updated consep & monusac configs to use ConvDecoderWithImage (#703)

kaiko-ai · Dec 2, 2024 · f5b0053 · f5b0053
1 parent 68eddbe
commit f5b0053
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Showing 2 changed files with 13 additions and 13 deletions.
diff --git a/configs/vision/pathology/online/segmentation/consep.yaml b/configs/vision/pathology/online/segmentation/consep.yaml
@@ -3,8 +3,8 @@ trainer:
   class_path: eva.Trainer
   init_args:
     n_runs: &N_RUNS ${oc.env:N_RUNS, 1}
-    default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, vit_small_patch16_224}/consep}
-    max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 513}
+    default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, vit_small_patch16_224_dino}/consep}
+    max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 2000}
     log_every_n_steps: 6
     callbacks:
       - class_path: eva.callbacks.ConfigurationLogger
@@ -26,7 +26,7 @@ trainer:
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
           min_delta: 0
-          patience: 100
+          patience: 34
           monitor: *MONITOR_METRIC
           mode: *MONITOR_METRIC_MODE
     logger:
@@ -45,10 +45,10 @@ model:
           out_indices: ${oc.env:OUT_INDICES, 1}
         model_extra_kwargs: ${oc.env:MODEL_EXTRA_KWARGS, null}
     decoder:
-      class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderMS
+      class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderWithImage
       init_args:
         in_features: ${oc.env:IN_FEATURES, 384}
-        num_classes:  &NUM_CLASSES 5
+        num_classes: &NUM_CLASSES 5
     criterion:
       class_path: eva.vision.losses.DiceLoss
       init_args:
@@ -58,7 +58,7 @@ model:
     optimizer:
       class_path: torch.optim.AdamW
       init_args:
-        lr: ${oc.env:LR_VALUE, 0.002}
+        lr: ${oc.env:LR_VALUE, 0.0001}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.PolynomialLR
       init_args:

diff --git a/configs/vision/pathology/online/segmentation/monusac.yaml b/configs/vision/pathology/online/segmentation/monusac.yaml
@@ -3,9 +3,9 @@ trainer:
   class_path: eva.Trainer
   init_args:
     n_runs: &N_RUNS ${oc.env:N_RUNS, 1}
-    default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, vit_small_patch16_224}/monusac}
-    max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 550}
-    log_every_n_steps: 4
+    default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, vit_small_patch16_224_dino}/monusac}
+    max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 2000}
+    log_every_n_steps: 6
     callbacks:
       - class_path: eva.callbacks.ConfigurationLogger
       - class_path: lightning.pytorch.callbacks.TQDMProgressBar
@@ -26,7 +26,7 @@ trainer:
       - class_path: lightning.pytorch.callbacks.EarlyStopping
         init_args:
           min_delta: 0
-          patience: 100
+          patience: 50
           monitor: *MONITOR_METRIC
           mode: *MONITOR_METRIC_MODE
     logger:
@@ -45,10 +45,10 @@ model:
           out_indices: ${oc.env:OUT_INDICES, 1}
         model_extra_kwargs: ${oc.env:MODEL_EXTRA_KWARGS, null}
     decoder:
-      class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderMS
+      class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderWithImage
       init_args:
         in_features: ${oc.env:IN_FEATURES, 384}
-        num_classes:  &NUM_CLASSES 5
+        num_classes: &NUM_CLASSES 5
     criterion:
       class_path: eva.vision.losses.DiceLoss
       init_args:
@@ -59,7 +59,7 @@ model:
     optimizer:
       class_path: torch.optim.AdamW
       init_args:
-        lr: ${oc.env:LR_VALUE, 0.002}
+        lr: ${oc.env:LR_VALUE, 0.0001}
     lr_scheduler:
       class_path: torch.optim.lr_scheduler.PolynomialLR
       init_args: