I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Environment
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the GeForce RTX 3090 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
sys.platform: linux
Python: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0]
CUDA available: True
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.0_bu.TC445_37.28540450_0
GPU 0: GeForce RTX 3090
GPU 1: GeForce GTX 1080 Ti
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.8.0
PyTorch compiling details: PyTorch built with:
GCC 7.3
C++ Version: 201402
Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
Prerequisite
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Environment
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70. If you want to use the GeForce RTX 3090 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name)) sys.platform: linux Python: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0] CUDA available: True CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.0_bu.TC445_37.28540450_0 GPU 0: GeForce RTX 3090 GPU 1: GeForce GTX 1080 Ti GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.8.0 PyTorch compiling details: PyTorch built with:
TorchVision: 0.9.0 OpenCV: 4.7.0 MMCV: 1.7.0 MMGen: 0.7.2+f9f00fd MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 10.2
Reproduces the problem - code sample
I used official code from your github. Nothing chaged.
Reproduces the problem - command or script
Comment that i used
For fish CUDA_VISIBLE_DEVICES=1 python3 tools/utils/singan_inference.py configs/positional_encoding_in_gans/singan_csg_fish.py \ /home/uhrgan/Neurocomputing/mmgeneration/infer_param/singan_csg_fis_20210406_175532-f0ec7b61.pth --samples-path output/fish --save-prev-res --seed 22
For bohemina - CSG CUDA_VISIBLE_DEVICES=1 python3 tools/utils/singan_inference.py configs/positional_encoding_in_gans/singan_csg_bohemian.py \ /home/uhrgan/Neurocomputing/mmgeneration/infer_param/singan_csg_bohemian_20210407_195455-5ed56db2.pth --samples-path output/bohemina_csg --save-prev-res --seed 22
For bohemina - SPE CUDA_VISIBLE_DEVICES=1 python3 tools/utils/singan_inference.py configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py \ /home/uhrgan/Neurocomputing/mmgeneration/infer_param/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth --samples-path output/bohemina_spe --save-prev-res --seed 22
Following guide line I also add test_cfg to sigan_bohemian.py & singan_fish.py file.
config file that i used.
base = [ '../base/models/singan/singan.py', '../base/datasets/singan.py', '../base/default_runtime.py' ]
num_scales = 10 # start from zero model = dict( generator=dict(num_scales=num_scales), discriminator=dict(num_scales=num_scales))
train_cfg = dict( noise_weight_init=0.1, iters_per_scale=2000, )
test_cfg = dict( delete = True, pkl_data = '/home/uhrgan/Neurocomputing/mmgeneration/infer_param/singan_csg_fis_20210406_175532-f0ec7b61.pkl' )
data = dict( train=dict( img_path='./data/singan/fish-crop.jpg', min_size=25, max_size=300))
optimizer = None lr_config = None checkpoint_config = dict(by_epoch=False, interval=2000, max_keep_ckpts=3)
custom_hooks = [ dict( type='MMGenVisualizationHook', output_dir='visual', interval=500, bgr2rgb=True, res_name_list=['fake_imgs', 'recon_imgs', 'real_imgs']), dict( type='PickleDataHook', output_dir='pickle', interval=-1, after_run=True, data_name_list=['noise_weights', 'fixed_noises', 'curr_stage']) ]
total_iters = 22000
But result have artifact
Reproduces the problem - error message
There is no error message, but result have artifacts
Additional information
No response