Change segmentation model argument names (microsoft#919)

* name change semgmentation model

* chmod evaluate.py

* fix variable name evaluate

* encoder -> backbone

* remove backbone_output_stride from confs

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

conf/chesapeake_cvpr.yaml

@@ -8,10 +8,9 @@ experiment:
   name: "chesapeake_cvpr_example"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
-    encoder_output_stride: 16
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 4

conf/etci2021.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "etci2021"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: "imagenet"
+    model: "unet"
+    backbone: "resnet18"
+    weights: "imagenet"
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 6

conf/inria.yaml

@@ -14,9 +14,9 @@ experiment:
   name: "inria_test"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: "imagenet"
+    model: "unet"
+    backbone: "resnet18"
+    weights: "imagenet"
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 3

conf/landcoverai.yaml

@@ -7,9 +7,9 @@ experiment:
   task: "landcoverai"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: "imagenet"
+    model: "unet"
+    backbone: "resnet18"
+    weights: "imagenet"
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 3

conf/naipchesapeake.yaml

@@ -7,10 +7,9 @@ experiment:
   task: "naipchesapeake"
   module:
     loss: "ce"
-    segmentation_model: "deeplabv3+"
-    encoder_name: "resnet34"
-    encoder_weights: "imagenet"
-    encoder_output_stride: 16
+    model: "deeplabv3+"
+    backbone: "resnet34"
+    weights: "imagenet"
     learning_rate: 1e-3
     learning_rate_schedule_patience: 2
     in_channels: 4

conf/oscd.yaml

@@ -7,9 +7,9 @@ experiment:
   task: "oscd"
   module:
     loss: "jaccard"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

conf/sen12ms.yaml

@@ -7,10 +7,9 @@ experiment:
   task: "sen12ms"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
-    encoder_output_stride: 16
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 2
     in_channels: 15

evaluate.py

@@ -176,18 +176,18 @@ def main(args: argparse.Namespace) -> None:
     elif issubclass(TASK, SemanticSegmentationTask):
         val_row = {
             "split": "val",
-            "segmentation_model": hparams["segmentation_model"],
-            "encoder_name": hparams["encoder_name"],
-            "encoder_weights": hparams["encoder_weights"],
+            "model": hparams["model"],
+            "backbone": hparams["backbone"],
+            "weights": hparams["weights"],
             "learning_rate": hparams["learning_rate"],
             "loss": hparams["loss"],
         }
 
         test_row = {
             "split": "test",
-            "segmentation_model": hparams["segmentation_model"],
-            "encoder_name": hparams["encoder_name"],
-            "encoder_weights": hparams["encoder_weights"],
+            "model": hparams["model"],
+            "backbone": hparams["backbone"],
+            "weights": hparams["weights"],
             "learning_rate": hparams["learning_rate"],
             "loss": hparams["loss"],
         }

experiments/run_chesapeake_cvpr_experiments.py

@@ -16,7 +16,7 @@
 # Hyperparameter options
 training_set_options = ["de"]
 model_options = ["unet"]
-encoder_options = ["resnet18", "resnet50"]
+backbone_options = ["resnet18", "resnet50"]
 lr_options = [1e-2, 1e-3, 1e-4]
 loss_options = ["ce", "jaccard"]
 weight_init_options = ["null", "imagenet"]
@@ -36,16 +36,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (train_state, model, encoder, lr, loss, weight_init) in itertools.product(
+    for (train_state, model, backbone, lr, loss, weight_init) in itertools.product(
         training_set_options,
         model_options,
-        encoder_options,
+        backbone_options,
         lr_options,
         loss_options,
         weight_init_options,
     ):
 
-        experiment_name = f"{train_state}_{model}_{encoder}_{lr}_{loss}_{weight_init}"
+        experiment_name = f"{train_state}_{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "chesapeake-cvpr_experiments")
         log_dir = os.path.join(output_dir, "logs")
@@ -57,9 +57,9 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
                 "python train.py"
                 + f" config_file={config_file}"
                 + f" experiment.name={experiment_name}"
-                + f" experiment.module.segmentation_model={model}"
-                + f" experiment.module.encoder_name={encoder}"
-                + f" experiment.module.encoder_weights={weight_init}"
+                + f" experiment.module.model={model}"
+                + f" experiment.module.backbone={backbone}"
+                + f" experiment.module.weights={weight_init}"
                 + f" experiment.module.learning_rate={lr}"
                 + f" experiment.module.loss={loss}"
                 + " experiment.module.class_set=7"

experiments/run_landcoverai_experiments.py

@@ -15,7 +15,7 @@
 
 # Hyperparameter options
 model_options = ["unet"]
-encoder_options = ["resnet18", "resnet50"]
+backbone_options = ["resnet18", "resnet50"]
 lr_options = [1e-2, 1e-3, 1e-4]
 loss_options = ["ce", "jaccard"]
 weight_init_options = ["null", "imagenet"]
@@ -35,11 +35,11 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, encoder, lr, loss, weight_init) in itertools.product(
-        model_options, encoder_options, lr_options, loss_options, weight_init_options
+    for (model, backbone, lr, loss, weight_init) in itertools.product(
+        model_options, backbone_options, lr_options, loss_options, weight_init_options
     ):
 
