device('cuda:0')) # creates a torch.cuda.DoubleTensor tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0') >>> torch.tensor(3.14159) # Create a scalar (zero-dimensional tensor) tensor(3.1416
import torch device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = torch.load("model_path") # pytorch模型加载 model.eval() x = torch.randn((1, 3, 320, 320)) # 生成张量 x = (device) torch.onnx.export(model, x, "ckpt/sgdn.onnx", verbose =True, ...
import torch # 1、tensor.view调整tensor的形状 a = torch.arange(0, 6) b1 = a.view(2, 3) b2 = a.view(-1, 2) # 当某一维为-1时,回自动计算它的大小 print(b1) print(b2) b3 = b1.unsqueeze(1) print(b3) print(b3.size()) # 注意形状,在第1维(下标从0开始)上增加“1” b4 = ...
Replicate:将Tensor拷贝n份,分布式放置在n个GPU上。 _Partial: 使得Tensor,在device mesh设备网格的特定维度上进行reduce,也就是在数个GPU设备(并非全部被)上进行reduce操作。 torch2.3给我们提供了5个ParallelStyle ColwiseParallel: 把一个module按照column维度进行分割,现在仅仅支持nn.Linear and nn.Embedding。当然大...
defconvert_tiny_llama_to_tflite(prefill_seq_len:int = 512,kv_cache_max_len:int = 1024,quantize:bool = True,):pytorch_model=tiny_llama.build_model(kv_cache_max_len=kv_cache_max_len)# Tensors used to trace the model graph during conversion.prefill_tokens=torch.full((1, prefill_seq_le...
to(device) ## padd batch = [ torch.Tensor(t).to(device) for t in batch ] batch = torch.nn.utils.rnn.pad_sequence(batch) ## compute mask mask = (batch != 0).to(device) return batch, lengths, mask (2)单机单卡-内存不可容纳 Dataset 处理内存不可容纳数据的方式就是按需读文件(虽然...
('model.pth',map_location=torch.device('cuda:0')))self.model.eval()def preprocess(self,batch):"""预处理输入数据"""images=[img.convert('RGB')forimginbatch]images=[img.resize((224,224))forimginimages]images=[torch.tensor(np.array(img)).permute(2,0,1).float()forimginimages]images=...
self.quant=torch.quantization.QuantStub()self.conv=torch.nn.Conv2d(1,1,1)self.relu=torch.nn.ReLU()# DeQuantStub converts tensors from quantized to floating point self.dequant=torch.quantization.DeQuantStub()defforward(self,x):# 自己指定开始量化的层 ...
1.在forward()方法中，创建一个新的输出Tensor，并使用output.to(input.device)将其分配到输入Tensor...
Point tensors eliminate this. When we do the reduceall, the:sum()operation, we keep the results on the gpu, like this: c=torch.Tensor(20,30):uniform():cl()--create a tensor on the GPUres=torch.ClTensor()--create a point tensor on the GPUres:sum(c)--sum c, and keep the res...