https://github.com/fg91/visualizing-cnn-feature-maps
“There are two main ways to try to understand how a neural network recognizes a certain pattern. If you want to know what kind of pattern significantly activates a certain feature map you could 1) either try to find images in a dataset that result in a high average activation of this feature map or you could 2) try to generate such a pattern by optimizing the pixel values in a random image. The latter idea was proposed by Erhan et al. 2009 “
from: https://towardsdatascience.com/how-to-visualize-convolutional-features-in-40-lines-of-code-70b7d87b0030
class FilterVisualizer():
def __init__(self, size=56, upscaling_steps=12, upscaling_factor=1.2):
self.size, self.upscaling_steps, self.upscaling_factor = size, upscaling_steps, upscaling_factor
self.model = vgg16(pre=True).cuda().eval()
set_trainable(self.model, False)
def visualize(self, layer, filter, lr=0.1, opt_steps=20, blur=None):
sz = self.size
img = np.uint8(np.random.uniform(150, 180, (sz, sz, 3)))/255 # generate random image
activations = SaveFeatures(list(self.model.children())[layer]) # register hook
for _ in range(self.upscaling_steps): # scale the image up upscaling_steps times
train_tfms, val_tfms = tfms_from_model(vgg16, sz)
img_var = V(val_tfms(img)[None], requires_grad=True) # convert image to Variable that requires grad
optimizer = torch.optim.Adam([img_var], lr=lr, weight_decay=1e-6)
for n in range(opt_steps): # optimize pixel values for opt_steps times
optimizer.zero_grad()
self.model(img_var)
loss = -activations.features[0, filter].mean()
loss.backward()
optimizer.step()
img = val_tfms.denorm(img_var.data.cpu().numpy()[0].transpose(1,2,0))
self.output = img
sz = int(self.upscaling_factor * sz) # calculate new image size
img = cv2.resize(img, (sz, sz), interpolation = cv2.INTER_CUBIC) # scale image up
if blur is not None: img = cv2.blur(img,(blur,blur)) # blur image to reduce high frequency patterns
self.save(layer, filter)
activations.close()
def save(self, layer, filter):
plt.imsave("layer_"+str(layer)+"_filter_"+str(filter)+".jpg", np.clip(self.output, 0, 1))
and use it like this:
layer = 40 filter = 265 FV = FilterVisualizer(size=56, upscaling_steps=12, upscaling_factor=1.2) FV.visualize(layer, filter, blur=5)