tests

config/datasets/detection-3d/lep_3d_dataset.yaml

@@ -11,15 +11,15 @@ _target_: innofw.core.integrations.mmdetection.datamodule.Mmdetection3DDataModul
 
 train:
   source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/train.zip
-  target: /home/karim/workdir/innofw/data/lep3d
+  target: ./data/lep3d
 
 test:
   source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip
-  target: /home/karim/workdir/innofw/data/lep3d
+  target: ./data/lep3d
 
 infer:
   source: https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip
-  target: /home/karim/workdir/innofw/data/lep3d
+  target: ./data/lep3d
 
 num_workers: 8
 

innofw/core/integrations/mmdetection/datamodule.py

@@ -24,6 +24,7 @@
 target_maxes = np.array([[136.7853946685791, 135.2938232421875, 45.29965019226074]])
 target_mins = np.array([[-136.7853946685791, -135.2938232421875, -45.29965019226074]])
 
+
 class Mmdetection3DDataModuleAdapter(BaseDataModule, ABC):
     """
     ...

innofw/core/integrations/mmdetection/model_adapter.py

@@ -126,8 +126,8 @@ def predict(self, data, ckpt_path=None):
 
 
 def setup_mmdetection():
-    logging.info('mmdetection-3d found')
     if os.path.exists('../mmdetection3d'):
+        logging.info('mmdetection-3d found')
         os.environ['MMDET_FOR_INNOFW'] = os.path.join(Path(os.getcwd()).parent, 'mmdetection3d')
     if 'MMDET_FOR_INNOFW' not in os.environ:
         logging.info("Cloning mmdetection-3d")
@@ -138,5 +138,6 @@ def setup_mmdetection():
 
     return os.environ['MMDET_FOR_INNOFW']
 
+
 def map_optimizer_to_mmdet_optim(optim_name):
-    return {'adam': 'Adam', 'adamw': 'AdamW', 'sgd': 'SGD'}[optim_name]
+    return {'adam': 'Adam', 'adamw': 'AdamW', 'sgd': 'SGD'}[optim_name]

run_tests.sh

@@ -1,4 +1,8 @@
 #!/bin/bash
+python -m pip install -U openmim
+mim install mmengine
+mim install 'mmcv>=2.0.0rc4'
+mim install 'mmdet>=3.0.0'
 export PYTHONPATH=.
 python -m pytest --cov=innofw --cov-report=xml  --junitxml=out_report.xml --cov-fail-under=10 # not percents
 if [ $? -ne 0 ]; then

tests/fixtures/config/trainers.py

@@ -23,6 +23,9 @@
 trainer_cfg_w_gpu_devices_1 = base_trainer_on_gpu_cfg.copy()
 trainer_cfg_w_gpu_devices_1["devices"] = 1
 
+trainer_cfg_w_gpu_devices_0 = base_trainer_on_gpu_cfg.copy()
+trainer_cfg_w_gpu_devices_0["devices"] = 0
+
 # case: accelerator = 'gpu', devices = 2  # uses two gpu devices
 trainer_cfg_w_gpu_devices_2 = base_trainer_on_gpu_cfg.copy()
 trainer_cfg_w_gpu_devices_2["devices"] = 2

tests/integration/models/torch/lighting_modules/test_mmdetection3d.py

@@ -0,0 +1,39 @@
+import shutil
+
+from omegaconf import DictConfig
+
+from innofw.constants import Stages
+from innofw.core.integrations.mmdetection.datamodule import Mmdetection3DDataModuleAdapter
+from innofw.core.integrations.mmdetection.model_adapter import Mmdetection3DDataModel, \
+    BaseMmdetModel
+from tests.fixtures.config import optimizers as fixt_optimizers
+from tests.fixtures.config import trainers as fixt_trainers
+
+
+def test_integration():
+    data_path = {
+        'source': 'https://api.blackhole.ai.innopolis.university/public-datasets/lep_3d_detection/test.zip',
+        'target': './tmp'}
+    datamodule = Mmdetection3DDataModuleAdapter(data_path, data_path, data_path, 4)
+    datamodule.setup_train_test_val()
+    datamodule.setup_infer()
+    assert datamodule.predict_dataloader() is None
+    assert datamodule.train_dataloader() is None
+    assert datamodule.test_dataloader() is None
+    assert datamodule.save_preds(None, Stages.test, '') is None
+
+    optimizer_cfg = DictConfig(fixt_optimizers.adam_optim_w_target)
+    model = Mmdetection3DDataModel(BaseMmdetModel(), './tmp/logs',
+                                   fixt_trainers.trainer_cfg_w_gpu_devices_0,
+                                   optimizers_cfg=optimizer_cfg)
+    model.train(datamodule)
+    ckpt_path = model.mmdet_path + '/work_dirs/pointpillars_hv_secfpn_8xb6_custom/epoch_10.pth'
+    model.test(datamodule, ckpt_path=ckpt_path)
+    model.predict(datamodule, ckpt_path=ckpt_path)
+
+    for i in range(3):
+        try:
+            shutil.rmtree('./tmp')
+            break
+        except:
+            pass