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Update geo-inference parameters #579
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RUN apt-get update \ | ||
&& apt-get install -y --no-install-recommends git wget unzip bzip2 build-essential sudo \ | ||
&& apt-key del 7fa2af80 \ | ||
&& wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb \ |
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dont we need these cuda keyring stuff anymore?
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Nope. Tested with a local docker image, the CI test image and on HPC. All works fine without these.
docs/source/mode.rst
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Thanks to keep the doc alive
inference_segmentation.py
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from tqdm import tqdm | ||
from pathlib import Path | ||
from numbers import Number | ||
from tempfile import mkstemp | ||
from omegaconf import DictConfig | ||
from typing import Dict, Sequence, Union | ||
from dataset.stacitem import SingleBandItemEO | ||
import rasterio | ||
from shutil import move |
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can we start organizing module loading according to PEP8? i.e. system libraries in the first block, external in the second, locally written libraries in the third.
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Changed it in this script.
Following the refactoring of all the other modules, this will be done in the other module as well.
@@ -1,15 +1,16 @@ | |||
import csv | |||
from math import sqrt | |||
import rasterio | |||
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it should look like this:
from shutil import move
from pathlib import Path
from numbers import Number
from tempfile import mkstemp
from typing import Dict, Sequence, Union
from tqdm import tqdm
import csv
import rasterio
from omegaconf import DictConfig
from utils.aoiutils import aois_from_csv
from dataset.stacitem import SingleBandItemEO
from utils.logger import get_logger, set_tracker
from geo_inference.geo_inference import GeoInference
from utils.utils import get_device_ids, get_key_def, set_device
but I think that PEP8 refactoring can be done later in a separate PR.
ccba099
@CharlesAuthier and @ms888ekb I added the support for Test-Time augmentation and corrected the model for the tests to pass. Final review, I swear. :) |
- pytest>=7.1 | ||
- python>=3.11 | ||
- pytorch>=2.3 |
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why do we need pytorch and pytorch-cuda at the same time? pytorch-cuda can work on CPU if needed. Won't having two different versions cause conflicts?
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It is the recommended way to install pytorch in a conda env.: https://pytorch.org/
Description
This PR updates the parameters from geo-inference (workers, prediction_threshold, patch_size).
It also reduces the size of the docker image created.
Type of change
What types of changes does your code introduce? Put an
x
in all the boxes that apply:How Has This Been Tested?
tst_gdl_3_1.sif
Checklist:
If you're unsure about any of these, don't hesitate to ask. We're here to help!