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2a1772a211
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confvnext_intrevse: add shallow
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2022-12-08 19:10:12 +00:00 |
|
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c27869630a
|
I hate VSCode's git commit interface
it doesn't let you ammend
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2022-12-08 18:58:54 +00:00 |
|
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b3345963f3
|
missing arg pass
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2022-12-08 18:58:32 +00:00 |
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3dde9b69da
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fixup
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2022-12-08 18:56:32 +00:00 |
|
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6fce39f696
|
WHY?!?!?!
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2022-12-08 18:55:53 +00:00 |
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26766366fc
|
I hate the python code intelligence
it's bad
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2022-12-08 18:55:15 +00:00 |
|
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ff56f591c7
|
I hate python
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2022-12-08 18:53:37 +00:00 |
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d37e7224f5
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train-mono: tidy up arg passing
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2022-12-08 18:47:03 +00:00 |
|
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b53db648bf
|
fixup
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2022-12-08 18:31:42 +00:00 |
|
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18c0210704
|
typo
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2022-12-08 17:00:25 +00:00 |
|
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a3c9416cf0
|
LossCrossentropy: don't sum
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2022-12-08 16:57:11 +00:00 |
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08046340f4
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dataset_mono: normalise heightmap
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2022-12-08 16:10:34 +00:00 |
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d997157f55
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dataset_mono: log when using heightmap
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2022-12-06 19:30:11 +00:00 |
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d0f2e3d730
|
readfile: do transparent gzip by default
....but there's a glad to turn it off if needed
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2022-12-06 19:27:39 +00:00 |
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eac6472c97
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Implement support for (optionally) taking a heightmap in
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2022-12-06 18:55:58 +00:00 |
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f92b2b3472
|
according to the equation it looks like it's 2
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2022-12-02 17:22:46 +00:00 |
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cad82cd1bc
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CBAM: unsure if it's 1 ro 3 dense ayers in the shared mlp
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2022-12-02 17:21:13 +00:00 |
|
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62f6a993bb
|
implement CBAM, but it's UNTESTED
Convolutional Block Attention Module.
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2022-12-02 17:17:45 +00:00 |
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9d666c3b38
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train mono: type=int → float
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2022-12-01 15:39:44 +00:00 |
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53dfa32685
|
model_mono: log learning rate
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2022-12-01 15:10:51 +00:00 |
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c384d55dff
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add arg to adjust learning rate
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2022-11-29 20:55:00 +00:00 |
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8e23e9d341
|
model_segmenter: we're no longer using sparse
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2022-11-29 19:28:27 +00:00 |
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9a2b4c6838
|
dsseg: fix reshape/onehot ordering
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2022-11-29 19:28:13 +00:00 |
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df774146d9
|
dataset_segmenter: reshape, not squeeze
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2022-11-29 19:24:54 +00:00 |
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77b8a1a8db
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dataset_segmenter: squeeze
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2022-11-29 19:16:15 +00:00 |
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01101ad30b
|
losscrossentropy: return the reduced value * facepalm *
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2022-11-29 19:07:08 +00:00 |
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37f196a785
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LossCrossentropy: add kwargs
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2022-11-29 15:40:35 +00:00 |
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838ff56a3b
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mono: fix loading checkpoint
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2022-11-29 15:25:11 +00:00 |
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dba6cbffcd
|
WHY. * facepalms *
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2022-11-28 19:33:42 +00:00 |
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57b8eb93fb
|
fixup
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2022-11-28 19:09:35 +00:00 |
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6640a41bb7
|
almost got it....? it's not what I expected....!
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2022-11-28 19:08:50 +00:00 |
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f48473b703
|
fixup
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2022-11-28 19:00:11 +00:00 |
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f6feb125e3
|
this iss ome serious debugging.
This commit will produce an extremely large volume of output.
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2022-11-28 18:57:41 +00:00 |
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09f81b0746
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train_mono: debug
this commit will generate a large amount of debug output.
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2022-11-28 16:46:17 +00:00 |
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f39e4ade70
|
LayerConvNextGamma: fix config serialisation bug
.....this is unlikely to be the problem as this bug is in an unused code path.
