Ds exp

configs/callbacks/default.yaml

@@ -8,14 +8,14 @@ defaults:
 model_checkpoint:
   dirpath: ${paths.output_dir}/checkpoints
   filename: "epoch_{epoch:03d}"
-  monitor: "val/acc"
+  monitor: "val/iou"
   mode: "max"
   save_last: True
   auto_insert_metric_name: False
 
 early_stopping:
-  monitor: "val/acc"
-  patience: 100
+  monitor: "val/iou"
+  patience: 2
   mode: "max"
 
 model_summary:

configs/data/building_data.yaml

@@ -0,0 +1,8 @@
+_target_: src.data.building_datasets.BuildingDataModule
+data_type: "acc_building"
+data_dir: "C:/Users/syedd/Documents/mine/DCLC3/data/dataset/New_dataset/Images"
+augment_factor: 1 
+building_threshold: 0.1
+batch_size: 32 
+num_workers: 4
+pin_memory: True

configs/experiment/building_seg.yaml

@@ -0,0 +1,65 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: building_data
+  - override /model: building_seg
+  - override /callbacks: default
+  - override /trainer: default
+  - override /logger: neptune
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["UNet"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 10
+  max_epochs: 10
+  gradient_clip_val: 0.5
+
+model:
+    optimizer:
+      _target_: torch.optim.Adam
+      _partial_: true
+      lr: 0.001
+      weight_decay: 0.0
+
+    scheduler:
+      _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+      _partial_: true
+      mode: min
+      factor: 0.1
+      patience: 10
+
+    net:
+      _target_: segmentation_models_pytorch.Unet
+      encoder_name: "resnet34"
+      encoder_weights: "imagenet"
+      in_channels: 3
+      classes: 1
+
+data:
+  batch_size: 64
+
+logger:
+  neptune:
+      project: mine-ai/DCLC3
+
+callbacks:
+    model_checkpoint:
+      dirpath: ${paths.output_dir}/checkpoints
+      filename: "epoch_{epoch:03d}"
+      monitor: "val/iou"
+      mode: "max"
+      save_last: True
+      auto_insert_metric_name: False
+
+    early_stopping:
+      monitor: "val/iou"
+      patience: 2
+      mode: "max"

configs/logger/neptune.yaml

@@ -2,8 +2,7 @@
 
 neptune:
   _target_: lightning.pytorch.loggers.neptune.NeptuneLogger
-  api_key: ${oc.env:NEPTUNE_API_TOKEN} # api key is loaded from environment variable
-  project: username/lightning-hydra-template
-  # name: ""
-  log_model_checkpoints: True
+  api_key: "eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI0ZTdkZWNlYS1jMGQwLTRkZTctYmUxMS1hZjRlZDY5NGU0OTUifQ=="
+  project: mine-ai/DCLC3
+  log_model_checkpoints: False
   prefix: ""

configs/model/building_seg.yaml

@@ -0,0 +1,24 @@
+_target_: src.models.building_seg_models.BuildingSegModule
+
+optimizer:
+  _target_: torch.optim.Adam
+  _partial_: true
+  lr: 0.001
+  weight_decay: 0.0
+
+scheduler:
+  _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
+  _partial_: true
+  mode: min
+  factor: 0.1
+  patience: 10
+
+net:
+  _target_: segmentation_models_pytorch.Unet
+  encoder_name: "resnet34"
+  encoder_weights: "imagenet"
+  in_channels: 3
+  classes: 1
+
+# compile model for faster training with pytorch 2.0
+compile: false