Содержание
Saved searches
Use saved searches to filter your results more quickly
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
AttributeError: ‘bytes’ object has no attribute ‘encode’ #43
AttributeError: ‘bytes’ object has no attribute ‘encode’ #43
Comments
Got error when running learning/main.py, my running environment :
ubuntu 16.04
pytorch 0.3.0 CUDA 9.0
Notice that I’m running with Semantic 3D datasets.
AttributeError: 'bytes' object has no attribute 'encode' AttributeError: 'Program' object has no attribute '_program' Will save to results/sema3d/trainval_best Total number of parameters: 213772 Module( (ecc): GraphNetwork( (0): RNNGraphConvModule( (_cell): GRUCellEx(32, 32)(ingate layernorm) (_fnet): Sequential( (0): Linear(in_features=13, out_features=32, bias=True) (1): ReLU(inplace) (2): Linear(in_features=32, out_features=128, bias=True) (3): ReLU(inplace) (4): Linear(in_features=128, out_features=64, bias=True) (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (6): ReLU(inplace) (7): Linear(in_features=64, out_features=32, bias=False) ) ) (1): Linear(in_features=352, out_features=8, bias=True) ) (ptn): PointNet( (stn): STNkD( (convs): Sequential( (0): Conv1d(11, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (8): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Linear(in_features=128, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) ) (proj): Linear(in_features=64, out_features=4, bias=True) ) (convs): Sequential( (0): Conv1d(11, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (8): ReLU(inplace) (9): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (10): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (11): ReLU(inplace) (12): Conv1d(128, 256, kernel_size=(1,), stride=(1,)) (13): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True) (14): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=257, out_features=256, bias=True) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Linear(in_features=256, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Linear(in_features=64, out_features=32, bias=True) ) ) ) Epoch 0/500 (results/sema3d/trainval_best): 0%| | 0/7 [00:00 main() File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/main.py", line 287, in main acc, loss, oacc, avg_iou = train() File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/main.py", line 185, in train outputs = model.ecc(embeddings) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/torch/nn/modules/module.py", line 357, in __call__ result = self.forward(*input, **kwargs) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/graphnet.py", line 97, in forward input = module(input) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/torch/nn/modules/module.py", line 357, in __call__ result = self.forward(*input, **kwargs) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/modules.py", line 54, in forward input = ecc.GraphConvFunction(nc, nc, idxn, idxe, degs, degs_gpu, self._edge_mem_limit)(hx, weights) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/GraphConvModule.py", line 67, in forward cuda_kernels.conv_aggregate_fw(output.narrow(0,startd,numd), products.view(-1,self._out_channels), self._degs_gpu.narrow(0,startd,numd)) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/cuda_kernels.py", line 120, in conv_aggregate_fw function, stream = get_kernel_func('conv_aggregate_fw_kernel_v2', conv_aggregate_fw_kernel_v2(), get_dtype(src)) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/cuda_kernels.py", line 33, in get_kernel_func prog = Program(ksrc.encode('utf-8'), (kname+dtype+'.cu').encode('utf-8')) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/compiler.py", line 52, in __init__ include_names) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/interface.py", line 200, in nvrtcCreateProgram c_char_p(encode_str(src)), c_char_p(encode_str(name)), File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/interface.py", line 54, in encode_str return s.encode("utf-8") AttributeError: 'bytes' object has no attribute 'encode' Exception ignored in: > Traceback (most recent call last): File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/compiler.py", line 56, in __del__ self._interface.nvrtcDestroyProgram(self._program) AttributeError: 'Program' object has no attribute '_program' The text was updated successfully, but these errors were encountered:
@loicland I have added the codes you mentioned above like this:
