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ThibaultGROUEIX
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‎README.md

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@@ -119,55 +119,37 @@ python ./training/train_AE_AtlasNet.py --env $env --nb_primitives $nb_primitives
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```
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The trained models accessible [here](TODO) have the following performances, slightly better than the one reported in [the paper](TODO). The number reported is the chamfer distance.
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| Method | Chamfer (2500 pts GT and 2500 pts Reconstruction) |
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| ---------- | --------------------- |
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| Autoencoder_Atlasnet_25prim | 0.0014476474650672833 |
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| Autoencoder_Atlasnet_1sphere | 0.0017207141972321953 |
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| Autoencoder_baseline | 0.001963350556556298 |
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| SingleViewReconstruction_Atlasnet_25prim | 0.004638490150569042 |
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| SingleViewReconstruction_Atlasnet_1sphere | 0.005198702077052366 |
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| SingleViewReconstruction_baseline | 0.0048062904884797605 |
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| Method | Chamfer⁽⁰⁾ |GPU memory⁽¹⁾ |Time by epoch⁽²⁾|
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| ---------- | --------------------- | --------------------- | --------------------- |
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| Autoencoder_Atlasnet_25prim | 0.0014476474650672833 | 4.1GB |6min55s|
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| Autoencoder_Atlasnet_1sphere | 0.0017207141972321953 |3.6GB|5min30s|
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| Autoencoder_Baseline | 0.001963350556556298 |1.9GB|2min05s|
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| SingleViewReconstruction_Atlasnet_25prim | 0.004638490150569042 |6.8GB|10min04s|
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| SingleViewReconstruction_Atlasnet_1sphere | 0.005198702077052366 |5.6GB|8min16s|
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| SingleViewReconstruction_Baseline | 0.0048062904884797605 |1.7GB|3min30s|
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⁽⁰⁾ computed between 2500 ground truth points and 2500 reconstructed points.
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⁽¹⁾ with the flag ```--accelerated_chamfer 1```.
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⁽²⁾this is only an estimate, the code is not optimised. The easiest way to enhance it would be to preload the training data to use the GPU at 100%. Time computed with the flag ```--accelerated_chamfer 1```.
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#### Autoencoder : 25 learned parameterization
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| val_loss | 0.0014795344685297** |
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| ---------- | --------------------- |
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| watercraft | 0.00127737027906 |
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| monitor | 0.0016588120616 |
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| car | 0.00152693425022 |
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| couch | 0.00171516126198 |
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| cabinet | 0.00168296881168 |
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| lamp | 0.00232362473947 |
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| plane | 0.000833268054194 |
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| speaker | 0.0025417242402 |
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| table | 0.00149979386376 |
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| chair | 0.00156113364435 |
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| bench | 0.00120812499892 |
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| firearm | 0.000626943988977 |
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| cellphone | 0.0012117530635 |
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| val_loss | watercraft | monitor | car | couch | cabinet | lamp | plane | speaker | table | chair | bench | firearm | cellphone |
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| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
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| 0.0014795344685297⁽³⁾ | 0.00127737027906 | 0.0016588120616 | 0.00152693425022 | 0.00171516126198 | 0.00168296881168 | 0.00232362473947 | 0.000833268054194 | 0.0025417242402 | 0.00149979386376 | 0.00156113364435 | 0.00120812499892 | 0.000626943988977 | 0.0012117530635 |
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#### Single View Reconstruction : 25 learned parameterization
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| val_loss | 0.00400863720389** |
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| ---------- | -------------------- |
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| watercraft | 0.00336707355723 |
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| monitor | 0.00456469316226 |
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| car | 0.00306795421868 |
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| couch | 0.00404269965806 |
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| cabinet | 0.00355917039209 |
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| lamp | 0.0114094304694 |
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| plane | 0.00192791500002 |
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| speaker | 0.00780984506137 |
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| table | 0.00368373458016 |
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| chair | 0.00407004468516 |
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| bench | 0.0030023689528 |
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| firearm | 0.00192803189235 |
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| cellphone | 0.00293665724291 |
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| val_loss | watercraft | monitor | car | couch | cabinet | lamp | plane | speaker | table | chair | bench | firearm | cellphone |
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| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
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| 0.00400863720389⁽³⁾ | 0.00336707355723 | 0.00456469316226 | 0.00306795421868 | 0.00404269965806 | 0.00355917039209 | 0.0114094304694 | 0.00192791500002 | 0.00780984506137 | 0.00368373458016 | 0.00407004468516 | 0.0030023689528 | 0.00192803189235 | 0.00293665724291 |
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⁽³⁾the number is slightly different for above because it comes from legacy code (Atlasnet v1).
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* Evaluate quantitatively the reconstructed meshes : [METRO DISTANCE](https://github.com/RobotLocomotion/meshConverters/tree/master/vcglib/apps/metro)
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**the number is slightly different for above because it comes from legacy code (Atlasnet v1).
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### Visualisation
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@@ -193,12 +175,12 @@ View [this paper](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/382
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| frame | Average recontruction error for SVR (x1000) : chamfer distance on input pointcloud and reconstruction of size 2500 pts|
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| ---------- | -------------------- |
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| object-centered | 4.87 |
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| object-centered | 4.87⁽⁴⁾ |
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| view-centered | 4.88 |
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<img src="pictures/chair_yana.png" style="zoom:55%" /><img src="pictures/car_yana.png" style="zoom:60%" />
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⁽⁴⁾ Trained with Atlasnet v2 (with learning rate scheduler : slightly better than the paper's result)
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## License
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‎data/ShapeNetCorev2Normalized/README.md

