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
|
#!/usr/bin/env python3
# Copyright (c) 2021 Arm Limited.
#
# SPDX-License-Identifier: MIT
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import json
import logging
import os
import sys
from argparse import ArgumentParser
import tflite
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
from utils.model_identification import identify_model_type
from utils.tflite_helpers import tflite_op2acl, tflite_typecode2name, tflite_typecode2aclname
SUPPORTED_MODEL_TYPES = ["tflite"]
logger = logging.getLogger("report_model_ops")
def get_ops_types_from_tflite_graph(model):
"""
Helper function that extract operator related meta-data from a TFLite model
Parameters
----------
model: str
Respective TFLite model to analyse
Returns
----------
supported_ops, unsupported_ops, data_types: tuple
A tuple with the sets of unique operator types and data-types that are present in the model
"""
logger.debug(f"Analysing TFLite mode '{model}'!")
with open(model, "rb") as f:
buf = f.read()
model = tflite.Model.GetRootAsModel(buf, 0)
# Extract unique operators
nr_unique_ops = model.OperatorCodesLength()
unique_ops = {tflite.opcode2name(model.OperatorCodes(op_id).BuiltinCode()) for op_id in range(0, nr_unique_ops)}
# Extract IO data-types
supported_data_types = set()
unsupported_data_types = set()
for subgraph_id in range(0, model.SubgraphsLength()):
subgraph = model.Subgraphs(subgraph_id)
for tensor_id in range(0, subgraph.TensorsLength()):
try:
supported_data_types.add(tflite_typecode2aclname(subgraph.Tensors(tensor_id).Type()))
except ValueError:
unsupported_data_types.add(tflite_typecode2name(subgraph.Tensors(tensor_id).Type()))
logger.warning(f"Data type {tflite_typecode2name(subgraph.Tensors(tensor_id).Type())} is not supported by ComputeLibrary")
# Perform mapping between TfLite ops to ComputeLibrary ones
supported_ops = set()
unsupported_ops = set()
for top in unique_ops:
try:
supported_ops.add(tflite_op2acl(top))
except ValueError:
unsupported_ops.add(top)
logger.warning(f"Operator {top} does not have ComputeLibrary mapping")
return (supported_ops, unsupported_ops, supported_data_types, unsupported_data_types)
def extract_model_meta(model, model_type):
"""
Function that calls the appropriate model parser to extract model related meta-data
Supported parsers: TFLite
Parameters
----------
model: str
Path to model that we want to analyze
model_type:
type of the model
Returns
----------
ops, data_types: (tuple)
A tuple with the list of unique operator types and data-types that are present in the model
"""
if model_type == "tflite":
return get_ops_types_from_tflite_graph(model)
else:
logger.warning(f"Model type '{model_type}' is unsupported!")
return ()
def generate_build_config(ops, data_types, data_layouts):
"""
Function that generates a compatible ComputeLibrary operator-based build configuration
Parameters
----------
ops: set
Set with the operators to add in the build configuration
data_types:
Set with the data types to add in the build configuration
data_layouts:
Set with the data layouts to add in the build configuration
Returns
----------
config_data: dict
Dictionary compatible with ComputeLibrary
"""
config_data = {}
config_data["operators"] = list(ops)
config_data["data_types"] = list(data_types)
config_data["data_layouts"] = list(data_layouts)
return config_data
if __name__ == "__main__":
parser = ArgumentParser(
description="""Report map of operations in a list of models.
The script consumes deep learning models and reports the type of operations and data-types used
Supported model types: TFLite """
)
parser.add_argument(
"-m",
"--models",
nargs="+",
required=True,
type=str,
help=f"List of models; supported model types: {SUPPORTED_MODEL_TYPES}",
)
parser.add_argument("-D", "--debug", action="store_true", help="Enable script debugging output")
parser.add_argument(
"-c",
"--config",
type=str,
help="JSON configuration file used that can be used for custom ComputeLibrary builds",
)
args = parser.parse_args()
# Setup Logger
logging_level = logging.INFO
if args.debug:
logging_level = logging.DEBUG
logging.basicConfig(level=logging_level)
# Extract operator mapping
final_supported_ops = set()
final_unsupported_ops = set()
final_supported_dts = set()
final_unsupported_dts = set()
final_layouts = {"nhwc"} # Data layout for TFLite is always NHWC
for model in args.models:
logger.debug(f"Starting analyzing {model} model")
model_type = identify_model_type(model)
supported_model_ops, unsupported_mode_ops, supported_model_dts, unsupported_model_dts = extract_model_meta(model, model_type)
final_supported_ops.update(supported_model_ops)
final_unsupported_ops.update(unsupported_mode_ops)
final_supported_dts.update(supported_model_dts)
final_unsupported_dts.update(unsupported_model_dts)
logger.info("=== Supported Operators")
logger.info(final_supported_ops)
if(len(final_unsupported_ops)):
logger.info("=== Unsupported Operators")
logger.info(final_unsupported_ops)
logger.info("=== Data Types")
logger.info(final_supported_dts)
if(len(final_unsupported_dts)):
logger.info("=== Unsupported Data Types")
logger.info(final_unsupported_dts)
logger.info("=== Data Layouts")
logger.info(final_layouts)
# Generate JSON file
if args.config:
logger.debug("Generating JSON build configuration file")
config_data = generate_build_config(final_supported_ops, final_supported_dts, final_layouts)
with open(args.config, "w") as f:
json.dump(config_data, f)
|
