def create_visdom_data(self, event: TemporalEvent,
model_trainer: ModelTrainer):
avg_grads, layers = [], []
for n, p in model_trainer.named_parameters():
if p.requires_grad and ("bias" not in n):
layers.append(n)
if p.grad is not None:
avg_grads.append(p.grad.abs().mean().item())
else:
avg_grads.append(0)
return VisdomData(model_trainer.name,
"Gradient Flow",
PlotType.BAR_PLOT,
event.frequency,
y=layers,
x=avg_grads,
params={
'opts': {
'xlabel':
"Layers",
'ylabel':
"Avg. Gradients",
'title':
"{} {} per {}".format(
model_trainer.name, "Avg. Gradient",
"Layer"),
'marginbottom':
200
}
})
def create_visdom_data(self, event: TemporalEvent, trainer):
if self._plot_type == PlotType.LINE_PLOT and "name" not in self._params[
'opts'].keys():
self._params['opts']['name'] = str(event.phase)
return [
VisdomData(trainer.name, self._variable_name, self._plot_type,
event.frequency, [event.iteration],
trainer.custom_variables[self._variable_name],
self._params)
]
def create_visdom_data(self, event, model_name, monitors):
return [
VisdomData(model_name,
loss_name,
PlotType.LINE_PLOT,
event.frequency, [[event.iteration]], [[loss_value]],
params={
'opts': {
'xlabel':
str(event.frequency),
'ylabel':
loss_name,
'title':
"{} {} per {}".format(model_name, loss_name,
str(event.frequency)),
'name':
str(event.phase),
'legend': [str(event.phase)]
}
}) for loss_name, loss_value in monitors.items()
]
def create_visdom_data(self, event, model_trainer: ModelTrainer):
return VisdomData(model_trainer.name,
"Learning Rate",
PlotType.LINE_PLOT,
event.frequency, [event.iteration],
model_trainer.optimizer_lr,
params={
'opts': {
'xlabel':
str(event.frequency),
'ylabel':
"Learning Rate",
'title':
"{} {} per {}".format(
model_trainer.name, "Learning Rate",
str(event.frequency)),
'name':
model_trainer.name,
'legend': [model_trainer.name]
}
})
if visdom_config.save_destination is not None:
save_folder = visdom_config.save_destination + os.path.join(
exp[0], exp[1],
os.path.basename(os.path.normpath(visdom_config.env)))
else:
save_folder = "saves/{}".format(
os.path.basename(os.path.normpath(visdom_config.env)))
[
os.makedirs("{}/{}".format(save_folder, model), exist_ok=True)
for model in ["Discriminator", "Generator", "Segmenter"]
]
visdom_logger = VisdomLogger(visdom_config)
visdom_logger(
VisdomData("Experiment", "Experiment Config", PlotType.TEXT_PLOT,
PlotFrequency.EVERY_EPOCH, None, config_html))
visdom_logger(
VisdomData(
"Experiment",
"Patch count",
PlotType.BAR_PLOT,
PlotFrequency.EVERY_EPOCH,
x=[
len(iSEG_train) if iSEG_train is not None else 0,
len(MRBrainS_train) if MRBrainS_train is not None else 0,
len(ABIDE_train) if ABIDE_train is not None else 0
],
y=["iSEG", "MRBrainS", "ABIDE"],
params={"opts": {
"title": "Patch count"
}}))