from torch_npu.contrib import transfer_to_npu 运行推理服务 python server.py 报错: python server.py 后就报错: logDevId 0 create HDC failed, error: 31 logDevId 0 create HDC failed, error: 31 logDevId 0 create HDC failed,
切换环境到一下项目,执行; import torch import torch.nn as nn import torch.nn.functional as F from torchsummary import summary class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20,...
dataset = timm.data.create_dataset("torch/CIFAR10","../",download=True) 必须的参数只有两个,数据集名称,root路径,如果路径下没有数据集会提醒你添加download=True来自动下载 data_loader = timm.data.create_loader(dataset,(3, 224, 224), 4) 必须传入的参数:dataset、input_size、batch_size 支持加载...
#coding=gbk from cgi import test import torch import numpy as np import torch.nn as nn import torch.optim as optim import torch.utils.data as Data import torch.nn.functional as F from data_process import get_data dtype = torch.FloatTensor device = torch.device("cuda" if torch.cuda.is_a...
nn.Module): def forward(self, x): return x + x def test_allow_in_graph_dynamo(): class Model(torch.nn.Module): def __init__(self): super().__init__() self.add = AllowInGraphLayer() def forward(self, x): return self.add(x) def backend(gm, _): gm.graph.print_tabular()...
(Activationsd, AddChanneld, AsDiscreted, CastToTyped, CopyItemsd, LoadImaged, NormalizeIntensityd, RandRotated, RandSpatialCropd, Resized, SaveImaged, SplitChanneld, ToNumpyd, ToTensord) from torch.nn import BCEWithLogitsLoss from torch.optim import Adam from torch.optim.lr_scheduler import ...
File "/opt/miniconda3/envs/py392/lib/python3.9/site-packages/torch/onnx/utils.py", line 377, in _trace_and_get_graph_from_model torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True) File "/opt/miniconda3/envs/py392/lib/python...
CPU进行推理# pip install onnxruntime-gpu # 使用GPU进行推理复制代码2.导出模型import torch.onnx # 转换的onnx格式的名称,文件后缀需为.onnxonnx_file_name...= "xxxxxx.onnx"# 我们需要转换的模型,将torch_model设置为自己的模型model = torch_model# 加载权重,将model.pth转换为自己的模型权重# ...
transforms.Normalize(mean=torch.tensor(mean),std=torch.tensor(std)) 图片转换为适合深度学习的tensor 并根据数据集均值和方差归一化 如果re_prob>0 则 使用RandomErasing RandomErasing(re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu') ...
🐛 Describe the bug import torch out = torch.empty(5).cuda() b = torch.compile(torch.sin)(torch.zeros(5).cuda(), out=out) results in the following triton code from ctypes import c_void_p, c_long import torch import math import random impo...