From 85da2f49221a4ddebebfc28144abe17717eb1e72 Mon Sep 17 00:00:00 2001
From: dcarron <daniel.carron@idiap.ch>
Date: Tue, 11 Apr 2023 17:08:06 +0200
Subject: [PATCH] Removed custom checkpointer, saving missing files
---
src/ptbench/engine/trainer.py | 91 +++++++---------------------
src/ptbench/utils/checkpointer.py | 99 -------------------------------
2 files changed, 21 insertions(+), 169 deletions(-)
delete mode 100644 src/ptbench/utils/checkpointer.py
diff --git a/src/ptbench/engine/trainer.py b/src/ptbench/engine/trainer.py
index 41ade3f3..ddacf63c 100644
--- a/src/ptbench/engine/trainer.py
+++ b/src/ptbench/engine/trainer.py
@@ -129,9 +129,9 @@ def save_model_summary(output_folder, model):
summary_path = os.path.join(output_folder, "model_summary.txt")
logger.info(f"Saving model summary at {summary_path}...")
with open(summary_path, "w") as f:
- summary = str(ModelSummary(model, max_depth=-1))
- f.write(summary)
- return summary
+ summary = ModelSummary(model, max_depth=-1)
+ f.write(str(summary))
+ return summary, ModelSummary(model).total_parameters
def static_information_to_csv(static_logfile_name, device, n):
@@ -374,62 +374,6 @@ def validate_epoch(loader, model, device, criterion, pbar_desc):
return numpy.average(batch_losses, weights=samples_in_batch)
-def checkpointer_process(
- checkpointer,
- checkpoint_period,
- valid_loss,
- lowest_validation_loss,
- arguments,
- epoch,
- max_epoch,
-):
- """Process the checkpointer, save the final model and keep track of the
- best model.
-
- Parameters
- ----------
-
- checkpointer : :py:class:`ptbench.utils.checkpointer.Checkpointer`
- checkpointer implementation
-
- checkpoint_period : int
- save a checkpoint every ``n`` epochs. If set to ``0`` (zero), then do
- not save intermediary checkpoints
-
- valid_loss : float
- Current epoch validation loss
-
- lowest_validation_loss : float
- Keeps track of the best (lowest) validation loss
-
- arguments : dict
- start and end epochs
-
- max_epoch : int
- end_potch
-
- Returns
- -------
-
- lowest_validation_loss : float
- The lowest validation loss currently observed
- """
- if checkpoint_period and (epoch % checkpoint_period == 0):
- checkpointer.save("model_periodic_save", **arguments)
-
- if valid_loss is not None and valid_loss < lowest_validation_loss:
- lowest_validation_loss = valid_loss
- logger.info(
- f"Found new low on validation set:" f" {lowest_validation_loss:.6f}"
- )
- checkpointer.save("model_lowest_valid_loss", **arguments)
-
- if epoch >= max_epoch:
- checkpointer.save("model_final_epoch", **arguments)
-
- return lowest_validation_loss
-
-
def write_log_info(
epoch,
current_time,
@@ -578,7 +522,7 @@ def run(
os.makedirs(output_folder, exist_ok=True)
# Save model summary
- _ = save_model_summary(output_folder, model)
+ r, n = save_model_summary(output_folder, model)
csv_logger = CSVLogger(output_folder, "logs_csv")
tensorboard_logger = TensorBoardLogger(output_folder, "logs_tensorboard")
@@ -590,6 +534,22 @@ def run(
logging_level=logging.ERROR,
)
+ checkpoint_callback = ModelCheckpoint(
+ output_folder,
+ "model_lowest_valid_loss",
+ save_last=True,
+ monitor="validation_loss",
+ mode="min",
+ save_on_train_epoch_end=False,
+ every_n_epochs=checkpoint_period,
+ )
+
+ checkpoint_callback.CHECKPOINT_NAME_LAST = "model_final_epoch"
+
+ # write static information to a CSV file
+ static_logfile_name = os.path.join(output_folder, "constants.csv")
+ static_information_to_csv(static_logfile_name, device, n)
+
with resource_monitor:
trainer = Trainer(
accelerator="auto",
@@ -597,16 +557,7 @@ def run(
max_epochs=max_epoch,
logger=[csv_logger, tensorboard_logger],
check_val_every_n_epoch=1,
- callbacks=[
- LoggingCallback(resource_monitor),
- ModelCheckpoint(
- output_folder,
- monitor="validation_loss",
- mode="min",
- save_on_train_epoch_end=False,
- every_n_epochs=checkpoint_period,
- ),
- ],
+ callbacks=[LoggingCallback(resource_monitor), checkpoint_callback],
)
_ = trainer.fit(model, data_loader, valid_loader)
diff --git a/src/ptbench/utils/checkpointer.py b/src/ptbench/utils/checkpointer.py
deleted file mode 100644
index 3e839b0e..00000000
--- a/src/ptbench/utils/checkpointer.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-import logging
-import os
-
-import torch
-
-logger = logging.getLogger(__name__)
-
-
-class Checkpointer:
- """A simple pytorch checkpointer.
-
- Parameters
- ----------
-
- model : torch.nn.Module
- Network model, eventually loaded from a checkpointed file
-
- optimizer : :py:mod:`torch.optim`, Optional
- Optimizer
-
- scheduler : :py:mod:`torch.optim`, Optional
- Learning rate scheduler
-
- path : :py:class:`str`, Optional
- Directory where to save checkpoints.
- """
-
- def __init__(self, model, optimizer=None, scheduler=None, path="."):
- self.model = model
- self.optimizer = optimizer
- self.scheduler = scheduler
- self.path = os.path.realpath(path)
-
- def save(self, name, **kwargs):
- data = {}
- data["model"] = self.model.state_dict()
- if self.optimizer is not None:
- data["optimizer"] = self.optimizer.state_dict()
- if self.scheduler is not None:
- data["scheduler"] = self.scheduler.state_dict()
- data.update(kwargs)
-
- name = f"{name}.pth"
- outf = os.path.join(self.path, name)
- logger.info(f"Saving checkpoint to {outf}")
- torch.save(data, outf)
- with open(self._last_checkpoint_filename, "w") as f:
- f.write(name)
-
- def load(self, f=None):
- """Loads model, optimizer and scheduler from file.
-
- Parameters
- ==========
-
- f : :py:class:`str`, Optional
- Name of a file (absolute or relative to ``self.path``), that
- contains the checkpoint data to load into the model, and optionally
- into the optimizer and the scheduler. If not specified, loads data
- from current path.
- """
- if f is None:
- f = self.last_checkpoint()
-
- if f is None:
- # no checkpoint could be found
- logger.warning("No checkpoint found (and none passed)")
- return {}
-
- # loads file data into memory
- logger.info(f"Loading checkpoint from {f}...")
- checkpoint = torch.load(f, map_location=torch.device("cpu"))
-
- # converts model entry to model parameters
- self.model.load_state_dict(checkpoint.pop("model"))
-
- if self.optimizer is not None:
- self.optimizer.load_state_dict(checkpoint.pop("optimizer"))
- if self.scheduler is not None:
- self.scheduler.load_state_dict(checkpoint.pop("scheduler"))
-
- return checkpoint
-
- @property
- def _last_checkpoint_filename(self):
- return os.path.join(self.path, "last_checkpoint")
-
- def has_checkpoint(self):
- return os.path.exists(self._last_checkpoint_filename)
-
- def last_checkpoint(self):
- if self.has_checkpoint():
- with open(self._last_checkpoint_filename) as fobj:
- return os.path.join(self.path, fobj.read().strip())
- return None
--
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