Lesson 6: pets revisited

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%reload_ext autoreload
%autoreload 2
%matplotlib inline

from fastai.vision import *


    

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bs = 64


    

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path = untar_data(URLs.PETS)/'images'


    

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tfms = get_transforms(max_rotate=20, max_zoom=1.3, max_lighting=0.4, max_warp=0.4,
                      p_affine=1., p_lighting=1.)


    

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doc(get_transforms)


    

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src = ImageList.from_folder(path).split_by_rand_pct(0.2, seed=2)


    

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def get_data(size, bs, padding_mode='reflection'):
    return (src.label_from_re(r'([^/]+)_\d+.jpg$')
           .transform(tfms, size=size, padding_mode=padding_mode)
           .databunch(bs=bs).normalize(imagenet_stats))


    

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data = get_data(224, bs, 'zeros')


    

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def _plot(i,j,ax):
    x,y = data.train_ds[3]
    x.show(ax, y=y)

plot_multi(_plot, 3, 3, figsize=(8,8))


    

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data = get_data(224,bs)


    

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plot_multi(_plot, 3, 3, figsize=(8,8))


    

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gc.collect()
learn = cnn_learner(data, models.resnet34, metrics=error_rate, bn_final=True)


    

    
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29237

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learn.fit_one_cycle(3, slice(1e-2), pct_start=0.8)


    

    





Total time: 01:08 

  

    

      
epoch
      
train_loss
      
valid_loss
      
error_rate
      
time
    
  
  

    

      
0
      
2.458192
      
1.221388
      
0.276049
      
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1.467777
      
0.333426
      
0.078484
      
00:22
    
    

      
2
      
0.906456
      
0.259250
      
0.066982
      
00:22
    
  

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learn.unfreeze()
learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-3), pct_start=0.8)


    

    





Total time: 00:59 

  

    

      
epoch
      
train_loss
      
valid_loss
      
error_rate
      
time
    
  
  

    

      
0
      
0.708001
      
0.269306
      
0.058187
      
00:29
    
    

      
1
      
0.666836
      
0.267181
      
0.060217
      
00:29
    
  

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data = get_data(352,bs)
learn.data = data


    

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learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-4))


    

    





Total time: 02:15 

  

    

      
epoch
      
train_loss
      
valid_loss
      
error_rate
      
time
    
  
  

    

      
0
      
0.633500
      
0.261447
      
0.054804
      
01:08
    
    

      
1
      
0.595398
      
0.259402
      
0.051421
      
01:06
    
  

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learn.save('352')


    

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data = get_data(352,16)


    

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learn = cnn_learner(data, models.resnet34, metrics=error_rate, bn_final=True).load('352')


    

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idx=0
x,y = data.valid_ds[idx]
x.show()
data.valid_ds.y[idx]


    

    
Out[ ]:





Category newfoundland






    

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k = tensor([
    [0.  ,-5/3,1],
    [-5/3,-5/3,1],
    [1.  ,1   ,1],
]).expand(1,3,3,3)/6


    

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k


    

    
Out[ ]:





tensor([[[[ 0.0000, -0.2778,  0.1667],
          [-0.2778, -0.2778,  0.1667],
          [ 0.1667,  0.1667,  0.1667]],

         [[ 0.0000, -0.2778,  0.1667],
          [-0.2778, -0.2778,  0.1667],
          [ 0.1667,  0.1667,  0.1667]],

         [[ 0.0000, -0.2778,  0.1667],
          [-0.2778, -0.2778,  0.1667],
          [ 0.1667,  0.1667,  0.1667]]]])

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k.shape


    

    
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torch.Size([1, 3, 3, 3])

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t = data.valid_ds[0][0].data; t.shape


    

    
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torch.Size([3, 352, 352])

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t[None].shape


    

    
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torch.Size([1, 3, 352, 352])

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edge = F.conv2d(t[None], k)


    

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show_image(edge[0], figsize=(5,5));


    

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data.c


    

    
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37

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learn.model


    

    
Out[ ]:





Sequential(
  (0): Sequential(
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace)
    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (4): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicBlock(
        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (5): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (3): BasicBlock(
        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (6): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (3): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (4): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (5): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (7): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): BasicBlock(
        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
  )
  (1): Sequential(
    (0): AdaptiveConcatPool2d(
      (ap): AdaptiveAvgPool2d(output_size=1)
      (mp): AdaptiveMaxPool2d(output_size=1)
    )
    (1): Flatten()
    (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (3): Dropout(p=0.25)
    (4): Linear(in_features=1024, out_features=512, bias=True)
    (5): ReLU(inplace)
    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (7): Dropout(p=0.5)
    (8): Linear(in_features=512, out_features=37, bias=True)
    (9): BatchNorm1d(37, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
  )
)

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print(learn.summary())


    

    




