torch.int32或torch.float32。希望存储为float,pytorch函数接受float量化值,它可能不接受整数输入。 unsigned:boolean,使用无符号整数范围。例如,对于num_bits=8,[0,255]。默认为False。 narrow_range:布尔值。使用对称整数范围进行有符号量化 例如,对于num_bits=8,用[-127,127]代替[-128,127]。默认为True。 Ret...
[Triton][Inductor] Infer Boolean Types #147416 opened Feb 18, 2025 Add cmake hints to USE_SYSTEM_NVTX for nvtx3 include dir #147418 opened Feb 18, 2025 [executorch hash update] update the pinned executorch hash #147422 opened Feb 19, 2025 To enable NCCL communication to suppor...
source_embedding_size (int): size of the source embedding vectors target_vocab_size (int): number of unique words in target language target_embedding_size (int): size of the target embedding vectors encoding_size (int): the size of the encoder RNN. target_bos_index (int): index for BEGI...
type(torch.LongTensor)) # Cast back to int-64, prints "tensor([0, 1, 2, 3])" 3在GPU上运算 这是上一节学过的,你还记得么? 下面的例子,讲解了几种非常有用的操作,切换 tensor 的运算设备,并且指定在哪个设备上运算,还可以转换数据类型。
boolean value, but got " "replacement={}".format(self.replacement)) if self._num_samples is not None and not replacement: raise ValueError("With replacement=False, num_samples should not be specified, " "since a random permute will be performed.") if not isinstance(self.num_samples, int...
Create empty boolean tensor on CUDA and initialize it with random values from urandom_gen: torch.empty(10,dtype=torch.bool,device='cuda').random_(generator=urandom_gen) Create empty int16 tensor on CUDA and initialize it with random values in range [0, 100) from urandom_gen: ...
not to ONNX runtime. So we only export fake quantized model into a form TensorRT will take. Fake quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. TensorRT will take the generated ONNX graph, and execute it in int8 in the most optimized way to its cap...
funinitOnnxModel(context:Context,rawid:Int):Boolean{try{val onnxDir:File=File(context.filesDir,"onnx")if(!onnxDir.exists()){onnxDir.mkdirs()}//判断模型是否存在是否存在,不存在复制过来 val onnxfile: File = File(onnxDir, "dnnNet.onnx") if (onnxfile.exists()){ return initOpenCVDNN...
mutex non_reentrant_device_thread_mutex_;// stop() must be called before the destruction path goes down to the base// class, in order to avoid a data-race-on-vptr. Use this boolean to guard// whether stop() has already been called, so we can call this in every// destructor of ...
(boolean): Whether to use new style (tensor field) or oldstyle (simple_value field). New style could lead to faster data loading.Examples::from torch.utils.tensorboard import SummaryWriterwriter = SummaryWriter()x = range(100)for i in x:writer.add_scalar('y=2x', i * 2, i)writer....