Revert "Merge branch 'vers_working_branch' into geosampler_prechipping"

This reverts commit c7f3e4c, reversing
changes made to d8cb4b2.

tests/samplers/test_batch.py

@@ -6,7 +6,6 @@
 from itertools import product
 
 import pytest
-import torch
 from _pytest.fixtures import SubRequest
 from rasterio.crs import CRS
 from torch.utils.data import DataLoader
@@ -145,20 +144,6 @@ def test_weighted_sampling(self) -> None:
             for bbox in batch:
                 assert bbox == BoundingBox(0, 10, 0, 10, 0, 10)
 
-    def test_random_seed(self) -> None:
-        ds = CustomGeoDataset()
-        ds.index.insert(0, (0, 10, 0, 10, 0, 10))
-        sampler = RandomBatchGeoSampler(ds, 1, 1, generator=torch.manual_seed(0))
-        for bbox in sampler:
-            sample1 = bbox
-            break
-
-        sampler = RandomBatchGeoSampler(ds, 1, 1, generator=torch.manual_seed(0))
-        for bbox in sampler:
-            sample2 = bbox
-            break
-        assert sample1 == sample2
-
     @pytest.mark.slow
     @pytest.mark.parametrize('num_workers', [0, 1, 2])
     def test_dataloader(

tests/samplers/test_single.py

@@ -7,7 +7,6 @@
 
 import geopandas as gpd
 import pytest
-import torch
 from _pytest.fixtures import SubRequest
 from geopandas import GeoDataFrame
 from rasterio.crs import CRS
@@ -223,21 +222,6 @@ def test_weighted_sampling(self) -> None:
         for bbox in sampler:
             assert bbox == BoundingBox(0, 10, 0, 10, 0, 10)
 
-    def test_random_seed(self) -> None:
-        ds = CustomGeoDataset()
-        ds.index.insert(0, (0, 10, 0, 10, 0, 10))
-        generator = torch.manual_seed(0)
-        sampler = RandomGeoSampler(ds, 1, 1, generator=generator)
-        for bbox in sampler:
-            sample1 = bbox
-            break
-
-        sampler = RandomGeoSampler(ds, 1, 1, generator=generator)
-        for bbox in sampler:
-            sample2 = bbox
-            break
-        assert sample1 == sample2
-
     @pytest.mark.slow
     @pytest.mark.parametrize('num_workers', [0, 1, 2])
     def test_dataloader(
@@ -387,25 +371,6 @@ def test_point_data(self) -> None:
         for _ in sampler:
             continue
 
-    def test_shuffle_seed(self) -> None:
-        ds = CustomGeoDataset()
-        ds.index.insert(0, (0, 10, 0, 10, 0, 10))
-        ds.index.insert(1, (0, 11, 0, 11, 0, 11))
-        generator = torch.manual_seed(2)
-        sampler1 = PreChippedGeoSampler(ds, shuffle=True, generator=generator)
-        for bbox in sampler1:
-            sample1 = bbox
-            print(sample1)
-            break
-
-        generator = torch.manual_seed(2)
-        sampler2 = PreChippedGeoSampler(ds, shuffle=True, generator=generator)
-        for bbox in sampler2:
-            sample2 = bbox
-            print(sample2)
-            break
-        assert sample1 == sample2
-
     @pytest.mark.slow
     @pytest.mark.parametrize('num_workers', [0, 1, 2])
     def test_dataloader(

torchgeo/datamodules/agrifieldnet.py

@@ -74,11 +74,7 @@ def setup(self, stage: str) -> None:
 
         if stage in ['fit']:
             self.train_batch_sampler = RandomBatchGeoSampler(
-                self.train_dataset,
-                self.patch_size,
-                self.batch_size,
-                self.length,
-                generator=generator,
+                self.train_dataset, self.patch_size, self.batch_size, self.length
             )
         if stage in ['fit', 'validate']:
             self.val_sampler = GridGeoSampler(

torchgeo/samplers/batch.py

@@ -70,7 +70,6 @@ def __init__(
         length: int | None = None,
         roi: BoundingBox | None = None,
         units: Units = Units.PIXELS,
-        generator: torch.Generator | None = None,
     ) -> None:
         """Initialize a new Sampler instance.
 
