[FIX][REL1.0] Fix Geti integration issues (#1885)

* Fix ote_config -> otx_config

* [FIX] hang issue when tracing a stack in certain scenario (#1868)
fix: use primitive library

* [FIX][POT] Set stat_requests_number parameter to 1 (#1870)
Set POT stat_requests_number parameter to 1 in order to lower RAM footprint

* [FIX] Training error when batch size is 1 (#1872)
fix: drop last batch

* Recover detection num_workers=2

* Remove nbmake from base requirements

* Add py.typed in package

* [FIX] Arrange scale between bbox preds and bbox targets in ATSS (#1880)
Arrange scale between bbox preds and bbox targets

---------

Co-authored-by: Inhyuk Cho <[email protected]>
Co-authored-by: Nikita Savelyev <[email protected]>
Co-authored-by: Jaeguk Hyun <[email protected]>

Author: 4 people

otx/algorithms/detection/adapters/mmdet/nncf/patches.py

@@ -129,14 +129,18 @@ def _wrap_is_in_onnx_export(ctx, fn):
     # possible alternatives
     #    - take the onnx branch in all cases
 
-    import inspect
+    import sys
 
-    stack = inspect.stack()
-    frame_info = stack[2]
+    frame = sys._getframe()
+    ctr = 2
+    while frame is not None and ctr:
+        frame = frame.f_back
+        ctr -= 1
     if (
-        frame_info.function == "forward"
-        and "self" in frame_info.frame.f_locals.keys()
-        and frame_info.frame.f_locals["self"].__class__.__name__ == "SingleRoIExtractor"
+        frame is not None
+        and frame.f_code.co_name == "forward"
+        and "self" in frame.f_locals.keys()
+        and frame.f_locals["self"].__class__.__name__ == "SingleRoIExtractor"
     ):
         return fn() or is_in_nncf_tracing()
     return fn()

otx/algorithms/detection/configs/detection/configuration.yaml

@@ -101,7 +101,7 @@ learning_parameters:
     warning: null
   num_workers:
     affects_outcome_of: NONE
-    default_value: 0
+    default_value: 2
     description:
       Increasing this value might improve training speed however it might
       cause out of memory errors. If the number of workers is set to zero, data loading

otx/algorithms/detection/configs/instance_segmentation/efficientnetb2b_maskrcnn/template.yaml

@@ -37,7 +37,7 @@ hyper_parameters:
         default_value: 100
     pot_parameters:
       stat_requests_number:
-        default_value: 2
+        default_value: 1
     nncf_optimization:
       enable_quantization:
         default_value: true

otx/algorithms/detection/configs/instance_segmentation/resnet50_maskrcnn/template.yaml

@@ -37,7 +37,7 @@ hyper_parameters:
         default_value: 100
     pot_parameters:
       stat_requests_number:
-        default_value: 2
+        default_value: 1
     nncf_optimization:
       enable_quantization:
         default_value: true

otx/algorithms/detection/configs/rotated_detection/efficientnetb2b_maskrcnn/template.yaml

@@ -37,7 +37,7 @@ hyper_parameters:
         default_value: 100
     pot_parameters:
       stat_requests_number:
-        default_value: 2
+        default_value: 1
     nncf_optimization:
       enable_quantization:
         default_value: true

otx/algorithms/detection/configs/rotated_detection/resnet50_maskrcnn/template.yaml

@@ -37,7 +37,7 @@ hyper_parameters:
         default_value: 100
     pot_parameters:
       stat_requests_number:
-        default_value: 2
+        default_value: 1
     nncf_optimization:
       enable_quantization:
         default_value: true

otx/api/configuration/__init__.py

@@ -14,7 +14,7 @@
 #
 
 
-import otx.api.configuration.helper as ote_config_helper  # for 'ote' backward compatibility
+import otx.api.configuration.helper as otx_config_helper  # for backward compatibility
 import otx.api.configuration.helper as cfg_helper  # pylint: disable=reimported
 from otx.api.configuration.elements import metadata_keys
 from otx.api.configuration.elements.configurable_enum import ConfigurableEnum
@@ -27,7 +27,7 @@
 __all__ = [
     "metadata_keys",
     "cfg_helper",
-    "ote_config_helper",
+    "otx_config_helper",
     "ConfigurableEnum",
     "ModelLifecycle",
     "Action",

otx/mpa/modules/models/heads/custom_atss_head.py

@@ -168,18 +168,16 @@ def loss_single(
             pos_centerness = centerness[pos_inds]
 
             centerness_targets = self.centerness_target(pos_anchors, pos_bbox_targets)
-            pos_decode_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
-            pos_decode_bbox_targets = self.bbox_coder.decode(pos_anchors, pos_bbox_targets)
+            if self.reg_decoded_bbox:
+                pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
 
             if self.use_qfl:
-                quality[pos_inds] = bbox_overlaps(
-                    pos_decode_bbox_pred.detach(), pos_decode_bbox_targets, is_aligned=True
-                ).clamp(min=1e-6)
+                quality[pos_inds] = bbox_overlaps(pos_bbox_pred.detach(), pos_bbox_targets, is_aligned=True).clamp(
+                    min=1e-6
+                )
 
             # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred, pos_decode_bbox_targets, weight=centerness_targets, avg_factor=1.0
-            )
+            loss_bbox = self.loss_bbox(pos_bbox_pred, pos_bbox_targets, weight=centerness_targets, avg_factor=1.0)
 
             # centerness loss
             loss_centerness = self.loss_centerness(pos_centerness, centerness_targets, avg_factor=num_total_samples)

otx/mpa/stage.py

@@ -306,8 +306,17 @@ def configure_samples_per_gpu(
 
         if distributed:
             dataset_len = dataset_len // dist.get_world_size()
+
+        # set batch size as a total dataset
+        # if it is smaller than total dataset
         if dataset_len < samples_per_gpu:
             dataloader_cfg.samples_per_gpu = dataset_len
+
+        # drop the last batch if the last batch size is 1
+        # batch size of 1 is a runtime error for training batch normalization layer
+        if subset in ("train", "unlabeled") and dataset_len % samples_per_gpu == 1:
+            dataloader_cfg.drop_last = True
+
         cfg.data[f"{subset}_dataloader"] = dataloader_cfg
 
     @staticmethod

requirements/base.txt

@@ -1,7 +1,6 @@
 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
 # Base Algo Requirements.                                                          #
 natsort>=6.0.0
-nbmake
 prettytable
 protobuf>=3.20.0
 pyyaml