'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'as_strided', 'as_strided_', 'as_tensor', 'asin', 'asin_', 'asinh', '
stride(): with torch.no_grad(): copy_param.set_(copy_param.clone() .as_strided(param.size(), param.stride()) .copy_(copy_param)) copy_param.requires_grad = param.requires_grad else: self._module_copies = [self.module] self.modules_params = [list(parameters(m)) for m in self....
stride() != copy_param.stride(): with torch.no_grad(): copy_param.set_(copy_param.clone() .as_strided(param.size(), param.stride()) .copy_(copy_param)) copy_param.requires_grad = param.requires_grad else: self._module_copies = [self.module] self.modules_params = [list(...
DeQuantStub()) ])) def make_divisible(x, divisible_by=8): import numpy as np return int(np.ceil(x * 1. / divisible_by) * divisible_by) class InvertedResidual(nn.Module): def __init__(self, inp, oup, stride, expand_ratio): super(InvertedResidual, self).__init__() self....
填充 torch.full(size, fill_value, ***, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor 使用fill_value 生成指定 size 的 tensor 裁剪 torch.clamp(input, min=None, max=None, ***, out=None) → Tensor 将过大或过小的元素裁剪成min/max6.1...
Change as_strided_scatter’s storage offset default to None from 0 (#87481) Make torch.histc consistent between CPU and CUDA (#87832) Add float to list of allowed ops for serialization (#94910) Fix numpy1.24 deprecations in unittests ([#93997] (#93997)) Properly moving segment_reduce to...
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (fc): Sequential( (0): Linear(in_features=1568, out_features=128, bias=True) (1): ReLU() (2): Linear(in_features=128, out_...
记录通过pytorch编写cnn 模型示例,包括训练、模型、预测全流程代码结构,数据采集公共调制方式识别数据集,编写代码简单,以便进行pytorch学习。 train.py import os import numpy as np import torch import torch.nn as nn
# 分别为来自CPU和GPU的模型保存参数, # 并再次将模型的参数加载到位于CPU或者GPU的模型上 import torch import torch.nn as nn torch.manual_seed(seed=20200910) class Model(torch.nn.Module): def __init__(self): super(Model,self).__init__() self.conv1=torch.nn.Sequential( # 输入torch.Size...
import torchclass Model(torch.nn.Module):def __init__(self):super(Model, self).__init__()self.conv = torch.nn.Conv2d(16, 33, 3, stride=2)self.relu = torch.nn.ReLU()def forward(self, x):x = self.conv(x)x = self.relu(x)return xmodel = Model()model.eval()data = torch....