7.Pt_shell –x “restore_session orca_savesession” :继续原来保存的信息 8.Set sh_enable_page_mode true :分页显示 9.Report_lib libname :看库的信息 10. Report_timing –group SYS_clk :看某个时钟的信息 11. Pre CTS clock Uncertainty = clock skew + clock jitter +margin Post CTS clock Unc...
7.Pt_shell –x “restore_session orca_savesession” :继续原来保存的信息8.Set sh_enable_page_mode true : 分页显示 9.Report_lib libname :看库的信息 10. Report_timing –group SYS_clk :看某个时钟的信息 11. Pre CTS clock Uncertainty = clock skew + clock jitter +margin Post CTS clock Unc...
ort_session = onnxruntime.InferenceSession(multi_input_model_output_path) # ONNX模型输入初始化 ort_inputs = {ort_session.get_inputs()[0].name: x.numpy(), ort_session.get_inputs()[1].name: y.numpy()} # ONNX模型推理 ort_outs = ort_session.run(None, ort_inputs) # print(ort_...
在link design之前,要设置以下变量来使工具生成hyperscale 模版脚本: 该特性影响linkdesign、updatetiming、savesession和writehier_data命令的行为。相应地,它必须在link design之前启用。 该特性的完整示例脚本如下: Choosing a Top-Down or Bottom-Up Flow 自顶向下流是首选的方法,因为时序收敛更容易更快地实现,并且...
save_session $session_directory -include_libraries # 检查保存会话是否保存 unix% ls -al $$session_directory # 检查曾经保存的会话版本 unix% more $session_diractory/Readme #检查使用的库 unix% more $session_diractory/lib_map #检查是否可以restore ...
7.Pt_shell –x “restore_session orca_savesession” :继续原来保存的信息 8.Set sh_enable_page_mode true :分页显示 9.Report_lib libname :看库的信息 10. Report_timing –group SYS_clk :看某个时钟的信息 11. Pre CTS clock Uncertainty = clock skew + clock jitter +margin Post CTS clock Unc...
7.Pt_shell –x “restore_session orca_savesession” :继续原来保存的信息 8.Set sh_enable_page_mode true : 分页显示 9.Report_lib libname :看库的信息 10. Report_timing –group SYS_clk :看某个时钟的信息 11. Pre CTS clock Uncertainty = clock skew + clock jitter +margin Post CTS clock ...
save-session=/etc/aria2.session force-save=true log-level=error # see --split option max-concurrent-downloads=5 continue=true max-overall-download-limit=8M max-overall-upload-limit=200K max-upload-limit=20 # Http/FTP options connect-timeout=120 ...
with tf.Session() as sess: saver.restore(sess, input_checkpoint) #恢复图并得到数据 output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化,将变量值固定 sess=sess, input_graph_def=sess.graph_def,# 等于:sess.graph_def ...
torch.save():将所有的组件保存到文件中 模型保存 importtorchimporttorch.nnasnn# 定义一个简单的模型classNet(nn.Module):def__init__(self):super(Net, self).__init__() self.fc1 = nn.Linear(10,5) self.fc2 = nn.Linear(5,1)defforward(self, x): ...