update readme and equilib versions

haruishi43 · Dec 22, 2020 · c071de4 · c071de4
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diff --git a/.readme/README.md b/.readme/README.md
@@ -10,3 +10,7 @@
 ## Grid Sample
 
 - [`grid_sample`](grid_sample.md)
+
+## Benchmarks
+
+- [`bencmarks`](benchmarks.md
diff --git a/.readme/benchmarks.md b/.readme/benchmarks.md
@@ -1,36 +1,99 @@
-tests/test_cube2equi.py test_numpy_single
-Func:run_cube2equi:     0.0360
-.test_numpy_batch
-Func:run_cube2equi:     0.1180
-.test_torch_single
-Func:run_cube2equi:     0.0218
-.test_torch_batch
-Func:run_cube2equi:     0.0199
-.
-tests/test_equi2cube.py test_numpy_single
-Func:run_equi2cube:     0.1174
-.test_numpy_batch
-Func:run_equi2cube:     0.4759
-.test_torch_single
-Func:run_equi2cube:     0.0060
-.test_torch_batch
-Func:run_equi2cube:     0.0205
-.
-tests/test_equi2equi.py test_numpy_single
-Func:run_equi2equi:     2.4446
-.test_numpy_batch
-Func:run_equi2equi:     9.8693
-.test_torch_single
-Func:run_equi2equi:     0.1816
-.test_torch_batch
-Func:run_equi2equi:     0.5867
-.
-tests/test_equi2pers.py test_numpy_single
-Func:run_equi2pers:     0.0734
-.test_numpy_batch
-Func:run_equi2pers:     0.2994
-.test_torch_single
-Func:run_equi2pers:     0.0026
-.test_torch_batch
-Func:run_equi2pers:     0.0084
-.
+# Benchmarks
+
+Environment:
+- CPU: Intel i9-9960X CPU @ 3.10 GHz
+- GPU: Nvidia Qudaro RTX 8000
+- RAM: 128 GB
+
+RAM usage doesn't exceed 20 GB, GPU memory usage at batch 64 is roughly 45 GB.
+
+**Transform**|** Type**|** Method**|** Input Size**|** Output Size**|** Batch Size**|** Time (seconds)**
+:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:
+cube2equi| numpy| default| 256x256| 480x240| 1| 0.0360
+cube2equi| numpy| default| 256x256| 480x240| 4| 0.1180
+cube2equi| numpy| default| 256x256| 480x240| 16| 0.4512
+cube2equi| numpy| default| 256x256| 480x240| 32| 1.0650
+cube2equi| numpy| default| 256x256| 480x240| 64| 2.1363
+cube2equi| torch| default| 256x256| 480x240| 1| 0.0218
+cube2equi| torch| default| 256x256| 480x240| 4| 0.0199
+cube2equi| torch| default| 256x256| 480x240| 16| 0.0228
+cube2equi| torch| default| 256x256| 480x240| 32| 0.0335
+cube2equi| torch| default| 256x256| 480x240| 64| 0.0438
+equi2cube| numpy| default| 4000x2000| 256x256| 1| 0.1174
+equi2cube| numpy| default| 4000x2000| 256x256| 4| 0.4759
+equi2cube| numpy| default| 4000x2000| 256x256| 16| 1.8907
+equi2cube| numpy| default| 4000x2000| 256x256| 32| 4.9468
+equi2cube| numpy| default| 4000x2000| 256x256| 64| 10.0229
+equi2cube| torch| default| 4000x2000| 256x256| 1| 0.0155
+equi2cube| torch| default| 4000x2000| 256x256| 4| 0.0328
+equi2cube| torch| default| 4000x2000| 256x256| 16| 0.0940
+equi2cube| torch| default| 4000x2000| 256x256| 32| 0.1698
+equi2cube| torch| default| 4000x2000| 256x256| 64| 0.3200
+equi2equi| numpy| default| 4000x2000| 640x320| 1| 2.4446
+equi2equi| numpy| default| 4000x2000| 640x320| 4| 9.8693
+equi2equi| numpy| default| 4000x2000| 640x320| 16| 42.6679
+equi2equi| numpy| default| 4000x2000| 640x320| 32| 96.5504
+equi2equi| numpy| default| 4000x2000| 640x320| 64| 193.8804
+equi2equi| torch| default| 4000x2000| 640x320| 1| 0.1816
+equi2equi| torch| default| 4000x2000| 640x320| 4| 0.5867
