[TorchSummary+TorchSnooper] 一次TorchSummary可视化网络的调试+TorchSnooper的第一次实际使用
2024-06-01 21:23:30
文章目录
- 问题描述：
- 调试过程记录：
- 调试1：
- 运行结果：
- 结果分析：
- 调试2：
- 运行结果：
- 结果分析：
- 最终代码1:
- 设置device = 'cpu'：
- 设置device = 'cuda'：
- 运行结果：
- 设置device = 'cpu'：
- 代码备份：
问题描述：
本次调试中遇到的错误，其原因主要是数据类型不符。具体表示为cpu、cuda、Tensor.cuda.FloatTensor、Tensor.FloatTensor的不匹配。
本次调试用到一个第三方库，名为torchsnooper，用于监测深度网络程序中的变量的类型、维度等信息，用以解决相应的不匹配问题。
GitHub链接：TorchSnooper
本次调试遇到的报错信息包括：
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 ‘weight’
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
p.s.
由于本人一开始对论文的网络架构architecture理解有误，致使网络结构与论文中的架构出现了较大的差异。
正确的网络架构如下：
而对应的最终的源代码如下：
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import torch
import os
from torchsummary import summary
import torchsnooperpatch_size = 17
batch_size = 20
device = "cuda" if torch.cuda.is_available() else "cpu"x = torch.randn(batch_size,1,103,patch_size,patch_size,device=device)
# -----------------------自加在构建网络的情况下获得维度---------------------------
# @torchsnooper.snoop()class Net(nn.Module):@staticmethoddef weight_init(m):if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d):init.xavier_uniform_(m.weight.data)init.constant_(m.bias.data, 0)def _get_final_flattened_size(self):with torch.no_grad():x = torch.zeros((1, 1, 103,patch_size, patch_size),device=device)x = self.pool1(self.conv1(x))x = self.pool2(self.conv2(x))x = self.conv3(x)_, t, c, w, h = x.size()return t * c * w * hdef __init__(self):super(Net,self).__init__()self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1)).cuda()self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1)).cuda()self.conv3 = nn.Conv3d(2*32, 4*32, (32, 3, 3), padding=(1, 0, 0)).cuda()self.pool1 = nn.MaxPool3d((1,2,2), stride = (1,2,2)).cuda()self.pool2 = nn.MaxPool3d((1,2,2), stride = (1,2,2)).cuda()self.features_size = self._get_final_flattened_size()self.fc = nn.Linear(self.features_size, 10).cuda()self.apply(self.weight_init)def forward(self,x):x = F.relu(self.conv1(x))x = self.pool1(x)x = F.relu(self.conv2(x))x = self.pool2(x)x = F.relu(self.conv3(x))x = x.view(-1, self.features_size)x = self.fc(x)return xnet = Net()
net.to(device)print(net.to(device))# os.system('pause')summary(net.to(device),(1, 103, patch_size, patch_size),device=device)
# -----------------------自加在构建网络的情况下获得维度---------------------------
这里通过将nn.MaxPool3d((1,2,2), stride = (1,2,2))将三维最大池化在Depth（Depth×Width×Height）的size和stride均设置为，从而达到只将W，H维度的size减半，保持D维度不变的目的。
对应的运行结果如下：
E:\Anaconda\python.exe C:/Users/73416/PycharmProjects/HSIproject/test.py
Net((conv1): Conv3d(1, 32, kernel_size=(32, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1))(conv2): Conv3d(32, 64, kernel_size=(32, 5, 5), stride=(1, 1, 1), padding=(1, 1, 1))(conv3): Conv3d(64, 128, kernel_size=(32, 3, 3), stride=(1, 1, 1), padding=(1, 0, 0))(pool1): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)(pool2): MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0, dilation=1, ceil_mode=False)(fc): Linear(in_features=2048, out_features=10, bias=True)
)
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv3d-1 [-1, 32, 74, 16, 16] 16,416MaxPool3d-2 [-1, 32, 74, 8, 8] 0Conv3d-3 [-1, 64, 45, 6, 6] 1,638,464MaxPool3d-4 [-1, 64, 45, 3, 3] 0Conv3d-5 [-1, 128, 16, 1, 1] 2,359,424Linear-6 [-1, 10] 20,490
================================================================
Total params: 4,034,794
Trainable params: 4,034,794
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.11
Forward/backward pass size (MB): 6.79
Params size (MB): 15.39
Estimated Total Size (MB): 22.29
----------------------------------------------------------------Process finished with exit code 0
调试过程记录：
调试1：
运行结果：
E:\Anaconda\python.exe C:/Users/73416/PycharmProjects/HSIproject/test.py
torch.Size([10, 1, 103, 17, 17])
Source path:... C:/Users/73416/PycharmProjects/HSIproject/test.py
