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dfef7db421
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moar debugging
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2022-10-31 18:26:34 +00:00 |
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172cf9d8ce
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tweak
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2022-10-31 18:19:43 +00:00 |
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dbe35ee943
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loss: comment l2 norm
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2022-10-31 18:09:03 +00:00 |
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5e60319024
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fixup
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2022-10-31 17:56:49 +00:00 |
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b986b069e2
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debug party time
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2022-10-31 17:50:29 +00:00 |
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458faa96d2
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loss: fixup
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2022-10-31 17:18:21 +00:00 |
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55dc05e8ce
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contrastive: comment weights that aren't needed
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2022-10-31 16:26:48 +00:00 |
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33391eaf16
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train_predict/jsonl: don't argmax
I'm interested inthe raw values
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2022-10-26 17:21:19 +01:00 |
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74f2cdb900
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train_predict: .list() → .tolist()
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2022-10-26 17:12:36 +01:00 |
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4f9d543695
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train_predict: don't pass model_code
it's redundant
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2022-10-26 17:11:36 +01:00 |
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1b489518d0
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segmenter: add LayerStack2Image to custom_objects
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2022-10-26 17:05:50 +01:00 |
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48ae8a5c20
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LossContrastive: normalise features as per the paper
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2022-10-26 16:52:56 +01:00 |
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843cc8dc7b
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contrastive: rewrite the loss function.
The CLIP paper *does* kinda make sense I think
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2022-10-26 16:45:45 +01:00 |
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fad1399c2d
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convnext: whitespace
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2022-10-26 16:45:20 +01:00 |
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1d872cb962
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contrastive: fix initial temperature value
It should be 1/0.07, but we had it set to 0.07......
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2022-10-26 16:45:01 +01:00 |
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f994d449f1
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Layer2Image: fix
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2022-10-25 21:32:17 +01:00 |
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6a29105f56
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model_segmentation: stack not reshape
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2022-10-25 21:25:15 +01:00 |
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98417a3e06
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prepare for NCE loss
.....but Tensorflow's implementation looks to be for supervised models :-(
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2022-10-25 21:15:05 +01:00 |
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bb0679a509
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model_segmentation: don't softmax twice
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2022-10-25 21:11:48 +01:00 |
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f2e2ca1484
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model_contrastive: make water encoder significantly shallower
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2022-10-24 20:52:31 +01:00 |
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a6b07a49cb
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count water/nowater pixels in Jupyter Notebook
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2022-10-24 18:05:34 +01:00 |
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a8b101bdae
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dataset_predict: add shape_water_desired
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2022-10-24 18:05:13 +01:00 |
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587c1dfafa
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train_predict: revamp jsonl handling
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2022-10-21 16:53:08 +01:00 |
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8195318a42
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SparseCategoricalAccuracy: losses → metrics
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2022-10-21 16:51:20 +01:00 |
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612735aaae
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rename shuffle arg
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2022-10-21 16:35:45 +01:00 |
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c98d8d05dd
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segmentation: use the right accuracy
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2022-10-21 16:17:05 +01:00 |
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bb0258f5cd
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flip squeeze operator ordering
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2022-10-21 15:38:57 +01:00 |
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af26964c6a
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batched_iterator: reset i_item after every time
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2022-10-21 15:35:43 +01:00 |
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c5b1501dba
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train-predict fixup
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2022-10-21 15:27:39 +01:00 |
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42aea7a0cc
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plt.close() fixup
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2022-10-21 15:23:54 +01:00 |
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12dad3bc87
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vis/segmentation: fix titles
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2022-10-21 15:22:35 +01:00 |
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0cb2de5d06
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train-preedict: close matplotlib after we've finished
they act like file handles
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2022-10-21 15:19:31 +01:00 |
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81e53efd9c
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PNG: create output dir if doesn't exist
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2022-10-21 15:17:39 +01:00 |
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3f7db6fa78
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fix embedding confusion
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2022-10-21 15:15:59 +01:00 |
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847cd97ec4
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fixup
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2022-10-21 14:26:58 +01:00 |
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0e814b7e98
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Contraster → Segmenter
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2022-10-21 14:25:43 +01:00 |
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1b658a1b7c
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train-predict: can't destructure array when iterating generator
....it seems to lead to undefined behaviour or something
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2022-10-20 19:34:04 +01:00 |
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aed2348a95
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train_predict: fixup
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2022-10-20 15:42:33 +01:00 |
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cc6679c609
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batch data; use generator
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2022-10-20 15:22:29 +01:00 |
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d306853c42
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use right daataset
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2022-10-20 15:16:24 +01:00 |
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59cfa4a89a
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basename paths
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2022-10-20 15:11:14 +01:00 |
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4d8ae21a45
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update cli help text
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2022-10-19 17:31:42 +01:00 |
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200076596b
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finish train_predict
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2022-10-19 17:26:40 +01:00 |
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488f78fca5
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pretrain_predict: default to parallel_reads=0
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2022-10-19 16:59:45 +01:00 |
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63e909d9fc
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datasets: add shuffle=True/False to get_filepaths.
This is important because otherwise it SCAMBLES the filenames, which is a disaster for making predictions in the right order....!
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2022-10-19 16:52:07 +01:00 |
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fe43ddfbf9
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start implementing driver for train_predict, but not finished yet
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2022-10-18 19:37:55 +01:00 |
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4ceec73e5b
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Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar
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2022-10-18 19:07:23 +01:00 |
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0c11ddca4b
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rainfallwrangler does NOT mess up the ordering of the data
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2022-10-18 19:07:14 +01:00 |
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b3ea189d37
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segmentation: softmax the output
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2022-10-13 21:02:57 +01:00 |
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f121bfb981
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fixup summaryfile
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2022-10-13 17:54:42 +01:00 |
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