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2022-11-25 21:16:31 +00:00 |
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e7410fb480
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train_mono_predict: limit label size to 64x64
that's the size the model predicts
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2022-11-25 17:47:17 +00:00 |
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51dd484d13
|
fixup
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2022-11-25 16:55:45 +00:00 |
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884c4eb150
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rainfall_stats: formatting again
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2022-11-24 19:08:07 +00:00 |
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bfe038086c
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rainfall_stats: formatting
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2022-11-24 19:07:44 +00:00 |
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7dba03200f
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fixup
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2022-11-24 19:06:48 +00:00 |
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e5258b9c66
|
typo
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2022-11-24 19:06:13 +00:00 |
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64d646bb13
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rainfall_stats: formatting
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2022-11-24 19:05:35 +00:00 |
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675c7a7448
|
fixup
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2022-11-24 19:03:28 +00:00 |
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afc1cdcf02
|
fixup
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2022-11-24 19:02:58 +00:00 |
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e4bea89c89
|
typo
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2022-11-24 19:01:52 +00:00 |
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a40cbe8705
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rainfall_stats: remove unused imports
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2022-11-24 19:01:18 +00:00 |
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fe57d6aab2
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rainfall_stats: initial implementation
this might reveal why we are having problems. If most/all the rainfall radar
data is v small numbers, normalising
might help.
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2022-11-24 18:58:16 +00:00 |
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3131b4f7b3
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debug2
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2022-11-24 18:25:32 +00:00 |
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d55a13f536
|
debug
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2022-11-24 18:24:03 +00:00 |
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1f60f2a580
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do_argmax
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2022-11-24 18:11:03 +00:00 |
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6c09d5254d
|
fixup
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2022-11-24 17:57:48 +00:00 |
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54a841efe9
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train_mono_predict: convert to correct format
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2022-11-24 17:56:07 +00:00 |
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105dc5bc56
|
missing kwargs
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2022-11-24 17:51:29 +00:00 |
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1e1d6dd273
|
fixup
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2022-11-24 17:48:19 +00:00 |
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011e0aef78
|
update cli docs
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2022-11-24 16:38:07 +00:00 |
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773944f9fa
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train_mono_predict: initial implementation
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2022-11-24 16:33:50 +00:00 |
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3a0356929c
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mono: drop the sparse
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2022-11-22 16:20:56 +00:00 |
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7e8f63f8ba
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fixup
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2022-11-21 19:38:24 +00:00 |
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ace4c8b246
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dataset_mono: debug
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2022-11-21 18:46:21 +00:00 |
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527b34942d
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convnext_inverse: kernel_size 4→2
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2022-11-11 19:29:37 +00:00 |
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0662d0854b
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model_mono: fix bottleneck
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2022-11-11 19:11:40 +00:00 |
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73acda6d9a
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fix debug logging
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2022-11-11 19:08:38 +00:00 |
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9da059d738
|
model shape logging
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2022-11-11 19:03:37 +00:00 |
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00917b2698
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dataset_mono: log shapes
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2022-11-11 19:02:43 +00:00 |
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54ae88b1b4
|
in this entire blasted project I have yet to get the rotation of anything correct....!
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2022-11-11 18:58:45 +00:00 |
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a7a475dcd1
|
debug 2
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2022-11-11 18:38:07 +00:00 |
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bf2f6e9b64
|
debug logging
it begins again
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2022-11-11 18:31:40 +00:00 |
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481eeb3759
|
mono: fix dataset preprocessing
rogue dimension
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2022-11-11 18:31:27 +00:00 |
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9035450213
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mono: instantiate right model
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2022-11-11 18:28:29 +00:00 |
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69a2d0cf04
|
fixup
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2022-11-11 18:27:01 +00:00 |
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65e801cf28
|
train_mono: fix crash
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2022-11-11 18:26:25 +00:00 |
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8ac5159adc
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dataset_mono: simplify param passing, onehot+threshold water depth data
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2022-11-11 18:23:50 +00:00 |
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3a3f7e85da
|
typo
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2022-11-11 18:03:09 +00:00 |
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3313f77c88
|
Add (untested) mono rainfall → water depth model
* sighs *
Unfortunately I can't seem to get contrastive learning to work.....
