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
|
"""Reader for training log.
See lib/Analysis/TrainingLogger.cpp for a description of the format.
"""
import ctypes
import dataclasses
import io
import json
import math
import sys
from typing import List, Optional
_element_types = {
"float": ctypes.c_float,
"double": ctypes.c_double,
"int8_t": ctypes.c_int8,
"uint8_t": ctypes.c_uint8,
"int16_t": ctypes.c_int16,
"uint16_t": ctypes.c_uint16,
"int32_t": ctypes.c_int32,
"uint32_t": ctypes.c_uint32,
"int64_t": ctypes.c_int64,
"uint64_t": ctypes.c_uint64,
}
@dataclasses.dataclass(frozen=True)
class TensorSpec:
name: str
port: int
shape: List[int]
element_type: type
@staticmethod
def from_dict(d: dict):
name = d["name"]
port = d["port"]
shape = [int(e) for e in d["shape"]]
element_type_str = d["type"]
if element_type_str not in _element_types:
raise ValueError(f"uknown type: {element_type_str}")
return TensorSpec(
name=name,
port=port,
shape=shape,
element_type=_element_types[element_type_str],
)
class TensorValue:
def __init__(self, spec: TensorSpec, buffer: bytes):
self._spec = spec
self._buffer = buffer
self._view = ctypes.cast(self._buffer, ctypes.POINTER(self._spec.element_type))
self._len = math.prod(self._spec.shape)
def spec(self) -> TensorSpec:
return self._spec
def __len__(self) -> int:
return self._len
def __getitem__(self, index):
if index < 0 or index >= self._len:
raise IndexError(f"Index {index} out of range [0..{self._len})")
return self._view[index]
def read_tensor(fs: io.BufferedReader, ts: TensorSpec) -> TensorValue:
size = math.prod(ts.shape) * ctypes.sizeof(ts.element_type)
data = fs.read(size)
return TensorValue(ts, data)
def pretty_print_tensor_value(tv: TensorValue):
print(f'{tv.spec().name}: {",".join([str(v) for v in tv])}')
def read_header(f: io.BufferedReader):
header = json.loads(f.readline())
tensor_specs = [TensorSpec.from_dict(ts) for ts in header["features"]]
score_spec = TensorSpec.from_dict(header["score"]) if "score" in header else None
advice_spec = TensorSpec.from_dict(header["advice"]) if "advice" in header else None
return tensor_specs, score_spec, advice_spec
def read_one_observation(
context: Optional[str],
event_str: str,
f: io.BufferedReader,
tensor_specs: List[TensorSpec],
score_spec: Optional[TensorSpec],
):
event = json.loads(event_str)
if "context" in event:
context = event["context"]
event = json.loads(f.readline())
observation_id = int(event["observation"])
features = []
for ts in tensor_specs:
features.append(read_tensor(f, ts))
f.readline()
score = None
if score_spec is not None:
score_header = json.loads(f.readline())
assert int(score_header["outcome"]) == observation_id
score = read_tensor(f, score_spec)
f.readline()
return context, observation_id, features, score
def read_stream(fname: str):
with io.BufferedReader(io.FileIO(fname, "rb")) as f:
tensor_specs, score_spec, _ = read_header(f)
context = None
while True:
event_str = f.readline()
if not event_str:
break
context, observation_id, features, score = read_one_observation(
context, event_str, f, tensor_specs, score_spec
)
yield context, observation_id, features, score
def main(args):
last_context = None
for ctx, obs_id, features, score in read_stream(args[1]):
if last_context != ctx:
print(f"context: {ctx}")
last_context = ctx
print(f"observation: {obs_id}")
for fv in features:
pretty_print_tensor_value(fv)
if score:
pretty_print_tensor_value(score)
if __name__ == "__main__":
main(sys.argv)
|
