def log_weights_sparsity(self, model, epoch):
params_size = 0
sparse_params_size = 0
for name, param in model.state_dict().items():
if param.dim() in [2, 4]:
_density = density(param)
params_size += torch.numel(param)
sparse_params_size += param.numel() * _density
self.tblogger.scalar_summary('sparsity/weights/' + name,
sparsity(param)*100, epoch)
self.tblogger.scalar_summary('sparsity-2D/weights/' + name,
sparsity_2D(param)*100, epoch)
self.tblogger.scalar_summary("sparsity/weights/total", 100*(1 - sparse_params_size/params_size), epoch)
self.tblogger.sync_to_file()
def __activation_sparsity_cb(module, input, output):
"""Record the activation sparsity of 'module'
This is a callback from the forward() of 'module'.
"""
module.sparsity.add(sparsity(output.data))
