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關(guān)于Pytorch MaxUnpool2d中size操作方式_python_腳本之家

主要通過排序的方法,將4x4里面的元素按行展開為(0,0,0,0,0,6,0,8,0,0,0,0,0,14...),然后按照次序放到5x5的矩陣里面。 以上這篇關(guān)于Pytorch MaxUnpool2d中size操作方式就是小編分享給大家的全部內(nèi)容了,希望能給大家一個參考,也希望大家多多支持腳本之家。
www.dbjr.com.cn/article/1777...htm 2025-5-30

pytorch中的上采樣以及各種反操作,求逆操作詳解_python_腳本之家

:class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero. .. note:: `MaxPool2d` can map several input sizes to the same output sizes. Hence, t...
www.dbjr.com.cn/article/1777...htm 2025-5-29

pytorch進行上采樣的種類實例_python_腳本之家

x52d=F.relu(self.bn52d(self.conv52d(x53d))) x51d=F.relu(self.bn51d(self.conv51d(x52d))) 測試例子: 1 2 3 4 5 6 7 8 9 10 11 #測試上采樣 m=nn.MaxPool2d((3,3),stride=(1,1),return_indices=True) upm=nn.MaxUnpool2d((3,3),stride=(1,1)) data4=torch.randn(1,1,3...
www.dbjr.com.cn/article/1807...htm 2025-5-25

pytorch1.60 torch.nn在pycharm中無法自動智能提示的解決_python_腳本之...

MaxPool2d as MaxPool2d, MaxPool3d as MaxPool3d, MaxUnpool1d as MaxUnpool1d, MaxUnpool2d as MaxUnpool2d, \ MaxUnpool3d as MaxUnpool3d from .rnn import GRU as GRU, GRUCell as GRUCell, LSTM as LSTM, LSTMCell as LSTMCell, RNN as RNN, RNNBase as RNNBase, \ RNNCell as RNNCell...
www.dbjr.com.cn/python/316373c...htm 2025-5-27

pytorch中獲取模型input/output shape實例_python_腳本之家

"MaxPool2d-3": { "input_shape": [1, 64, 32, 128], "output_shape": [1, 64, 16, 64], "nb_params": 0 }, "Conv2d-4": { "input_shape": [1, 64, 16, 64], "output_shape": [1, 128, 16, 64], "trainable": true, "nb_params": 73728 }, "ReLU-5": { "input_shape...
www.dbjr.com.cn/article/1774...htm 2025-5-28

Pytorch模型定義與深度學(xué)習(xí)自查手冊_python_腳本之家

max_unpool = nn.MaxUnpool2d((2, 2), stride=(2, 2)) img_unpool = max_unpool(img_unpool, indices) 組合池化 組合池化同時利用最大值池化與均值池化兩種的優(yōu)勢而引申的一種池化策略。常見組合策略有兩種:Cat與Add。其代碼描述如下: 1 2 3 4 5 6 7 8 def add_avgmax_pool2d(x, output_si...
www.dbjr.com.cn/article/2502...htm 2025-5-25