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2022-11-10 22:36:11 +00:00 |
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9384b89165
|
model_segmentation: spare → normal crossentropy, activation functions at end
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2022-11-10 20:53:37 +00:00 |
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b6676e7361
|
switch from sparse to normal crossentropy
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2022-11-10 20:50:56 +00:00 |
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d8be26d476
|
Merge branch 'main' of git.starbeamrainbowlabs.com:sbrl/PhD-Rainfall-Radar
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2022-11-10 20:49:01 +00:00 |
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b03388de60
|
dataset_segmenter: DEBUG: fix water shape
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2022-11-10 20:48:21 +00:00 |
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daf691bf43
|
typo
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2022-11-10 19:55:00 +00:00 |
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0aa2ce19f5
|
read_metadata: support file inputs as well as dirs
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2022-11-10 19:53:30 +00:00 |
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aa7d9b8cf6
|
fixup
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2022-11-10 19:46:09 +00:00 |
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0894bd09e8
|
train_predict: add error message for parrams.json not found
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2022-11-10 19:45:41 +00:00 |
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44ad51f483
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CallbackNBatchCsv: bugfix .sort() → sorted()
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2022-11-04 16:40:21 +00:00 |
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4dddcfcb42
|
pretrain_predict: missing \n
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2022-11-04 16:01:28 +00:00 |
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1375201c5f
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CallbackNBatchCsv: open_handle mode
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2022-11-03 18:29:00 +00:00 |
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f2ae74ce7b
|
how could I be so stupid..... round 2
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2022-11-02 17:38:26 +00:00 |
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5f8d6dc6ea
|
Add metrics every 64 batches
this is important, because with large batches it can be difficult to tell what's happening inside each epoch.
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2022-10-31 19:26:10 +00:00 |
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cf872ef739
|
how could I be so *stupid*......
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2022-10-31 18:40:58 +00:00 |
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da32d75778
|
make_callbacks: display steps, not samples
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2022-10-31 18:36:28 +00:00 |
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dfef7db421
|
moar debugging
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2022-10-31 18:26:34 +00:00 |
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172cf9d8ce
|
tweak
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2022-10-31 18:19:43 +00:00 |
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dbe35ee943
|
loss: comment l2 norm
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2022-10-31 18:09:03 +00:00 |
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5e60319024
|
fixup
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2022-10-31 17:56:49 +00:00 |
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b986b069e2
|
debug party time
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2022-10-31 17:50:29 +00:00 |
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458faa96d2
|
loss: fixup
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2022-10-31 17:18:21 +00:00 |
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55dc05e8ce
|
contrastive: comment weights that aren't needed
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2022-10-31 16:26:48 +00:00 |
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33391eaf16
|
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
|
train_predict: .list() → .tolist()
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2022-10-26 17:12:36 +01:00 |
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4f9d543695
|
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
|
segmenter: add LayerStack2Image to custom_objects
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2022-10-26 17:05:50 +01:00 |
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48ae8a5c20
|
LossContrastive: normalise features as per the paper
|
2022-10-26 16:52:56 +01:00 |
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843cc8dc7b
|
contrastive: rewrite the loss function.
The CLIP paper *does* kinda make sense I think
|
2022-10-26 16:45:45 +01:00 |
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fad1399c2d
|
convnext: whitespace
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2022-10-26 16:45:20 +01:00 |
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1d872cb962
|
contrastive: fix initial temperature value
It should be 1/0.07, but we had it set to 0.07......