+equi2equi| torch| default| 4000x2000| 640x320| 16| 2.5047
+equi2equi| torch| default| 4000x2000| 640x320| 32| 4.4535
+equi2equi| torch| default| 4000x2000| 640x320| 64| 8.7202
+equi2pers| numpy| default| 4000x2000| 640x480| 1| 0.0734
+equi2pers| numpy| default| 4000x2000| 640x480| 4| 0.2994
+equi2pers| numpy| default| 4000x2000| 640x480| 16| 1.1730
+equi2pers| numpy| default| 4000x2000| 640x480| 32| 2.7934
+equi2pers| numpy| default| 4000x2000| 640x480| 64| 5.4712
+equi2pers| torch| default| 4000x2000| 640x480| 1| 0.0026
+equi2pers| torch| default| 4000x2000| 640x480| 4| 0.0084
+equi2pers| torch| default| 4000x2000| 640x480| 16| 0.0293
+equi2pers| torch| default| 4000x2000| 640x480| 32| 0.0447
+equi2pers| torch| default| 4000x2000| 640x480| 64| 0.0770
+
+
+---
+
+CSV:
+```
+Transform, Type, Method, Input Size, Output Size, Batch Size, Time (seconds)
+cube2equi, numpy, default, 256x256, 480x240, 1, 0.0360
+cube2equi, numpy, default, 256x256, 480x240, 4, 0.1180
+cube2equi, numpy, default, 256x256, 480x240, 16, 0.4512
+cube2equi, numpy, default, 256x256, 480x240, 32, 1.0650
+cube2equi, numpy, default, 256x256, 480x240, 64, 2.1363
+cube2equi, torch, default, 256x256, 480x240, 1, 0.0218
+cube2equi, torch, default, 256x256, 480x240, 4, 0.0199
+cube2equi, torch, default, 256x256, 480x240, 16, 0.0228
+cube2equi, torch, default, 256x256, 480x240, 32, 0.0335
+cube2equi, torch, default, 256x256, 480x240, 64, 0.0438
+equi2cube, numpy, default, 4000x2000, 256x256, 1, 0.1174
+equi2cube, numpy, default, 4000x2000, 256x256, 4, 0.4759
+equi2cube, numpy, default, 4000x2000, 256x256, 16, 1.8907
+equi2cube, numpy, default, 4000x2000, 256x256, 32, 4.9468
+equi2cube, numpy, default, 4000x2000, 256x256, 64, 10.0229
+equi2cube, torch, default, 4000x2000, 256x256, 1, 0.0155
+equi2cube, torch, default, 4000x2000, 256x256, 4, 0.0328
+equi2cube, torch, default, 4000x2000, 256x256, 16, 0.0940
+equi2cube, torch, default, 4000x2000, 256x256, 32, 0.1698
+equi2cube, torch, default, 4000x2000, 256x256, 64, 0.3200
+equi2equi, numpy, default, 4000x2000, 640x320, 1, 2.4446
+equi2equi, numpy, default, 4000x2000, 640x320, 4, 9.8693
+equi2equi, numpy, default, 4000x2000, 640x320, 16, 42.6679
+equi2equi, numpy, default, 4000x2000, 640x320, 32, 96.5504
+equi2equi, numpy, default, 4000x2000, 640x320, 64, 193.8804
+equi2equi, torch, default, 4000x2000, 640x320, 1, 0.1816
+equi2equi, torch, default, 4000x2000, 640x320, 4, 0.5867
+equi2equi, torch, default, 4000x2000, 640x320, 16, 2.5047
+equi2equi, torch, default, 4000x2000, 640x320, 32, 4.4535
+equi2equi, torch, default, 4000x2000, 640x320, 64, 8.7202
+equi2pers, numpy, default, 4000x2000, 640x480, 1, 0.0734
+equi2pers, numpy, default, 4000x2000, 640x480, 4, 0.2994
+equi2pers, numpy, default, 4000x2000, 640x480, 16, 1.1730
+equi2pers, numpy, default, 4000x2000, 640x480, 32, 2.7934
+equi2pers, numpy, default, 4000x2000, 640x480, 64, 5.4712
+equi2pers, torch, default, 4000x2000, 640x480, 1, 0.0026
+equi2pers, torch, default, 4000x2000, 640x480, 4, 0.0084
+equi2pers, torch, default, 4000x2000, 640x480, 16, 0.0293
+equi2pers, torch, default, 4000x2000, 640x480, 32, 0.0447
+equi2pers, torch, default, 4000x2000, 640x480, 64, 0.0770
+```
diff --git a/.readme/cube2equi.md b/.readme/cube2equi.md
@@ -2,15 +2,6 @@
 
