from __future__ import absolute_import
import argparse
from analysis.CaffeA import *
from Caffe import caffe_net
from analysis.utils import save_csv
"""
Before you analyse your network, [Netscope](http://ethereon.github.io/netscope/#/editor)
is recommended to visiualize your network.
Command：`python caffe_analyser.py [-h] prototxt outdir shape`
- The prototxt is the path of the prototxt file.
- The outdir is path to save the csv file.
- The shape is the input shape of the network(split by comma `,`), image shape should be: channel, batch_size, image_height, image_width.
For example `python caffe_analyser.py resnet_18_deploy.prototxt analys_result.csv 1,3,224,224`
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('prototxt', help='path of the prototxt file', type=str)
parser.add_argument('outdir', help='path to save the csv file', type=str)
parser.add_argument('shape',
help='input shape of the network(split by comma `,`), image shape should be: batch,c,h,w',
type=str)
args = parser.parse_args()
shape = [int(i) for i in args.shape.split(',')]
net = caffe_net.Prototxt(args.prototxt)
blob_dict, layers = profiling(net, Blob(shape))
save_csv(layers, args.outdir)
'--shape',
help=
'input shape of the network(split by comma `,`), image shape should be: batch,c,h,w',
type=str)
parser.add_argument('--out',
help='path to save the csv file',
default='/tmp/pytorch_analyse.csv',
type=str)
parser.add_argument('--class_args',
help='args to init the class in python file',
default='',
type=str)
args = parser.parse_args()
path, filename = os.path.split(args.path)
filename = os.path.splitext(filename)[0]
sys.path.insert(0, path)
exec('from %s import %s as Net' % (filename, args.name))
if isinstance(Net, nn.Module):
net = Net
elif issubclass(Net, nn.Module):
net = Net(*args.class_args.split())
else:
assert (
"Error, The Net is not a instance of nn.Module or subclass of nn.Module"
)
shape = [int(i) for i in args.shape.split(',')]
x = Variable(torch.rand(shape))
blob_dict, layers = analyse(net, x)
save_csv(layers, args.out)
import argparse
from analysis.CaffeA import *
from Caffe import caffe_net
from analysis.utils import save_csv
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('prototxt', help='path of the prototxt file', type=str)
parser.add_argument('outdir', help='path to save the csv file', type=str)
parser.add_argument(
'shape',
help=
'input shape of the network(split by comma `,`), image shape should be: batch,h,w,c',
type=str)
args = parser.parse_args()
shape = [int(i) for i in args.shape.split(',')]
net = caffe_net.Prototxt(args.prototxt)
blob_dict, not_ref = profilling(net, Blob(shape))
save_csv(not_ref, args.outdir)
type=str)
parser.add_argument('--func_kwargs',
help='kwargs parse to the function, eg. a=1,b=2',
default='',
type=str)
args = parser.parse_args()
path, filename = os.path.split(args.path)
path = os.path.abspath(path)
print(path)
filename = os.path.splitext(filename)[0]
sys.path.insert(0, path)
exec('from %s import %s as sym' % (filename, args.name))
if isinstance(sym, mxnet.sym.Symbol):
sym = sym
elif hasattr(sym, '__call__'):
_kwargs = args.func_kwargs.split(',')
kwargs = []
for a in _kwargs:
if a == '':
continue
kwargs.append(a.split('='))
kwargs = dict(kwargs)
sym = sym(*args.func_args.split(), **kwargs)
else:
assert (
"Error, The sym is not a instance of mxnet.sym.Symbol or function")
shape = [int(i) for i in args.shape.split(',')]
profiling_symbol(sym, shape)
save_csv(tracked_layers, '/tmp/a.csv')
help=
'kwargs dict parse to the function, eg. --func_kwargs {a=1,c="OP"}',
default='',
type=str)
args = parser.parse_args()
path, filename = os.path.split(args.path)
path = os.path.abspath(path)
print(path)
filename = os.path.splitext(filename)[0]
sys.path.insert(0, path)
exec('from %s import %s as sym' % (filename, args.name))
if isinstance(sym, mxnet.sym.Symbol):
sym = sym
elif hasattr(sym, '__call__'):
if args.func_kwargs != '':
kwargs = eval(args.func_kwargs)
else:
kwargs = {}
if args.func_args != '':
func_args = eval(args.func_args)
else:
func_args = []
sym = sym(*func_args, **kwargs)
else:
assert (
"Error, The sym is not a instance of mxnet.sym.Symbol or function")
shape = [int(i) for i in args.shape.split(',')]
profiling_symbol(sym, shape)
save_csv(tracked_layers, '/tmp/mxnet_analyse.csv')