def get_kernel_func(kname, ksrc, dtype): if kname+dtype not in modules: ksrc = ksrc.replace('DTYPE', dtype) prog = Program(ksrc.encode('utf-8'), (kname+dtype+'.cu').encode('utf-8')) ksrc.encode('utf-8') (kname + dtype + '.cu').encode('utf-8') ptx = prog.compile() log = prog._interface.nvrtcGetProgramLog(prog._program) if len(log.strip()) > 0: print(log) module = cupy.cuda.function.Module() module.load(bytes(ptx.encode())) modules[kname+dtype] = module else: module = modules[kname+dtype] but after running it, problem occurred again, like the following:
Will save to results/sema3d/trainval_best Total number of parameters: 213772 Module( (ecc): GraphNetwork( (0): RNNGraphConvModule( (_cell): GRUCellEx(32, 32)(ingate layernorm) (_fnet): Sequential( (0): Linear(in_features=13, out_features=32, bias=True) (1): ReLU(inplace) (2): Linear(in_features=32, out_features=128, bias=True) (3): ReLU(inplace) (4): Linear(in_features=128, out_features=64, bias=True) (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (6): ReLU(inplace) (7): Linear(in_features=64, out_features=32, bias=False) ) ) (1): Linear(in_features=352, out_features=8, bias=True) ) (ptn): PointNet( (stn): STNkD( (convs): Sequential( (0): Conv1d(11, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (8): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Linear(in_features=128, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) ) (proj): Linear(in_features=64, out_features=4, bias=True) ) (convs): Sequential( (0): Conv1d(11, 64, kernel_size=(1,), stride=(1,)) (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,)) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,)) (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (8): ReLU(inplace) (9): Conv1d(128, 128, kernel_size=(1,), stride=(1,)) (10): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True) (11): ReLU(inplace) (12): Conv1d(128, 256, kernel_size=(1,), stride=(1,)) (13): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True) (14): ReLU(inplace) ) (fcs): Sequential( (0): Linear(in_features=257, out_features=256, bias=True) (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True) (2): ReLU(inplace) (3): Linear(in_features=256, out_features=64, bias=True) (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True) (5): ReLU(inplace) (6): Linear(in_features=64, out_features=32, bias=True) ) ) ) Epoch 0/500 (results/sema3d/trainval_best): 0%| | 0/7 [00:00 main() File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/main.py", line 287, in main acc, loss, oacc, avg_iou = train() File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/main.py", line 185, in train outputs = model.ecc(embeddings) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/torch/nn/modules/module.py", line 357, in __call__ result = self.forward(*input, **kwargs) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/graphnet.py", line 97, in forward input = module(input) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/torch/nn/modules/module.py", line 357, in __call__ result = self.forward(*input, **kwargs) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/modules.py", line 54, in forward input = ecc.GraphConvFunction(nc, nc, idxn, idxe, degs, degs_gpu, self._edge_mem_limit)(hx, weights) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/GraphConvModule.py", line 67, in forward cuda_kernels.conv_aggregate_fw(output.narrow(0,startd,numd), products.view(-1,self._out_channels), self._degs_gpu.narrow(0,startd,numd)) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/cuda_kernels.py", line 122, in conv_aggregate_fw function, stream = get_kernel_func('conv_aggregate_fw_kernel_v2', conv_aggregate_fw_kernel_v2(), get_dtype(src)) File "/home/xuyan/pytorch_xuyan/superpoint_graph-release/learning/ecc/cuda_kernels.py", line 33, in get_kernel_func prog = Program(ksrc.encode('utf-8'), (kname+dtype+'.cu').encode('utf-8')) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/compiler.py", line 52, in __init__ include_names) File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/interface.py", line 200, in nvrtcCreateProgram c_char_p(encode_str(src)), c_char_p(encode_str(name)), File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/interface.py", line 54, in encode_str return s.encode("utf-8") AttributeError: 'bytes' object has no attribute 'encode' Exception ignored in: > Traceback (most recent call last): File "/home/xuyan/tensorflow_xuyan/virtual_3.5/lib/python3.5/site-packages/pynvrtc/compiler.py", line 56, in __del__ self._interface.nvrtcDestroyProgram(self._program) AttributeError: 'Program' object has no attribute '_program' Notice that I ran this code in Python 3.5 and Pytorch 0.3.1