Closed frgfm closed 2 years ago
Merging #64 (76aca8b) into main (f11e201) will decrease coverage by
1.42%
. The diff coverage is40.00%
.
@@ Coverage Diff @@
## main #64 +/- ##
==========================================
- Coverage 94.35% 92.93% -1.43%
==========================================
Files 10 10
Lines 656 665 +9
==========================================
- Hits 619 618 -1
- Misses 37 47 +10
Impacted Files | Coverage Δ | |
---|---|---|
torchscan/crawler.py | 84.32% <ø> (ø) |
|
torchscan/process/memory.py | 39.13% <40.00%> (-32.30%) |
:arrow_down: |
checkout to this branch fisrt, and install it in notebook as following command:
import sys
!{sys.executable} -m pip uninstall torchscan -y
!{sys.executable} -m pip install torchscan/.
got result:
Processing ./torchscan Installing build dependencies ... done Getting requirements to build wheel ... done Preparing wheel metadata ... done Requirement already satisfied: torch>=1.5.0 in /home/jupyter/.conda/envs/joonas/lib/python3.9/site-packages (from torchscan==0.1.2.dev0) (1.11.0) Requirement already satisfied: typing-extensions in /home/jupyter/.conda/envs/joonas/lib/python3.9/site-packages (from torch>=1.5.0->torchscan==0.1.2.dev0) (3.7.4.3) Building wheels for collected packages: torchscan Building wheel for torchscan (PEP 517) ... done Created wheel for torchscan: filename=torchscan-0.1.2.dev0-py3-none-any.whl size=30391 sha256=9fb4bc758c8f16683bdef0ec1cf9cd684a9a6d15d04eac11f02ab15cd39cb0da Stored in directory: /tmp/pip-ephem-wheel-cache-te9qtths/wheels/73/72/2c/7aef77450243410db62e4ec62b085f39cdaaf84259bda8aef1 Successfully built torchscan Installing collected packages: torchscan Successfully installed torchscan-0.1.2.dev0
But it still prints negative size:
Model size (params + buffers): 13.65 Mb
Framework & CUDA overhead: -24.21 Mb
Total RAM usage: -10.56 Mb
checkout to this branch fisrt, and install it in notebook as following command:
import sys !{sys.executable} -m pip uninstall torchscan -y !{sys.executable} -m pip install torchscan/.
Thanks but are you positive this is the snippet you used to install it? If so, apart from checkout out, you need to install for the folder, which is called "torch-scan" not "torchscan". So I think it should be:
!{sys.executable} -m pip install -e torch-scan/.
Let me know if that fixes the problem :)
d'oh! I missed option -e
, i will try again.
the reason for "torchscan" is, that is the name of directory I unzipped
thanks for letting me know :)
Script
netG = Generator().to(device)
summary(netG, (nz, 1, 1))
netD = Discriminator().to(device)
summary(netD, (3, 64, 64))
Output
______________________________________________________________
Layer Type Output Shape Param #
==============================================================
generator Generator (-1, 3, 64, 64) 0
├─main Sequential (-1, 3, 64, 64) 0
| └─0 ConvTranspose2d (-1, 512, 4, 4) 819,200
| └─1 BatchNorm2d (-1, 512, 4, 4) 2,049
| └─2 ReLU (-1, 512, 4, 4) 0
| └─3 ConvTranspose2d (-1, 256, 8, 8) 2,097,152
| └─4 BatchNorm2d (-1, 256, 8, 8) 1,025
| └─5 ReLU (-1, 256, 8, 8) 0
| └─6 ConvTranspose2d (-1, 128, 16, 16) 524,288
| └─7 BatchNorm2d (-1, 128, 16, 16) 513
| └─8 ReLU (-1, 128, 16, 16) 0
| └─9 ConvTranspose2d (-1, 64, 32, 32) 131,072
| └─10 BatchNorm2d (-1, 64, 32, 32) 257
| └─11 ReLU (-1, 64, 32, 32) 0
| └─12 ConvTranspose2d (-1, 3, 64, 64) 3,072
| └─13 Tanh (-1, 3, 64, 64) 0
==============================================================
Trainable params: 3,576,704
Non-trainable params: 0
Total params: 3,576,704
--------------------------------------------------------------
Model size (params + buffers): 13.65 Mb
Framework & CUDA overhead: 1914.35 Mb
Total RAM usage: 1928.00 Mb
--------------------------------------------------------------
Floating Point Operations on forward: 857.74 MFLOPs
Multiply-Accumulations on forward: 428.96 MMACs
Direct memory accesses on forward: 432.46 MDMAs
______________________________________________________________
________________________________________________________________
Layer Type Output Shape Param #
================================================================
discriminator Discriminator (-1, 1, 1, 1) 0
├─main Sequential (-1, 1, 1, 1) 0
| └─0 Conv2d (-1, 64, 32, 32) 3,072
| └─1 LeakyReLU (-1, 64, 32, 32) 0
| └─2 Conv2d (-1, 128, 16, 16) 131,072
| └─3 BatchNorm2d (-1, 128, 16, 16) 513
| └─4 LeakyReLU (-1, 128, 16, 16) 0
| └─5 Conv2d (-1, 256, 8, 8) 524,288
| └─6 BatchNorm2d (-1, 256, 8, 8) 1,025
| └─7 LeakyReLU (-1, 256, 8, 8) 0
| └─8 Conv2d (-1, 512, 4, 4) 2,097,152
| └─9 BatchNorm2d (-1, 512, 4, 4) 2,049
| └─10 LeakyReLU (-1, 512, 4, 4) 0
| └─11 Conv2d (-1, 1, 1, 1) 8,192
================================================================
Trainable params: 2,765,568
Non-trainable params: 0
Total params: 2,765,568
----------------------------------------------------------------
Model size (params + buffers): 10.56 Mb
Framework & CUDA overhead: 1923.74 Mb
Total RAM usage: 1934.30 Mb
----------------------------------------------------------------
Floating Point Operations on forward: 208.47 MFLOPs
Multiply-Accumulations on forward: 104.11 MMACs
Direct memory accesses on forward: 106.95 MDMAs
________________________________________________________________
Installed version with commit 76aca8b
There is no more negatives 👍
Ah perfect :)
This PR fixes the GPU RAM estimation problem by:
What this PR will not solve:
Closes #63
cc @joonas-yoon