Starting var:.. self = REPR FAILED
Starting var:.. __class__ = <class '__main__.Net'>
22:19:30.891404 call 63 def __init__(self):
22:19:30.892400 line 64 super(Net, self).__init__()
Modified var:.. self = Net()
22:19:30.892400 line 65 self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1)))
22:19:30.893395 line 66 self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...(32, 5, 5), stride=(1, 1, 1), padding=(1, 1, 1)))
22:19:30.904366 line 67 self.conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...=(4, 3, 3), stride=(1, 1, 1), padding=(1, 0, 0)))
22:19:30.906358 line 68 self.pool1 = nn.MaxPool3d(2, stride = 2)
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))
22:19:30.907359 line 69 self.pool2 = nn.MaxPool3d(2, stride = 2)
22:19:30.907359 return 69 self.pool2 = nn.MaxPool3d(2, stride = 2)
Return value:.. None
Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))
Starting var:.. x = tensor<(2, 1, 103, 17, 17), float32, cuda:0>
22:19:33.521693 call 71 def forward(self,x):
22:19:33.523689 line 72 x = F.relu(self.conv1(x))
conv1: torch.Size([2, 32, 74, 16, 16])
Modified var:.. x = tensor<(2, 32, 74, 16, 16), float32, cuda:0, grad>
22:19:34.297627 line 73 print('conv1:', x.size())
22:19:34.312655 line 74 x = self.pool1(x)
pool1: torch.Size([2, 32, 37, 8, 8])
Modified var:.. x = tensor<(2, 32, 37, 8, 8), float32, cuda:0, grad>
22:19:34.315645 line 75 print('pool1:', x.size())
22:19:34.317638 line 76 x = F.relu(self.conv2(x))
Modified var:.. x = tensor<(2, 64, 8, 6, 6), float32, cuda:0, grad>
22:19:34.318636 line 77 print('conv2:', x.size())
conv2: torch.Size([2, 64, 8, 6, 6])
pool2: torch.Size([2, 64, 4, 3, 3])
22:19:34.322625 line 78 x = self.pool2(x)
Modified var:.. x = tensor<(2, 64, 4, 3, 3), float32, cuda:0, grad>
22:19:34.323624 line 79 print('pool2:', x.size())
22:19:34.324619 line 80 x = F.relu(self.conv3(x))
Modified var:.. x = tensor<(2, 128, 3, 1, 1), float32, cuda:0, grad>
22:19:34.325617 line 81 print('conv3:', x.size())
conv3: torch.Size([2, 128, 3, 1, 1])
22:19:34.326614 line 82 features_size = self._get_final_flattened_size()Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))22:19:34.327611 call 53 def _get_final_flattened_size(self):22:19:34.327611 line 54 with torch.no_grad():22:19:34.327611 line 55 x = torch.zeros((batch_size, 1, 103,22:19:34.327611 line 56 patch_size, patch_size))New var:....... x = tensor<(10, 1, 103, 17, 17), float32, cpu>22:19:34.328577 line 57 x = self.pool1(self.conv1(x))22:19:34.332566 exception 57 x = self.pool1(self.conv1(x))RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'Call ended by exception
22:19:34.337552 exception 82 features_size = self._get_final_flattened_size()
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
Call ended by exception
Traceback (most recent call last):File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 92, in <module>summary(net,(1, 103, patch_size, patch_size),batch_size,device='cuda')File "E:\Anaconda\lib\site-packages\torchsummary\torchsummary.py", line 72, in summarymodel(*x)File "E:\Anaconda\lib\site-packages\torch\nn\modules\module.py", line 489, in __call__result = self.forward(*input, **kwargs)File "E:\Anaconda\lib\site-packages\pysnooper\tracer.py", line 256, in simple_wrapperreturn function(*args, **kwargs)File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 82, in forwardfeatures_size = self._get_final_flattened_size()File "E:\Anaconda\lib\site-packages\pysnooper\tracer.py", line 256, in simple_wrapperreturn function(*args, **kwargs)File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 57, in _get_final_flattened_sizex = self.pool1(self.conv1(x))File "E:\Anaconda\lib\site-packages\torch\nn\modules\module.py", line 489, in __call__result = self.forward(*input, **kwargs)File "E:\Anaconda\lib\site-packages\torch\nn\modules\conv.py", line 448, in forwardself.padding, self.dilation, self.groups)
