From ded809b1f198e0b41686b9c27117967337d8ba3f Mon Sep 17 00:00:00 2001
From: manonBlanco <blanco@teklia.com>
Date: Thu, 17 Aug 2023 16:11:14 +0200
Subject: [PATCH] Rename predict and evaluation functions
---
dan/ocr/manager/training.py | 10 +++++-----
dan/ocr/train.py | 2 +-
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/dan/ocr/manager/training.py b/dan/ocr/manager/training.py
index 97d37a23..2b13c515 100644
--- a/dan/ocr/manager/training.py
+++ b/dan/ocr/manager/training.py
@@ -670,7 +670,7 @@ class GenericTrainingManager:
):
for valid_set_name in self.dataset.valid_loaders:
# evaluate set and compute metrics
- eval_values = self.evaluate(
+ eval_values = self.validate(
valid_set_name, mlflow_logging=mlflow_logging
)
# log valid metrics in tensorboard file
@@ -702,7 +702,7 @@ class GenericTrainingManager:
self.save_model(epoch=num_epoch, name="last")
self.writer.flush()
- def evaluate(self, set_name, mlflow_logging=False, **kwargs):
+ def validate(self, set_name, mlflow_logging=False, **kwargs):
"""
Main loop for validation
"""
@@ -720,7 +720,7 @@ class GenericTrainingManager:
tokens=self.tokens,
)
with tqdm(total=len(loader.dataset)) as pbar:
- pbar.set_description("Evaluation E{}".format(self.latest_epoch))
+ pbar.set_description("Validation E{}".format(self.latest_epoch))
with torch.no_grad():
# iterate over batch data
for ind_batch, batch_data in enumerate(loader):
@@ -751,7 +751,7 @@ class GenericTrainingManager:
)
return display_values
- def predict(
+ def evaluate(
self, custom_name, sets_list, metric_names, mlflow_logging=False, output=False
):
"""
@@ -772,7 +772,7 @@ class GenericTrainingManager:
)
with tqdm(total=len(loader.dataset)) as pbar:
- pbar.set_description("Prediction")
+ pbar.set_description("Evaluation")
with torch.no_grad():
for ind_batch, batch_data in enumerate(loader):
# iterates over batch data
diff --git a/dan/ocr/train.py b/dan/ocr/train.py
index 7aea40fd..bb0bc48e 100644
--- a/dan/ocr/train.py
+++ b/dan/ocr/train.py
@@ -50,7 +50,7 @@ def train_and_test(rank, params, mlflow_logging=False):
metrics = ["cer", "wer", "wer_no_punct", "time"]
for dataset_name in params["dataset"]["datasets"]:
for set_name in ["test", "val", "train"]:
- model.predict(
+ model.evaluate(
"{}-{}".format(dataset_name, set_name),
[
(dataset_name, set_name),
--
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