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pytorch torch.nn.AdaptiveAvgPool2d()自適應(yīng)平均池化函數(shù)詳解_python...
CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of o
www.dbjr.com.cn/article/1777...htm 2025-6-1
PyTorch的自適應(yīng)池化Adaptive Pooling實(shí)例_python_腳本之家
自適應(yīng)池化Adaptive Pooling是PyTorch含有的一種池化層,在PyTorch的中有六種形式:自適應(yīng)最大池化Adaptive Max Pooling:torch.nn.AdaptiveMaxPool1d(output_size) torch.nn.AdaptiveMaxPool2d(output_size) torch.nn.AdaptiveMaxPool3d(output_size)自適應(yīng)平均池化Adaptive Average Pooling:torch.nn.AdaptiveAvgPool1d(...
www.dbjr.com.cn/article/1777...htm 2025-6-5
YOLOv5改進(jìn)教程之添加注意力機(jī)制_python_腳本之家
self.avgpool=nn.AdaptiveAvgPool2d(1) self.l1=nn.Linear(c1, c1//r, bias=False) self.relu=nn.ReLU(inplace=True) self.l2=nn.Linear(c1//r, c1, bias=False) self.sig=nn.Sigmoid() defforward(self, x): b, c, _, _=x.size() y=self.avgpool(x).view(b, c) y=self.l1(y) y...
www.dbjr.com.cn/article/2530...htm 2025-5-29
PyTorch一小時(shí)掌握之遷移學(xué)習(xí)篇_python_腳本之家
AdaptiveAvgPool2d-513 [-1, 2048, 1, 1] 0 Linear-514 [-1, 100] 204,900 LogSoftmax-515 [-1, 100] 0 === Total params: 58,348,708 Trainable params: 204,900 Non-trainable params: 58,143,808 --- Input size (MB): 0.01 Forward/backward pass size (MB): 12.40 Params size (MB)...
www.dbjr.com.cn/article/2222...htm 2025-5-30
PyTorch一小時(shí)掌握之圖像識別實(shí)戰(zhàn)篇_python_腳本之家
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_ru...
www.dbjr.com.cn/article/2222...htm 2025-5-20
Swin Transformer圖像處理深度學(xué)習(xí)模型_python_腳本之家
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(embed_dim * 2**num_layers, num_classes) # add relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * (2 * window_sizes[-1] - 1), embed_dim // 8, embed_dim // 8)), requi...
www.dbjr.com.cn/article/2794...htm 2025-6-8
淺談pytorch池化maxpool2D注意事項(xiàng)_python_腳本之家
nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 0)), 這樣在用交叉熵做損失函數(shù)的時(shí)候,有時(shí)候會出現(xiàn)loss為nan的情況,檢查的時(shí)候發(fā)現(xiàn),某些樣本的提取出來的feature全為nan。 以上這篇淺談pytorch池化maxpool2D注意事項(xiàng)就是小編分享給大家的全部內(nèi)容了,希望能給大家一個(gè)參考,也希望大家多多支持腳本之...
www.dbjr.com.cn/article/1806...htm 2025-5-25
pytorch如何獲得模型的計(jì)算量和參數(shù)量_python_腳本之家
支持layer:Conv1d/2d/3d,ConvTranspose2d,BatchNorm1d/2d/3d,激活(ReLU, PReLU, ELU, ReLU6, LeakyReLU),Linear,Upsample,Poolings (AvgPool1d/2d/3d、MaxPool1d/2d/3d、adaptive ones) 安裝要求:Pytorch >= 0.4.1, torchvision >= 0.2.1 get_model_complexity_info() get_model_complexity_info是ptflops...
www.dbjr.com.cn/article/2137...htm 2025-5-30
Python機(jī)器學(xué)習(xí)從ResNet到DenseNet示例詳解_python_腳本之家
nn.BatchNorm2d(num_channels), nn.ReLU(), nn.AdaptiveMaxPool2d((1, 1)), nn.Flatten(), nn.Linear(num_channels, 10) ) 訓(xùn)練模型 由于這里使用了比較深的網(wǎng)絡(luò),本節(jié)里我們將輸入高和寬從224降到96來簡化計(jì)算。 1 2 3 lr, num_epochs, batch_size = 0.1, 10, 256 train_iter, test_iter = ...
www.dbjr.com.cn/article/2249...htm 2025-5-19