======================================================================
Layer (type)         Output Shape         Param #    Trainable 
======================================================================
Conv2d               [64, 176, 176]       9,408      False     
______________________________________________________________________
BatchNorm2d          [64, 176, 176]       128        True      
______________________________________________________________________
ReLU                 [64, 176, 176]       0          False     
______________________________________________________________________
MaxPool2d            [64, 88, 88]         0          False     
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
ReLU                 [64, 88, 88]         0          False     
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
ReLU                 [64, 88, 88]         0          False     
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
ReLU                 [64, 88, 88]         0          False     
______________________________________________________________________
Conv2d               [64, 88, 88]         36,864     False     
______________________________________________________________________
BatchNorm2d          [64, 88, 88]         128        True      
______________________________________________________________________
Conv2d               [128, 44, 44]        73,728     False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
ReLU                 [128, 44, 44]        0          False     
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
Conv2d               [128, 44, 44]        8,192      False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
ReLU                 [128, 44, 44]        0          False     
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
ReLU                 [128, 44, 44]        0          False     
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
ReLU                 [128, 44, 44]        0          False     
______________________________________________________________________
Conv2d               [128, 44, 44]        147,456    False     
______________________________________________________________________
BatchNorm2d          [128, 44, 44]        256        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        294,912    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        32,768     False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
ReLU                 [256, 22, 22]        0          False     
______________________________________________________________________
Conv2d               [256, 22, 22]        589,824    False     
______________________________________________________________________
BatchNorm2d          [256, 22, 22]        512        True      
______________________________________________________________________
Conv2d               [512, 11, 11]        1,179,648  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
ReLU                 [512, 11, 11]        0          False     
______________________________________________________________________
Conv2d               [512, 11, 11]        2,359,296  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
Conv2d               [512, 11, 11]        131,072    False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
Conv2d               [512, 11, 11]        2,359,296  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
ReLU                 [512, 11, 11]        0          False     
______________________________________________________________________
Conv2d               [512, 11, 11]        2,359,296  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
Conv2d               [512, 11, 11]        2,359,296  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
ReLU                 [512, 11, 11]        0          False     
______________________________________________________________________
Conv2d               [512, 11, 11]        2,359,296  False     
______________________________________________________________________
BatchNorm2d          [512, 11, 11]        1,024      True      
______________________________________________________________________
AdaptiveAvgPool2d    [512, 1, 1]          0          False     
______________________________________________________________________
AdaptiveMaxPool2d    [512, 1, 1]          0          False     
______________________________________________________________________
Flatten              [1024]               0          False     
______________________________________________________________________
BatchNorm1d          [1024]               2,048      True      
______________________________________________________________________
Dropout              [1024]               0          False     
______________________________________________________________________
Linear               [512]                524,800    True      
______________________________________________________________________
ReLU                 [512]                0          False     
______________________________________________________________________
BatchNorm1d          [512]                1,024      True      
______________________________________________________________________
Dropout              [512]                0          False     
______________________________________________________________________
Linear               [37]                 18,981     True      
______________________________________________________________________
BatchNorm1d          [37]                 74         True      
______________________________________________________________________

Total params: 21,831,599
Total trainable params: 563,951
Total non-trainable params: 21,267,648

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m = learn.model.eval();


    

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xb,_ = data.one_item(x)
xb_im = Image(data.denorm(xb)[0])
xb = xb.cuda()


    

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from fastai.callbacks.hooks import *


    

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def hooked_backward(cat=y):
    with hook_output(m[0]) as hook_a: 
        with hook_output(m[0], grad=True) as hook_g:
            preds = m(xb)
            preds[0,int(cat)].backward()
    return hook_a,hook_g


    

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hook_a,hook_g = hooked_backward()


    

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acts  = hook_a.stored[0].cpu()
acts.shape


    

    
Out[ ]:





torch.Size([512, 11, 11])

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avg_acts = acts.mean(0)
avg_acts.shape


    

    
Out[ ]:





torch.Size([11, 11])

In [ ]:

    

def show_heatmap(hm):
    _,ax = plt.subplots()
    xb_im.show(ax)
    ax.imshow(hm, alpha=0.6, extent=(0,352,352,0),
              interpolation='bilinear', cmap='magma');


    

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show_heatmap(avg_acts)


    

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grad = hook_g.stored[0][0].cpu()
grad_chan = grad.mean(1).mean(1)
grad.shape,grad_chan.shape


    

    
Out[ ]:





(torch.Size([512, 11, 11]), torch.Size([512]))

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mult = (acts*grad_chan[...,None,None]).mean(0)


    

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show_heatmap(mult)


    

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fn = path/'../other/bulldog_maine.jpg' #Replace with your own image


    

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x = open_image(fn); x


    

    
Out[ ]:

In [ ]:

    

xb,_ = data.one_item(x)
xb_im = Image(data.denorm(xb)[0])
xb = xb.cuda()


    

In [ ]:

    

hook_a,hook_g = hooked_backward()


    

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acts = hook_a.stored[0].cpu()
grad = hook_g.stored[0][0].cpu()

grad_chan = grad.mean(1).mean(1)
mult = (acts*grad_chan[...,None,None]).mean(0)


    

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show_heatmap(mult)


    

In [ ]:

    

data.classes[0]


    

    
Out[ ]:





'Abyssinian'

In [ ]:

    

hook_a,hook_g = hooked_backward(0)


    

In [ ]:

    

acts = hook_a.stored[0].cpu()
grad = hook_g.stored[0][0].cpu()

grad_chan = grad.mean(1).mean(1)
mult = (acts*grad_chan[...,None,None]).mean(0)


    

In [ ]:

    

show_heatmap(mult)


    

In [ ]: