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| "padH": 1, |
| "padW": 1 |
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| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
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| "w": { "shape": [64, 48, 3, 3], "dtype": "float32", "dist": "normal", "seed": 7809, "scale": 0.05 } |
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| { |
| "name": "fusedconv-3x3-alignedK-c64m64-32x32", |
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| "vars": { |
| "batch": 1, |
| "inChannels": 64, |
| "outChannels": 64, |
| "inH": 32, |
| "inW": 32, |
| "kernelH": 3, |
| "kernelW": 3, |
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| "padW": 1 |
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| "x": { "shape": [1, 64, 32, 32], "dtype": "float32", "dist": "normal", "seed": 7770, "scale": 0.2 }, |
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| { |
| "name": "fusedconv-3x3-depthwise-c256-32x32", |
| "preset": "smoke", |
| "vars": { |
| "batch": 1, |
| "inChannels": 256, |
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| { |
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| "name": "fusedconv-3x3-dense-bias-c256m256-32x32", |
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| "w": { "shape": [256, 256, 3, 3], "dtype": "float32", "dist": "normal", "seed": 3532, "scale": 0.05 }, |
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| "outputs": { "y": { "shape": [1, 256, 32, 32], "dtype": "float32" } }, |
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| } |
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| "name": "fusedconv-stem-cin3-k7s2-224", |
| "preset": "smoke", |
| "vars": { |
| "batch": 1, |
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| "padW": 3 |
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| "attrs": { "activation": "Relu", "strides": [2, 2], "pads": [3, 3, 3, 3] }, |
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| "vars": { |
| "batch": 1, |
| "inChannels": 32, |
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| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
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| { |
| "name": "fusedconv-1x1-oddM66-c64-32x32", |
| "preset": "smoke", |
| "vars": { |
| "batch": 1, |
| "inChannels": 64, |
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| "padH": 0, |
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| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [0, 0, 0, 0] }, |
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| { |
| "name": "fusedconv-1x1-pow2M64-c64-32x32", |
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| "vars": { |
| "batch": 1, |
| "inChannels": 64, |
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| "padH": 0, |
| "padW": 0 |
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| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [0, 0, 0, 0] }, |
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| "outputs": { "y": { "shape": [1, 64, 32, 32], "dtype": "float32" } }, |
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| "notes": "A group-4, 5x5 convolution exercises the four-wide grouped large-kernel route near its kernel-size boundary." |
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| "preset": "stress", |
| "vars": { |
| "batch": 1, |
| "inChannels": 128, |
| "outChannels": 128, |
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| "strideW": 1, |
| "padH": 2, |
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| "group": 4 |
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| "name": "fusedconv-conv1d-hardswish-b1c80m512-w3000-k3s1", |
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| "source": "ONNX Runtime FusedConv provider semantics", |
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| "outputs": { "y": { "shape": [1, 512, 3000], "dtype": "float32" } }, |
| "bench": { |
| "primary": true, |
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| }, |
| { |
| "name": "fusedconv-splitk-grid32-b1m128-32x32", |
| "preset": "smoke", |
| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [1, 64, 32, 32], "dtype": "float32", "dist": "normal", "seed": 7770, "scale": 0.2 }, |
| "w": { "shape": [128, 64, 3, 3], "dtype": "float32", "dist": "normal", "seed": 2676, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [1, 128, 32, 32], "dtype": "float32" } }, |
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| }, |
| { |
| "name": "fusedconv-splitk-grid32-b1m64-32x64", |
| "preset": "smoke", |
| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [1, 64, 32, 64], "dtype": "float32", "dist": "normal", "seed": 7770, "scale": 0.2 }, |
| "w": { "shape": [64, 64, 3, 3], "dtype": "float32", "dist": "normal", "seed": 2676, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [1, 64, 32, 64], "dtype": "float32" } }, |
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| "metrics": [ |
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| "provenance": { |
| "source": "synthetic", |
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| }, |
| { |
| "name": "fusedconv-splitk-grid32-b2m64-32x32", |
| "preset": "smoke", |
| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [2, 64, 32, 32], "dtype": "float32", "dist": "normal", "seed": 7770, "scale": 0.2 }, |
| "w": { "shape": [64, 64, 3, 3], "dtype": "float32", "dist": "normal", "seed": 2676, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [2, 64, 32, 32], "dtype": "float32" } }, |
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| "source": "synthetic", |
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| }, |
| { |
| "name": "fusedconv-direct-inputs-row-band-m32-k288-n256", |
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| "vars": { |
| "batch": 1, |
| "inChannels": 32, |
| "outChannels": 32, |
| "inH": 16, |
| "inW": 16, |
| "kernelH": 3, |
| "kernelW": 3, |
| "strideH": 1, |
| "strideW": 1, |
| "padH": 1, |
| "padW": 1 |
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| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [1, 32, 16, 16], "dtype": "float32", "dist": "normal", "seed": 839, "scale": 0.2 }, |
| "w": { "shape": [32, 32, 3, 3], "dtype": "float32", "dist": "normal", "seed": 1375, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [1, 32, 16, 16], "dtype": "float32" } }, |
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| "metrics": [ |
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| "type": "gflops", |
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| } |
| }, |
| { |
| "name": "fusedconv-direct-inputs-row-band-m64-k288-n256", |
| "preset": "smoke", |
| "vars": { |
| "batch": 1, |
