Pooling over channels in pytorchPytorch maxpooling over channels dimensionPassing Individual Channels of Tensors to Layers in KerasWhat is output tensor of Max Pooling 2D Layer in TensorFlow?Pytorch maxpooling over channels dimensionDifferent behavior of gather() function as seen in tensorflow and pytorchtensorflow equivalent of torch.gatherpytorch equivalent tf.gatherPytorch Inner Product of 3D tensor with 1D Tensor to generate 2D TensorAdd channel to MNIST via transform?Concat tensors in PyTorchImport LSTM from Tensorflow to PyTorch by hand
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Pooling over channels in pytorch
Pytorch maxpooling over channels dimensionPassing Individual Channels of Tensors to Layers in KerasWhat is output tensor of Max Pooling 2D Layer in TensorFlow?Pytorch maxpooling over channels dimensionDifferent behavior of gather() function as seen in tensorflow and pytorchtensorflow equivalent of torch.gatherpytorch equivalent tf.gatherPytorch Inner Product of 3D tensor with 1D Tensor to generate 2D TensorAdd channel to MNIST via transform?Concat tensors in PyTorchImport LSTM from Tensorflow to PyTorch by hand
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
In tensorflow, I can pool over the depth dimension which would reduce the channels and leave the spatial dimensions unchanged. I'm trying to do the same in pytorch but the documentation seems to say pooling can only be done over the height and width dimensions. Is there a way I can pool over channels in pytorch?
I've a tensor of shape [1,512,50,50] I'm trying to use pooling to bring the number of channels down to 3.
I saw this question but did not find the answer helpful.
conv-neural-network pytorch max-pooling
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
add a comment |
In tensorflow, I can pool over the depth dimension which would reduce the channels and leave the spatial dimensions unchanged. I'm trying to do the same in pytorch but the documentation seems to say pooling can only be done over the height and width dimensions. Is there a way I can pool over channels in pytorch?
I've a tensor of shape [1,512,50,50] I'm trying to use pooling to bring the number of channels down to 3.
I saw this question but did not find the answer helpful.
conv-neural-network pytorch max-pooling
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19
add a comment |
In tensorflow, I can pool over the depth dimension which would reduce the channels and leave the spatial dimensions unchanged. I'm trying to do the same in pytorch but the documentation seems to say pooling can only be done over the height and width dimensions. Is there a way I can pool over channels in pytorch?
I've a tensor of shape [1,512,50,50] I'm trying to use pooling to bring the number of channels down to 3.
I saw this question but did not find the answer helpful.
conv-neural-network pytorch max-pooling
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
In tensorflow, I can pool over the depth dimension which would reduce the channels and leave the spatial dimensions unchanged. I'm trying to do the same in pytorch but the documentation seems to say pooling can only be done over the height and width dimensions. Is there a way I can pool over channels in pytorch?
I've a tensor of shape [1,512,50,50] I'm trying to use pooling to bring the number of channels down to 3.
I saw this question but did not find the answer helpful.
conv-neural-network pytorch max-pooling
conv-neural-network pytorch max-pooling
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
asked Mar 26 at 11:08
Judy T RajJudy T Raj
4861 gold badge6 silver badges20 bronze badges
4861 gold badge6 silver badges20 bronze badges
can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19
add a comment |
can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19
can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19
can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19
add a comment |
1 Answer
1
active
oldest
votes
The easiest way to reduce the number of channels is using a 1x1 kernel:
import torch
x = torch.rand(1, 512, 50, 50)
conv = torch.nn.Conv2d(512, 3, 1)
y = conv(x)
print(y.size())
# torch.Size([1, 3, 50, 50])
If you really need to perform pooling along the channels dimension due to some reason, you may want to permute the dimensions so that the channels dimension is swapped with some other dimension (e.g. width).
This idea was referenced here.
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
The easiest way to reduce the number of channels is using a 1x1 kernel:
import torch
x = torch.rand(1, 512, 50, 50)
conv = torch.nn.Conv2d(512, 3, 1)
y = conv(x)
print(y.size())
# torch.Size([1, 3, 50, 50])
If you really need to perform pooling along the channels dimension due to some reason, you may want to permute the dimensions so that the channels dimension is swapped with some other dimension (e.g. width).
This idea was referenced here.
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
add a comment |
The easiest way to reduce the number of channels is using a 1x1 kernel:
import torch
x = torch.rand(1, 512, 50, 50)
conv = torch.nn.Conv2d(512, 3, 1)
y = conv(x)
print(y.size())
# torch.Size([1, 3, 50, 50])
If you really need to perform pooling along the channels dimension due to some reason, you may want to permute the dimensions so that the channels dimension is swapped with some other dimension (e.g. width).
This idea was referenced here.
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
add a comment |
The easiest way to reduce the number of channels is using a 1x1 kernel:
import torch
x = torch.rand(1, 512, 50, 50)
conv = torch.nn.Conv2d(512, 3, 1)
y = conv(x)
print(y.size())
# torch.Size([1, 3, 50, 50])
If you really need to perform pooling along the channels dimension due to some reason, you may want to permute the dimensions so that the channels dimension is swapped with some other dimension (e.g. width).
This idea was referenced here.
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
The easiest way to reduce the number of channels is using a 1x1 kernel:
import torch
x = torch.rand(1, 512, 50, 50)
conv = torch.nn.Conv2d(512, 3, 1)
y = conv(x)
print(y.size())
# torch.Size([1, 3, 50, 50])
If you really need to perform pooling along the channels dimension due to some reason, you may want to permute the dimensions so that the channels dimension is swapped with some other dimension (e.g. width).
This idea was referenced here.
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
edited Mar 26 at 14:01
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
answered Mar 26 at 12:57
penkovskypenkovsky
5697 silver badges13 bronze badges
5697 silver badges13 bronze badges
add a comment |
add a comment |
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can you describe, mathematically, how you plan to reduce 512 numbers to just 3 (per channel)? dividing 512 to 3 groups and then taking the max over each group?
– Shai
Mar 26 at 12:19