if "bn" not in name and "output" not in name:
if weights_vector is None:
weights_vector = param.flatten()
else:
weights_vector = torch.cat((weights_vector, param.flatten()),
0)
ewcData = maybe_cuda(ewcData, use_cuda=True)
loss = (lambd / 2) * torch.dot(ewcData[1],
(weights_vector - ewcData[0])**2)
return loss
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if __name__ == "__main__":
from models.mobilenet import MyMobilenetV1
model = MyMobilenetV1(pretrained=True)
replace_bn_with_brn(model, "net")
ewcData, synData = create_syn_data(model)
extract_weights(model, ewcData[0])
# do not remove this line
start_time = time.time()
# Create the dataset object
dataset = CORE50(
root='/home/admin/ssd_data/cvpr_competition/cvpr_competition_data/',
scenario=scenario,
preload=False)
preproc = preprocess_imgs
# Get the fixed test set
full_valdidset = dataset.get_full_valid_set()
# Model setup
model = MyMobilenetV1(pretrained=True, latent_layer_num=latent_layer_num)
replace_bn_with_brn(model,
momentum=init_update_rate,
r_d_max_inc_step=inc_step,
max_r_max=max_r_max,
max_d_max=max_d_max)
model.saved_weights = {}
model.past_j = {i: 0 for i in range(50)}
model.cur_j = {i: 0 for i in range(50)}
if reg_lambda != 0:
ewcData, synData = create_syn_data(model)
# Optimizer setup
optimizer = torch.optim.SGD(model.parameters(),
lr=init_lr,
momentum=momentum,