refactoring and running experiments

experiments/meta_task_small_utility.py

@@ -1,12 +1,5 @@
-from pathlib import Path
-
 import torch
-import torchmetrics
-
 from torch.utils.data import Dataset
-from tqdm import tqdm
-
-from experiments.meta_task_utility import set_checkpoint
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
@@ -24,31 +17,6 @@ class AddTaskDataset(Dataset):
         return ab, ab.sum(axis=-1, keepdims=True)
 
 
-def validate(checkpoint_path, valid_d, ratio=1, validmetric=torchmetrics.MeanAbsoluteError()):
-    checkpoint_path = Path(checkpoint_path)
-
-    # initialize metric
-    model = torch.load(checkpoint_path, map_location=DEVICE).eval()
-    n_samples = int(len(valid_d) * ratio)
-
-    with tqdm(total=n_samples, desc='Validation Run: ') as pbar:
-        for idx, (valid_batch_x, valid_batch_y) in enumerate(valid_d):
-            valid_batch_x, valid_batch_y = valid_batch_x.to(DEVICE), valid_batch_y.to(DEVICE)
-            y_valid = model(valid_batch_x)
-
-            # metric on current batch
-            acc = validmetric(y_valid.cpu(), valid_batch_y.cpu())
-            pbar.set_postfix_str(f'Acc: {acc}')
-            pbar.update()
-            if idx == n_samples:
-                break
-
-    # metric on all batches using custom accumulation
-    acc = validmetric.compute()
-    tqdm.write(f"Avg. Accuracy on all data: {acc}")
-    return acc
-
-
 def train_task(model, optimizer, loss_func, btch_x, btch_y) -> (dict, torch.Tensor):
     # Zero your gradients for every batch!
     optimizer.zero_grad()
@@ -66,12 +34,5 @@ def train_task(model, optimizer, loss_func, btch_x, btch_y) -> (dict, torch.Tens
     return stp_log, y_prd
 
 
-def checkpoint_and_validate(model, out_path, epoch_n, valid_d, final_model=False):
-    out_path = Path(out_path)
-    ckpt_path = set_checkpoint(model, out_path, epoch_n, final_model=final_model)
-    result = validate(ckpt_path, valid_d)
-    return result
-
-
 if __name__ == '__main__':
     raise(NotImplementedError('Get out of here'))