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