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'Process finished with exit code 1
结果分析：
可以看到报错原因是：
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'weight'
可以看出程序报错是cpu、cuda类型不匹配造成的。
通过@torchsnooper.snoop()来监测程序中的变量。发现：
只有这个变量的类型是cpu，而其他的变量的类型都是cuda。查找上文发现这个报错的位置是在程序的第56行。
22:19:34.327611 line 55 x = torch.zeros((batch_size, 1, 103,
22:19:34.327611 line 56 patch_size, patch_size))
New var:....... x = tensor<(10, 1, 103, 17, 17), float32, cpu>
即在相应的位置将变量的类型指定为cuda即可。
x = torch.zeros((batch_size, 1, 103, patch_size, patch_size), device = 'cuda')
调试2：
运行结果：
E:\Anaconda\python.exe C:/Users/73416/PycharmProjects/HSIproject/test.py
Source path:... C:/Users/73416/PycharmProjects/HSIproject/test.py
torch.Size([10, 1, 103, 17, 17])
Starting var:.. self = REPR FAILED
Starting var:.. __class__ = <class '__main__.Net'>
22:28:30.503370 call 63 def __init__(self):
22:28:30.503370 line 64 super(Net, self).__init__()
Modified var:.. self = Net()
22:28:30.503370 line 65 self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1)))
22:28:30.504392 line 66 self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...(32, 5, 5), stride=(1, 1, 1), padding=(1, 1, 1)))
22:28:30.516367 line 67 self.conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...=(4, 3, 3), stride=(1, 1, 1), padding=(1, 0, 0)))
22:28:30.518328 line 68 self.pool1 = nn.MaxPool3d(2, stride = 2)
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))
22:28:30.518328 line 69 self.pool2 = nn.MaxPool3d(2, stride = 2)
22:28:30.518328 line 70 self.features_size = self._get_final_flattened_size()Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))22:28:30.519359 call 53 def _get_final_flattened_size(self):22:28:30.519359 line 54 with torch.no_grad():22:28:30.519359 line 55 x = torch.zeros((batch_size, 1, 103,22:28:30.519359 line 56 patch_size, patch_size),device='cuda')New var:....... x = tensor<(10, 1, 103, 17, 17), float32, cuda:0>22:28:30.520324 line 57 x = self.pool1(self.conv1(x))22:28:30.523339 exception 57 x = self.pool1(self.conv1(x))RuntimeError: Input type (torch.cuda.FloatTensor...eight type (torch.FloatTensor) should be the sameCall ended by exception
22:28:30.526306 exception 70 self.features_size = self._get_final_flattened_size()
RuntimeError: Input type (torch.cuda.FloatTensor...eight type (torch.FloatTensor) should be the same
Call ended by exception
Traceback (most recent call last):File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 92, in <module>net = Net().to("cuda")File "E:\Anaconda\lib\site-packages\pysnooper\tracer.py", line 256, in simple_wrapperreturn function(*args, **kwargs)File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 70, in __init__self.features_size = self._get_final_flattened_size()File "E:\Anaconda\lib\site-packages\pysnooper\tracer.py", line 256, in simple_wrapperreturn function(*args, **kwargs)File "C:/Users/73416/PycharmProjects/HSIproject/test.py", line 57, in _get_final_flattened_sizex = self.pool1(self.conv1(x))File "E:\Anaconda\lib\site-packages\torch\nn\modules\module.py", line 489, in __call__result = self.forward(*input, **kwargs)File "E:\Anaconda\lib\site-packages\torch\nn\modules\conv.py", line 448, in forwardself.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the sameProcess finished with exit code 1
结果分析：
可以看到报错是：
究其原因是因为输入的x与模型net()所在的位置不同，一个在CPU，一个在GPU。
解决方式有两种：
- 将输入数据
x和模型net()的所在位置均设定为CPU。 - 将输入数据
x和模型net()的所在位置均设定为GPU。
本次的处理是将位置设定为CPU。（因为设定为GPU后仍报错，且报错信息不变）。
修改过的源码见“最终代码”部分。
最终代码1:
设置device = ‘cpu’：
这部分就是将模型和所用的网络的device均设定为‘cuda’。
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import torch
import os
from torchsummary import summary
import torchsnooperpatch_size = 17
batch_size = 20