@@ -98,11 +97,9 @@ def __init__(
             roi: region of interest to sample from (minx, maxx, miny, maxy, mint, maxt)
                 (defaults to the bounds of ``dataset.index``)
             units: defines if ``size`` is in pixel or CRS units
-            generator: random number generator
         """
         super().__init__(dataset, roi)
         self.size = _to_tuple(size)
-        self.generator = generator
 
         if units == Units.PIXELS:
             self.size = (self.size[0] * self.res, self.size[1] * self.res)
@@ -147,9 +144,7 @@ def __iter__(self) -> Iterator[list[BoundingBox]]:
             # Choose random indices within that tile
             batch = []
             for _ in range(self.batch_size):
-                bounding_box = get_random_bounding_box(
-                    bounds, self.size, self.res, self.generator
-                )
+                bounding_box = get_random_bounding_box(bounds, self.size, self.res)
                 batch.append(bounding_box)
 
             yield batch

torchgeo/samplers/single.py

@@ -5,7 +5,6 @@
 
 import abc
 from collections.abc import Callable, Iterable, Iterator
-from functools import partial
 
 import geopandas as gpd
 import numpy as np
@@ -211,7 +210,6 @@ def __init__(
         length: int | None = None,
         roi: BoundingBox | None = None,
         units: Units = Units.PIXELS,
-        generator: torch.Generator | None = None,
     ) -> None:
         """Initialize a new Sampler instance.
 
@@ -238,16 +236,13 @@ def __init__(
             roi: region of interest to sample from (minx, maxx, miny, maxy, mint, maxt)
                 (defaults to the bounds of ``dataset.index``)
             units: defines if ``size`` is in pixel or CRS units
-            generator: The random generator used for sampling.
-
         """
         super().__init__(dataset, roi)
         self.size = _to_tuple(size)
 
         if units == Units.PIXELS:
             self.size = (self.size[0] * self.res, self.size[1] * self.res)
 
-        self.generator = generator
         self.length = 0
         self.hits = []
         areas = []
@@ -309,7 +304,7 @@ def get_chips(self) -> GeoDataFrame:
             bounds = BoundingBox(*hit.bounds)
 
             # Choose a random index within that tile
-            bbox = get_random_bounding_box(bounds, self.size, self.res, self.generator)
+            bbox = get_random_bounding_box(bounds, self.size, self.res)
             minx, maxx, miny, maxy, mint, maxt = tuple(bbox)
             chip = {
                 'geometry': box(minx, miny, maxx, maxy),
@@ -452,11 +447,7 @@ class PreChippedGeoSampler(GeoSampler):
     """
 
     def __init__(
-        self,
-        dataset: GeoDataset,
-        roi: BoundingBox | None = None,
-        shuffle: bool = False,
-        generator: torch.Generator | None = None,
+        self, dataset: GeoDataset, roi: BoundingBox | None = None, shuffle: bool = False
     ) -> None:
         """Initialize a new Sampler instance.
 
@@ -467,12 +458,9 @@ def __init__(
             roi: region of interest to sample from (minx, maxx, miny, maxy, mint, maxt)
                 (defaults to the bounds of ``dataset.index``)
             shuffle: if True, reshuffle data at every epoch
-            generator: The random number generator used in combination with shuffle.
-
         """
         super().__init__(dataset, roi)
         self.shuffle = shuffle
-        self.generator = generator
 
         self.hits = []
         for hit in self.index.intersection(tuple(self.roi), objects=True):
@@ -489,7 +477,7 @@ def get_chips(self) -> GeoDataFrame:
         """
         generator: Callable[[int], Iterable[int]] = range
         if self.shuffle:
-            generator = partial(torch.randperm, generator=self.generator)
+            generator = torch.randperm
 
         print('generating samples... ')
         chips = []

torchgeo/samplers/utils.py

@@ -35,10 +35,7 @@ def _to_tuple(value: tuple[float, float] | float) -> tuple[float, float]:
 
 
 def get_random_bounding_box(
-    bounds: BoundingBox,
-    size: tuple[float, float] | float,
-    res: float,
-    generator: torch.Generator | None = None,
+    bounds: BoundingBox, size: tuple[float, float] | float, res: float
 ) -> BoundingBox:
     """Returns a random bounding box within a given bounding box.
 
@@ -53,7 +50,6 @@ def get_random_bounding_box(
         bounds: the larger bounding box to sample from
         size: the size of the bounding box to sample
         res: the resolution of the image
-        generator: random number generator
 
     Returns:
         randomly sampled bounding box from the extent of the input
@@ -68,8 +64,8 @@ def get_random_bounding_box(
     miny = bounds.miny
 
     # Use an integer multiple of res to avoid resampling
-    minx += int(torch.rand(1, generator=generator).item() * width) * res
-    miny += int(torch.rand(1, generator=generator).item() * height) * res
+    minx += int(torch.rand(1).item() * width) * res
+    miny += int(torch.rand(1).item() * height) * res
 
     maxx = minx + t_size[1]
     maxy = miny + t_size[0]