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‎data/ShapeNetRendering/README.md

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‎data/customShapeNet/__init__.py

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‎training/train_AE_AtlasNet.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=8)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=120, help='number of epochs to train for')
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parser.add_argument('--model', type=str, default = '', help='optional reload model path')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of points')
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parser.add_argument('--nb_primitives', type=int, default = 25, help='number of primitives in the atlas')
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parser.add_argument('--super_points', type=int, default = 2500, help='number of input points to pointNet, not used by default')
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parser.add_argument('--env', type=str, default ="AE_AtlasNet" , help='visdom environment')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

‎training/train_AE_AtlasNet_sphere.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=120, help='number of epochs to train for')
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parser.add_argument('--model', type=str, default = '', help='optional reload model path')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of points')
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parser.add_argument('--nb_primitives', type=int, default = 1, help='number of primitives in the atlas')
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parser.add_argument('--super_points', type=int, default = 2500, help='number of input points to pointNet, not used by default')
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parser.add_argument('--env', type=str, default ="AE_AtlasNet_SPHERE" , help='visdom environment')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

‎training/train_AE_Baseline.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=120, help='number of epochs to train for')
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parser.add_argument('--model', type=str, default = '', help='optional reload model path')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of points')
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parser.add_argument('--nb_primitives', type=int, default = 1, help='number of primitives in the atlas')
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parser.add_argument('--super_points', type=int, default = 2500, help='number of input points to pointNet, not used by default')
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parser.add_argument('--env', type=str, default ="AE_Baseline" , help='visdom environment')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

‎training/train_SVR_AtlasNet.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=400, help='number of epochs to train for')
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parser.add_argument('--model_preTrained_AE', type=str, default = 'trained_models/ae_atlasnet_25.pth', help='model path')
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parser.add_argument('--model', type=str, default = '', help='model path')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of points')
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parser.add_argument('--nb_primitives', type=int, default = 25, help='number of primitives')
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parser.add_argument('--env', type=str, default ="SVR_AtlasNet" , help='visdom env')
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parser.add_argument('--fix_decoder', type=bool, default = True , help='if set to True, on the the resnet encoder is trained')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

‎training/train_SVR_AtlasNet_sphere.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=400, help='number of epochs to train for')
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parser.add_argument('--model_preTrained_AE', type=str, default = 'trained_models/ae_atlasnet_sphere.pth', help='model path')
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parser.add_argument('--model', type=str, default = 'trained_models/svr_atlas_sphere.pth', help='model path')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of points')
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parser.add_argument('--nb_primitives', type=int, default = 1, help='number of primitives')
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parser.add_argument('--env', type=str, default ="SVR_AtlasNet_SPHERE" , help='visdom env')
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parser.add_argument('--fix_decoder', type=bool, default = True , help='if set to True, on the the resnet encoder is trained')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

‎training/train_SVR_Baseline.py

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# =============PARAMETERS======================================== #
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parser = argparse.ArgumentParser()
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parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=6)
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=12)
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parser.add_argument('--nepoch', type=int, default=400, help='number of epochs to train for')
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parser.add_argument('--model_preTrained_AE', type=str, default = 'trained_models/ae_baseline.pth', help='model path for fixed decoder')
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parser.add_argument('--model', type=str, default = '', help='model path for pretraining')
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parser.add_argument('--num_points', type=int, default = 2500, help='number of generated points')
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parser.add_argument('--env', type=str, default ="SVR_Baseline" , help='visdom env')
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parser.add_argument('--fix_decoder', type=bool, default = False , help='if set to True, on the the resnet encoder is trained')
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parser.add_argument('--accelerated_chamfer', type=int, default =1 , help='use custom build accelarated chamfer')
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parser.add_argument('--accelerated_chamfer', type=int, default =0 , help='use custom build accelarated chamfer')
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opt = parser.parse_args()
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print (opt)

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