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Update onnx-metadata.json
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@lutzroeder
lutzroeder committed Jun 18, 2024
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"summary": "convtranspose_dilations",
"code": "x = np.array(\n [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\nW = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], dilations=[2, 2]\n)\n\ny = np.array(\n [\n [\n [\n [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5]\n [63.0, 35.0, 67.0, 10.0, 14.0],\n [24.0, 22.0, 76.0, 76.0, 21.0],\n [9.0, 5.0, 88.0, 45.0, 63.0],\n [3.0, 2.0, 33.0, 18.0, 54.0],\n ]\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_dilations\")"
},
{
"summary": "convtranspose_group_2",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ]\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2\")"
},
{
"summary": "convtranspose_group_2_image_3",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [18.0, 37.0, 57.0, 39.0, 20.0],\n [39.0, 80.0, 123.0, 84.0, 43.0],\n [63.0, 129.0, 198.0, 135.0, 69.0],\n [45.0, 92.0, 141.0, 96.0, 49.0],\n [24.0, 49.0, 75.0, 51.0, 26.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n ]\n).astype(np.float32)\n\nexpect(\n node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2_image_3\"\n)"
},
{
"summary": "convtranspose_pads",
"code": "x = np.array(\n [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\n\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ]\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], strides=[3, 2], pads=[1, 2, 1, 2]\n)\n\ny = np.array(\n [\n [\n [\n [1.0, 1.0, 3.0], # (1, 2, 7, 3)\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n [\n [1.0, 1.0, 3.0],\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_pads\")"
Expand Down Expand Up @@ -10162,6 +10170,14 @@
"summary": "convtranspose_dilations",
"code": "x = np.array(\n [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\nW = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], dilations=[2, 2]\n)\n\ny = np.array(\n [\n [\n [\n [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5]\n [63.0, 35.0, 67.0, 10.0, 14.0],\n [24.0, 22.0, 76.0, 76.0, 21.0],\n [9.0, 5.0, 88.0, 45.0, 63.0],\n [3.0, 2.0, 33.0, 18.0, 54.0],\n ]\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_dilations\")"
},
{
"summary": "convtranspose_group_2",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ]\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2\")"
},
{
"summary": "convtranspose_group_2_image_3",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [18.0, 37.0, 57.0, 39.0, 20.0],\n [39.0, 80.0, 123.0, 84.0, 43.0],\n [63.0, 129.0, 198.0, 135.0, 69.0],\n [45.0, 92.0, 141.0, 96.0, 49.0],\n [24.0, 49.0, 75.0, 51.0, 26.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n ]\n).astype(np.float32)\n\nexpect(\n node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2_image_3\"\n)"
},
{
"summary": "convtranspose_pads",
"code": "x = np.array(\n [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\n\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ]\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], strides=[3, 2], pads=[1, 2, 1, 2]\n)\n\ny = np.array(\n [\n [\n [\n [1.0, 1.0, 3.0], # (1, 2, 7, 3)\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n [\n [1.0, 1.0, 3.0],\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_pads\")"
Expand Down Expand Up @@ -10293,6 +10309,14 @@
"summary": "convtranspose_dilations",
"code": "x = np.array(\n [[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\nW = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], dilations=[2, 2]\n)\n\ny = np.array(\n [\n [\n [\n [21.0, 56.0, 13.0, 16.0, 2.0], # [1, 1, 5, 5]\n [63.0, 35.0, 67.0, 10.0, 14.0],\n [24.0, 22.0, 76.0, 76.0, 21.0],\n [9.0, 5.0, 88.0, 45.0, 63.0],\n [3.0, 2.0, 33.0, 18.0, 54.0],\n ]\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_dilations\")"
},
{
"summary": "convtranspose_group_2",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ]\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2\")"
},
{
"summary": "convtranspose_group_2_image_3",
"code": "x = np.array(\n [\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n [\n [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],\n [[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]],\n ],\n ]\n).astype(np.float32)\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ],\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], group=2)\n\ny = np.array(\n [\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [18.0, 37.0, 57.0, 39.0, 20.0],\n [39.0, 80.0, 123.0, 84.0, 43.0],\n [63.0, 129.0, 198.0, 135.0, 69.0],\n [45.0, 92.0, 141.0, 96.0, 49.0],\n [24.0, 49.0, 75.0, 51.0, 26.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n [\n [\n [0.0, 1.0, 3.0, 3.0, 2.0],\n [3.0, 8.0, 15.0, 12.0, 7.0],\n [9.0, 21.0, 36.0, 27.0, 15.0],\n [9.0, 20.0, 33.0, 24.0, 13.0],\n [6.0, 13.0, 21.0, 15.0, 8.0],\n ],\n [\n [9.0, 19.0, 30.0, 21.0, 11.0],\n [21.0, 44.0, 69.0, 48.0, 25.0],\n [36.0, 75.0, 117.0, 81.0, 42.0],\n [27.0, 56.0, 87.0, 60.0, 31.0],\n [15.0, 31.0, 48.0, 33.0, 17.0],\n ],\n ],\n ]\n).astype(np.float32)\n\nexpect(\n node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_group_2_image_3\"\n)"
},
{
"summary": "convtranspose_pads",
"code": "x = np.array(\n [[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)\n).astype(np.float32)\n\nW = np.array(\n [\n [\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)\n [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],\n ]\n ]\n).astype(np.float32)\n\nnode = onnx.helper.make_node(\n \"ConvTranspose\", [\"X\", \"W\"], [\"Y\"], strides=[3, 2], pads=[1, 2, 1, 2]\n)\n\ny = np.array(\n [\n [\n [\n [1.0, 1.0, 3.0], # (1, 2, 7, 3)\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n [\n [1.0, 1.0, 3.0],\n [1.0, 1.0, 3.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [7.0, 4.0, 9.0],\n [13.0, 7.0, 15.0],\n [13.0, 7.0, 15.0],\n ],\n ]\n ]\n).astype(np.float32)\n\nexpect(node, inputs=[x, W], outputs=[y], name=\"test_convtranspose_pads\")"
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