-        experiment_name = f"{model}_{encoder}_{lr}_{loss}_{weight_init}"
+        experiment_name = f"{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "landcoverai_experiments")
         log_dir = os.path.join(output_dir, "logs")
@@ -54,8 +54,8 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
                 + f" experiment.module.segmentation_model={model}"
                 + f" experiment.module.learning_rate={lr}"
                 + f" experiment.module.loss={loss}"
-                + f" experiment.module.encoder_name={encoder}"
-                + f" experiment.module.encoder_weights={weight_init}"
+                + f" experiment.module.backbone={backbone}"
+                + f" experiment.module.weights={weight_init}"
                 + f" program.output_dir={output_dir}"
                 + f" program.log_dir={log_dir}"
                 + f" program.data_dir={DATA_DIR}"

experiments/run_landcoverai_seed_experiments.py

@@ -15,7 +15,7 @@
 
 # Hyperparameter options
 model_options = ["unet"]
-encoder_options = ["resnet18", "resnet50"]
+backbone_options = ["resnet18", "resnet50"]
 lr_options = [1e-2, 1e-3, 1e-4]
 loss_options = ["ce", "jaccard"]
 weight_init_options = ["null", "imagenet"]
@@ -35,11 +35,11 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, encoder, lr, loss, weight_init) in itertools.product(
-        model_options, encoder_options, lr_options, loss_options, weight_init_options
+    for (model, backbone, lr, loss, weight_init) in itertools.product(
+        model_options, backbone_options, lr_options, loss_options, weight_init_options
     ):
 
-        experiment_name = f"{model}_{encoder}_{lr}_{loss}_{weight_init}"
+        experiment_name = f"{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "landcoverai_experiments")
         log_dir = os.path.join(output_dir, "logs")
@@ -51,11 +51,11 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
                 "python train.py"
                 + f" config_file={config_file}"
                 + f" experiment.name={experiment_name}"
-                + f" experiment.module.segmentation_model={model}"
+                + f" experiment.module.model={model}"
                 + f" experiment.module.learning_rate={lr}"
                 + f" experiment.module.loss={loss}"
-                + f" experiment.module.encoder_name={encoder}"
-                + f" experiment.module.encoder_weights={weight_init}"
+                + f" experiment.module.backbone={backbone}"
+                + f" experiment.module.weights={weight_init}"
                 + f" program.output_dir={output_dir}"
                 + f" program.log_dir={log_dir}"
                 + f" program.data_dir={DATA_DIR}"

tests/conf/chesapeake_cvpr_5.yaml

@@ -2,10 +2,9 @@ experiment:
   task: "chesapeake_cvpr"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet50"
-    encoder_weights: null
-    encoder_output_stride: 16
+    model: "unet"
+    backbone: "resnet50"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 4

tests/conf/chesapeake_cvpr_7.yaml

@@ -2,7 +2,7 @@ experiment:
   task: "chesapeake_cvpr"
   module:
     loss: "ce"
-    segmentation_model: "unet"
+    model: "unet"
     encoder_name: "resnet18"
     encoder_weights: null
     encoder_output_stride: 16

tests/conf/chesapeake_cvpr_prior.yaml

@@ -2,7 +2,7 @@ experiment:
   task: "chesapeake_cvpr"
   module:
     loss: "ce"
-    segmentation_model: "unet"
+    model: "unet"
     encoder_name: "resnet50"
     encoder_weights: null
     encoder_output_stride: 16

tests/conf/deepglobelandcover_0.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "deepglobelandcover"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

tests/conf/deepglobelandcover_5.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "deepglobelandcover"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

tests/conf/etci2021.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "etci2021"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 6

tests/conf/inria.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "inria"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: "imagenet"
+    model: "unet"
+    backbone: "resnet18"
+    weights: "imagenet"
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     in_channels: 3

tests/conf/landcoverai.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "landcoverai"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

tests/conf/naipchesapeake.yaml

@@ -2,10 +2,9 @@ experiment:
   task: "naipchesapeake"
   module:
     loss: "ce"
-    segmentation_model: "deeplabv3+"
-    encoder_name: "resnet34"
-    encoder_weights: null
-    encoder_output_stride: 16
+    model: "deeplabv3+"
+    backbone: "resnet34"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 2
     in_channels: 4

tests/conf/oscd_all.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "oscd"
   module:
     loss: "jaccard"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

tests/conf/oscd_rgb.yaml

@@ -2,9 +2,9 @@ experiment:
   task: "oscd"
   module:
     loss: "jaccard"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 6
     verbose: false

tests/conf/sen12ms_all.yaml

@@ -2,10 +2,9 @@ experiment:
   task: "sen12ms"
   module:
     loss: "ce"
-    segmentation_model: "unet"
-    encoder_name: "resnet18"
-    encoder_weights: null
-    encoder_output_stride: 16
+    model: "unet"
+    backbone: "resnet18"
+    weights: null
     learning_rate: 1e-3
     learning_rate_schedule_patience: 2
     in_channels: 15