1. Seperate all the code and Test it on the device
Successfully I can get the result what we want, the benchmark, latency time. But even though I seperate the code, but it still looks complicated. 😂
Now I’m getting the benchmarks of 19200 samples (still running).
run_bazel.py
import os
import subprocess
import sqlite3
import numpy as np
import pandas as pd
def run_bazel(model_name):
proc = subprocess.Popen(
['sh', 'gsoc_proj/run.sh', './gsoc_proj/MODELS/{}'.format(model_name)],
stdout = subprocess.PIPE
)
out, err = proc.communicate()
latency_time = [float(i.replace('ms', '')) for i in out.decode('utf-8').split('\n')[-11:-1]] # only gpu inference latency time..
return sum(latency_time) / len(latency_time)
def test(model_name):
proc = subprocess.Popen(
['sh', 'gsoc_proj/run.sh', './gsoc_proj/MODELS/{}'.format(model_name)],
stdout = subprocess.PIPE
)
out, err = proc.communicate()
print(out)
print("\n", err)
return out
if __name__ == "__main__":
#sqlite3 connect
conn = sqlite3.connect('gsoc.db')
curr = conn.cursor()
# read model and run
path = os.path.dirname(os.path.abspath('__file__'))
path = os.path.join(path, 'gsoc_proj', 'MODELS')
model_list = os.listdir(path)
for model_name in model_list:
print(model_name, " bazel run start!")
latency = run_bazel(model_name)
print(latency)
sql = f"UPDATE gsoc SET latency_time={latency} WHERE tflite_name={model_name}"
curr.execute(sql)
generate_model.py
import os
import subprocess
import sqlite3
from itertools import product
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
kernel_list = [2*i+1 for i in range(10)]
filter_list = [2**i for i in range(4, 8)]
input_img_list = [(i, i, 3) for i in range(32, 512)]
def init():
conn = sqlite3.connect('gsoc.db', isolation_level=None)
cursor = conn.cursor()
cursor.execute(
"""CREATE TABLE gsoc(
tflite_name varchar,
kernel int,
filter int,
input_shape varchar,
latency_time varchar
)"""
)
conn.close()
def generate_model(kernel_list=kernel_list, filter_list=filter_list, input_img_list=input_img_list):
comb = list(product(kernel_list, filter_list, input_img_list))
conn = sqlite3.connect('gsoc.db', isolation_level=None)
curr = conn.cursor()
for com in comb:
_kernel, _filter, _input_shape = com
model = keras.Sequential(
keras.layers.Conv2D(filters=_filter, kernel_size=_kernel, input_shape=_input_shape,
padding='same', activation='relu')
)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
model_name = "kernel_{}_filter_{}_input_shape_{}.tflite".format(_kernel, _filter, _input_shape)
with open("./MODELS/" + model_name, 'wb') as f:
f.write(tflite_model)
sql = f'INSERT INTO gsoc VALUES(tflite_name={model_name}, {_kernel}, {_filter}, {_input_shape}, 0)'.replace('"', '')
try:
curr.execute('''CREATE TABLE gsoc(
tflite_name varchar(100),
kernel int,
filter int,
input_shape varchar(100),
latency_time varchar(100))
''')
except:
pass
finally:
try:
curr.execute(sql)
except:
print(sql)
break
conn.close()
if __name__ == "__main__":
generate_model()
2. How to get Conv2D Params
I checked the visualization.py from our slack channel.
import re
def CamelCaseToSnakeCase(camel_case_input):
"""Converts an identifier in CamelCase to snake_case."""
s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", camel_case_input)
return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower()
def FlatbufferToDict(fb, preserve_as_numpy):
"""Converts a hierarchy of FB objects into a nested dict.
We avoid transforming big parts of the flat buffer into python arrays. This
speeds conversion from ten minutes to a few seconds on big graphs.
Args:
fb: a flat buffer structure. (i.e. ModelT)
preserve_as_numpy: true if all downstream np.arrays should be preserved.
false if all downstream np.array should become python arrays
Returns:
A dictionary representing the flatbuffer rather than a flatbuffer object.
"""
if isinstance(fb, int) or isinstance(fb, float) or isinstance(fb, str):
return fb
elif hasattr(fb, "__dict__"):
result = {}
for attribute_name in dir(fb):
attribute = fb.__getattribute__(attribute_name)
if not callable(attribute) and attribute_name[0] != "_":
snake_name = CamelCaseToSnakeCase(attribute_name)
preserve = True if attribute_name == "buffers" else preserve_as_numpy
result[snake_name] = FlatbufferToDict(attribute, preserve)
return result
elif isinstance(fb, np.ndarray):
return fb if preserve_as_numpy else fb.tolist()
elif hasattr(fb, "__len__"):
return [FlatbufferToDict(entry, preserve_as_numpy) for entry in fb]
else:
return fb
def CreateDictFromFlatbuffer(buffer_data):
model_obj = schema_fb.Model.GetRootAsModel(buffer_data, 0)
model = schema_fb.ModelT.InitFromObj(model_obj)
return FlatbufferToDict(model, preserve_as_numpy=False)
data = CreateDictFromFlatbuffer(file_data)
print(data)
And below is the result.
