debug(
"Broadcasting metrics to all worker processes to execute a "
"validation step end callback")
metrics = hvd.broadcast_object(metrics, root_rank=0)
for callback in self.callbacks.values():
if util.is_overridden(callback.on_validation_step_end,
pytorch.PyTorchCallback):
logging.warning(
"on_validation_step_end is now deprecated, please use on_validation_end instead"
)
callback.on_validation_step_end(metrics)
for callback in self.callbacks.values():
callback.on_validation_end(metrics)
if not self.is_chief:
return {}
# Skip reporting timings if evaluate_full_dataset() was defined. This is far less common
# than evaluate_batch() and we can't know how the user processed their validation data.
if self._evaluate_batch_defined():
step_duration = time.time() - step_start_time
logging.info(
det.util.make_timing_log("validated", step_duration,
num_inputs, num_batches))
return {"num_inputs": num_inputs, "validation_metrics": metrics}
