From f2312c118445d9bdea9810e0d3a2e05dc8de3dd7 Mon Sep 17 00:00:00 2001 From: Starbeamrainbowlabs Date: Wed, 31 Aug 2022 16:25:27 +0100 Subject: [PATCH] fix crash --- README.md | 4 ++-- aimodel/src/lib/dataset/dataset.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 12458fa..e17a8b6 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This is the 3rd major version of this model. -Unfortunately using this model is rather complicated and involves a large number of steps. This README (will) explain it the best I can though. +Unfortunately using this model is rather complicated and involves a large number of steps. There is no way around this. This README (will) explain it the best I can though. ## System Requirements @@ -24,7 +24,7 @@ The process of using this model is as follows. 3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this) 4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data] 5. Push through [HAIL-CAESAR](*https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename) -6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .tfrecord files +6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files 7. Pretrain a contrastive learning model 8. Encode the rainfall radar data with the contrastive learning model you pretrained 9. Train the *actual* model to predict water depth diff --git a/aimodel/src/lib/dataset/dataset.py b/aimodel/src/lib/dataset/dataset.py index f44d655..59e03d9 100644 --- a/aimodel/src/lib/dataset/dataset.py +++ b/aimodel/src/lib/dataset/dataset.py @@ -7,7 +7,7 @@ from loguru import logger import tensorflow as tf -from shuffle import shuffle +from .shuffle import shuffle # TO PARSE: