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
|
#include "caffe2/operators/quantized/int8_average_pool_op.h"
namespace caffe2 {
REGISTER_CPU_OPERATOR(
Int8AveragePool,
int8::Int8AveragePoolOp<int8::Activation::NONE>);
REGISTER_CPU_OPERATOR(
Int8AveragePoolRelu,
int8::Int8AveragePoolOp<int8::Activation::RELU>);
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
const char kAveragePoolDoc_int8[] = R"DOC(
consumes an input blob X and applies average pooling across the
the blob according to kernel sizes, stride sizes, and pad lengths defined by the
ConvPoolOpBase operator. Average pooling consisting of averaging all values of a
subset of the input tensor according to the kernel size and downsampling the
data into the output blob Y for further processing.
)DOC";
std::function<void(OpSchema&)> AveragePoolDocGenerator(
const char* dim,
bool relu_fused = false) {
return [=](OpSchema& schema) {
string doc = "AveragePool{dim} {pool_doc}";
c10::ReplaceAll(doc, "{dim}", dim);
c10::ReplaceAll(doc, "{pool_doc}", kAveragePoolDoc_int8);
schema.SetDoc(doc);
schema.Input(
0,
"X",
"Input data tensor from the previous operator; dimensions depend on "
"whether the NCHW or NHWC operators are being used. For example, in "
"the former, the input has size (N x C x H x W), where N is the batch "
"size, C is the number of channels, and H and W are the height and the "
"width of the data. The corresponding permutation of dimensions is "
"used in the latter case.");
schema.Output(0, "Y", relu_fused ?
"Output data tensor from average pooling across the input "
"tensor. Dimensions will vary based on various kernel, stride, and pad "
"sizes. Output will go through rectified linear "
"function, where y = max(0, x)." :
"Output data tensor from average pooling across the input "
"tensor. Dimensions will vary based on various kernel, stride, and pad "
"sizes.");
};
}
OPERATOR_SCHEMA(Int8AveragePool)
.NumInputs(1)
.NumOutputs(1)
.Arg("Y_scale", "Output tensor quantization scale")
.Arg("Y_zero_point", "Output tensor quantization offset")
.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForPool)
.FillUsing(AveragePoolDocGenerator(""));
OPERATOR_SCHEMA(Int8AveragePoolRelu)
.NumInputs(1)
.NumOutputs(1)
.Arg("Y_scale", "Output tensor quantization scale")
.Arg("Y_zero_point", "Output tensor quantization offset")
.TensorInferenceFunction(ConvPoolOpBase<CPUContext>::TensorInferenceForPool)
.FillUsing(AveragePoolDocGenerator("", true));
} // namespace caffe2
|
