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
|
#include "caffe2/operators/summarize_op.h"
namespace caffe2 {
template <>
bool SummarizeOp<float, CPUContext>::RunOnDevice() {
auto& X = Input(0);
const auto N = X.numel();
CAFFE_ENFORCE_GT(N, 0);
const float* Xdata = X.data<float>();
double mean = 0;
float max = Xdata[0];
float min = Xdata[0];
for (auto i = 0; i < N; ++i) {
mean += static_cast<double>(Xdata[i]) / N;
max = std::max(max, Xdata[i]);
min = std::min(min, Xdata[i]);
}
// We will simply do a two-pass. More efficient solutions can be written but
// I'll keep code simple for now.
double standard_deviation = 0;
for (auto i = 0; i < N; ++i) {
double diff = Xdata[i] - mean;
standard_deviation += diff * diff;
}
// Unbiased or biased? Let's do unbiased now.
standard_deviation = N == 1 ? 0 : std::sqrt(standard_deviation / (N - 1));
if (to_file_) {
(*log_file_) << min << " " << max << " " << mean << " "
<< standard_deviation << std::endl;
}
if (OutputSize()) {
auto* Y = Output(0, {NUM_STATS}, at::dtype<float>());
float* Ydata = Y->template mutable_data<float>();
Ydata[MIN_IDX] = min;
Ydata[MAX_IDX] = max;
Ydata[MEAN_IDX] = static_cast<float>(mean);
Ydata[STD_IDX] = static_cast<float>(standard_deviation);
}
return true;
}
REGISTER_CPU_OPERATOR(Summarize, SummarizeOp<float, CPUContext>);
// Input: X; output: if set, a summarized Tensor of shape 4, with the values
// being min, max, mean and std respectively.
OPERATOR_SCHEMA(Summarize)
.NumInputs(1)
.NumOutputs(0, 1)
.SetDoc(R"DOC(
Summarize computes four statistics of the input tensor (Tensor)- min,
max, mean and standard deviation. The output will be written to a 1-D tensor of
size 4 if an output tensor is provided. Else, if the argument 'to_file' is
greater than 0, the values are written to a log file in the root folder.
)DOC")
.Arg(
"to_file",
"(int, default 0) flag to indicate if the summarized "
"statistics have to be written to a log file.")
.Input(0, "data", "The input data as Tensor.")
.Output(
0,
"output",
"1-D tensor (Tensor) of size 4 containing min, "
"max, mean and standard deviation");
SHOULD_NOT_DO_GRADIENT(Summarize);
} // namespace caffe2
|
