no improvement in new module

.hydra/config.yaml

@@ -6,22 +6,22 @@ test: false
 ckpt_path: null
 seed: 420
 datamodule:
-  _target_: src.datamodules.kitti_datamodule.KITTIDataModule2
+  _target_: src.datamodules.kitti_datamodule.KITTIDataModule3
   dataset_path: ${paths.data_dir}
   train_sets: ${paths.data_dir}/train_80.txt
   val_sets: ${paths.data_dir}/val_80.txt
   test_sets: ${paths.data_dir}/test_80.txt
   batch_size: 128
   num_worker: 32
 model:
-  _target_: src.models.regressor.RegressorModel2
+  _target_: src.models.regressor.RegressorModel3
   net:
-    _target_: src.models.components.base.RegressorNet2
+    _target_: src.models.components.base.RegressorNet
     backbone:
       _target_: torchvision.models.resnet18
       pretrained: true
     bins: 2
-  lr: 0.0001
+  lr: 0.001
   momentum: 0.9
   w: 0.4
   alpha: 0.6
@@ -101,4 +101,6 @@ extras:
   ignore_warnings: false
   enforce_tags: true
   print_config: true
-name: new_network_&_dataset
+wandb:
+  name: model3
+name: model3

.hydra/hydra.yaml

@@ -126,11 +126,11 @@ hydra:
     - hydra.job.name=train_ddp_process_3
     - hydra.mode=RUN
     task:
-    - experiment=model2
+    - experiment=model3
   job:
     name: train_ddp_process_3
     chdir: null
-    override_dirname: experiment=model2
+    override_dirname: experiment=model3
     id: ???
     num: ???
     config_name: train.yaml
@@ -163,15 +163,15 @@ hydra:
       debug: null
       local: default.yaml
       hparams_search: null
-      experiment: model2
+      experiment: model3
       hydra: default.yaml
       extras: default.yaml
       paths: default.yaml
       trainer: dgx.yaml
       logger: wandb.yaml
       callbacks: default.yaml
-      model: regressor2.yaml
-      datamodule: kitti_datamodule2.yaml
+      model: regressor3.yaml
+      datamodule: kitti_datamodule3.yaml
       hydra/env: default
       hydra/callbacks: null
       hydra/job_logging: colorlog

.hydra/overrides.yaml

@@ -1 +1 @@
-- experiment=model2
+- experiment=model3

configs/convert.yaml

@@ -19,7 +19,7 @@ ignore_warnings: True
 root: ${hydra:runtime.cwd}
 
 # checkpoint directory
-checkpoint_dir: ${root}/logs/train/runs/2022-09-05_13-50-07/checkpoints/epoch_009.ckpt
+checkpoint_dir: ${root}/logs/train/runs/2022-09-06_10-37-38/checkpoints/epoch_009.ckpt
 
 # dump dir
 dump_dir: ${root}/weights
@@ -33,4 +33,4 @@ input_sample:
 convert_to: "pytorch" # [pytorch, torchscript, onnx, tensorrt]
 
 # model name without extension
-name: ${dump_dir}/regressor_resnet18_new3
+name: ${dump_dir}/regressor_new_module

configs/datamodule/kitti_datamodule.yaml

@@ -1,8 +1,8 @@
 _target_: src.datamodules.kitti_datamodule.KITTIDataModule
 
 dataset_path: ${paths.data_dir} # data_dir is specified in config.yaml
-train_sets: ${paths.data_dir}/train.txt
-val_sets: ${paths.data_dir}/val.txt
-test_sets: ${paths.data_dir}/test.txt
+train_sets: ${paths.data_dir}/train_80.txt
+val_sets: ${paths.data_dir}/val_80.txt
+test_sets: ${paths.data_dir}/test_80.txt
 batch_size: 128
 num_worker: 32

configs/datamodule/kitti_datamodule3.yaml

@@ -0,0 +1,8 @@
+_target_: src.datamodules.kitti_datamodule.KITTIDataModule3
+
+dataset_path: ${paths.data_dir} # data_dir is specified in config.yaml
+train_sets: ${paths.data_dir}/train_80.txt
+val_sets: ${paths.data_dir}/val_80.txt
+test_sets: ${paths.data_dir}/test_80.txt
+batch_size: 128
+num_worker: 32

configs/datamodule/kitti_dataset3.yaml

@@ -0,0 +1,11 @@
+_target_: src.datamodules.components.kitti_dataset.KITTIDataset3
+
+dataset_path: ${paths.data_dir}
+dataset_sets: ${paths.data_dir}/train_80.txt
+bins: 2
+overlap: 0.1
+image_size: 224
+categories: 
+  - "car"
+  - "pedestrian"
+  - "cyclist"

