forked from sunshines14/DCASE2021
-
Notifications
You must be signed in to change notification settings - Fork 0
/
model_size.py
251 lines (194 loc) · 7.2 KB
/
model_size.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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
import numpy
import dcase_util
import tensorflow as tf
import sys
def get_keras_model_size(keras_model, verbose=True, ui=None):
"""Calculate keras model size (non-zero parameters on disk)
Parameters
----------
keras_model : keras.models.Model
keras model for the size calculation
verbose : bool
Print layer by layer information
Default value True
ui : dcase_util.ui.FancyLogger or dcase_util.ui.FancyPrinter
Print handler
Default value None
excluded_layers : list
List of layers to be excluded from the calculation
Default value [keras.layers.normalization.BatchNormalization, kapre.time_frequency.Melspectrogram]
Returns
-------
nothing
"""
parameters_count = 0
parameters_count_nonzero = 0
parameters_bytes = 0
parameters_bytes_nonzero = 0
if verbose and ui is None:
# Initialize print handler
ui = dcase_util.ui.ui.FancyPrinter()
if verbose:
# Set up printing
ui.row_reset()
ui.row(
'Name', 'Param', 'NZ Param', 'Size', 'NZ Size',
widths=[30, 12, 12, 30, 30],
types=['str', 'int', 'int', 'str', 'str'],
separators=[True, False, True, False]
)
ui.row_sep()
for l in keras_model.layers:
# Loop layer by layer
current_parameters_count = 0
current_parameters_count_nonzero = 0
current_parameters_bytes = 0
current_parameters_bytes_nonzero = 0
weights = l.get_weights()
for w in weights:
current_parameters_count += numpy.prod(w.shape)
current_parameters_count_nonzero += numpy.count_nonzero(w.flatten())
if w.dtype in ['single', 'float32', 'int32', 'uint32']:
bytes = 32 / 8
elif w.dtype in ['float16', 'int16', 'uint16']:
bytes = 16 / 8
elif w.dtype in ['double', 'float64', 'int64', 'uint64']:
bytes = 64 / 8
elif w.dtype in ['int8', 'uint8']:
bytes = 8 / 8
else:
print('UNKNOWN TYPE', w.dtype)
current_parameters_bytes += numpy.prod(w.shape) * bytes
current_parameters_bytes_nonzero += numpy.count_nonzero(w.flatten()) * bytes
parameters_count += current_parameters_count
parameters_count_nonzero += current_parameters_count_nonzero
parameters_bytes += current_parameters_bytes
parameters_bytes_nonzero += current_parameters_bytes_nonzero
if verbose:
ui.row(
l.name,
current_parameters_count,
current_parameters_count_nonzero,
dcase_util.utils.get_byte_string(current_parameters_bytes, show_bytes=False),
dcase_util.utils.get_byte_string(current_parameters_bytes_nonzero, show_bytes=False),
)
if verbose:
ui.row_sep()
ui.row(
'Total',
parameters_count,
parameters_count_nonzero,
dcase_util.utils.get_byte_string(parameters_bytes, show_bytes=True),
dcase_util.utils.get_byte_string(parameters_bytes_nonzero, show_bytes=True),
)
ui.line()
return {
'parameters': {
'all': {
'count': parameters_count,
'bytes': parameters_bytes
},
'non_zero': {
'count': parameters_count_nonzero,
'bytes': parameters_bytes_nonzero
}
}
}
def get_lite_model_size(interpreter, verbose=True, ui=None):
"""Calculate TFLite model size (non-zero parameters on disk)
Parameters
----------
interpreter : TFLite model
for the size calculation
verbose : bool
Print layer by layer information
Default value True
ui : dcase_util.ui.FancyLogger or dcase_util.ui.FancyPrinter
Print handler
Default value None
Returns
-------
nothing
"""
parameters_count = 0
parameters_count_nonzero = 0
parameters_bytes = 0
parameters_bytes_nonzero = 0
if verbose and ui is None:
# Initialize print handler
ui = dcase_util.ui.ui.FancyPrinter()
if verbose:
# Set up printing
ui.row_reset()
ui.row(
'Name', 'Param', 'NZ Param', 'Size', 'NZ Size',
widths=[30, 12, 12, 30, 30],
types=['str', 'int', 'int', 'str', 'str'],
separators=[True, False, True, False]
)
ui.row_sep()
layer_details = interpreter.get_tensor_details()
interpreter.allocate_tensors()
for l in layer_details:
# Loop layer by layer
current_parameters_count = 0
current_parameters_count_nonzero = 0
current_parameters_bytes = 0
current_parameters_bytes_nonzero = 0
w = interpreter.get_tensor(l['index'])
# Calculate only the quantize layers
if l['dtype'] is not None:
current_parameters_count += numpy.prod(w.shape)
current_parameters_count_nonzero += numpy.count_nonzero(w.flatten())
if w.dtype in ['single', 'float32', 'int32', 'uint32']:
bytes = 32 / 8
elif w.dtype in ['float16', 'int16', 'uint16']:
bytes = 16 / 8
elif w.dtype in ['double', 'float64', 'int64', 'uint64']:
bytes = 64 / 8
elif w.dtype in ['int8', 'uint8']:
bytes = 8 / 8
else:
print('UNKNOWN TYPE', w.dtype)
current_parameters_bytes += numpy.prod(w.shape) * bytes
current_parameters_bytes_nonzero += numpy.count_nonzero(w.flatten()) * bytes
# Add to total
parameters_count += current_parameters_count
parameters_count_nonzero += current_parameters_count_nonzero
parameters_bytes += current_parameters_bytes
parameters_bytes_nonzero += current_parameters_bytes_nonzero
if verbose:
ui.row(
l['name'].split('/')[-1],
current_parameters_count,
current_parameters_count_nonzero,
dcase_util.utils.get_byte_string(current_parameters_bytes, show_bytes=False),
dcase_util.utils.get_byte_string(current_parameters_bytes_nonzero, show_bytes=False))
if verbose:
ui.row_sep()
ui.row(
'Total',
parameters_count,
parameters_count_nonzero,
dcase_util.utils.get_byte_string(parameters_bytes, show_bytes=True),
dcase_util.utils.get_byte_string(parameters_bytes_nonzero, show_bytes=True),
)
ui.line()
return {
'parameters': {
'all': {
'count': parameters_count,
'bytes': parameters_bytes
},
'non_zero': {
'count': parameters_count_nonzero,
'bytes': parameters_bytes_nonzero
}
}
}
hdf5_path = sys.argv[1]
tflite_path = sys.argv[2]
model = tf.keras.models.load_model(hdf5_path)
interpreter = tf.lite.Interpreter(model_path=tflite_path)
get_keras_model_size(model)
get_lite_model_size(interpreter)