{'buffers': [{'data': None},
{'data': array([154, 224, 234, 190, 186, 156, 181, 62, 165, 62, 10, 64, 67,
95, 193, 191, 132, 0, 133, 190, 96, 33, 18, 63, 241, 171,
57, 64, 119, 46, 77, 64, 170, 189, 109, 191, 212, 110, 50,
64, 59, 158, 35, 64, 81, 135, 219, 61, 76, 231, 72, 64,
81, 186, 66, 64, 238, 177, 23, 64, 84, 240, 179, 60, 197,
50, 56, 192, 168, 143, 29, 64, 29, 151, 81, 64, 70, 95,
86, 64, 18, 153, 31, 191, 162, 34, 202, 191, 1, 96, 233,
63, 124, 27, 16, 64, 91, 9, 127, 63, 220, 173, 67, 64,
32, 192, 17, 64, 201, 95, 8, 64, 67, 240, 89, 64, 80,
206, 30, 64, 187, 245, 232, 63, 140, 255, 134, 63], dtype=uint8)},
{'data': None},
{'data': array([ 72, 153, 63, ..., 232, 41, 59], dtype=uint8)},
{'data': None}],
'description': [84,
79,
67,
79,
32,
67,
111,
110,
118,
101,
114,
116,
101,
100,
46],
'metadata': None,
'metadata_buffer': None,
'operator_codes': [{'builtin_code': 0,
'custom_code': None,
'deprecated_builtin_code': 3,
'version': 1}],
'signature_defs': None,
'subgraphs': [{'inputs': [3],
'name': None,
'operators': [{'builtin_options': {'dilation_h_factor': 1,
'dilation_w_factor': 1,
'fused_activation_function': 3,
'padding': 0,
'stride_h': 2,
'stride_w': 2},
'builtin_options_type': 1,
'custom_options': None,
'custom_options_format': 0,
'inputs': [3, 0, 2],
'intermediates': None,
'mutating_variable_inputs': None,
'opcode_index': 0,
'outputs': [1]}],
'outputs': [1],
'tensors': [{'buffer': 3,
'is_variable': False,
'name': [77,
111,
98,
105,
108,
101,
110,
101,
116,
86,
49,
47,
67,
111,
110,
118,
50,
100,
95,
48,
47,
119,
101,
105,
103,
104,
116,
115],
'quantization': {'details': None,
'details_type': 0,
'max': None,
'min': None,
'quantized_dimension': 0,
'scale': None,
'zero_point': None},
'shape': [32, 3, 3, 3],
'shape_signature': None,
'sparsity': None,
'type': 0},
{'buffer': 2,
'is_variable': False,
'name': [77,
111,
98,
105,
108,
101,
110,
101,
116,
86,
49,
47,
77,
111,
98,
105,
108,
101,
110,
101,
116,
86,
49,
47,
67,
111,
110,
118,
50,
100,
95,
48,
47,
82,
101,
108,
117,
54],
'quantization': {'details': None,
'details_type': 0,
'max': None,
'min': None,
'quantized_dimension': 0,
'scale': None,
'zero_point': None},
'shape': [1, 112, 112, 32],
'shape_signature': None,
'sparsity': None,
'type': 0},
{'buffer': 1,
'is_variable': False,
'name': [77,
111,
98,
105,
108,
101,
110,
101,
116,
86,
49,
47,
77,
111,
98,
105,
108,
101,
110,
101,
116,
86,
49,
47,
67,
111,
110,
118,
50,
100,
95,
48,
47,
99,
111,
110,
118,
111,
108,
117,
116,
105,
111,
110,
95,
98,
105,
97,
115],
'quantization': {'details': None,
'details_type': 0,
'max': None,
'min': None,
'quantized_dimension': 0,
'scale': None,
'zero_point': None},
'shape': [32],
'shape_signature': None,
'sparsity': None,
'type': 0},
{'buffer': 4,
'is_variable': False,
'name': [105, 110, 112, 117, 116],
'quantization': {'details': None,
'details_type': 0,
'max': [255.0],
'min': [0.0],
'quantized_dimension': 0,
'scale': None,
'zero_point': None},
'shape': [1, 224, 224, 3],
'shape_signature': None,
'sparsity': None,
'type': 0}]}],
'version': 3}
I couldn’t go through all the codes in detail, but maybe I can get the conv2d params like stride, padding, etc.
Thinks to check
- SQLite3 Syntax Error
import sqlite3
conn = sqlite3.connect('gsoc.db')
curr = conn.cursor()
sql = "INSERT INTO gsoc VALUES(kernel_1_filter_16_input_shape_(32, 32, 3).tflite, 1, 16, (32, 32, 3), 0)"
curr.execute(sql)
>>>
Traceback (most recent call last):
File "generate_model.py", line 84, in <module>
generate_model()
File "generate_model.py", line 59, in generate_model
curr.execute(sql)
sqlite3.OperationalError: near ".": syntax error
-> But this is very small and minor error
- Finish making database
- Data Analysis
- Simple Regression Model