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Merge branch 'geosampler_prechipping' into vers_working_branch
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‎docs/conf.py‎

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('py:class', 'torchvision.models._api.WeightsEnum'),
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('py:class', 'torchvision.models.resnet.ResNet'),
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('py:class', 'torchvision.models.swin_transformer.SwinTransformer'),
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('py:class', 'geopandas.GeoDataFrame'),
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]
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'torch': ('https://pytorch.org/docs/stable', None),
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'torchmetrics': ('https://lightning.ai/docs/torchmetrics/stable/', None),
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'torchvision': ('https://pytorch.org/vision/stable', None),
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'geopandas': ('https://geopandas.org/en/stable/', None),
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}
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# nbsphinx

‎docs/index.rst‎

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:caption: Tutorials
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tutorials/getting_started
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tutorials/visualizing_samples
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tutorials/custom_raster_dataset
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tutorials/transforms
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tutorials/indices

‎docs/tutorials/visualizing_samples.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualizing Samples\n",
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"\n",
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"This tutorial shows how to visualize and save the extent of your samples before and during training. In this particular example, we compare a vanilla RandomGeoSampler with one bounded by multiple ROI's and show how easy it is to gain insight on the distribution of your samples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import tempfile\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"from torchgeo.datasets import NAIP, stack_samples\n",
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"from torchgeo.datasets.utils import download_url\n",
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"from torchgeo.samplers import RandomGeoSampler\n",
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"\n",
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"\n",
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"def run_epochs(dataset, sampler):\n",
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" dataloader = DataLoader(\n",
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" dataset, sampler=sampler, batch_size=1, collate_fn=stack_samples, num_workers=0\n",
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" )\n",
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" fig, ax = plt.subplots()\n",
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" num_epochs = 5\n",
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" for epoch in range(num_epochs):\n",
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" color = plt.cm.viridis(epoch / num_epochs)\n",
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" # sampler.chips.to_file(f'naip_chips_epoch_{epoch}') # Optional: save chips to file for display in GIS software\n",
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" ax = sampler.chips.plot(ax=ax, color=color)\n",
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" for sample in dataloader:\n",
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" pass\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Generate dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"naip_root = os.path.join(tempfile.gettempdir(), 'naip')\n",
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"naip_url = (\n",
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" 'https://naipeuwest.blob.core.windows.net/naip/v002/de/2018/de_060cm_2018/38075/'\n",
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")\n",
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"tiles = ['m_3807511_ne_18_060_20181104.tif', 'm_3807512_sw_18_060_20180815.tif']\n",
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"for tile in tiles:\n",
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" download_url(naip_url + tile, naip_root)\n",
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"\n",
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"naip = NAIP(naip_root)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First we create the default sampler for our dataset (3 samples) and run it for 5 epochs and plot its results. Each color displays a different epoch, so we can see how the RandomGeoSampler has distributed it's samples for every epoch."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sampler = RandomGeoSampler(naip, size=1000, length=3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"run_epochs(naip, sampler)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we split our dataset by two bounding boxes and re-inspect the samples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"from torchgeo.datasets import roi_split\n",
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"from torchgeo.datasets.utils import BoundingBox\n",
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"\n",
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"rois = [\n",
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" BoundingBox(440854, 442938, 4299766, 4301731, 0, np.inf),\n",
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" BoundingBox(449070, 451194, 4289463, 4291746, 0, np.inf),\n",
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"]\n",
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"datasets = roi_split(naip, rois)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"combined = datasets[0] | datasets[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sampler = RandomGeoSampler(combined, size=1000, length=3)\n",
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"run_epochs(combined, sampler)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "cca",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎pyproject.toml‎

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"einops>=0.3",
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# fiona 1.8.21+ required for Python 3.10 wheels
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"fiona>=1.8.21",
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# geopandas 0.13.2 is the last version to support pandas 1.3, but has feather support
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"geopandas>=0.13.2",
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# kornia 0.7.3+ required for instance segmentation support in AugmentationSequential
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"kornia>=0.7.3",
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# lightly 1.4.5+ required for LARS optimizer
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"pandas>=1.3.3",
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# pillow 8.4+ required for Python 3.10 wheels
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"pillow>=8.4",
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# pyarrow 12.0+ required for feather support
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"pyarrow>=17.0.0",
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# pyproj 3.3+ required for Python 3.10 wheels
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"pyproj>=3.3",
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# rasterio 1.3+ required for Python 3.10 wheels

‎requirements/min-reqs.old‎

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# install
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einops==0.3.0
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fiona==1.8.21
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geopandas==0.13.2
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kornia==0.7.3
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lightly==1.4.5
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lightning[pytorch-extra]==2.0.0
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matplotlib==3.5.0
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numpy==1.21.2
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pandas==1.3.3
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pillow==8.4.0
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pyarrow==17.0.0
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pyproj==3.3.0
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rasterio==1.3.0.post1
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rtree==1.0.0

‎requirements/required.txt‎

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# install
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einops==0.8.0
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fiona==1.10.1
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geopandas==0.14.4
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kornia==0.7.3
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lightly==1.5.12
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lightning[pytorch-extra]==2.4.0
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matplotlib==3.9.2
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numpy==2.1.1
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pandas==2.2.3
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pillow==10.4.0
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pyarrow==17.0.0
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pyproj==3.6.1
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rasterio==1.3.11
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rtree==1.3.0

‎tests/data/samplers/filtering_4x4.feather‎

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‎tests/data/samplers/filtering_4x4/filtering_4x4.cpg‎

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ISO-8859-1

‎tests/data/samplers/filtering_4x4/filtering_4x4.dbf‎

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‎tests/data/samplers/filtering_4x4/filtering_4x4.prj‎

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PROJCS["NAD_1983_BC_Environment_Albers",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",1000000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-126.0],PARAMETER["Standard_Parallel_1",50.0],PARAMETER["Standard_Parallel_2",58.5],PARAMETER["Latitude_Of_Origin",45.0],UNIT["Meter",1.0]]

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