How visualise feature map in original size of input?

This snippet visualise the feature map after up2 layer (model was UNet).
First question is how can I display this in the original size of input image. (mapping output of activation in original size).
Second question is how can I get average of all activation and display one image with the original size of input image.

criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.00001)  # 1e-3

def visulization():  
    epoch = 100
    PATH = 'D:/Neda/Pytorch/U-net/plots_U_Net/UNet_exp3_epoch/UNet_exp3_epoch{}.pth'.format(epoch)
    checkpoint = torch.load(PATH)
    epoch = checkpoint['epoch']
    loss = checkpoint['loss']

    activations = {}
    test = {}

    def get_activation(name):
        def hook(model, input, output):
            activations[name] = output.detach()
            test = copy.deepcopy(activations)
        return hook  
    im_paths = []
    target_paths = []
    count = 0
    for i, data in enumerate(test_loader, 0):
            t_image, mask, im_path, target_path = data
            t_image, mask =,
            flattened_im_paths = [item for sublist in im_paths for item in sublist]
            flattened_target_paths = [item for sublist in target_paths for item in sublist]
            count +=1 
            print(count, os.path.basename(flattened_target_paths[i]))
            model.up2.register_forward_hook(get_activation('up2')) #torch.Size([1, 128, 120, 160])

            outputs = model(t_image)
            imgs = activations['up2'].squeeze().split(1, 0) # squeeze the batch dimension and use torch.split to get each channel separately

For example it produced for me this output:

each of this channel has size of [120, 160] but how can I display it in original size of image which is [240, 320] and how can I display one image which is average of all channel? (this layer had 128 channels however the above image displays 64 channels)

any comment would be appreciated.

Cannot answer about an average map. You can use torch.nn.functional. interpolate and bilinear modeto upsample images (per channel) up to original size.

It works quite well, I upsampled 8x8 to 224x224.

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@JuanFMontesinos How should I specify the size for interpolate? For example, the output of up2 layer is #torch.Size([1, 128, 120, 160]) so, it does have 128 channels, and batch is one. the question is what is should be size of input for interpolate to reach to the input original size which was [240, 320]?

#torch.Size([1, 128, 120, 160])
imgs = activations['up2'].squeeze().split(1, 0) 
imgs = torch.nn.functional.interpolate(imgs, size=None, mode='bilinear')

I think you have to specify the HxW size like:

imgs = torch.nn.functional.interpolate(imgs, size=(240,320), mode='bilinear')

It should deal with channels and batches but I’m not 100 % sure as I have no torch on this device. It deals with batches (at least) for sure.

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Thank you @JuanFMontesinos . it is causing an error

if input.dim() == 3 and mode == 'nearest': AttributeError: 'tuple' object has no attribute 'dim'

if I remove the torch.split like imgs = activations['up2'].squeeze() then, the size of img is torch.Size([128, 120, 160]) but it’s causing an error that Got 3D input, but bilinear mode needs 4D input

I’m running it and it works for me.

In [7]: o=it(torch.rand(1, 128, 120, 160),size=(240,320))                       

In [8]: o.size()                                                                
Out[8]: torch.Size([1, 128, 240, 320]

it calls


Which pytorch version are you using? (0.4.1 here)
I don’t know if they modified it in pytorch 1
I think you are putting size in first place, is it possible? the ordering is

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@JuanFMontesinos yes, you are right. thanks a lot. It fixed like this:

imgs = activations['up2']  
print(imgs.size()) #torch.Size([1, 128, 120, 160])
imgs = torch.nn.functional.interpolate(imgs, size=(240, 320), mode='bilinear')
print(imgs.size()) #torch.Size([1, 128, 240, 320])

how can I display the image after interpolation?

fig, axarr = plt.subplots(imgs.size(0))
for idx in range(imgs.size(0)):
       axarr[idx].imshow(imgs[idx].cpu().squeeze().numpy(), cmap = 'jet')
       save_results_to = 'D:/Neda/Pytorch/U-net/plots_U_Net/UNet_exp3_epoch/visulize_layers_epoch_100/new_plots/test_'         
       plt.savefig(save_results_to  + str(os.path.basename(flattened_im_paths[i])))

not it doesn’t let to plot. TypeError: 'AxesSubplot' object does not support indexing how can I plot all of the 128 channels?

Hi, you can you from torchvision.utils import make_grid
make_grid transforms a batch into a single image by concatenating them.
(Remember to convert the output into a numpy array)

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