From 0ce6787ac105276abefa295b6f2eefc9b74ba43d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=C3=A9lodie=20Boillet?= <boillet@teklia.com>
Date: Wed, 24 May 2023 14:28:31 +0200
Subject: [PATCH] Remove list of applied data augmentations
---
dan/manager/dataset.py | 2 +-
dan/manager/ocr.py | 6 +-----
dan/transforms.py | 6 ++----
3 files changed, 4 insertions(+), 10 deletions(-)
diff --git a/dan/manager/dataset.py b/dan/manager/dataset.py
index 2b28e6dc..942ba5c2 100644
--- a/dan/manager/dataset.py
+++ b/dan/manager/dataset.py
@@ -336,7 +336,7 @@ class GenericDataset(Dataset):
for aug, set_name in zip(augs, ["train", "val", "test"]):
if aug and self.set_name == set_name:
return apply_data_augmentation(img, aug)
- return img, list()
+ return img
def get_sample_img(self, i):
"""
diff --git a/dan/manager/ocr.py b/dan/manager/ocr.py
index bd464073..0ca7ab3d 100644
--- a/dan/manager/ocr.py
+++ b/dan/manager/ocr.py
@@ -136,9 +136,7 @@ class OCRDataset(GenericDataset):
sample = self.generate_synthetic_data(sample)
# Data augmentation
- sample["img"], sample["applied_da"] = self.apply_data_augmentation(
- sample["img"]
- )
+ sample["img"] = self.apply_data_augmentation(sample["img"])
if "max_size" in self.params["config"] and self.params["config"]["max_size"]:
max_ratio = max(
@@ -523,7 +521,6 @@ class OCRCollateFunction:
batch_data[i]["name"].split("/")[-1].split(".")[0]
for i in range(len(batch_data))
]
- applied_da = [batch_data[i]["applied_da"] for i in range(len(batch_data))]
labels = [batch_data[i]["token_label"] for i in range(len(batch_data))]
labels = pad_sequences_1D(labels, padding_value=self.label_padding_value)
@@ -630,7 +627,6 @@ class OCRCollateFunction:
"word_raw": word_raw,
"word_labels": pad_word_token,
"word_labels_len": word_len,
- "applied_da": applied_da,
}
return formatted_batch_data
diff --git a/dan/transforms.py b/dan/transforms.py
index 89cb5e29..957a6453 100644
--- a/dan/transforms.py
+++ b/dan/transforms.py
@@ -328,9 +328,8 @@ def apply_data_augmentation(img, da_config):
"""
Apply data augmentation strategy on input image
"""
- applied_da = list()
if da_config["proba"] != 1 and rand() > da_config["proba"]:
- return img, applied_da
+ return img
# Convert to PIL Image
img = img[:, :, 0] if img.shape[2] == 1 else img
@@ -345,12 +344,11 @@ def apply_data_augmentation(img, da_config):
for augmenter in augmenters:
img = augmenter(img)
- applied_da.append(type(augmenter).__name__)
# convert to numpy array
img = np.array(img)
img = np.expand_dims(img, axis=2) if len(img.shape) == 2 else img
- return img, applied_da
+ return img
def apply_transform(img, transform):
--
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