Patch eval metrics to markdown

benchmarks/imagenet/resnet50/finetune_eval.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import Dict
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
@@ -47,7 +48,7 @@ def finetune_eval(
     devices: int,
     precision: str,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     """Runs fine-tune evaluation on the given model.
 
     Parameters follow SimCLR [0] settings.
@@ -131,7 +132,8 @@ def finetune_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: Dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
-        print_rank_zero(
-            f"max finetune {metric}: {max(metric_callback.val_metrics[metric])}"
-        )
+        print(f"max finetune {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/resnet50/knn_eval.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule, Trainer
@@ -23,7 +24,7 @@ def knn_eval(
     accelerator: str,
     devices: int,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     """Runs KNN evaluation on the given model.
 
     Parameters follow InstDisc [0] settings.
@@ -89,5 +90,8 @@ def knn_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
-        print_rank_zero(f"knn {metric}: {max(metric_callback.val_metrics[metric])}")
+        print(f"knn {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/resnet50/linear_eval.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import Dict
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
@@ -24,7 +25,7 @@ def linear_eval(
     devices: int,
     precision: str,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     """Runs a linear evaluation on the given model.
 
     Parameters follow SimCLR [0] settings.
@@ -108,7 +109,8 @@ def linear_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: Dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
-        print_rank_zero(
-            f"max linear {metric}: {max(metric_callback.val_metrics[metric])}"
-        )
+        print(f"max linear {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/resnet50/main.py

@@ -1,7 +1,7 @@
 from argparse import ArgumentParser
 from datetime import datetime
 from pathlib import Path
-from typing import Sequence, Union
+from typing import Sequence, Union, Dict
 
 import barlowtwins
 import byol
@@ -121,11 +121,11 @@ def main(
                 precision=precision,
                 ckpt_path=ckpt_path,
             )
-
+        eval_metrics: Dict[str, Dict[str, float]] = Dict()
         if skip_knn_eval:
             print_rank_zero("Skipping KNN eval.")
         else:
-            knn_eval.knn_eval(
+            eval_metrics["knn"] = knn_eval.knn_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -140,7 +140,7 @@ def main(
         if skip_linear_eval:
             print_rank_zero("Skipping linear eval.")
         else:
-            linear_eval.linear_eval(
+            eval_metrics["linear"] = linear_eval.linear_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -156,7 +156,7 @@ def main(
         if skip_finetune_eval:
             print_rank_zero("Skipping fine-tune eval.")
         else:
-            finetune_eval.finetune_eval(
+            eval_metrics["finetune"] = finetune_eval.finetune_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -169,6 +169,10 @@ def main(
                 precision=precision,
             )
 
+        if eval_metrics:
+            print(f"Results for {method}:")
+            print(eval_metrics_to_markdown(eval_metrics))
+
 
 def pretrain(
     model: LightningModule,
@@ -246,6 +250,38 @@ def pretrain(
         print_rank_zero(f"max {metric}: {max(metric_callback.val_metrics[metric])}")
 
 
+def eval_metrics_to_markdown(metrics: Dict[str, Dict[str, float]]) -> str:
+    EVAL_NAME_COLUMN_NAME = "Eval Name"
+    METRIC_COLUMN_NAME = "Metric Name"
+    VALUE_COLUMN_NAME = "Value"
+
+    eval_name_max_len = max(
+        len(eval_name) for eval_name in list(metrics.keys()) + [EVAL_NAME_COLUMN_NAME]
+    )
+    metric_name_max_len = max(
+        len(metric_name)
+        for metric_dict in metrics.values()
+        for metric_name in list(metric_dict.keys()) + [METRIC_COLUMN_NAME]
+    )
+    value_max_len = max(
+        len(metric_value)
+        for metric_dict in metrics.values()
+        for metric_value in list(f"{value:.2f}" for value in metric_dict.values())
+        + [VALUE_COLUMN_NAME]
+    )
+
+    header = f"| {EVAL_NAME_COLUMN_NAME.ljust(eval_name_max_len)} | {METRIC_COLUMN_NAME.ljust(metric_name_max_len)} | {VALUE_COLUMN_NAME.ljust(value_max_len)} |"
+    separator = f"|:{'-' * (eval_name_max_len)}:|:{'-' * (metric_name_max_len)}:|:{'-' * (value_max_len)}:|"
+
+    lines = [header, separator] + [
+        f"| {eval_name.ljust(eval_name_max_len)} | {metric_name.ljust(metric_name_max_len)} | {f'{metric_value:.2f}'.ljust(value_max_len)} |"
+        for eval_name, metric_dict in metrics.items()
+        for metric_name, metric_value in metric_dict.items()
+    ]
+
+    return "\n".join(lines)
+
+
 if __name__ == "__main__":
     args = parser.parse_args()
     main(**vars(args))

benchmarks/imagenet/vitb16/finetune_eval.py

@@ -136,7 +136,7 @@ def finetune_eval(
     devices: int,
     precision: str,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     """Runs fine-tune evaluation on the given model.
 