| "inChannels": 32, |
| "outChannels": 64, |
| "inH": 16, |
| "inW": 16, |
| "kernelH": 3, |
| "kernelW": 3, |
| "strideH": 1, |
| "strideW": 1, |
| "padH": 1, |
| "padW": 1 |
| }, |
| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [1, 32, 16, 16], "dtype": "float32", "dist": "normal", "seed": 839, "scale": 0.2 }, |
| "w": { "shape": [64, 32, 3, 3], "dtype": "float32", "dist": "normal", "seed": 1375, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [1, 64, 16, 16], "dtype": "float32" } }, |
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| "metrics": [ |
| { |
| "type": "gflops", |
| "value": "2 * args.batch * args.outChannels * dim(shapes.y, 2) * dim(shapes.y, 3) * dim(shapes.w, 1) * args.kernelH * args.kernelW" |
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| }, |
| { |
| "name": "fusedconv-half-direct-inputs-row-band-m32-k288-n256", |
| "preset": "smoke", |
| "vars": { |
| "batch": 1, |
| "inChannels": 32, |
| "outChannels": 32, |
| "inH": 16, |
| "inW": 16, |
| "kernelH": 3, |
| "kernelW": 3, |
| "strideH": 1, |
| "strideW": 1, |
| "padH": 1, |
| "padW": 1 |
| }, |
| "attrs": { "activation": "Relu", "strides": [1, 1], "pads": [1, 1, 1, 1] }, |
| "inputs": { |
| "x": { "shape": [1, 32, 16, 16], "dtype": "float16", "dist": "normal", "seed": 839, "scale": 0.2 }, |
| "w": { "shape": [32, 32, 3, 3], "dtype": "float16", "dist": "normal", "seed": 1375, "scale": 0.05 } |
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| "outputs": { "y": { "shape": [1, 32, 16, 16], "dtype": "float16" } }, |
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| "metrics": [ |
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| "w": { "dtype": "float32", "shape": [64, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k7x7_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k7x7_d2x2_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k7x7_d3x3_s1x1_bias0_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [9, 9, 9, 9], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k9x9_d1x1_s1x1_bias0_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [4, 4, 4, 4], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k9x9_d2x2_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k9x9_d3x3_s1x1_bias0_asym0-Tanh", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k11x11_d1x1_s1x1_bias0_asym0-Tanh", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k11x11_d2x2_s1x1_bias0_asym0-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [10, 10, 10, 10], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m16_32x33_k11x11_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [15, 15, 15, 15], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "HardSwish" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 8, 11, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k3x3_d1x1_s1x1_bias0_asym0-Tanh", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k3x3_d2x2_s1x1_bias0_asym0-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k3x3_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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 3x3, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k5x5_d2x2_s1x1_bias0_asym0-Clip", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [4, 4, 4, 4], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "Clip", |
| "activation_params": [-0.15, 0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Clip activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k5x5_d3x3_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [3, 3] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k7x7_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k7x7_d2x2_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k7x7_d3x3_s1x1_bias0_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [9, 9, 9, 9], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k9x9_d1x1_s1x1_bias0_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [4, 4, 4, 4], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k9x9_d2x2_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [8, 8, 8, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k9x9_d3x3_s1x1_bias0_asym0-Tanh", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [12, 12, 12, 12], "strides": [1, 1], "dilations": [3, 3], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k11x11_d1x1_s1x1_bias0_asym0-Tanh", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [5, 5, 5, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k11x11_d2x2_s1x1_bias0_asym0-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [10, 10, 10, 10], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m16_32x33_k11x11_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [15, 15, 15, 15], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "HardSwish" |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 32, 33], "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, 33] } }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c2_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 8, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 2, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 2 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c8_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 8, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-identity", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-identity", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Relu", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Tanh", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSwish" |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Clip", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "Clip", |
| "activation_params": [-0.15, 0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Clip activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-identity", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Relu", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Tanh", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [10, 10, 11, 12], "strides": [1, 1], "dilations": [2, 2], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSwish" |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k11x11_d2x2_s1x1_bias0_asym1-Clip", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [10, 10, 11, 12], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "Clip", |
| "activation_params": [-0.15, 0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 11, 11], "dist": "normal", "seed": 602, "scale": 0.4 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, Clip activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k3x3_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k7x7_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k7x7_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k11x11_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 16, |
