Small CorrDiff fixes

examples/weather/corrdiff/conf/base/generation/base_all.yaml

@@ -46,6 +46,6 @@ perf:
     # Use Apex GroupNorm (optimized normalization for performance with channelslast layout)
   profile_mode: false
     # Enable NVTX annotations for performance profiling
-  io_syncronous: true
+  io_synchronous: true
     # Synchronize I/O operations for writing inference results
 

examples/weather/corrdiff/generate.py

@@ -357,7 +357,7 @@ def generate_fn():
                     has_lead_time=has_lead_time,
                 )
 
-                if cfg.generation.perf.io_syncronous:
+                if cfg.generation.perf.io_synchronous:
                     writer_executor = ThreadPoolExecutor(
                         max_workers=cfg.generation.perf.num_writer_workers
                     )
@@ -383,8 +383,9 @@ def elapsed_time(self, _):
                 start = end = DummyEvent()
 
             times = dataset.time()
-            for index, (image_tar, image_lr, *lead_time_label) in enumerate(
-                iter(data_loader)
+            for dataset_index, (image_tar, image_lr, *lead_time_label) in zip(
+                sampler,
+                iter(data_loader),
             ):
                 time_index += 1
                 if dist.rank == 0:
@@ -407,7 +408,7 @@ def elapsed_time(self, _):
                 image_out = generate_fn()
                 if dist.rank == 0:
                     batch_size = image_out.shape[0]
-                    if cfg.generation.perf.io_syncronous:
+                    if cfg.generation.perf.io_synchronous:
                         # write out data in a seperate thread so we don't hold up inferencing
                         writer_threads.append(
                             writer_executor.submit(
@@ -419,8 +420,7 @@ def elapsed_time(self, _):
                                 image_tar.cpu(),
                                 image_lr.cpu(),
                                 time_index,
-                                index,
-                                has_lead_time,
+                                dataset_index,
                             )
                         )
                     else:
@@ -432,8 +432,7 @@ def elapsed_time(self, _):
                             image_tar.cpu(),
                             image_lr.cpu(),
                             time_index,
-                            index,
-                            has_lead_time,
+                            dataset_index,
                         )
             end.record()
             end.synchronize()
@@ -451,7 +450,7 @@ def elapsed_time(self, _):
                 )
 
             # make sure all the workers are done writing
-            if dist.rank == 0 and cfg.generation.perf.io_syncronous:
+            if dist.rank == 0 and cfg.generation.perf.io_synchronous:
                 for thread in list(writer_threads):
                     thread.result()
                     writer_threads.remove(thread)

examples/weather/corrdiff/helpers/generate_helpers.py

@@ -51,8 +51,7 @@ def save_images(
     image_tar,
     image_lr,
     time_index,
-    t_index,
-    has_lead_time,
+    dataset_index,
 ):
     """
     Saves inferencing result along with the baseline
@@ -71,7 +70,7 @@ def save_images(
     image_tar (torch.Tensor): Ground truth data
     image_lr (torch.Tensor): Low resolution input data
     time_index (int): Epoch number
-    t_index (int): index where times are located
+    dataset_index (int): index where times are located
     """
     # weather sub-plot
     image_lr2 = image_lr[0].unsqueeze(0)
@@ -95,7 +94,7 @@ def save_images(
         image_out2 = image_out2.cpu().numpy()
         image_out2 = dataset.denormalize_output(image_out2)
 
-        time = times[t_index]
+        time = times[dataset_index]
         writer.write_time(time_index, time)
         for channel_idx in range(image_out2.shape[1]):
             info = dataset.output_channels()[channel_idx]
@@ -107,10 +106,10 @@ def save_images(
                 channel_name, time_index, idx, image_out2[0, channel_idx]
             )
 
-        input_channel_info = dataset.input_channels()
-        for channel_idx in range(len(input_channel_info)):
-            info = input_channel_info[channel_idx]
-            channel_name = info.name + info.level
-            writer.write_input(channel_name, time_index, image_lr2[0, channel_idx])
-            if channel_idx == image_lr2.shape[1] - 1:
-                break
+    input_channel_info = dataset.input_channels()
+    for channel_idx in range(len(input_channel_info)):
+        info = input_channel_info[channel_idx]
+        channel_name = info.name + info.level
+        writer.write_input(channel_name, time_index, image_lr2[0, channel_idx])
+        if channel_idx == image_lr2.shape[1] - 1:
+            break

examples/weather/corrdiff/train.py

@@ -830,13 +830,13 @@ def main(cfg: DictConfig) -> None:
                         epoch=cur_nimg,
                     )
 
-            # Retain only the recent n checkpoints, if desired
-            if cfg.training.io.save_n_recent_checkpoints > 0:
-                for suffix in [".mdlus", ".pt"]:
-                    ckpts = checkpoint_list(checkpoint_dir, suffix=suffix)
-                    while len(ckpts) > cfg.training.io.save_n_recent_checkpoints:
-                        os.remove(os.path.join(checkpoint_dir, ckpts[0]))
-                        ckpts = ckpts[1:]
+                    # Retain only the recent n checkpoints, if desired
+                    if cfg.training.io.save_n_recent_checkpoints > 0:
+                        for suffix in [".mdlus", ".pt"]:
+                            ckpts = checkpoint_list(checkpoint_dir, suffix=suffix)
+                            while len(ckpts) > cfg.training.io.save_n_recent_checkpoints:
+                                os.remove(os.path.join(checkpoint_dir, ckpts[0]))
+                                ckpts = ckpts[1:]
 
     # Done.
     logger0.info("Training Completed.")