def weights_sparsity_summary(model, opt=None):
try:
df = distiller.weights_sparsity_summary(
model.module, return_total_sparsity=True
)
except AttributeError:
df = distiller.weights_sparsity_summary(model, return_total_sparsity=True)
return df[0]["NNZ (dense)"].sum() // 2
def save_model_stats(model, save_path, name=None):
if not os.path.exists(save_path):
os.makedirs(save_path)
sparsity_df, _ = distiller.weights_sparsity_summary(model, True)
performance_df = distiller.model_performance_summary(
model, torch.rand([1, 3, 352, 352]), 1)
if name:
sparsity_df.to_csv('{}/{}_{}.sparsity.csv'.format(
save_path, model.name, name))
performance_df.to_csv('{}/{}_{}.performance.csv'.format(
save_path, model.name, name))
else:
sparsity_df.to_csv('{}/{}.sparsity.csv'.format(save_path, model.name))
performance_df.to_csv('{}/{}.performance.csv'.format(
save_path, model.name))
def sparsity_display(model, sparsity_file):
"""
分析网络权重的稀疏性, 就是权重中有多少0
Arguments:
model (class Net): 已训练好的模型 \n
sparsity_file (str): 存储稀疏性分析结果的文件名称和位置
Examples:
>>> from apputils.platform_summaries import *
>>> sparsity_display(model, 'spars_file.xlsx')
"""
df_sparsity = distiller.weights_sparsity_summary(model)
# Remove these two columns which contains uninteresting values
df_sparsity = df_sparsity.drop(['Cols (%)', 'Rows (%)'], axis=1)
df_sparsity.to_csv(sparsity_file)