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Study/Code Review 2024. 7. 4. 22:35
멀리뛰기

잘못시도 1. binary tree잘못시도 2. queue 접근- 정답 참고) 점화식
Study/Code Review 2024. 6. 26. 23:30
Pose Estimation Study Site

https://heartbeat.fritz.ai/heatmaps-and-convolutional-neural-networks-using-fast-ai-16d5b7d02a86 Heatmaps and Convolutional Neural Networks Using Fast.ai Learn all about CNNs and a visual representation technique to intuitively understand their outputs heartbeat.fritz.ai https://nanonets.com/blog/human-pose-estimation-3d-guide/ A 2019 guide to 3D Pose Estimation Human Pose Estimation is an impor..
Study/Code Review 2020. 9. 7. 11:09
Github 모음(weight freeze)

1. https://discuss.pytorch.org/t/how-the-pytorch-freeze-network-in-some-layers-only-the-rest-of-the-training/7088/23 How the pytorch freeze network in some layers, only the rest of the training? Thanks for the clarification! As a follow-up to my point regarding the speed up - I am not observing a speedup when I freeze the initial 80% of the network. I expected the training to be faster, since it..
Study/Code Review 2020. 8. 30. 17:01
Github 모음(weight copy , pruning)

--------------Weight 복사---------- https://discuss.pytorch.org/t/copying-weights-from-one-net-to-another/1492/3 Copying weights from one net to another How does deep copy / canonical copy differ from normal weights loading? discuss.pytorch.org https://discuss.pytorch.org/t/copy-deepcopy-vs-clone/55022/3 Copy.deepcopy() vs clone() Hi, Thanks a lot. So this means, when I do clone() on tensors, thei..
Study/Code Review 2020. 8. 16. 23:09
3D Convolution

a=torch.Tensor ([[[[[1, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[1, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]]]]) print(a.size()) c=nn.Conv3d(3,1,kernel_size=3,stride=1,pa..
Study/Code Review 2020. 4. 1. 16:42
Learning both weights and connections for Efficient Neural Networks-(2)

import torch.nn as nn import torch.nn.functional as F class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channel,channel,stride): super(BasicBlock,self).__init__() self.conv1 = nn.Conv2d(in_channel,channel,kernel_size=3,stride=stride, padding=1,bias=False) self.bnorm1 = nn.BatchNorm2d(channel) self.conv2 = nn.Conv2d(channel,channel,kernel_size=3,stride=1,padding=1,bias=False) self...
Study/Code Review 2020. 3. 22. 22:10
Learning both weights and connections for Efficient Neural Networks-(1)

learning rate 확인하고 싶을 때 def get_lr(optimizer): for param_group in optimizer.param_groups: return param_group['lr'] model.children() returns model.children() is a generator that returns layers of the model from which you can extract your parameter tensors using .weight or .bias[1] print(list(model.children())) [Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1)), Conv2d(6, 16, kernel_size=(3, 3), str..
Study/Code Review 2020. 3. 22. 20:05
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