| "pads": [10, 10, 10, 10], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSwish" |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k11x11_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 16, |
| "pads": [15, 15, 15, 15], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "HardSwish" |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k3x3_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [3, 3], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k7x7_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k7x7_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [9, 9, 9, 9], "strides": [1, 1], "dilations": [3, 3], "activation": "HardSwish" }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k11x11_d2x2_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 16, |
| "pads": [10, 10, 10, 10], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSwish" |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k11x11_d3x3_s1x1_bias0_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { |
| "group": 16, |
| "pads": [15, 15, 15, 15], |
| "strides": [1, 1], |
| "dilations": [3, 3], |
| "activation": "HardSwish" |
| }, |
| "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 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 32, 33, 35] } }, |
| "bench": { |
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| ] |
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| "provenance": { |
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| }, |
| { |
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| "preset": "model", |
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| "pads": [1, 10, 2, 12], |
| "strides": [1, 1], |
| "dilations": [1, 2], |
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| "activation_params": [0.2] |
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| "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 } |
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| } |
| }, |
| { |
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| "preset": "model", |
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| "pads": [10, 1, 11, 3], |
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| "w": { "dtype": "float32", "shape": [24, 3, 11, 3], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_33x35_k5x9_d1x3_s2x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [2, 12, 3, 14], |
| "strides": [2, 1], |
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| "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 } |
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| "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, stride 2x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_33x35_k7x5_d2x2_s1x2_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [6, 4, 7, 6], |
| "strides": [1, 2], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
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| "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, stride 1x2, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_33x35_k11x11_d6x6_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [30, 30, 31, 32], |
| "strides": [1, 1], |
| "dilations": [6, 6], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c3_m6_33x35_k3x11_d1x2_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [1, 10, 2, 12], |
| "strides": [1, 1], |
| "dilations": [1, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c3_m6_33x35_k11x3_d2x1_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [10, 1, 11, 3], |
| "strides": [1, 1], |
| "dilations": [2, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c3_m6_33x35_k5x9_d1x3_s2x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [2, 12, 3, 14], |
| "strides": [2, 1], |
| "dilations": [1, 3], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 2x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c3_m6_33x35_k7x5_d2x2_s1x2_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [6, 4, 7, 6], |
| "strides": [1, 2], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 1x2, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c3_m6_33x35_k11x11_d6x6_s1x1_bias0_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 4, |
| "pads": [30, 30, 31, 32], |
| "strides": [1, 1], |
| "dilations": [6, 6], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "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 } |
| }, |
| "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, stride 1x1, LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x7_k5x5_d1x2_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 7], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x8_k5x5_d1x2_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 8], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| }, |
| "tunableSpace": { |
| "GROUPED_ROW_LOOP_MIN_KERNEL_AREA": [0, 64, 121], |
| "GROUPED_ROW_LOOP_MAX_SMALL_CHANNEL_BYTES": [0, 32, 64], |
| "GROUPED_DILATED_MIN_PLAIN_SPAN": [0, 32] |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x9_k5x5_d1x2_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 9], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 9] } }, |
| "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 3 input and 6 output channels per group, dilation 1x2, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x11_k5x5_d1x3_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 11], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x12_k5x5_d1x3_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 12], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x13_k5x5_d1x3_s1x1_bias0_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 13], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [24, 3, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 24, 40, 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, stride 1x1, Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k3x3_d1x1_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 16, 3, 3], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 16, 3, 3], "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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k5x5_d1x1_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
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| "w": { "dtype": "float32", "shape": [64, 16, 5, 5], "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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k5x5_d2x2_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
