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net_flops.py
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# __
# / *_)
# _.----. _ /../
# /............./
# __/..(...|.(...|
# /__.-|_|--|_|
#
# Christos Kyrkou, PhD
# 2019
# Estimator for model FLOPS in keras
# Use: net_flops(model, table=False)
#Supported Layers: Conv2D, DepthwiseConv2D, SeparableConv2D, Activation, BatchNormalization, InputLayer, Reshape, Add, Maximum,
# Concatenate, Average, pool, Flatten, Global Pooling,
def net_flops(model, table=False):
if (table == True):
print('%25s | %16s | %16s | %16s | %16s | %6s | %6s' % (
'Layer Name', 'Input Shape', 'Output Shape', 'Kernel Size', 'Filters', 'Strides', 'FLOPS'))
print('-' * 170)
t_flops = 0
t_macc = 0
for l in model.layers:
o_shape, i_shape, strides, ks, filters = ['', '', ''], ['', '', ''], [1, 1], [0, 0], [0, 0]
flops = 0
macc = 0
name = l.name
factor = 1
if ('InputLayer' in str(l)):
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = i_shape
if ('Reshape' in str(l)):
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = l.output.get_shape()[1:4].as_list()
if ('Add' in str(l) or 'Maximum' in str(l) or 'Concatenate' in str(l)):
i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)]
o_shape = l.output.get_shape()[1:4].as_list()
flops = (len(l.input) - 1) * i_shape[0] * i_shape[1] * i_shape[2]
if ('Average' in str(l) and 'pool' not in str(l)):
i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)]
o_shape = l.output.get_shape()[1:4].as_list()
flops = len(l.input) * i_shape[0] * i_shape[1] * i_shape[2]
if ('BatchNormalization' in str(l)):
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = l.output.get_shape()[1:4].as_list()
bflops = 1
for i in range(len(i_shape)):
bflops *= i_shape[i]
flops /= factor
if ('Activation' in str(l) or 'activation' in str(l)):
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = l.output.get_shape()[1:4].as_list()
bflops = 1
for i in range(len(i_shape)):
bflops *= i_shape[i]
flops /= factor
if ('pool' in str(l) and ('Global' not in str(l))):
i_shape = l.input.get_shape()[1:4].as_list()
strides = l.strides
ks = l.pool_size
flops = ((i_shape[0] / strides[0]) * (i_shape[1] / strides[1]) * (ks[0] * ks[1] * i_shape[2]))
if ('Flatten' in str(l)):
i_shape = l.input.shape[1:4].as_list()
flops = 1
out_vec = 1
for i in range(len(i_shape)):
flops *= i_shape[i]
out_vec *= i_shape[i]
o_shape = flops
flops = 0
if ('Dense' in str(l)):
print(l.input)
i_shape = l.input.shape[1:4].as_list()[0]
if (i_shape == None):
i_shape = out_vec
o_shape = l.output.shape[1:4].as_list()
flops = 2 * (o_shape[0] * i_shape)
macc = flops / 2
if ('Padding' in str(l)):
flops = 0
if (('Global' in str(l))):
i_shape = l.input.get_shape()[1:4].as_list()
flops = ((i_shape[0]) * (i_shape[1]) * (i_shape[2]))
o_shape = [l.output.get_shape()[1:4].as_list(), 1, 1]
out_vec = o_shape
if ('Conv2D ' in str(l) and 'DepthwiseConv2D' not in str(l) and 'SeparableConv2D' not in str(l)):
strides = l.strides
ks = l.kernel_size
filters = l.filters
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = l.output.get_shape()[1:4].as_list()
if (filters == None):
filters = i_shape[2]
flops = 2 * ((filters * ks[0] * ks[1] * i_shape[2]) * (
(i_shape[0] / strides[0]) * (i_shape[1] / strides[1])))
macc = flops / 2
if ('Conv2D ' in str(l) and 'DepthwiseConv2D' in str(l) and 'SeparableConv2D' not in str(l)):
strides = l.strides
ks = l.kernel_size
filters = l.filters
i_shape = l.input.get_shape()[1:4].as_list()
o_shape = l.output.get_shape()[1:4].as_list()
if (filters == None):
filters = i_shape[2]
flops = 2 * (
(ks[0] * ks[1] * i_shape[2]) * ((i_shape[0] / strides[0]) * (i_shape[1] / strides[1])))
macc = flops / 2
t_macc += macc
t_flops += flops
if (table == True):
print('%25s | %16s | %16s | %16s | %16s | %6s | %5.4f' % (
name, str(i_shape), str(o_shape), str(ks), str(filters), str(strides), flops))
t_flops = t_flops / factor
print('\nTotal FLOPS:', int(t_flops))
print('\nTotal MACCs:', int(t_macc))
return