little update to the reader

dronescapes_reader/__init__.py

@@ -1,13 +1,14 @@
 """init file"""
 from .multitask_dataset import MultiTaskDataset, NpzRepresentation
-from .dronescapes_representations import DepthRepresentation, OpticalFlowRepresentation, SemanticRepresentation
+from .dronescapes_representations import DepthRepresentation, OpticalFlowRepresentation, SemanticRepresentation, \
+    ColorRepresentation
 
 _color_map = [[0, 255, 0], [0, 127, 0], [255, 255, 0], [255, 255, 255],
               [255, 0, 0], [0, 0, 255], [0, 255, 255], [127, 127, 63]]
 _m2f_name = "semantic_mask2former_swin_mapillary_converted"
 dronescapes_task_types = { # some pre-baked representations
-    "rgb": NpzRepresentation("rgb"),
-    "edges_dexined": NpzRepresentation("edges_dexined"),
+    "rgb": ColorRepresentation("rgb"),
+    "edges_dexined": ColorRepresentation("edges_dexined"),
     "depth_dpt": DepthRepresentation("depth_dpt", min_depth=0, max_depth=0.999),
     "depth_sfm_manual202204": DepthRepresentation("depth_sfm_manual202204", min_depth=0, max_depth=300),
     "opticalflow_rife": OpticalFlowRepresentation,

dronescapes_reader/dronescapes_representations.py

@@ -1,4 +1,5 @@
 """Dronescapes representations -- adds various loading/writing/image showing capabilities to dronescapes tasks"""
+from __future__ import annotations
 from pathlib import Path
 import numpy as np
 import torch as tr
@@ -6,29 +7,48 @@ import flow_vis
 from overrides import overrides
 from matplotlib.cm import hot # pylint: disable=no-name-in-module
 from .multitask_dataset import NpzRepresentation
+from torch.nn import functional as F
+
+class ColorRepresentation(NpzRepresentation):
+    def load_from_disk(self, path: Path) -> tr.Tensor:
+        res = super().load_from_disk(path)
+        return res.float() / 255
+
+    def save_to_disk(self, data: tr.Tensor, path: Path):
+        return super().save_to_disk((data * 255).byte(), path)
 
 class DepthRepresentation(NpzRepresentation):
     """DepthRepresentation. Implements depth task-specific stuff, like hotmap."""
     def __init__(self, *args, min_depth: float, max_depth: float, **kwargs):
         super().__init__(*args, **kwargs)
+        assert 0 <= min_depth < max_depth, (min_depth, max_depth)
         self.min_depth = min_depth
         self.max_depth = max_depth
 
     @overrides
     def plot_fn(self, x: tr.Tensor) -> np.ndarray:
         x = x.detach().cpu().numpy()
-        x = np.clip(x, self.min_depth, self.max_depth)
-        x = np.nan_to_num((x - x.min()) / (x.max() - x.min()), 0)
+        x = np.clip(x, 0, 1)
         y = hot(x.squeeze())[..., 0:3]
         y = np.uint8(y * 255)
         return y
 
+    def load_from_disk(self, path: Path) -> tr.Tensor:
+        res = super().load_from_disk(path)
+        res = res.float().clip(self.min_depth, self.max_depth)
+        res = (res - self.min_depth) / (self.max_depth - self.min_depth)
+        return res
+
 class OpticalFlowRepresentation(NpzRepresentation):
     """OpticalFlowRepresentation. Implements depth task-specific stuff, like using flow_vis."""
     @overrides
     def plot_fn(self, x: tr.Tensor) -> np.ndarray:
         return flow_vis.flow_to_color(x.squeeze().nan_to_num(0).detach().cpu().numpy())
 
+    def load_from_disk(self, path: Path) -> tr.Tensor:
+        res = super().load_from_disk(path).float()
+        return res
+
 class SemanticRepresentation(NpzRepresentation):
     """SemanticRepresentation. Implements depth task-specific stuff, like using flow_vis."""
     def __init__(self, *args, classes: int | list[str], color_map: list[tuple[int, int, int]], **kwargs):
@@ -36,17 +56,18 @@ class SemanticRepresentation(NpzRepresentation):
         self.classes = list(range(classes)) if isinstance(classes, int) else classes
         self.n_classes = len(self.classes)
         self.color_map = color_map
-        assert len(color_map) == self.n_classes, (color_map, self.n_classes)
+        assert len(color_map) == self.n_classes and self.n_classes > 1, (color_map, self.n_classes)
 
     @overrides
     def load_from_disk(self, path: Path) -> tr.Tensor:
         res = super().load_from_disk(path)
         assert len(res.shape) == 2, f"Only argmaxed data supported, got: {res.shape}"
+        res = F.one_hot(res.long(), num_classes=self.n_classes).float()
         return res
 
     @overrides
     def plot_fn(self, x: tr.Tensor) -> np.ndarray:
-        x = x.squeeze().nan_to_num(0).detach().cpu().numpy()
+        x = x.squeeze().nan_to_num(0).detach().argmax(-1).cpu().numpy()
         new_images = np.zeros((*x.shape, 3), dtype=np.uint8)
         for i in range(self.n_classes):
             new_images[x == i] = self.color_map[i]

dronescapes_reader/multitask_dataset.py

@@ -2,6 +2,7 @@
 """MultiTask Dataset module compatible with torch.utils.data.Dataset & DataLoader."""
 from __future__ import annotations
 from pathlib import Path
+from typing import Dict, List, Tuple
 from argparse import ArgumentParser
 from pprint import pprint
 from natsort import natsorted
@@ -12,8 +13,8 @@ from torch.utils.data import Dataset, DataLoader
 from lovely_tensors import monkey_patch
 
 monkey_patch()
-BuildDatasetTuple = tuple[dict[str, list[Path]], list[str]]
-MultiTaskItem = tuple[dict[str, tr.Tensor], str, list[str]] # [{task: data}, stem(name) | list[stem(name)], [tasks]]
+BuildDatasetTuple = Tuple[Dict[str, List[Path]], List[str]]
+MultiTaskItem = Tuple[Dict[str, tr.Tensor], str, List[str]] # [{task: data}, stem(name) | list[stem(name)], [tasks]]
 
 class NpzRepresentation:
     """Generic Task with data read from/saved to npz files. Tries to read data as-is from disk and store it as well"""