x = torch.randn(batch_size,1,103,patch_size,patch_size,device='cpu')
# -----------------------自加在构建网络的情况下获得维度---------------------------
@torchsnooper.snoop()class Net(nn.Module):def _get_final_flattened_size(self):with torch.no_grad():x = torch.zeros((batch_size, 1, 103,patch_size, patch_size),device='cpu')x = self.pool1(self.conv1(x))x = self.pool2(self.conv2(x))x = self.conv3(x)_, t, c, w, h = x.size()return t * c * w * hdef __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1))self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1))self.conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0))self.pool1 = nn.MaxPool3d(2, stride = 2)self.pool2 = nn.MaxPool3d(2, stride = 2)self.features_size = self._get_final_flattened_size()self.fc = nn.Linear(self.features_size, 10)def forward(self,x):x = F.relu(self.conv1(x))x = self.pool1(x)x = F.relu(self.conv2(x))x = self.pool2(x)x = F.relu(self.conv3(x))x = x.view(-1, self.features_size)x = self.fc(x)return xdef print(self):print(self.features_size)net = Net()
summary(net,(1, 103, patch_size, patch_size),device='cpu')
net.print()
# -----------------------自加在构建网络的情况下获得维度---------------------------
# # torch.Size([10, 1, 103, 17, 17])
# # conv1: torch.Size([10, 32, 74, 16, 16])
# # pool1: torch.Size([10, 32, 37, 8, 8])
# # conv2: torch.Size([10, 64, 8, 6, 6])
# # pool2: torch.Size([10, 64, 4, 3, 3])
# # conv3: torch.Size([10, 128, 3, 1, 1])
# # features_size: 384
# # final_size: torch.Size([10, 10])
设置device = ‘cuda’：
这部分的操作是在网络Net的类的初始化中，对网络的层加上.cuda()的后缀，将网络层放到GPU上。
之前的报错原因是因为网络（或者说网络的层）在CPU而不在GPU。
当然这种操作我是第一次见（2019.9.20）
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import torch
import os
from torchsummary import summary
import torchsnooperpatch_size = 17
batch_size = 20
device = "cuda" if torch.cuda.is_available() else "cpu"x = torch.randn(batch_size,1,103,patch_size,patch_size,device=device)
# -----------------------自加在构建网络的情况下获得维度---------------------------
# @torchsnooper.snoop()class Net(nn.Module):@staticmethoddef weight_init(m):if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d):init.xavier_uniform_(m.weight.data)init.constant_(m.bias.data, 0)def _get_final_flattened_size(self):with torch.no_grad():x = torch.zeros((1, 1, 103,patch_size, patch_size),device=device)x = self.pool1(self.conv1(x))x = self.pool2(self.conv2(x))x = self.conv3(x)_, t, c, w, h = x.size()return t * c * w * hdef __init__(self):super(Net,self).__init__()self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1)).cuda()self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1)).cuda()self.conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0)).cuda()self.pool1 = nn.MaxPool3d(2, stride = 2).cuda()self.pool2 = nn.MaxPool3d(2, stride = 2).cuda()self.features_size = self._get_final_flattened_size()self.fc = nn.Linear(self.features_size, 10).cuda()self.apply(self.weight_init)def forward(self,x):x = F.relu(self.conv1(x))x = self.pool1(x)x = F.relu(self.conv2(x))x = self.pool2(x)x = F.relu(self.conv3(x))x = x.view(-1, self.features_size)x = self.fc(x)return xnet = Net()
net.to(device)print(net.to(device))# os.system('pause')summary(net.to(device),(1, 103, patch_size, patch_size),device=device)
# -----------------------自加在构建网络的情况下获得维度---------------------------
# -----------------------自加在构建网络的情况下获得维度---------------------------
# # torch.Size([10, 1, 103, 17, 17])
# # conv1: torch.Size([10, 32, 74, 16, 16])
# # pool1: torch.Size([10, 32, 37, 8, 8])
# # conv2: torch.Size([10, 64, 8, 6, 6])
# # pool2: torch.Size([10, 64, 4, 3, 3])
# # conv3: torch.Size([10, 128, 3, 1, 1])
# # features_size: 384
# # final_size: torch.Size([10, 10])
这里我要强调一点：这是我这么长时间以来（2019.9.20）第一次见在网络的类的初始化模块，就将在网络层的后面加上.cuda()。
运行结果：
设置device = ‘cpu’：
E:\Anaconda\python.exe C:/Users/73416/PycharmProjects/HSIproject/test.py
Done!