 Cubemap to equirectangular transformation
 
-## Usage
-
-```Python
-
-
-```
-
-
-
 - `w_out` and `h_out` must be divisible by 8.
 - if `w_out, h_out` is large respect to the input cubemap, it may leave some artifacts
 

diff --git a/.readme/equi2cube.md b/.readme/equi2cube.md
@@ -1,31 +1,9 @@
 ## equi2cube
 
-equirectangular to cubemap transformation
-
-```Python
-class SomeEqui2Cube(BaseEqui2Cube):
-    def __init__(self, w_face: int):
-        ...
-    def run(self, equi, rot, cube_format, **kwargs):
-        ...
-```
-
-### Numpy
-
-```Python
-from equilib.equi2cube import NumpyEqui2Cube
-```
-
-### PyTorch
-
-```Python
-from equilib.equi2cube import TorchEqui2Cube
-```
-
 ### TODO:
 
 - [x] Implement `numpy`
 - [x] Implement `torch`
 - [x] Implement `torch` with batches
 - [x] Fix rotation axis
-- [ ] Implement `c++` with `cuda`
+- [ ] Implement `c++` with `cuda`
diff --git a/.readme/equi2equi.md b/.readme/equi2equi.md
@@ -1,29 +1,5 @@
 ## equi2equi
 
-equirectangular to equirectangular transformation
-
-I try to keep a common api that can be used in both `c++`, `numpy`, and `pytorch`.
-
-```Python
-class SomeEqui2Equi(BaseEqui2Equi):
-    def __init__(self, h_out: int, w_out: int):
-        ...
-    def run(self, src, rot, **kwargs):
-        ...
-```
-
-### Numpy
-
-```Python
-from equilib.equi2equi import NumpyEqui2Equi
-```
-
-### PyTorch
-
-```Python
-from equilib.equi2equi import TorchEqui2Equi
-```
-
 ### TODO:
 
 - [x] Implement `numpy`

diff --git a/.readme/equi2pers.md b/.readme/equi2pers.md
@@ -2,31 +2,6 @@
 
 equirectangular to perspective transformation
 
-I try to keep a common api that can be used in both `c++`, `numpy`, and `pytorch`.
-
-```Python
-class SomeEqui2Pers(BaseEqui2Pers):
-    def __init__(self, w_pers, h_pers, fov_x):
-        ...
-    def run(self, equi, rot, **kwargs):
-        ...
-```
-
-### Numpy
-
-```Python
-from equilib.equi2pers import NumpyEqui2Pers
-```
-
-### PyTorch
-
-```Python
-from equilib.equi2pers import TorchEqui2Pers
-```
-
-### C++
-
-__WIP__
 
 ### TODO:
 

diff --git a/README.md b/README.md
@@ -145,6 +145,7 @@ pers_img = equi2pers(
 </table>
 
 For more information about how each APIs work, take a look in [.readme](.readme/) or go through example codes in the `tests` or `demo`.
+See performance and benchmarking results of the APIs in [.readme/benchmark.md](.readme/benchmark.md).
 
 
 ### Coordinate System:
@@ -185,7 +186,7 @@ Check [CONTRIBUTING.md](./CONTRIBUTING.md) for more information
 ### TODO:
 
 - [ ] Documentations for each transform
-- [ ] Add graphs and statistics for speed improvements
+- [x] Add table and statistics for speed improvements
 
 
 ## Acknowledgements:

diff --git a/equilib/__init__.py b/equilib/__init__.py
@@ -16,7 +16,7 @@
     "equi2pers",
 ]
 
-__version__ = "0.2.0"
+__version__ = "0.2.1"
 __author__ = "Haruya Ishikawa"
 __homepage__ = "www.hoge.com"
 __description__ = "equirectangular processing"