My goal is to create a tensor in pytorch (possibly using torch.from_numpy()?) from the CUDAarray, without the data leaving the GPU. Someone has a working example of creating a tensor from an ndarray using CuPy, at least. My CUDAarray is coming from a cudaGraphicsResource I get from ...
In this article, we show how to create a tensor with random values assigned to it in Python using the PyTorch library. A tensor is one of the most basic building blocks of PyTorch. It is basically the equivalent of a numpy array. When you create a tensor, you can assign random ...
from time import time from pathlib import Path import numpy as np #from PIL import Image # OpenVINO # from openvino.inference_engine import (IECore as Core, # # Tensor # ) # API v2.0 from openvino.runtime import Core, Tensor import psutil # from memory_profiler import...
import torch import json from einops import rearrange import cv2 import numpy as np from PIL import Image from huggingface_hub import hf_hub_download from safetensors import safe_open from safetensors.torch import load_file as load_sft from optimum.quanto import requantize from .model import Fl...
U-Net– A U-Net model takes token embeddings from CLIP along with an array of noisy inputs and produces a denoised output. This happens though a series of iterative steps, where each step processes an input latent tensor and produces a new latent...
(i)) input_data = torch.Tensor(input_data) input_label= torch.LongTensor(input_label) dataset = Data.TensorDataset(input_data, input_label) # 此时得到的输入数据是index形式的,不是向量形式 dataloader = Data.DataLoader(dataset, batch_size, True) model = TextRNN() criterion = nn.CrossEntropy...
from time import time from pathlib import Path import numpy as np #from PIL import Image # OpenVINO # from openvino.inference_engine import (IECore as Core, # # Tensor # ) # API v2.0 from openvino.runtime import Core, Tensor import psutil # from memory_profiler import...
U-Net– A U-Net model takes token embeddings from CLIP along with an array of noisy inputs and produces a denoised output. This happens though a series of iterative steps, where each step processes an input latent tensor and produces a new latent...
insert(0, R) returns = torch.tensor(returns) returns = (returns - returns.mean()) / (returns.std() + 1e-5) return returns def compute_advantages(self, rewards, values): returns = self.compute_returns(rewards) advantages = returns - values return advantages def compute_log_probs(self,...
return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) elif isinstance(batch[0], int_classes): elif isinstance(batch[0], int): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], string_classes): elif...