/
flops.py
140 lines (124 loc) · 6.33 KB
/
flops.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import numpy as np
from ..core import GraphWrapper, dygraph2program
__all__ = ["flops", "dygraph_flops"]
def flops(model, inputs=None, dtypes=None, only_conv=True, detail=False):
"""
Compute the FLOPs of nn.Layer of paddle.Program.
Args:
model(paddle.nn.Layer|paddle.static.Program): The target model.
inputs(list): It is only used when model is instance of 'paddle.nn.Layer'. The dummy inputs used for 'model.forward'. It can be:
1. list<int>|tuple<int>: means 'model.forward' accepts
only one variable as argument and the shape of
variable is 'inputs'.
2. list<list<list>>: means 'model.forward' accepts multiple
variables as arguments and the shapes of variables is 'inputs'.
3. others: 'inputs' will be used as argument list by calling
'model.forward(*inputs)'.
dtypes(str|list<str>): It only used when 'inputs' is shape or shapes that means
data type of each input. None means all the inputs is 'float32'.
Default: None.
only_conv(bool): Just return number of mul-adds in convolution and FC layer if `only_conv` is true.
default: True.
detail(bool): Whether to return detail of each convolution layer.
"""
if isinstance(model, paddle.static.Program):
return _static_flops(model, only_conv=only_conv, detail=detail)
elif isinstance(model, paddle.nn.Layer):
return dygraph_flops(
model, inputs, dtypes=dtypes, only_conv=only_conv, detail=detail)
def _static_flops(program, only_conv=True, detail=False):
"""Get FLOPs of target graph.
Args:
program(Program): The program used to calculate FLOPS.
only_conv(bool): Just return number of mul-adds in convolution and FC layer if `only_conv` is true.
default: True.
detail(bool): Whether to return detail of each convolution layer.
Returns:
int|tuple: If `detail` is true, then return a tuple in format `(FLOPs, details)`, otherwise it will just return `FlOPs`. The details is a dict whose key is the parameter name of convlution layer and value is the FLOPs of each convolution layer.
"""
graph = GraphWrapper(program)
return _graph_flops(graph, only_conv=only_conv, detail=detail)
def _graph_flops(graph, only_conv=True, detail=False):
assert isinstance(graph, GraphWrapper)
flops = 0
params2flops = {}
for op in graph.ops():
if op.type() in ['conv2d', 'depthwise_conv2d']:
filter_shape = op.inputs("Filter")[0].shape()
output_shape = op.outputs("Output")[0].shape()
c_out, c_in, k_h, k_w = filter_shape
_, _, h_out, w_out = output_shape
# c_in is the channel number of filter. It is (input_channel // groups).
kernel_ops = k_h * k_w * float(c_in)
if len(op.inputs("Bias")) > 0:
with_bias = 1
else:
with_bias = 0
op_flops = h_out * w_out * c_out * (kernel_ops + with_bias)
flops += op_flops
params2flops[op.inputs("Filter")[0].name()] = op_flops
elif op.type() == 'pool2d' and not only_conv:
output_shape = op.outputs("Out")[0].shape()
_, c_out, h_out, w_out = output_shape
k_size = op.attr("ksize")
if op.attr('pooling_type') == 'avg':
flops += (h_out * w_out * c_out * (k_size[0]**2) * 2)
elif op.type() in ['mul', 'matmul', 'matmul_v2']:
x_shape = list(op.inputs("X")[0].shape())
y_shape = op.inputs("Y")[0].shape()
if x_shape[0] == -1:
x_shape[0] = 1
op_flops = x_shape[0] * x_shape[1] * y_shape[1]
flops += op_flops
params2flops[op.inputs("Y")[0].name()] = op_flops
elif op.type() in ['relu', 'sigmoid', 'batch_norm', 'relu6'
] and not only_conv:
input_shape = list(op.inputs("X")[0].shape())
if input_shape[0] == -1:
input_shape[0] = 1
if op.type() == 'batch_norm':
op_flops = np.product(input_shape) * 2
else:
op_flops = np.product(input_shape)
flops += op_flops
if detail:
return flops, params2flops
else:
return flops
def dygraph_flops(model, inputs, dtypes=None, only_conv=False, detail=False):
"""
Compute the FLOPs of nn.Layer.
Args:
model(nn.Layer): The target model.
inputs(list): The dummy inputs used for 'model.forward'. It can be:
1. list<int>|tuple<int>: means 'model.forward' accepts
only one variable as argument and the shape of
variable is 'inputs'.
2. list<list<list>>: means 'model.forward' accepts multiple
variables as arguments and the shapes of variables is 'inputs'.
3. others: 'inputs' will be used as argument list by calling
'model.forward(*inputs)'.
dtypes(str|list<str>): It only used when 'inputs' is shape or shapes that means
data type of each input. None means all the inputs is 'float32'.
Default: None.
only_conv(bool): Just return number of mul-adds in convolution and FC layer if `only_conv` is true.
default: True.
detail(bool): Whether to return detail of each convolution layer.
"""
program = dygraph2program(model, inputs, dtypes=dtypes)
graph = GraphWrapper(program)
return _graph_flops(graph, only_conv=only_conv, detail=detail)