Source path:... C:/Users/73416/PycharmProjects/HSIproject/test.py
Starting var:.. self = REPR FAILED
Starting var:.. __class__ = <class '__main__.Net'>
14:08:20.721270 call 62 def __init__(self):
14:08:20.721270 line 63 super(Net, self).__init__()
Modified var:.. self = Net()
14:08:20.721270 line 64 self.conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1)))
14:08:20.767246 line 65 self.conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...(32, 5, 5), stride=(1, 1, 1), padding=(1, 1, 1)))
14:08:20.797150 line 66 self.conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0))
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...=(4, 3, 3), stride=(1, 1, 1), padding=(1, 0, 0)))
14:08:20.799145 line 67 self.pool1 = nn.MaxPool3d(2, stride = 2)
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))
14:08:20.799145 line 68 self.pool2 = nn.MaxPool3d(2, stride = 2)
14:08:20.799145 line 69 self.features_size = self._get_final_flattened_size()Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...tride=2, padding=0, dilation=1, ceil_mode=False))14:08:20.799145 call 52 def _get_final_flattened_size(self):14:08:20.799145 line 53 with torch.no_grad():14:08:20.800142 line 54 x = torch.zeros((batch_size, 1, 103,14:08:20.800142 line 55 patch_size, patch_size),device='cpu')New var:....... x = tensor<(20, 1, 103, 17, 17), float32, cpu>14:08:20.811084 line 56 x = self.pool1(self.conv1(x))Modified var:.. x = tensor<(20, 32, 37, 8, 8), float32, cpu>14:08:21.515198 line 57 x = self.pool2(self.conv2(x))Modified var:.. x = tensor<(20, 64, 4, 3, 3), float32, cpu>14:08:21.967987 line 58 x = self.conv3(x)Modified var:.. x = tensor<(20, 128, 3, 1, 1), float32, cpu>14:08:21.985939 line 59 _, t, c, w, h = x.size()New var:....... _ = 20New var:....... t = 128New var:....... c = 3New var:....... w = 1New var:....... h = 114:08:21.986969 line 60 return t * c * w * h14:08:21.986969 return 60 return t * c * w * hReturn value:.. 384
14:08:21.986969 line 70 self.fc = nn.Linear(self.features_size, 10)
Modified var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...ear(in_features=384, out_features=10, bias=True))
14:08:21.987934 return 70 self.fc = nn.Linear(self.features_size, 10)
Return value:.. None
Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...ear(in_features=384, out_features=10, bias=True))
Starting var:.. x = tensor<(2, 1, 103, 17, 17), float32, cpu>
14:08:22.012867 call 73 def forward(self,x):
14:08:22.013864 line 74 x = F.relu(self.conv1(x))
Modified var:.. x = tensor<(2, 32, 74, 16, 16), float32, cpu, grad>
14:08:22.148504 line 75 x = self.pool1(x)
Modified var:.. x = tensor<(2, 32, 37, 8, 8), float32, cpu, grad>
14:08:22.165459 line 76 x = F.relu(self.conv2(x))
Modified var:.. x = tensor<(2, 64, 8, 6, 6), float32, cpu, grad>
14:08:22.208344 line 77 x = self.pool2(x)
Modified var:.. x = tensor<(2, 64, 4, 3, 3), float32, cpu, grad>
14:08:22.209340 line 78 x = F.relu(self.conv3(x))
Modified var:.. x = tensor<(2, 128, 3, 1, 1), float32, cpu, grad>
14:08:22.210337 line 79 x = x.view(-1, self.features_size)
Modified var:.. x = tensor<(2, 384), float32, cpu, grad>
14:08:22.221308 line 80 x = self.fc(x)
Modified var:.. x = tensor<(2, 10), float32, cpu, grad>
14:08:22.238264 line 81 return x
14:08:22.239262 return 81 return x
Return value:.. tensor<(2, 10), float32, cpu, grad>
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv3d-1 [-1, 32, 74, 16, 16] 16,416MaxPool3d-2 [-1, 32, 37, 8, 8] 0Conv3d-3 [-1, 64, 8, 6, 6] 1,638,464MaxPool3d-4 [-1, 64, 4, 3, 3] 0Conv3d-5 [-1, 128, 3, 1, 1] 295,040Linear-6 [-1, 10] 3,850