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| "x": { "dtype": "float32", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 16, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k7x7_d1x1_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 16, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m16_32x33_k7x7_d2x2_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [64, 16, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 32, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k3x3_d1x1_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 1, 1, 1], "strides": [1, 1], "dilations": [1, 1], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k5x5_d1x1_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k5x5_d2x2_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k7x7_d1x1_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 1x1, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m16_32x33_k7x7_d2x2_s1x1_bias1_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [6, 6, 6, 6], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 32, 33], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [64, 16, 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, 33] } }, |
| "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 16 input and 16 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c7_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 28, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 7, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 7 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c7_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 28, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 7, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 7 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c7_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 28, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 7, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 7 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c7_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 28, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 7, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 7 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 8 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 8 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c8_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 32, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 8, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 8 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c9_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 36, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 9, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 9 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c9_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 36, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 9, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 9 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c9_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 36, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 9, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 9 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c9_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 36, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 9, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 9 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c15_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 60, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 15, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 15 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c15_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 60, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 15, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 15 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c15_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 60, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 15, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 15 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c15_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 60, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 15, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 15 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c17_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 68, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 17, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 17 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c17_m32_48x48_k5x5_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 68, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 17, 5, 5], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 17 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c17_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 68, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 17, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 17 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c17_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 68, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 17, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 17 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k4x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 2, 1, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 4, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 4x6 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k3x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [1, 4, 1, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 3, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 3x9 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k6x6_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 6, 6], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x6 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-identity", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Relu", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Tanh", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Clip", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "Clip", |
| "activation_params": [-0.15, 0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float32", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float32", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Clip activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-identity", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2] }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and identity activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Relu", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Tanh", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "Tanh" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Tanh activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-HardSigmoid", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "HardSigmoid", |
