Hello @ptrblck
I have a efficient issue with some tensor for loop.
I’m extracting the features from the last layer of a CNN through a image data loader (I’m using batch size 8). Im  getting the euclidean distance of the batch tensor and a table with previous features.
I want to add to the table a tensor every time all the tensors in the table are above a threshole. I have implemented a successful running code but the loop i use its not efficient and im wondering how i could do something similar using something more efficient rather than this secuential way.
for i, data in enumerate(dataloader, 0):
input, label = data
input, label = input.to(device), label.to(device)
n,c h,w = input.size()
outputs = model(input)
if (i == 0):
features_list = torch.cat( (features_list, outputs[0].view(1,-1)), 0)
dist_tensores = torch.cdist(outputs, features_list, p=2.0)
activation = torch.gt(dist_tensores, AVG, out=torch.cuda.FloatTensor(len(outputs), len(features_list)))
counter = len(features_list)
activation_list = torch.sum(activation, dim=0)
for x in range(len(activation)):
if (torch.sum(activation[x], dim=0) == counter):
features_list = torch.cat( (features_list, outputs[x].view(1,-1)), 0)
Any ideas?