fix: restore training data augmentation behavior for det model

1. Restore set_epoch_as_seed in SimpleDataSet to re-enable adaptive
   shrink_ratio (0.4→0.6) curriculum learning in MakeBorderMap and
   MakeShrinkMap across training epochs.
2. Remove default p=1.0 for Affine augmentation, restoring
   albumentations default p=0.5 (50% rotation probability).

These two changes caused V5 dataset precision to drop significantly
(e.g. blur 0.904→0.709, printing_ch 0.926→0.708) while V4 remained
unaffected.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: leo-q8

ppocr/data/imaug/iaa_augment.py

@@ -158,8 +158,6 @@ def map_arguments(self, augmenter_type, augmenter_args):
             augmenter_args["rotate"] = rotate
             # Set fit_output=True to expand canvas when rotating to prevent content loss
             # Use BORDER_CONSTANT with value 0 (black padding) for clean borders
-            if "p" not in augmenter_args:
-                augmenter_args["p"] = 1.0
             if "fit_output" not in augmenter_args:
                 augmenter_args["fit_output"] = True
             if "border_mode" not in augmenter_args:

ppocr/data/simple_dataset.py

@@ -51,6 +51,7 @@ def __init__(self, config, mode, logger, seed=None):
         self.data_idx_order_list = list(range(len(self.data_lines)))
         if self.mode == "train" and self.do_shuffle:
             self.shuffle_data_random()
+        self.set_epoch_as_seed(seed, dataset_config)
         self.ops = create_operators(dataset_config["transforms"], global_config)
         self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", 2)
         self.need_reset = True in [x < 1 for x in ratio_list]
@@ -73,6 +74,18 @@ def shuffle_data_random(self):
         random.shuffle(self.data_lines)
         return
 
+    def set_epoch_as_seed(self, seed, dataset_config):
+        if self.mode == "train":
+            try:
+                dataset_config["transforms"][5]["MakeBorderMap"][
+                    "epoch"
+                ] = (seed if seed is not None else 0)
+                dataset_config["transforms"][6]["MakeShrinkMap"][
+                    "epoch"
+                ] = (seed if seed is not None else 0)
+            except Exception:
+                return
+
     def _try_parse_filename_list(self, file_name):
         # multiple images -> one gt label
         if len(file_name) > 0 and file_name[0] == "[":