| "activation_params": [0.2, 0.5] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and HardSigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 3, "pads": [6, 6, 7, 8], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m6_17x29_k7x7_d2x2_s1x1_bias1_asym1-Clip", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [6, 6, 7, 8], |
| "strides": [1, 1], |
| "dilations": [2, 2], |
| "activation": "Clip", |
| "activation_params": [-0.15, 0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 2 }, |
| "w": { "dtype": "float16", "shape": [18, 3, 7, 7], "dist": "normal", "seed": 602, "scale": 0.4 }, |
| "bias": { "dtype": "float16", "shape": [18], "dist": "normal", "seed": 603, "scale": 0.5 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [2, 18, 18, 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 3 input and 6 output channels per group, dilation 2x2, stride 1x1, bias and Clip activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m1_33x35_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m1_33x35_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g16_c1_m2_33x35_k3x3_d2x2_s1x1_bias1_asym0-HardSwish", |
| "preset": "model", |
| "attrs": { "group": 16, "pads": [2, 2, 2, 2], "strides": [1, 1], "dilations": [2, 2], "activation": "HardSwish" }, |
| "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, stride 1x1, bias and HardSwish activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m1_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [3, 3, 5, 5], "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 5x5 convolution with 3 input and 1 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m3_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [9, 3, 5, 5], "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 5x5 convolution with 3 input and 3 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b2_g3_c3_m5_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [15, 3, 5, 5], "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 5x5 convolution with 3 input and 5 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m1_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [3, 3, 5, 5], "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 5x5 convolution with 3 input and 1 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m3_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [9, 3, 5, 5], "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 5x5 convolution with 3 input and 3 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b2_g3_c3_m5_17x29_k5x5_d1x1_s1x1_bias1_asym0-LeakyRelu", |
| "preset": "model", |
| "attrs": { |
| "group": 3, |
| "pads": [2, 2, 2, 2], |
| "strides": [1, 1], |
| "dilations": [1, 1], |
| "activation": "LeakyRelu", |
| "activation_params": [0.2] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [2, 9, 17, 29], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [15, 3, 5, 5], "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 5x5 convolution with 3 input and 5 output channels per group, dilation 1x1, stride 1x1, bias and LeakyRelu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x7_k5x5_d1x2_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x8_k5x5_d1x2_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x9_k5x5_d1x2_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 4, 2, 4], "strides": [1, 1], "dilations": [1, 2], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x11_k5x5_d1x3_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x12_k5x5_d1x3_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c3_m6_40x13_k5x5_d1x3_s1x1_bias1_asym0-Sigmoid", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 6, 2, 6], "strides": [1, 1], "dilations": [1, 3], "activation": "Sigmoid" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 12, 40, 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, 40, 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, stride 1x1, bias and Sigmoid activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k7x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 7, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k8x8_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 8, 8], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x8 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k6x11_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 5, 2, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 6, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x11 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k8x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 8, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c16_m32_48x48_k9x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 16, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k7x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 7, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x9 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k8x8_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 8, 8], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x8 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k6x11_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 5, 2, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 6, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x11 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k8x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 8, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x9 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float32_b1_g4_c32_m32_48x48_k9x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float32", "shape": [128, 32, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 128, 48, 48] } }, |
