Clay over aoi #116
Clay over aoi #116
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I merged this PR into #118. Which would close this one when merged. |
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Thanks @yellowcap, just some minor recommendations/questions below. I think we're trying to move fast, so I'll pre-approve this, but feel free to handle any of the suggestions below if you have time before merging.
docs/clay_over_aoi.ipynb
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| "source": [ | ||
| "aoi_src = \"\"\"{\n", | ||
| " \"type\": \"FeatureCollection\",\n", | ||
| " \"name\": \"puri\",\n", | ||
| " \"crs\": {\n", | ||
| " \"type\": \"name\",\n", | ||
| " \"properties\": { \"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\" }\n", | ||
| " },\n", | ||
| " \"features\": [\n", | ||
| " {\n", | ||
| " \"type\": \"Feature\",\n", | ||
| " \"properties\": {\n", | ||
| " \"Region\": \"Puri\"\n", | ||
| " },\n", | ||
| " \"geometry\": {\n", | ||
| " \"type\": \"Polygon\",\n", | ||
| " \"coordinates\": [ [ [ 85.050328768992927, 19.494998302498729 ], [ 85.169749958884921, 19.441346370958311 ], [ 85.968028516820738, 19.799945705366934 ], [ 86.029742823006544, 20.15322442052609 ], [ 86.104280881127053, 20.516965603502392 ], [ 85.404584916189307, 20.564248936237991 ], [ 84.945334300027469, 20.339711117597215 ], [ 84.816295296184407, 19.799945705366934 ], [ 85.050328768992927, 19.494998302498729 ] ] ]\n", | ||
| " }\n", | ||
| " }\n", | ||
| " ]\n", | ||
| "}\"\"\"\n", | ||
| "aoi = gpd.read_file(aoi_src, driver=\"GeoJSON\")\n", | ||
| "aoi.geometry[0]" |
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Can we show how to specify the AOI via shapely.Point or shapely.box instead? GeoJSON is a little harder to construct manually.
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Good point, changed to use shapely.box
docs/clay_over_aoi.ipynb
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| "! wandb disabled\n", | ||
| "! python trainer.py predict --ckpt_path=data/checkpoints/Clay_v0.1_epoch-24_val-loss-0.46.ckpt \\\n", |
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Yeah, we should look to disable wandb by default in trainer.py as mentioned at #109 (comment). Relevant lines are here:
Lines 53 to 56 in 0145e55
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Most definitively, we can remove this line once the trainer has been udpated.
scripts/datacube.py
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| required=False, | ||
| default="", | ||
| type=str, | ||
| help="Comma separated list of date ranges, each provided as yy-mm-dd/yy-mm-dd.", |
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Is it year as 4 numbers (e.g. 2024) or as 2 numbers (e.g. 24)?
| help="Comma separated list of date ranges, each provided as yy-mm-dd/yy-mm-dd.", | |
| help="Comma separated list of date ranges, each provided as yyyy-mm-dd/yyyy-mm-dd.", |
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Good catch, I will update accordingly.
scripts/datacube.py
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| "--subset", | ||
| required=False, | ||
| default="", | ||
| help="For debugging, subset x and y to this pixel window as a comma" | ||
| "separated string of 4 integers.", |
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I see that this is for debugging only, but just to clarify, this option is for cropping the tiles/chips to a specified height and width?
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I didn't understand what this does either.
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I see this is creating confusion, I'll try to work this better in the help string.
The goal of this is to reduce the amount of data when processing in test mode. Normally, the script would download the entire scene, which is 10k pixels squared. Whit this one can reduce the amount of pixels downloaded and passed to the tiler script. So this is cutting a window from the scene data. The tiler will then work on this smaller input, but be unaltered otherwise.
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Not to make it more complex, but I understand we download the entire MGRS tile, even when many of the chips might be outside of the requested AOI. If we drop chips outside of the AoI, we can just use a point or small polygon for testing.
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That is correct and would be the best approach, but for that the datacube.py script needs much more refactoring. Currently it's fully focused on downloading the entire MGRS tile. Filtering that spatially will require passing the AOI itself and handling spatial intersections, so that indeed is quite a bit more complicated.
For now I can state also that more clearly, that the AOI approach right now means getting data for full MGRS tiles that intersect with the AOI.
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| with rasterio.open(name, "r+") as rst: | ||
| rst.colorinterp = color | ||
| rst.update_tags(date=date) |
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Do you want to handle #70 here, i.e. use ACQUISITIONDATETIME or TIFFTAG_DATETIME? Or do that in a separate PR?
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Separate PR, coming up.
| "source": [ | ||
| "import geopandas as gpd\n", | ||
| "\n", | ||
| "! wget https://clay-mgrs-samples.s3.amazonaws.com/mgrs_full.fgb -O data/mgrs/mgrs_full.fgb" |
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Should we mention the original KML file this mgrs_full.fgb file was converted from? Assuming that you got it from https://sentinel.esa.int/web/sentinel/missions/sentinel-2/data-products at https://sentinel.esa.int/documents/247904/1955685/S2A_OPER_GIP_TILPAR_MPC__20151209T095117_V20150622T000000_21000101T000000_B00.kml?
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Good idea, the full sample file is constructed in the landcover.sh script. There I actually got it from the NASA site but its the same file. I'll link to this in the notebook.
| "source": [ | ||
| "for index, row in mgrs_aoi.iterrows():\n", | ||
| " print(index, row)\n", | ||
| " ! python scripts/datacube.py --sample data/mgrs/mgrs_aoi.fgb --subset 1500,1500,2524,2524 --localpath data/chips --index {index} --dateranges 2020-01-01/2020-04-01,2021-06-01/2021-09-15" |
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This line requires a PC_SDK_SUBSCRIPTION_KEY token to be set I think, otherwise users will get a requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://planetarycomputer.microsoft.com/api/sas/v1/token/sentinel1euwestrtc/sentinel1-grd-rtc. Need to document this inline perhaps, xref #119.
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Added reference to the markdown
docs/clay_over_aoi.ipynb
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| "source": [ | ||
| "## Create the embeddings for each training chip\n", | ||
| "\n", | ||
| "The checkpoints can be downloaded from huggingface." |
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Link to HuggingFace page perhaps?
| "The checkpoints can be downloaded from huggingface." | |
| "The checkpoints can be downloaded from huggingface at https://huggingface.co/made-with-clay/Clay." |
docs/clay_over_aoi.ipynb
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| ], | ||
| "source": [ | ||
| "! wandb disabled\n", | ||
| "! python trainer.py predict --ckpt_path=data/checkpoints/Clay_v0.1_epoch-24_val-loss-0.46.ckpt \\\n", |
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Lightning supports passing in a checkpoint URL (see https://lightning.ai/docs/pytorch/2.1.2/common/checkpointing_advanced.html#resume-training-from-a-cloud-checkpoint), so can remove the wget line above and predict directly from the HuggingFace checkpoint URL. The checkpoint will be downloaded automatically to ~/.cache/torch/hub/checkpoints/
| "! python trainer.py predict --ckpt_path=data/checkpoints/Clay_v0.1_epoch-24_val-loss-0.46.ckpt \\\n", | |
| "! python trainer.py predict --ckpt_path=https://huggingface.co/made-with-clay/Clay/resolve/main/Clay_v0.1_epoch-24_val-loss-0.46.ckpt \\\n", |
The pyproject.toml based config is not always being picked up.
Updated pipeline and added notebook to docs to run clay over custom aois over custom date ranges.
I tested the script locally extensively and it works, having more people trying it won't hurt to see if there are any things that are missing / that I did not catch in my local runs.