def module_visitor(self, input, output, df, model, weights_vol, macs, attrs=None):
in_features_shape = input[0].size()
out_features_shape = output.size()
mod_name = distiller.model_find_module_name(model, self)
df.loc[len(df.index)] = ([mod_name, self.__class__.__name__,
attrs if attrs is not None else '',
distiller.size_to_str(in_features_shape), distiller.volume(input[0]),
distiller.size_to_str(out_features_shape), distiller.volume(output),
int(weights_vol), int(macs)])
def module_visitor(self,
input,
output,
df,
model,
weights_vol,
macs,
attrs=None):
in_features_shape = input[0].size()
out_features_shape = output.size()
param_name = distiller.model_find_param_name(model, self.weight.data)
if param_name is None:
return
mod_name = param_name[:param_name.find(".weight")]
df.loc[len(df.index)] = ([
mod_name, self.__class__.__name__, attrs if attrs is not None else '',
distiller.size_to_str(in_features_shape),
distiller.volume(input[0]),
distiller.size_to_str(out_features_shape),
distiller.volume(output), weights_vol,
int(macs)
])
def weights_sparsity_summary(model,
return_total_sparsity=False,
param_dims=[2, 4]):
df = pd.DataFrame(columns=[
'Name', 'Shape', 'NNZ (dense)', 'NNZ (sparse)', 'Cols (%)', 'Rows (%)',
'Ch (%)', '2D (%)', '3D (%)', 'Fine (%)', 'Std', 'Mean', 'Abs-Mean'
])
pd.set_option('precision', 2)
params_size = 0
sparse_params_size = 0
summary_param_types = ['weight', 'bias']
for name, param in model.state_dict().items():
# Extract just the actual parameter's name, which in this context we treat as its "type"
curr_param_type = name.split('.')[-1]
if param.dim(
) in param_dims and curr_param_type in summary_param_types:
_density = distiller.density(param)
params_size += torch.numel(param)
sparse_params_size += param.numel() * _density
df.loc[len(df.index)] = ([
name,
distiller.size_to_str(param.size()),
torch.numel(param),
int(_density * param.numel()),
distiller.sparsity_cols(param) * 100,
distiller.sparsity_rows(param) * 100,
distiller.sparsity_ch(param) * 100,
distiller.sparsity_2D(param) * 100,
distiller.sparsity_3D(param) * 100, (1 - _density) * 100,
param.std().item(),
param.mean().item(),
param.abs().mean().item()
])
total_sparsity = (1 - sparse_params_size / params_size) * 100
df.loc[len(df.index)] = ([
'Total sparsity:', '-', params_size,
int(sparse_params_size), 0, 0, 0, 0, 0, total_sparsity, 0, 0, 0
])
if return_total_sparsity:
return df, total_sparsity
return df
def weights_sparsity_summary(model, return_total_sparsity=False, param_dims=[2,4]):
df = pd.DataFrame(columns=['Name', 'Shape', 'NNZ (dense)', 'NNZ (sparse)',
'Cols (%)','Rows (%)', 'Ch (%)', '2D (%)', '3D (%)',
'Fine (%)', 'Std', 'Mean', 'Abs-Mean'])
pd.set_option('precision', 2)
params_size = 0
sparse_params_size = 0
for name, param in model.state_dict().items():
if (param.dim() in param_dims) and any(type in name for type in ['weight', 'bias']):
_density = distiller.density(param)
params_size += torch.numel(param)
sparse_params_size += param.numel() * _density
df.loc[len(df.index)] = ([
name,
distiller.size_to_str(param.size()),
torch.numel(param),
int(_density * param.numel()),
distiller.sparsity_cols(param)*100,
distiller.sparsity_rows(param)*100,
distiller.sparsity_ch(param)*100,
distiller.sparsity_2D(param)*100,
distiller.sparsity_3D(param)*100,
(1-_density)*100,
param.std().item(),
param.mean().item(),
param.abs().mean().item()
])
total_sparsity = (1 - sparse_params_size/params_size)*100
df.loc[len(df.index)] = ([
'Total sparsity:',
'-',
params_size,
int(sparse_params_size),
0, 0, 0, 0, 0,
total_sparsity,
0, 0, 0])
if return_total_sparsity:
return df, total_sparsity
return df
def weights_sparsity_summary(model,
return_total_sparsity=False,
param_dims=[2, 4]):
df = pd.DataFrame(columns=[
"Name",
"Shape",
"NNZ (dense)",
"NNZ (sparse)",
"Cols (%)",
"Rows (%)",
"Ch (%)",
"2D (%)",
"3D (%)",
"Fine (%)",
"Std",
"Mean",
"Abs-Mean",
])
pd.set_option("precision", 2)
params_size = 0
sparse_params_size = 0
for name, param in model.state_dict().items():
# Extract just the actual parameter's name, which in this context we treat as its "type"
if param.dim() in param_dims and any(type in name
for type in ["weight", "bias"]):
_density = distiller.density(param)
params_size += torch.numel(param)
sparse_params_size += param.numel() * _density
df.loc[len(df.index)] = [
name,
distiller.size_to_str(param.size()),
torch.numel(param),
int(_density * param.numel()),
distiller.sparsity_cols(param) * 100,
distiller.sparsity_rows(param) * 100,
distiller.sparsity_ch(param) * 100,
distiller.sparsity_2D(param) * 100,
distiller.sparsity_3D(param) * 100,
(1 - _density) * 100,
param.std().item(),
param.mean().item(),
param.abs().mean().item(),
]
total_sparsity = (1 - sparse_params_size / params_size) * 100
df.loc[len(df.index)] = [
"Total sparsity:",
"-",
params_size,
int(sparse_params_size),
0,
0,
0,
0,
0,
total_sparsity,
0,
0,
0,
]
if return_total_sparsity:
return df, total_sparsity
return df