|
2022-10-26 16:45:01 +01:00 |
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f994d449f1
|
Layer2Image: fix
|
2022-10-25 21:32:17 +01:00 |
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6a29105f56
|
model_segmentation: stack not reshape
|
2022-10-25 21:25:15 +01:00 |
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98417a3e06
|
prepare for NCE loss
.....but Tensorflow's implementation looks to be for supervised models :-(
|
2022-10-25 21:15:05 +01:00 |
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bb0679a509
|
model_segmentation: don't softmax twice
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2022-10-25 21:11:48 +01:00 |
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f2e2ca1484
|
model_contrastive: make water encoder significantly shallower
|
2022-10-24 20:52:31 +01:00 |
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a6b07a49cb
|
count water/nowater pixels in Jupyter Notebook
|
2022-10-24 18:05:34 +01:00 |
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a8b101bdae
|
dataset_predict: add shape_water_desired
|
2022-10-24 18:05:13 +01:00 |
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587c1dfafa
|
train_predict: revamp jsonl handling
|
2022-10-21 16:53:08 +01:00 |
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8195318a42
|
SparseCategoricalAccuracy: losses → metrics
|
2022-10-21 16:51:20 +01:00 |
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612735aaae
|
rename shuffle arg
|
2022-10-21 16:35:45 +01:00 |
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c98d8d05dd
|
segmentation: use the right accuracy
|
2022-10-21 16:17:05 +01:00 |
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bb0258f5cd
|
flip squeeze operator ordering
|
2022-10-21 15:38:57 +01:00 |
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af26964c6a
|
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
|
train-predict fixup
|
2022-10-21 15:27:39 +01:00 |
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42aea7a0cc
|
plt.close() fixup
|
2022-10-21 15:23:54 +01:00 |
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12dad3bc87
|
vis/segmentation: fix titles
|
2022-10-21 15:22:35 +01:00 |
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0cb2de5d06
|
train-preedict: close matplotlib after we've finished
they act like file handles
|
2022-10-21 15:19:31 +01:00 |
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81e53efd9c
|
PNG: create output dir if doesn't exist
|
2022-10-21 15:17:39 +01:00 |
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3f7db6fa78
|
fix embedding confusion
|
2022-10-21 15:15:59 +01:00 |
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847cd97ec4
|
fixup
|
2022-10-21 14:26:58 +01:00 |
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0e814b7e98
|
Contraster → Segmenter
|
2022-10-21 14:25:43 +01:00 |
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1b658a1b7c
|
train-predict: can't destructure array when iterating generator
....it seems to lead to undefined behaviour or something
|
2022-10-20 19:34:04 +01:00 |
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aed2348a95
|
train_predict: fixup
|
2022-10-20 15:42:33 +01:00 |
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cc6679c609
|
batch data; use generator
|
2022-10-20 15:22:29 +01:00 |
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d306853c42
|
use right daataset
|
2022-10-20 15:16:24 +01:00 |
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59cfa4a89a
|
basename paths
|
2022-10-20 15:11:14 +01:00 |
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4d8ae21a45
|
update cli help text
|
2022-10-19 17:31:42 +01:00 |
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200076596b
|
finish train_predict
|
2022-10-19 17:26:40 +01:00 |
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488f78fca5
|
pretrain_predict: default to parallel_reads=0
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2022-10-19 16:59:45 +01:00 |
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63e909d9fc
|
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....!
|
2022-10-19 16:52:07 +01:00 |
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fe43ddfbf9
|
start implementing driver for train_predict, but not finished yet
|
2022-10-18 19:37:55 +01:00 |
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b3ea189d37
|
segmentation: softmax the output
|
2022-10-13 21:02:57 +01:00 |
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f121bfb981
|
fixup summaryfile
|
2022-10-13 17:54:42 +01:00 |
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5c35c0cee4
|
model_segmentation: document; remove unused args
|
2022-10-13 17:50:16 +01:00 |
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f12e6ab905
|
No need for a CLI arg for feature_dim_in - metadata should contain this
|
2022-10-13 17:37:16 +01:00 |
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e201372252
|
write quick Jupyter notebook to test data
....I'm paranoid
|
2022-10-13 17:27:17 +01:00 |
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ae53130e66
|
layout
|
2022-10-13 14:54:20 +01:00 |
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6423bf6702
|
LayerConvNeXtGamma: avoid adding an EagerTensor to config
Very weird how this is a problem when it wasn't before..
|
2022-10-12 17:12:07 +01:00 |
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32f5200d3b
|
pass model_arch properly
|
2022-10-12 16:50:06 +01:00 |
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5933fb1061
|
fixup
|
2022-10-11 19:23:41 +01:00 |
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c45b90764e
|
segmentation: adds xxtiny, but unsure if it's small enough
|
2022-10-11 19:22:37 +01:00 |
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f4a2c742d9
|
typo
|
2022-10-11 19:19:23 +01:00 |
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11f91a7cf4
|
train: add --arch; default to convnext_i_xtiny
|
2022-10-11 19:18:01 +01:00 |
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5666c5a0d9
|
typo
|
2022-10-10 18:12:51 +01:00 |
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131c0a0a5b
|
pretrain-predict: create dir if not exists
|
2022-10-10 18:00:55 +01:00 |
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f883986eaa
|
Bugfix: modeset to enable TFRecordWriter instead of bare handle
|
2022-10-06 20:07:59 +01:00 |
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