================================================================
Total params: 1,953,770
Trainable params: 1,953,770
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.11
Forward/backward pass size (MB): 5.36
Params size (MB): 7.45
Estimated Total Size (MB): 12.93
----------------------------------------------------------------
384
Starting var:.. self = Net( (conv1): Conv3d(1, 32, kernel_size=(32, 4,...ear(in_features=384, out_features=10, bias=True))
14:08:22.299100 call 83 def print(self):
14:08:22.300098 line 84 print(self.features_size)
14:08:22.300098 return 84 print(self.features_size)
Return value:.. NoneProcess finished with exit code 0
#####设置device = ‘cuda’
E:\Anaconda\python.exe C:/Users/73416/PycharmProjects/HSIproject/test.py
Net((conv1): Conv3d(1, 32, kernel_size=(32, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1))(conv2): Conv3d(32, 64, kernel_size=(32, 5, 5), stride=(1, 1, 1), padding=(1, 1, 1))(conv3): Conv3d(64, 128, kernel_size=(4, 3, 3), stride=(1, 1, 1), padding=(1, 0, 0))(pool1): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(pool2): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(fc): Linear(in_features=384, out_features=10, bias=True)
)
----------------------------------------------------------------Layer (type) Output Shape Param #
================================================================Conv3d-1 [-1, 32, 74, 16, 16] 16,416MaxPool3d-2 [-1, 32, 37, 8, 8] 0Conv3d-3 [-1, 64, 8, 6, 6] 1,638,464MaxPool3d-4 [-1, 64, 4, 3, 3] 0Conv3d-5 [-1, 128, 3, 1, 1] 295,040Linear-6 [-1, 10] 3,850
================================================================
Total params: 1,953,770
Trainable params: 1,953,770
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.11
Forward/backward pass size (MB): 5.36
Params size (MB): 7.45
Estimated Total Size (MB): 12.93
----------------------------------------------------------------Process finished with exit code 0
代码备份：
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
import torch
import os
from torchsummary import summary
import torchsnooperpatch_size = 17
batch_size = 20
x = torch.randn(batch_size,1,103,patch_size,patch_size,device='cpu')
# -----------------------自加在不建立网络的情况下获得维度-------------------------
# conv1 = nn.Conv3d(1, 32, (32, 4, 4), padding=(1, 1, 1))
# conv2 = nn.Conv3d(32, 2*32, (32, 5, 5), padding=(1, 1, 1))
# conv3 = nn.Conv3d(2*32, 4*32, (4, 3, 3), padding=(1, 0, 0))
# pool1 = nn.MaxPool3d(2, stride = 2)
# pool2 = nn.MaxPool3d(2, stride = 2)
#
# def _get_final_flattened_size():
# with torch.no_grad():
# x = torch.zeros((batch_size, 1, 103,
# patch_size, patch_size))
# x = pool1(conv1(x))
# x = pool2(conv2(x))
# x = conv3(x)
# _, t, c, w, h = x.size()
# return t * c * w * h
#
# x = F.relu(conv1(x))
# print('conv1:', x.size())
# x = pool1(x)
# print('pool1:', x.size())
# x = F.relu(conv2(x))
# print('conv2:', x.size())
# x = pool2(x)
# print('pool2:', x.size())
# x = F.relu(conv3(x))
# print('conv3:', x.size())
# features_size = _get_final_flattened_size()
# print('features_size:', features_size)
# fc = nn.Linear(features_size, 10)
# x = x.view(-1, features_size)
# x = fc(x)
# print('final_size:', x.size())
print('Done!')
# -----------------------自加在不建立网络的情况下获得维度-------------------------
# # torch.Size([10, 1, 103, 17, 17])
# # conv1: torch.Size([10, 32, 74, 16, 16])
# # pool1: torch.Size([10, 32, 37, 8, 8])
# # conv2: torch.Size([10, 64, 8, 6, 6])
# # pool2: torch.Size([10, 64, 4, 3, 3])
# # conv3: torch.Size([10, 128, 3, 1, 1])
# # features_size: 384
# # final_size: torch.Size([10, 10])
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