File size: 3,926 Bytes
a0e12a2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
{
  "op": "ai.onnx.BatchNormalization",
  "cases": [
    {
      "name": "nchw_4x64x112x112_vec4",
      "preset": "smoke",
      "vars": { "dtype": "float32", "batch": 4, "channels": 64, "height": 112, "width": 112 },
      "inputs": {
        "x": { "dtype": "float32", "shape": [4, 64, 112, 112], "dist": "normal", "seed": 770, "scale": 0.5 },
        "scale": { "dtype": "float32", "shape": [64], "dist": "uniform", "seed": 771, "scale": 0.25, "offset": 1 },
        "b": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 772, "scale": 0.1 },
        "inputMean": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 773, "scale": 0.1 },
        "inputVar": { "dtype": "float32", "shape": [64], "data": { "kind": "constant", "value": 1.0 } }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [4, 64, 112, 112] } },
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "bandwidth",
            "value": "args.batch * args.channels * args.height * args.width * dtypeBytes(args.dtype) * 2 + args.channels * dtypeBytes(args.dtype) * 4"
          }
        ]
      }
    },
    {
      "name": "nchw_1x64x112x112",
      "vars": { "dtype": "float32", "batch": 1, "channels": 64, "height": 112, "width": 112 },
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 64, 112, 112] },
        "scale": { "dtype": "float32", "shape": [64] },
        "b": { "dtype": "float32", "shape": [64] },
        "inputMean": { "dtype": "float32", "shape": [64] },
        "inputVar": { "dtype": "float32", "shape": [64], "data": { "kind": "constant", "value": 1.0 } }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 112, 112] } },
      "bench": {
        "primary": true,
        "metrics": [
          {
            "type": "bandwidth",
            "value": "args.batch * args.channels * args.height * args.width * dtypeBytes(args.dtype) * 2 + args.channels * dtypeBytes(args.dtype) * 4"
          }
        ]
      }
    },
    {
      "name": "nc_4096x768",
      "vars": { "dtype": "float32", "batch": 4096, "channels": 768, "height": 1, "width": 1 },
      "inputs": {
        "x": { "dtype": "float32", "shape": [4096, 768] },
        "scale": { "dtype": "float32", "shape": [768] },
        "b": { "dtype": "float32", "shape": [768] },
        "inputMean": { "dtype": "float32", "shape": [768] },
        "inputVar": { "dtype": "float32", "shape": [768], "dist": "constant", "value": 1 }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [4096, 768] } },
      "bench": {
        "metrics": [
          {
            "type": "bandwidth",
            "value": "args.batch * args.channels * dtypeBytes(args.dtype) * 2 + args.channels * dtypeBytes(args.dtype) * 4"
          }
        ]
      }
    },
    {
      "name": "nchw_inference_scalar_no_vec4_1x64x63x63",
      "preset": "edge",
      "vars": { "dtype": "float32", "batch": 1, "channels": 64, "height": 63, "width": 63 },
      "inputs": {
        "x": { "dtype": "float32", "shape": [1, 64, 63, 63], "dist": "normal", "seed": 6301, "scale": 1 },
        "scale": { "dtype": "float32", "shape": [64], "dist": "uniform", "seed": 6302, "scale": 0.25, "offset": 1 },
        "b": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 6303, "scale": 0.1 },
        "inputMean": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 6304, "scale": 0.1 },
        "inputVar": { "dtype": "float32", "shape": [64], "dist": "uniform", "seed": 6305, "scale": 0.5, "offset": 1 }
      },
      "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 63, 63] } },
      "bench": {
        "metrics": [
          {
            "type": "bandwidth",
            "value": "args.batch * args.channels * args.height * args.width * dtypeBytes(args.dtype) * 2 + args.channels * dtypeBytes(args.dtype) * 4"
          }
        ]
      }
    }
  ]
}