Piv
aa423cc38a
Start adding unsupervised train loop
2021-11-20 13:37:26 +10:30
Piv
2bb37b2722
Fix up generator to include intrinsics
2021-08-29 19:26:15 +09:30
Piv
90b73bf420
Start adding generators for unsupervised training
2021-08-29 18:06:37 +09:30
Piv
42fcf5554a
Update warp test to verify output shape
2021-08-24 21:39:10 +09:30
Piv
c164c9720a
Finish Projective Inverse Warp algorithm
2021-08-24 20:13:30 +09:30
Piv
b7917ec465
More warp implementation
2021-08-21 17:32:16 +09:30
Piv
df1ac89a81
Add euler to rotation matrix, grid flattening
2021-08-10 20:39:52 +09:30
Piv
8016f0f945
Add coordinates generation implementation
2021-08-08 22:11:50 +09:30
Piv
ece37843ce
Merge branch 'main' into unsupervised
2021-08-08 18:58:39 +09:30
Michael Pivato
a72f04006f
Merge branch 'packnet_small' into 'main'
...
Add small option to packnet, fix docs and first/final conv layers (prev 32)
See merge request vato007/fast-depth-tf!8
2021-08-08 09:25:56 +00:00
Michael Pivato
625ecba731
Add small option to packnet, fix docs and first/final conv layers (prev 32)
2021-08-08 09:25:55 +00:00
Michael Pivato
5373dc6b65
Merge branch 'packnet_fixes' into 'main'
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Fix packnet residual block and layers, refactor to support different amount of residual layers
See merge request vato007/fast-depth-tf!7
2021-08-07 11:31:22 +00:00
Piv
58b8e53986
Fix packnet residual block and layers, refactor to support different amount of residual layers
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I noticed the number of parameters didn't match up to the paper (~128 million)
Fixed this by doing the following:
- Kernel size of 1 for 3rd conv2d in residual block
- Use add rather than concat in residual block
- Fixed add/concat features in decode layers
- Fixed final layers -> this also allows features_3d == 16 to work
2021-08-07 21:00:17 +09:30
Piv
cd278e683f
Start adding pose warp conversions
2021-08-07 17:18:06 +09:30
Michael Pivato
f56e663fca
Merge branch 'unsupervised' into 'main'
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Smooth Loss
See merge request vato007/fast-depth-tf!6
2021-08-05 08:20:31 +00:00
Michael Pivato
26dda68523
Add Smooth Loss
2021-08-05 08:20:31 +00:00
Piv
5996d6eaf0
Merge branch 'main' into unsupervised
2021-08-05 17:49:48 +09:30
Piv
8be4ce4e6d
Add smooth loss
2021-08-05 17:48:44 +09:30
Michael Pivato
e96e6c2c2b
Merge branch 'unsupervised' into 'main'
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PoseNet
See merge request vato007/fast-depth-tf!5
2021-08-04 11:36:30 +00:00
Michael Pivato
5d0731b60f
PoseNet
...
Adds the nns required for unsupervised learning
- PoseNet (based on ResNet18 like Monodepth)
- Wrapper for fast depth to pull out intermediary layers to smooth out gradients during training
2021-08-04 11:36:30 +00:00
Piv
b95442bb23
Finish off pose net
2021-08-04 20:51:46 +09:30
Piv
a111f89722
Start adding pose decoder
2021-08-03 20:25:19 +09:30
Piv
2372b906df
Add resnet18
2021-08-01 10:44:33 +09:30
Michael Pivato
d3a63c6bcd
Merge branch 'unsupervised' into 'main'
...
Packnet
See merge request vato007/fast-depth-tf!4
2021-07-31 01:31:25 +00:00
Piv
6514fb0e86
Add sample openvino inference
2021-07-29 20:52:14 +09:30
Piv
3254eef4bf
Add compiling packnet model, refactor modules to not duplicate loaders and trainers
2021-07-23 22:41:46 +09:30
Piv
66cbc7faf6
Add pack layer tests, fix unpack_3d layer
2021-07-19 20:37:54 +09:30
Piv
38e7ad069e
Refactor load/util, start fixing packnet to support NHWC format
2021-07-19 12:32:56 +09:30
Piv
d8bf493999
More packnet implementation
2021-07-18 19:54:28 +09:30
Piv
603de2bc9f
Start adding packnet model
2021-07-18 18:59:25 +09:30
Piv
e372fe33ba
Add per-pixel loss functions
2021-07-13 20:32:45 +09:30
Piv
b9457f17fe
Add pixel loss functions, move warp
2021-07-13 19:20:24 +09:30
Piv
6f7da21977
Add spatial transformer network sampler
2021-07-12 18:19:59 +09:30
Piv
f501beb6f2
Start implementing unsupervised train loop, add sfmlearner train and utils files for reference
2021-07-05 20:50:12 +09:30
Piv
ba0ba609a3
Add utils from sfm learner, start adding sfm learner loss function to work with keras
2021-06-22 22:03:23 +09:30
Piv
101fe08924
Start adding stubs for unsupervised training
2021-06-16 21:55:13 +09:30
Piv
e5b07fb766
Fix loss function
2021-06-16 21:52:31 +09:30
Michael Pivato
0547509689
Merge branch 'kitti_depth_dataset' into 'main'
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Kitti depth dataset
See merge request vato007/fast-depth-tf!2
2021-04-22 12:13:48 +00:00
Michael Pivato
070aec6eed
Add Kitti depth dataset
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Warning: Using this requires >175gb of disk space (tensorflow will also generate examples that will take up space)
2021-04-22 12:13:48 +00:00
Piv
02d8cd5810
Remove half-features from dense_depth
2021-04-22 12:30:30 +09:30
Piv
acdb58396c
Remove usage of keras
2021-04-14 12:45:01 +09:30
Piv
cf7d2561ec
Implement details of dense depth paper
2021-04-14 12:38:51 +09:30
Piv
f598005b73
Add basic coreml and mlkit conversion scripts
2021-03-29 19:08:55 +10:30
Piv
f3fc0f8fbb
Add coreml conversion
2021-03-29 18:58:16 +10:30
Michael Pivato
f2a42cca4c
Merge branch 'dense-depth' into 'main'
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Dense depth
See merge request vato007/fast-depth-tf!1
2021-03-29 07:31:42 +00:00
Piv
d88e9d3f12
Add dense-depth and experimental dense-net-nnconv5 models
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Since dense-depth will use half labels by default, the nyu train/eval datasets can be loaded from here at half resolutions for labels
2021-03-29 17:59:18 +10:30
Piv
870429c3ef
Refactor fast-depth
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Addresses the following:
- Rename nnconv5 block to nnconv5
- Add skip connections directly to nnconv5 block
- Allow custom metrics, loss and optimizer (keep defaults that reflect original paper) to train
- Correctly use nyu evaluation dataset only when no dataset is provided
2021-03-29 17:57:12 +10:30
Piv
3325ea0c0c
Format pep8, include pass shaped to mobilenet
2021-03-25 21:56:50 +10:30
Piv
9449ddef01
Add notebook, gitignore
2021-03-25 21:50:19 +10:30
Piv
78d5aace15
Remove Experimental model
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It didn't perform any better than the regular model
Removing batch normalisation significantly harmed training performance
2021-03-25 21:28:07 +10:30