     Parameters follow MAE settings.
@@ -211,5 +211,8 @@ def finetune_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: Dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
         print(f"max finetune {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/vitb16/knn_eval.py

@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import Dict
 
 import torch
 from pytorch_lightning import LightningModule, Trainer
@@ -22,7 +23,7 @@ def knn_eval(
     accelerator: str,
     devices: int,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     print("Running KNN evaluation...")
 
     # Setup training data.
@@ -76,5 +77,8 @@ def knn_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
         print(f"knn {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/vitb16/linear_eval.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Tuple
+from typing import Dict, Tuple
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import DeviceStatsMonitor, LearningRateMonitor
@@ -74,7 +74,7 @@ def linear_eval(
     devices: int,
     precision: str,
     num_classes: int,
-) -> None:
+) -> Dict[str, float]:
     """Runs a linear evaluation on the given model.
 
     Parameters follow MAE settings.
@@ -145,5 +145,8 @@ def linear_eval(
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
     )
+    metrics_dict: Dict[str, float] = dict()
     for metric in ["val_top1", "val_top5"]:
         print(f"max linear {metric}: {max(metric_callback.val_metrics[metric])}")
+        metrics_dict[metric] = max(metric_callback.val_metrics[metric])
+    return metrics_dict

benchmarks/imagenet/vitb16/main.py

@@ -1,7 +1,7 @@
 from argparse import ArgumentParser
 from datetime import datetime
 from pathlib import Path
-from typing import Sequence, Union
+from typing import Sequence, Union, Dict
 
 import aim
 import finetune_eval
@@ -100,10 +100,11 @@ def main(
                 strategy=strategy,
             )
 
+        eval_metrics: Dict[str, Dict[str, float]] = Dict()
         if skip_knn_eval:
             print("Skipping KNN eval.")
         else:
-            knn_eval.knn_eval(
+            eval_metrics["knn"] = knn_eval.knn_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -118,7 +119,7 @@ def main(
         if skip_linear_eval:
             print("Skipping linear eval.")
         else:
-            linear_eval.linear_eval(
+            eval_metrics["linear"] = linear_eval.linear_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -134,7 +135,7 @@ def main(
         if skip_finetune_eval:
             print("Skipping fine-tune eval.")
         else:
-            finetune_eval.finetune_eval(
+            eval_metrics["finetune"] = finetune_eval.finetune_eval(
                 model=model,
                 num_classes=num_classes,
                 train_dir=train_dir,
@@ -147,6 +148,10 @@ def main(
                 precision=precision,
             )
 
+        if eval_metrics:
+            print(f"Results for {method}:")
+            print(eval_metrics_to_markdown(eval_metrics))
+
 
 def pretrain(
     model: LightningModule,
@@ -218,6 +223,38 @@ def pretrain(
         print(f"max {metric}: {max(metric_callback.val_metrics[metric])}")
 
 
+def eval_metrics_to_markdown(metrics: Dict[str, Dict[str, float]]) -> str:
+    EVAL_NAME_COLUMN_NAME = "Eval Name"
+    METRIC_COLUMN_NAME = "Metric Name"
+    VALUE_COLUMN_NAME = "Value"
+
+    eval_name_max_len = max(
+        len(eval_name) for eval_name in list(metrics.keys()) + [EVAL_NAME_COLUMN_NAME]
+    )
+    metric_name_max_len = max(
+        len(metric_name)
+        for metric_dict in metrics.values()
+        for metric_name in list(metric_dict.keys()) + [METRIC_COLUMN_NAME]
+    )
+    value_max_len = max(
+        len(metric_value)
+        for metric_dict in metrics.values()
+        for metric_value in list(f"{value:.2f}" for value in metric_dict.values())
+        + [VALUE_COLUMN_NAME]
+    )
+
+    header = f"| {EVAL_NAME_COLUMN_NAME.ljust(eval_name_max_len)} | {METRIC_COLUMN_NAME.ljust(metric_name_max_len)} | {VALUE_COLUMN_NAME.ljust(value_max_len)} |"
+    separator = f"|:{'-' * (eval_name_max_len)}:|:{'-' * (metric_name_max_len)}:|:{'-' * (value_max_len)}:|"
+
+    lines = [header, separator] + [
+        f"| {eval_name.ljust(eval_name_max_len)} | {metric_name.ljust(metric_name_max_len)} | {f'{metric_value:.2f}'.ljust(value_max_len)} |"
+        for eval_name, metric_dict in metrics.items()
+        for metric_name, metric_value in metric_dict.items()
+    ]
+
+    return "\n".join(lines)
+
+
 if __name__ == "__main__":
     args = parser.parse_args()
     main(**vars(args))