def log_weights_sparsity(self, model, epoch):
fname = self.get_fname("weights_sparsity")
with open(fname, "w") as csv_file:
params_size = 0
sparse_params_size = 0
writer = csv.writer(csv_file)
# write the header
writer.writerow([
"parameter", "shape", "volume", "sparse volume",
"sparsity level"
])
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
writer.writerow([
name,
size_to_str(param.size()),
torch.numel(param),
int(_density * param.numel()),
(1 - _density) * 100,
])
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 log_weights_sparsity(self, model, epoch):
with open(self.fname, 'w') as csv_file:
params_size = 0
sparse_params_size = 0
writer = csv.writer(csv_file)
# write the header
writer.writerow([
'parameter', 'shape', 'volume', 'sparse volume',
'sparsity level'
])
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
writer.writerow([
name,
size_to_str(param.size()),
torch.numel(param),
int(_density * param.numel()), (1 - _density) * 100
])