| "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 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k7x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 7, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k8x8_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 8, 8], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x8 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k6x11_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 5, 2, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 6, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x11 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k8x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 8, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x9 convolution with 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c16_m32_48x48_k9x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [4, 4, 4, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 64, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 16, 9, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "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 16 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k7x9_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 4, 3, 4], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 7, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 48, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 7x9 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k8x8_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 8, 8], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 47] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 8x8 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
| "name": "fused-grouped_float16_b1_g4_c32_m32_48x48_k6x11_d1x1_s1x1_bias0_asym0-Relu", |
| "preset": "model", |
| "attrs": { "group": 4, "pads": [2, 5, 2, 5], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 128, 48, 48], "dist": "normal", "seed": 601, "scale": 0.2 }, |
| "w": { "dtype": "float16", "shape": [128, 32, 6, 11], "dist": "normal", "seed": 602, "scale": 0.02 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 48] } }, |
| "bench": { |
| "metrics": [ |
| { "type": "gflops", "value": "2 * numel(shapes.y) * dim(shapes.w, 1) * dim(shapes.w, 2) * dim(shapes.w, 3)" } |
| ] |
| }, |
| "provenance": { |
| "notes": "Grouped 6x11 convolution with 32 input and 32 output channels per group, dilation 1x1, stride 1x1, Relu activation. Checks shared row-window reuse and output coverage." |
| } |
| }, |
| { |
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| "w": { "dtype": "float16", "shape": [128, 32, 8, 9], "dist": "normal", "seed": 602, "scale": 0.02 } |
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| "outputs": { "y": { "dtype": "float16", "shape": [1, 128, 47, 48] } }, |
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| } |
| }, |
| { |
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| "bench": { |
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| } |
| }, |
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| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
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| "w": { "dtype": "float32", "shape": [64, 16, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float32", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "bench": { |
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| }, |
| { |
| "name": "fused-bias-adjacent-float16-b1-c64-m64-g4-32x32-k7", |
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| "attrs": { "group": 4, "pads": [3, 3, 3, 3], "strides": [1, 1], "dilations": [1, 1], "activation": "Relu" }, |
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| "w": { "dtype": "float16", "shape": [64, 16, 7, 7], "dist": "normal", "seed": 602, "scale": 0.02 }, |
| "bias": { "dtype": "float16", "shape": [64], "dist": "normal", "seed": 603, "scale": 0.03 } |
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| "outputs": { "y": { "dtype": "float16", "shape": [1, 64, 32, 32] } }, |
| "bench": { |
| "metrics": [ |
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| }, |
| "provenance": { |
| "notes": "Grouped 7x7 convolution with 16 input and output channels per group, width 32, unit dilation, bias and ReLU. Checks the adjacent four-column layout with a complete output vector." |
| } |
| }, |
| { |
| "name": "tile-partial-float32-b1c4m8-14x14-k3", |
| "preset": "edge", |
| "attrs": { |
| "activation": "Elu", |
| "activation_params": [0.5], |
| "kernel_shape": [3, 3], |
| "pads": [1, 1, 1, 1], |
| "strides": [1, 1], |
| "dilations": [1, 1] |
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| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 4, 14, 14], "dist": "normal", "seed": 1111, "scale": 0.3 }, |
| "w": { "dtype": "float32", "shape": [8, 4, 3, 3], "dist": "normal", "seed": 1112, "scale": 0.2 }, |
| "bias": { "dtype": "float32", "shape": [8], "dist": "normal", "seed": 1113, "scale": 0.3 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 8, 14, 14] } }, |
| "bench": { "metrics": [{ "type": "gflops", "value": 112896 }] } |
| }, |
| { |
| "name": "tile-partial-float32-b1c36m8-14x14-k1", |
| "preset": "edge", |
| "attrs": { |
| "activation": "Elu", |
| "activation_params": [0.5], |
| "kernel_shape": [1, 1], |
| "pads": [0, 0, 0, 0], |
| "strides": [1, 1], |
| "dilations": [1, 1] |
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| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 36, 14, 14], "dist": "normal", "seed": 1111, "scale": 0.3 }, |
| "w": { "dtype": "float32", "shape": [8, 36, 1, 1], "dist": "normal", "seed": 1112, "scale": 0.2 }, |
| "bias": { "dtype": "float32", "shape": [8], "dist": "normal", "seed": 1113, "scale": 0.3 } |
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| "outputs": { "y": { "dtype": "float32", "shape": [1, 8, 14, 14] } }, |
| "bench": { "metrics": [{ "type": "gflops", "value": 112896 }] } |
| }, |
| { |
| "name": "tile-partial-float32-b1c5m17-13x15-k3", |
| "preset": "edge", |
| "attrs": { |
| "activation": "Elu", |
| "activation_params": [0.5], |
| "kernel_shape": [3, 3], |
| "pads": [1, 1, 1, 1], |
| "strides": [1, 1], |
| "dilations": [1, 1] |
| }, |
| "inputs": { |
| "x": { "dtype": "float32", "shape": [1, 5, 13, 15], "dist": "normal", "seed": 1111, "scale": 0.3 }, |
| "w": { "dtype": "float32", "shape": [17, 5, 3, 3], "dist": "normal", "seed": 1112, "scale": 0.2 }, |
| "bias": { "dtype": "float32", "shape": [17], "dist": "normal", "seed": 1113, "scale": 0.3 } |
| }, |
| "outputs": { "y": { "dtype": "float32", "shape": [1, 17, 13, 15] } }, |
| "bench": { "metrics": [{ "type": "gflops", "value": 298350 }] } |
| }, |
| { |
| "name": "tile-partial-float16-b1c4m8-14x14-k3", |
| "preset": "edge", |
| "attrs": { |
| "activation": "Elu", |
| "activation_params": [0.5], |
| "kernel_shape": [3, 3], |
| "pads": [1, 1, 1, 1], |
| "strides": [1, 1], |
| "dilations": [1, 1] |
| }, |
| "inputs": { |
| "x": { "dtype": "float16", "shape": [1, 4, 14, 14], "dist": "normal", "seed": 1111, "scale": 0.3 }, |
| "w": { "dtype": "float16", "shape": [8, 4, 3, 3], "dist": "normal", "seed": 1112, "scale": 0.2 }, |
| "bias": { "dtype": "float16", "shape": [8], "dist": "normal", "seed": 1113, "scale": 0.3 } |
| }, |
| "outputs": { "y": { "dtype": "float16", "shape": [1, 8, 14, 14] } }, |
| "bench": { "metrics": [{ "type": "gflops", "value": 112896 }] } |
| } |
| ] |
| } |
|
|
