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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
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| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k7x7_d2x2_s1x1_bias1_asym0", |
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| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k7x7_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
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| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
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| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k9x9_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
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| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k9x9_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
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| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
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| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k9x9_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k9x9_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k11x11_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k11x11_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
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| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k11x11_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x32_k11x11_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 32] } }, |
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| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c8_m16_32x31_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
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| "x": { "dtype": "float32", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k3x3_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k3x3_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k3x3_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k3x3_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
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| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
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| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
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| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k3x3_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
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| "w": { "dtype": "float16", "shape": [64, 8, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k3x3_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
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| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
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| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 3x3 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
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| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k5x5_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k5x5_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
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| }, |
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| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k5x5_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k5x5_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
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| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k5x5_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k5x5_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k7x7_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k7x7_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k7x7_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k7x7_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k9x9_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k9x9_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k9x9_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k9x9_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k11x11_d1x1_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k11x11_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k11x11_d2x2_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 2x2, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x32_k11x11_d3x3_s1x1_bias0_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 32], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 32 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c8_m16_32x31_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 31], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 31] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 8 input and 16 output channels per group, dilation 3x3, and stride 1x1. The output has 32 rows and 31 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m1_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [3, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [3], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 3, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m1_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [3, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [3], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 3, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m2_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [6, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [6], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 6, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m2_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [6, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [6], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 6, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m3_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [9, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [9], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 9, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 3 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m3_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [9, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [9], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 9, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 3 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m5_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [15, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [15], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 15, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 5 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m5_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [15, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [15], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 15, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 5 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 6 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 6 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b2_g3_c3_m8_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 24, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 8 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b2_g3_c3_m8_17x29_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 24, 17, 29] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 3 input and 8 output channels per group, dilation 2x2, and stride 1x1. The output has 17 rows and 29 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x7_k5x5_d1x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 7], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 7] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x2, and stride 1x1. The output has 17 rows and 7 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x8_k5x5_d1x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 8], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 8] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x2, and stride 1x1. The output has 17 rows and 8 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x9_k5x5_d1x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 9], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 9] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x2, and stride 1x1. The output has 17 rows and 9 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x11_k5x5_d1x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 11], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 11] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x3, and stride 1x1. The output has 17 rows and 11 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x12_k5x5_d1x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 12], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 12] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x3, and stride 1x1. The output has 17 rows and 12 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_17x13_k5x5_d1x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 17, 13], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 13] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 3 input and 6 output channels per group, dilation 1x3, and stride 1x1. The output has 17 rows and 13 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k3x11_d1x2_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 10, 2, 12], "strides": [1, 1], "dilations": [1, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 3, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x11 convolution with 3 input and 6 output channels per group, dilation 1x2, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k11x3_d2x1_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 1, 11, 3], "strides": [1, 1], "dilations": [2, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 11, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x3 convolution with 3 input and 6 output channels per group, dilation 2x1, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k5x9_d1x3_s2x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 12, 3, 14], "strides": [2, 1], "dilations": [1, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 17, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x9 convolution with 3 input and 6 output channels per group, dilation 1x3, and stride 2x1. The output has 17 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k7x5_d2x2_s1x2_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 4, 7, 6], "strides": [1, 2], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 7, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 34, 19] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x5 convolution with 3 input and 6 output channels per group, dilation 2x2, and stride 1x2. The output has 34 rows and 19 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k11x11_d6x6_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [30, 30, 31, 32], "strides": [1, 1], "dilations": [6, 6] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 3 input and 6 output channels per group, dilation 6x6, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g4_c3_m6_33x35_k11x11_d7x7_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [35, 35, 36, 37], "strides": [1, 1], "dilations": [7, 7] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 3 input and 6 output channels per group, dilation 7x7, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k3x11_d1x2_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 10, 2, 12], "strides": [1, 1], "dilations": [1, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 3, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x11 convolution with 3 input and 6 output channels per group, dilation 1x2, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k11x3_d2x1_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [10, 1, 11, 3], "strides": [1, 1], "dilations": [2, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 11, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x3 convolution with 3 input and 6 output channels per group, dilation 2x1, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k5x9_d1x3_s2x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 12, 3, 14], "strides": [2, 1], "dilations": [1, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 5, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 17, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x9 convolution with 3 input and 6 output channels per group, dilation 1x3, and stride 2x1. The output has 17 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k7x5_d2x2_s1x2_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 4, 7, 6], "strides": [1, 2], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 7, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 34, 19] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x5 convolution with 3 input and 6 output channels per group, dilation 2x2, and stride 1x2. The output has 34 rows and 19 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k11x11_d6x6_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [30, 30, 31, 32], "strides": [1, 1], "dilations": [6, 6] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 3 input and 6 output channels per group, dilation 6x6, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g4_c3_m6_33x35_k11x11_d7x7_s1x1_bias1_asym1", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [35, 35, 36, 37], "strides": [1, 1], "dilations": [7, 7] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 12, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [24, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [24], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 24, 34, 37] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 3 input and 6 output channels per group, dilation 7x7, and stride 1x1. The output has 34 rows and 37 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k3x3_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k3x3_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k3x3_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k3x3_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k3x3_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k3x3_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x3 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k7x7_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k7x7_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k11x11_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [10, 10, 10, 10], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 1 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k11x11_d3x3_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [15, 15, 15, 15], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 11x11 convolution with 1 input and 2 output channels per group, dilation 3x3, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k4x4_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 32, 34] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 34 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k4x4_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 32, 34] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 34 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k4x4_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k4x4_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k5x5_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k5x5_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k5x5_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k5x5_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m1_33x35_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [16, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float32_b1_g16_c1_m2_33x35_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [32, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k4x4_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 32, 34] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 34 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k4x4_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 32, 34] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 32 rows and 34 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k4x4_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k4x4_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 4, 4], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x4 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k5x5_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k5x5_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k5x5_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k5x5_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 5x5 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 1 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k9x9_d1x1_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 2 output channels per group, dilation 1x1, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m1_33x35_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [16, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [16], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 16, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 1 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| }, |
| { |
| "name": "grouped_float16_b1_g16_c1_m2_33x35_k9x9_d2x2_s1x1_bias1_asym0", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 16, 33, 35], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [32, 1, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [32], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 9x9 convolution with 1 input and 2 output channels per group, dilation 2x2, and stride 1x1. The output has 33 rows and 35 columns per channel." |
| } |
| } |
| ] |
| } |
|
|
