Delete train_resnet_pokemon.py

train_resnet_pokemon.py

@@ -1,82 +0,0 @@
-import torch
-import torchvision.transforms.v2 as T
-
-import fiftyone as fo
-from fiftyone import ViewField as F
-import fiftyone.train as fot
-
-def main():
-
-    dataset = fo.load_dataset("pokemon")
-
-    dataset = dataset.match_tags("train")
-
-    model_config = fot.model.classification.TorchvisionResnetModelConfig(
-        get_item_class_name='ImageClassificationGetItem',
-        get_item_inference_class_name='ImageClassificationInferenceGetItem',
-        train_transforms_list='standard',
-        inference_transforms_list='standard',
-        classes=dataset.distinct('ground_truth.label'),
-        weights=None
-    )
-    train_run_config = fot.run.TrainConfig(
-        model_config,
-        data={
-            'dataloader': {
-                'batch_size': 128,
-            }
-        },
-        train_regime = {
-            "num_steps": 3800,  # 100 epochs × 38 steps/epoch
-            "validate_every": 38,  # Validate every epoch
-            "early_stop_after": 456,  # 6 epochs without improvement
-            "grad_clip": 15,
-        },
-        optimizer = {
-            "name": "SGD",
-            "kwargs": {
-                "lr": 1e-2,  # Lower initial learning rate
-                "momentum": 0.9,
-                "weight_decay": 1e-3,
-            },
-            "scheduler": {
-                "name": "CosineAnnealingLR",
-                "kwargs": {
-                    "T_max": 3800,  # Total steps
-                    "eta_min": 1e-6,
-                }
-            }
-        }
-    )
-
-    incidentals={
-            "logging" : {
-                "wandb" : {
-                    'project':'resnet-pokemon'
-                },
-                "log_every" : 6,
-            },
-            # "gi_kwargs" : {
-            #     "field_mapping" : {
-            #         "labels_field" : "ground_truth.label"
-            #     }
-            # }
-        }
-
-    with fot.context(embeddings_model="resnet18-imagenet-torch",
-                     embeddings_field='embed-resnet18-imagenet',
-                     checkpoint_dir="./.checkpoints"):
-        print(fot.context)
-        print(fot.context.checkpoint_dir)
-        results = fot.run.train(
-            samples=dataset,
-            config=train_run_config,
-            key=f"resnet18_classifier",
-            incidentals=incidentals
-        )
-
-    return results
-
-
-if __name__ == "__main__":
-    main()