ResNet + Flatten - "number of dims don't match in permute"

Discussion started in #2. `plot_gcam` does not work with a CNN using a pre-trained ResNet-50. There are two issues that arise. This occurs with the following versions:

- fastai==2.2.5
- fastcore==1.3.19
- fastai-amalgam==0.0.26
- torch==1.7.0+cu101
- torchvision==0.8.1+cu101
- using Google Colab with no GPU

## 1. Missing 1 required positional argument: 'losses'

This code:

    interp = ClassificationInterpretationEx.from_learner(learn)

Results in this error:

    TypeError: __init__() missing 1 required positional argument: 'losses'

The same error happens when you use `ClassificationInterpretation` from the base fastai class. The learner does have a `loss_func` attached, which is `FlattenedLoss of CrossEntropyLoss()`.

## 2. Number of dims don't match in permute

When trying to replicate the code in [interpret_gradcam.ipynb](https://github.com/Synopsis/amalgam/blob/master/nbs/interpret_gradcam.ipynb), the `plot_gcam` function gives the following error:

![](https://user-images.githubusercontent.com/7775919/106608332-45474180-6532-11eb-991c-ab298e09c309.png)

The same error happens when using any of the following:

* `learn.gradcam('File_008.jpeg', target_layer = learn.model[1][0])` (the adaptive pooling layer of the last `Sequential`)
* `learn.gradcam('File_008.jpeg', target_layer = learn.model[0][-1])` (the last `Sequential` of the first `Sequential`, which contains the final conv layer
* `learn.gradcam('File_008.jpeg', target_layer = learn.model[0][-1][-1])` (the last `Bottleneck` of the last `Sequential`)
* `learn.gradcam('File_008.jpeg', target_layer = learn.model[0][-1][-1].conv3)` (the final conv layer)

And the following results in various `Dimension out of range (expected to be in range of [-1, 0], but got 1)` errors:

* `learn.gradcam('File_008.jpeg', target_layer = learn.model[1][1])` (the `Flatten` layer after the last conv layer
* `learn.gradcam('File_008.jpeg', target_layer = learn.model[1][8])` (the final `Linear` layer

## Network structure
 
    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=True)
        (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (4): Sequential(
        (0): Bottleneck(
            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
        )
        (1): Bottleneck(
            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (2): Bottleneck(
            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        )
        (5): Sequential(
        (0): Bottleneck(
            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=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): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (2): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (3): Bottleneck(
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        )
        (6): Sequential(
        (0): Bottleneck(
            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
        )
        (1): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (2): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (3): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (4): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (5): Bottleneck(
            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        )
        (7): Sequential(
        (0): Bottleneck(
            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
            (downsample): Sequential(
            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            )
        )
        (1): Bottleneck(
            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        (2): Bottleneck(
            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (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)
            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu): ReLU(inplace=True)
        )
        )
    )
    (1): Sequential(
        (0): AdaptiveConcatPool2d(
        (ap): AdaptiveAvgPool2d(output_size=1)
        (mp): AdaptiveMaxPool2d(output_size=1)
        )
        (1): Flatten(full=False)
        (2): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (3): Dropout(p=0.25, inplace=False)
        (4): Linear(in_features=4096, out_features=512, bias=False)
        (5): ReLU(inplace=True)
        (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (7): Dropout(p=0.5, inplace=False)
        (8): Linear(in_features=512, out_features=150, bias=False)
    )
    )

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ResNet + Flatten - "number of dims don't match in permute" #3

1. Missing 1 required positional argument: 'losses'

2. Number of dims don't match in permute

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ResNet + Flatten - "number of dims don't match in permute" #3

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1. Missing 1 required positional argument: 'losses'

2. Number of dims don't match in permute

Network structure

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

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