动手学深度学习pytorch.pdf,动⼿学深度学习 Release2.0.0-alpha2 AstonZhang,ZacharyC.Lipton,MuLi,andAlexanderJ.Smola Aug26,2021 ⽬录 序⾔ 1 安装 9 符号 13 1 前⾔ 17 2 预备知识 39 2.1 数据操作 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
parser.add_argument('--seed', type=int, default=118, help="random seed") parser.add_argument('--cuda', action='store_true', default=True, help='enables cuda') parser.add_argument('--checkpoint_path',type=str,default='', help='Path to load a previous trained model if not empty (d...
'action'), ('action', 'scenes'), ('scenes', 'were'), ('were', 'top'), ('top', 'notch'), ('notch', 'in'), ('in', 'this'), ('this', 'movie.'), ('movie.', 'Thor'), ('Thor', 'has'), ('has', 'never'), ('never', 'been'), ('been', 'this'), (...
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论文连接:https://arxiv.org/pdf/1911.08947.pdfgithub链接:github.com网络结构首先,图像输入特征提取主干,提取特征; 其次,特征金字塔上采样到相同的尺寸,并进行特征级联得到特征F; 然后,特征F用于预测概率图(probability map P)和阈值图(threshold map T) 最后,通过P和F计算近似二值图(approximate binary map...
1000- R2Plus1D-PyTorch: PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition" 1000- StackNN: A PyTorch implementation of differentiable stacks for use in neural networks. 1000- transl...
Currently, on NVIDIA L20, RTX 4090 and RTX 3080 Laptop, compared with cuBLAS's default Tensor Cores algorithm, the HGEMM (WMMA/MMA/CuTe) in this repo (blue🔵) can achieve 98%~100% of its (orange🟠) performance. Please check toy-hgemm library⚡️⚡️ or HGEMM⚡️⚡...
1000- NALU-pytorch: Basic pytorch implementation of NAC/NALU from Neural Arithmetic Logic Units arxiv.org/pdf/1808.00508.pdf 1000- LOLA_DiCE: Pytorch 使用DiCE实现LOLA。 1000- generative-query-network-pytorch: Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and...
论文连接:https://arxiv.org/pdf/1911.08947.pdf github链接:github.com 网络结构 首先,图像输入特征提取主干,提取特征; 其次,特征金字塔上采样到相同的尺寸,并进行特征级联得到特征F; 然后,特征F用于预测概率图(probability map P)和阈值图(threshold map T) ...
for data, target in test_loader:data, target = data.to(device), target.to(device)output = model(data)test_loss += criterion(output, target).item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(...