I have created network to classify emotions of the people using CAER dataset.
For the model I am using resnet architecture (not the pretrained one) and trying to save feature map for the last convolutional layer of last block.
I am able to save the maps but when I try to view them its quite difficult to interpret not sure whether this is expected or I am doing something wrong in either capturing or saving them.
Below is the code and feature maps.
Visualize code
writer = SummaryWriter('runs/caer')
class LayerActivations():
features=None
def __init__(self,model,layer_num):
self.hook = model.layer4[layer_num].conv2.register_forward_hook(self.hook_fn)
def hook_fn(self,module,input,output):
self.features = output.detach()
def remove(self):
self.hook.remove()
def imshow_image(image):
mean = torch.tensor([0.370, 0.225, 0.189])
std = torch.tensor([0.172, 0.130, 0.122])
for img,m,s in zip(image,mean,std):
img.mul_(s).add_(m)
writer.add_image('Image_for_activation',img)
writer.close()
def load_checkpoint(filepath):
checkpoint = torch.load(filepath)
model = checkpoint['model']
model.load_state_dict(checkpoint['state_dict'])
model.eval()
return model
def show_activation(train_loader,dev):
dataiter = iter(train_loader)
images,labels = dataiter.next()
imshow_image(images[0:2])
model = load_checkpoint('caer_model.pth')
features = model.module
conv_out = LayerActivations(features,1)
out_pred = model(images[0:2].to(dev))
#conv_out.remove()
act = conv_out.features
act = torch.nn.Upsample(size=(224,224),mode='bilinear')(act)
for i in range(512):
filename = './features/feature_'+str(i)+'.jpg'
img = act[0][i]
torchvision.utils.save_image(img, filename, nrow=8 )
Feature maps:-
Note:- I am able to post only one feature map.
