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Zero
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import time
from typing import Mapping, Union
from ignite.contrib.handlers import TensorboardLogger
from ignite.handlers import global_step_from_engine
from ignite.contrib.handlers.base_logger import BaseHandler
from ignite.engine import Engine, EventEnum, Events
import torch
def add_time_handlers(engine: Engine):
iteration_time_handler = TimeHandler("iter", freq=True, period=True)
batch_time_handler = TimeHandler("get_batch", freq=False, period=True)
engine.add_event_handler(
Events.ITERATION_STARTED, iteration_time_handler.start_timing
)
engine.add_event_handler(
Events.ITERATION_COMPLETED, iteration_time_handler.end_timing
)
engine.add_event_handler(Events.GET_BATCH_STARTED, batch_time_handler.start_timing)
engine.add_event_handler(Events.GET_BATCH_COMPLETED, batch_time_handler.end_timing)
class MetricLoggingHandler(BaseHandler):
def __init__(
self,
tag,
optimizer=None,
log_loss=True,
log_metrics=True,
log_timings=True,
global_step_transform=None,
):
self.tag = tag
self.optimizer = optimizer
self.log_loss = log_loss
self.log_metrics = log_metrics
self.log_timings = log_timings
self.gst = global_step_transform
super(MetricLoggingHandler, self).__init__()
def __call__(
self,
engine: Engine,
logger: TensorboardLogger,
event_name: Union[str, EventEnum],
):
if not isinstance(logger, TensorboardLogger):
raise RuntimeError(
"Handler 'MetricLoggingHandler' works only with TensorboardLogger"
)
if self.gst is None:
gst = global_step_from_engine(engine)
else:
gst = self.gst
global_step = gst(engine, event_name) # type: ignore[misc]
if not isinstance(global_step, int):
raise TypeError(
f"global_step must be int, got {type(global_step)}."
" Please check the output of global_step_transform."
)
writer = logger.writer
# Optimizer parameters
if self.optimizer is not None:
params = {
k: float(param_group["lr"])
for k, param_group in enumerate(self.optimizer.param_groups)
}
for k, param in params.items():
writer.add_scalar(f"lr-{self.tag}/{k}", param, global_step)
if self.log_loss:
# Plot losses
loss_dict = engine.state.output["loss_dict"]
for k, v in loss_dict.items():
# TODO: is this needed?
# if not isinstance(v, (float, int)):
# print(f"{k}: {type(v)}")
writer.add_scalar(f"loss-{self.tag}/{k}", v, global_step)
if self.log_metrics:
# Plot metrics
metrics_dict = engine.state.metrics
metrics_dict_custom = engine.state.output["metrics_dict"]
for k, v in metrics_dict.items():
# Avoid dictionaries because of weird ignite handling of Mapping metrics
if isinstance(v, Mapping) or k.endswith("assignment"): # TODO: Remove hard-coded assignment
continue
if isinstance(v, torch.Tensor) and v.ndim > 0:
writer.add_histogram(f"metrics-{self.tag}/{k}", v, global_step)
else:
writer.add_scalar(f"metrics-{self.tag}/{k}", v, global_step)
for k, v in metrics_dict_custom.items():
if isinstance(v, Mapping):
continue
if isinstance(v, torch.Tensor) and v.ndim > 0:
writer.add_histogram(f"metrics-{self.tag}/{k}", v, global_step)
else:
writer.add_scalar(f"metrics-{self.tag}/{k}", v, global_step)
if self.log_timings:
# Plot timings
timings_dict = engine.state.times
timings_dict_custom = engine.state.output["timings_dict"]
for k, v in timings_dict.items():
if k == "COMPLETED":
continue
writer.add_scalar(f"timing-{self.tag}/{k}", v, global_step)
for k, v in timings_dict_custom.items():
writer.add_scalar(f"timing-{self.tag}/{k}", v, global_step)
engine.state.output = None # For memory efficiency, val results do not need to stay in the state
class TimeHandler:
def __init__(self, name: str, freq: bool = False, period: bool = False) -> None:
self.name = name
self.freq = freq
self.period = period
if not self.period and not self.freq:
print(f"Warning: No timings logged for {name}")
self._start_time = None
def start_timing(self, engine):
self._start_time = time.time()
def end_timing(self, engine):
if self._start_time is None:
period = 0
freq = 0
else:
period = max(time.time() - self._start_time, 1e-6)
freq = 1 / period
if not hasattr(engine.state, "times"):
engine.state.times = {}
else:
if self.period:
engine.state.times[f"secs_per_{self.name}"] = period
if self.freq:
engine.state.times[f"num_{self.name}_per_sec"] = freq
class VisualizationHandler(BaseHandler):
def __init__(self, tag, visualizer, global_step_transform=None):
self.tag = tag
self.visualizer = visualizer
self.gst = global_step_transform
super(VisualizationHandler, self).__init__()
def __call__(
self,
engine: Engine,
logger: TensorboardLogger,
event_name: Union[str, EventEnum],
) -> None:
if not isinstance(logger, TensorboardLogger):
raise RuntimeError(
"Handler 'VisualizationHandler' works only with TensorboardLogger"
)
if self.gst is None:
gst = global_step_from_engine(engine)
else:
gst = self.gst
global_step = gst(engine, event_name) # type: ignore[misc]
if not isinstance(global_step, int):
raise TypeError(
f"global_step must be int, got {type(global_step)}."
" Please check the output of global_step_transform."
)
self.visualizer(engine, logger, global_step, self.tag)
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