configs/experiment/model3.yaml

@@ -0,0 +1,32 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /datamodule: kitti_datamodule3.yaml
+  - override /model: regressor3.yaml
+  - override /callbacks: default.yaml
+  - override /logger: wandb.yaml
+  - override /trainer: dgx.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+seed: 420
+
+trainer:
+  min_epochs: 1
+  max_epochs: 10
+  gpus: [0,1,2,3]
+  strategy: ddp
+
+datamodule:
+  train_sets: ${paths.data_dir}/train_80.txt
+  val_sets: ${paths.data_dir}/val_80.txt
+
+wandb:
+  name: "model3"
+
+# name of the run determines folder name in logs
+name: "model3"

configs/inference.yaml

@@ -4,7 +4,7 @@
 defaults:
   - _self_
   - detector: yolov5.yaml
-  - model: regressor2.yaml
+  - model: regressor.yaml
   - augmentation: inference_preprocessing.yaml
 
   # debugging config (enable through command line, e.g. `python train.py debug=default)
@@ -35,7 +35,7 @@ save_result: True
 
 # regressor weights
 # regressor_weights: ${root}/weights/regressor_resnet18.pt
-regressor_weights: ${root}/weights/regressor_resnet18_new3.pt
+regressor_weights: ${root}/weights/regressor_old1.pt
 
 # inference type
 inference_type: pytorch # [pytorch, onnx, openvino, tensorrt]

configs/model/regressor3.yaml

@@ -0,0 +1,13 @@
+_target_: src.models.regressor.RegressorModel3
+
+net:
+  _target_: src.models.components.base.RegressorNet
+  backbone:
+    _target_: torchvision.models.resnet18 # change model on this
+    pretrained: True
+  bins: 2
+
+lr: 0.001
+momentum: 0.9
+w: 0.4
+alpha: 0.6

configs/train.yaml

@@ -4,10 +4,10 @@
 # order of defaults determines the order in which configs override each other
 defaults:
   - _self_
-  - datamodule: kitti_datamodule2.yaml
-  - model: regressor2.yaml
+  - datamodule: kitti_datamodule.yaml
+  - model: regressor.yaml
   - callbacks: default.yaml
-  - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
+  - logger: wandb.yaml # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
   - trainer: dgx.yaml
   - paths: default.yaml
   - extras: default.yaml

src/datamodules/kitti_datamodule.py

@@ -6,7 +6,7 @@
 from torch.utils.data import DataLoader
 from torchvision.transforms import transforms
 
-from src.datamodules.components.kitti_dataset import KITTIDataset, KITTIDataLoader
+from src.datamodules.components.kitti_dataset import KITTIDataset, KITTIDataset3
 
 class KITTIDataModule(LightningDataModule):
     def __init__(
@@ -99,6 +99,52 @@ def val_dataloader(self):
             shuffle=False
         )
 
+class KITTIDataModule3(LightningDataModule):
+    def __init__(
+        self,
+        dataset_path: str = './data/KITTI',
+        train_sets: str = './data/KITTI/train.txt',
+        val_sets: str = './data/KITTI/val.txt',
+        test_sets: str = './data/KITTI/test.txt',
+        batch_size: int = 32,
+        num_worker: int = 4,
+    ):
+        super().__init__()
+
+        # save hyperparameters
+        self.save_hyperparameters(logger=False)
+
+        # transforms
+        # TODO: using albumentations
+        self.dataset_transforms = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))
+        ])
+
+    def setup(self, stage=None):
+        """ Split dataset to training and validation """
+        self.KITTI_train = KITTIDataset3(self.hparams.dataset_path, self.hparams.train_sets)
+        self.KITTI_val = KITTIDataset3(self.hparams.dataset_path, self.hparams.val_sets)
+        # self.KITTI_test = KITTIDataset(self.hparams.dataset_path, self.hparams.test_sets)
+        # TODO: add test datasets dan test sets
+
+    def train_dataloader(self):
+        return DataLoader(
+            dataset=self.KITTI_train,
+            batch_size=self.hparams.batch_size,
+            num_workers=self.hparams.num_worker,
+            shuffle=True
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            dataset=self.KITTI_val,
+            batch_size=self.hparams.batch_size,
+            num_workers=self.hparams.num_worker,
+            shuffle=False
+        )
+
+
 if __name__ == '__main__':
 
     # dataset = KITTIDataModule(batch_size=1)
@@ -109,12 +155,14 @@ def val_dataloader(self):
     #     print(label)
     #     break
 
-    dataset = KITTIDataModule2(batch_size=1)
+    dataset = KITTIDataModule3(batch_size=1)
     dataset.setup()
     train = dataset.train_dataloader()
 
     for img, label in train:
-        print(label["orientation"])
+        print(img.shape)
+        print(label)
+        break
 
     # output
     # torch.Size([1, 3, 224, 224])