semantic mapper script and multitask reader fixes

dronescapes_reader/multitask_dataset.py

@@ -107,7 +107,10 @@ class MultiTaskDataset(Dataset):
         if self._statistics is not None:
             for task_name, task in self.name_to_task.items():
                 if not task.is_classification:
-                    task.set_normalization(self.normalization[task_name], self._statistics[task_name])
+                    try:
+                        task.set_normalization(self.normalization[task_name], self._statistics[task_name])
+                    except:
+                        breakpoint()
 
     # Public methods and properties
 
@@ -264,7 +267,7 @@ class MultiTaskDataset(Dataset):
         if self.cache_task_stats and cache_path.exists():
             res = np.load(cache_path, allow_pickle=True)["arr_0"].item()
             logger.info(f"Loaded task statistics: { {k: tuple(v[0].shape) for k, v in res.items()} } from {cache_path}")
-        missing_tasks = list(set(self.task_names).difference(res.keys()))
+        missing_tasks = [t for t in set(self.task_names).difference(res) if not self.name_to_task[t].is_classification]
         if len(missing_tasks) == 0:
             return res
         logger.info(f"Computing global task statistics (dataset len {len(self)}) for {missing_tasks}")
@@ -287,27 +290,25 @@ class MultiTaskDataset(Dataset):
             assert not new_mean.isnan().any() and not new_M2.isnan().any(), (mean, new_mean, counts, counts_delta)
             return new_count, new_mean, new_M2
 
-        missing_tasks_no_classif = [t for t in missing_tasks if not self.name_to_task[t].is_classification]
+        assert not any(mt := [self.name_to_task[t].is_classification for t in missing_tasks]), mt
+        assert len(missing_tasks) > 0, missing_tasks
         ch = {k: v[-1] if len(v) == 3 else 1 for k, v in self.data_shape.items()}
-        counts = {task_name: tr.zeros(ch[task_name]).long() for task_name in missing_tasks_no_classif}
-        mins = {task_name: tr.zeros(ch[task_name]).type(tr.float64) + 10**10 for task_name in missing_tasks_no_classif}
-        maxs = {task_name: tr.zeros(ch[task_name]).type(tr.float64) - 10**10 for task_name in missing_tasks_no_classif}
-        means_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks_no_classif}
-        M2s_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks_no_classif}
+        counts = {task_name: tr.zeros(ch[task_name]).long() for task_name in missing_tasks}
+        mins = {task_name: tr.zeros(ch[task_name]).type(tr.float64) + 10**10 for task_name in missing_tasks}
+        maxs = {task_name: tr.zeros(ch[task_name]).type(tr.float64) - 10**10 for task_name in missing_tasks}
+        means_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks}
+        M2s_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks}
 
         old_names, old_normalization = self.task_names, self.normalization
-        self.task_names, self.normalization = missing_tasks_no_classif, None # for self[ix]
-
-        if len(missing_tasks_no_classif) == 0:
-            return {}
-
+        self.task_names, self.normalization = missing_tasks, None # for self[ix]
         res = {}
         BS = min(len(self), self.batch_size_stats)
         n = (len(self) // BS) + (len(self) % BS != 0)
+
         logger.debug(f"Global task statistics. Batch size: {BS}. N iterations: {n}.")
         for ix in trange(n, disable=os.getenv("STATS_PBAR", "0") == "0", desc="Computing stats"):
             item = self[ix * BS: min(len(self), (ix + 1) * BS)][0]
-            for task in missing_tasks_no_classif:
+            for task in missing_tasks:
                 item_flat_ch = item[task].reshape(-1, ch[task])
                 item_no_nan = item_flat_ch.nan_to_num(0).type(tr.float64)
                 mins[task] = tr.minimum(mins[task], item_no_nan.min(0)[0])
@@ -316,7 +317,7 @@ class MultiTaskDataset(Dataset):
                 counts[task], means_vec[task], M2s_vec[task] = \
                     update(counts[task], counts_delta, means_vec[task], M2s_vec[task], item_no_nan)
 
-        for task in missing_tasks_no_classif:
+        for task in missing_tasks:
             res[task] = (mins[task], maxs[task], means_vec[task], (M2s_vec[task] / counts[task]).sqrt())
             assert not any(x[0].isnan().any() for x in res[task]), (task, res[task])
         self.task_names, self.normalization = old_names, old_normalization