From 378a86c087e23f495c9cc1252a0f377be0da641b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=C3=A9lodie=20Boillet?= <boillet@teklia.com>
Date: Tue, 30 May 2023 09:14:54 +0000
Subject: [PATCH] Remove growing models
---
dan/manager/training.py | 18 ------------------
1 file changed, 18 deletions(-)
diff --git a/dan/manager/training.py b/dan/manager/training.py
index 1c7f157b..72acce30 100644
--- a/dan/manager/training.py
+++ b/dan/manager/training.py
@@ -357,7 +357,6 @@ class GenericTrainingManager:
Load the optimizer of each model
"""
for model_name in self.models.keys():
- new_params = dict()
if (
checkpoint
and "optimizer_named_params_{}".format(model_name) in checkpoint
@@ -365,16 +364,6 @@ class GenericTrainingManager:
self.optimizers_named_params_by_group[model_name] = checkpoint[
"optimizer_named_params_{}".format(model_name)
]
- # for progressively growing models
- for name, param in self.models[model_name].named_parameters():
- existing = False
- for gr in self.optimizers_named_params_by_group[model_name]:
- if name in gr:
- gr[name] = param
- existing = True
- break
- if not existing:
- new_params.update({name: param})
else:
self.optimizers_named_params_by_group[model_name] = [
dict(),
@@ -420,13 +409,6 @@ class GenericTrainingManager:
checkpoint["lr_scheduler_{}_state_dict".format(model_name)]
)
- # for progressively growing models, keeping learning rate
- if checkpoint and new_params:
- self.optimizers_named_params_by_group[model_name].append(new_params)
- self.optimizers[model_name].add_param_group(
- {"params": list(new_params.values())}
- )
-
@staticmethod
def set_model_learnable(model, learnable=True):
for p in list(model.parameters()):
--
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