update desc

src/distmetrics/transformer.py

@@ -293,7 +293,7 @@ def _estimate_logit_params_via_streamed_patches(
     unfold_gen = unfolding_stream(pre_imgs_stack_t, P, stride, batch_size)
 
     for patch_batch, slices in tqdm(
-        unfold_gen, total=n_batches, desc='Chips Traversed', mininterval=2, disable=(not tqdm_enabled)
+        unfold_gen, total=n_batches, desc='Chips Traversed (dual pol)', mininterval=2, disable=(not tqdm_enabled)
     ):
         chip_mean, chip_logvar = model(patch_batch)
         for k, (sy, sx) in enumerate(slices):
@@ -395,7 +395,7 @@ def _estimate_logit_params_via_folding(
     pred_means_p = torch.zeros(*target_chip_shape).to(device)
     pred_logvars_p = torch.zeros(*target_chip_shape).to(device)
 
-    for i in tqdm(range(n_batches), desc='Chips Traversed', mininterval=2, disable=(not tqdm_enabled)):
+    for i in tqdm(range(n_batches), desc='Chips Traversed (dual pol)', mininterval=2, disable=(not tqdm_enabled)):
         # change last dimension from P**2 to P, P; use -1 because won't always have batch_size as 0th dimension
         batch_s = slice(batch_size * i, batch_size * (i + 1))
         patch_batch = patches[batch_s, ...].view(-1, T, C, P, P)