In [1]:

#hide
from utils import *

CNN interpretation with CAM¶

CAM and hooks¶

In [2]:

path = untar_data(URLs.PETS)/'images'
def is_cat(x): return x[0].isupper()
dls = ImageDataLoaders.from_name_func(
    path, get_image_files(path), valid_pct=0.2, seed=42,
    label_func=is_cat, item_tfms=Resize(224))
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

epoch	train_loss	valid_loss	error_rate	time
0	0.166899	0.026620	0.005413	00:13

epoch	train_loss	valid_loss	error_rate	time
0	0.061859	0.009713	0.004060	00:18

In [3]:

img = PILImage.create('images/chapter1_cat_example.jpg')
x, = first(dls.test_dl([img]))

In [4]:

class Hook():
    def hook_func(self, m, i, o): self.stored = o.detach().clone()

In [5]:

hook_output = Hook()
hook = learn.model[0].register_forward_hook(hook_output.hook_func)

In [6]:

with torch.no_grad(): output = learn.model.eval()(x)

In [7]:

act = hook_output.stored[0]

In [14]:

F.softmax(output, dim=-1)

Out[14]:

tensor([[1.4325e-06, 1.0000e+00]], device='cuda:0')

In [15]:

dls.vocab

Out[15]:

(#2) [False,True]

In [8]:

x.shape

Out[8]:

torch.Size([1, 3, 224, 224])

In [9]:

cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, act)
cam_map.shape

Out[9]:

torch.Size([2, 7, 7])

In [18]:

x_dec = TensorImage(dls.train.decode((x,))[0][0])
_,ax = plt.subplots()
x_dec.show(ctx=ax)
ax.imshow(cam_map[0].detach().cpu(), alpha=0.6, extent=(0,224,224,0),
              interpolation='bilinear', cmap='magma');

No description has been provided for this image

In [19]:

_,ax = plt.subplots()
x_dec.show(ctx=ax)
ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,224,224,0),
              interpolation='bilinear', cmap='magma');

In [20]:

hook.remove()

In [21]:

class Hook():
    def __init__(self, m):
        self.hook = m.register_forward_hook(self.hook_func)   
    def hook_func(self, m, i, o): self.stored = o.detach().clone()
    def __enter__(self, *args): return self
    def __exit__(self, *args): self.hook.remove()

In [22]:

with Hook(learn.model[0]) as hook:
    with torch.no_grad(): output = learn.model.eval()(x.cuda())
    act = hook.stored

Gradient CAM¶

In [23]:

class HookBwd():
    def __init__(self, m):
        self.hook = m.register_backward_hook(self.hook_func)   
    def hook_func(self, m, gi, go): self.stored = go[0].detach().clone()
    def __enter__(self, *args): return self
    def __exit__(self, *args): self.hook.remove()

In [24]:

cls = 0
with HookBwd(learn.model[0]) as hookg:
    with Hook(learn.model[0]) as hook:
        output = learn.model.eval()(x.cuda())
        act = hook.stored
    output[0,cls].backward()
    grad = hookg.stored

In [25]:

w = grad[0].mean(dim=[1,2], keepdim=True)
cam_map = (w * act[0]).sum(0)

In [26]:

_,ax = plt.subplots()
x_dec.show(ctx=ax)
ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),
              interpolation='bilinear', cmap='magma');

In [27]:

with HookBwd(learn.model[0][-2]) as hookg:
    with Hook(learn.model[0][-2]) as hook:
        output = learn.model.eval()(x.cuda())
        act = hook.stored
    output[0,cls].backward()
    grad = hookg.stored

In [28]:

w = grad[0].mean(dim=[1,2], keepdim=True)
cam_map = (w * act[0]).sum(0)

In [29]:

_,ax = plt.subplots()
x_dec.show(ctx=ax)
ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),
              interpolation='bilinear', cmap='magma');

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