This the model info （Because of the length limitation for each reply I split the model into two replies ）
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
print(mod)
+++++++++++++++++++++++++++++(1)
shape_dict {'0': [1, 3, 256, 256]}
Extract tasks...
def @main(%v0: Tensor[(1, 3, 256, 256), float32], %v1015: Tensor[(32, 3, 3, 3), float32], %v1016: Tensor[(32), float32], %v1018: Tensor[(32, 1, 3, 3), float32], %v1019: Tensor[(32), float32], %v1021: Tensor[(24, 32, 1, 1), float32], %v1022: Tensor[(24), float32], %v1024: Tensor[(144, 24, 1, 1), float32], %v1025: Tensor[(144), float32], %v1027: Tensor[(144, 1, 3, 3), float32], %v1028: Tensor[(144), float32], %v1030: Tensor[(32, 144, 1, 1), float32], %v1031: Tensor[(32), float32], %v1033: Tensor[(192, 32, 1, 1), float32], %v1034: Tensor[(192), float32], %v1036: Tensor[(192, 1, 3, 3), float32], %v1037: Tensor[(192), float32], %v1039: Tensor[(32, 192, 1, 1), float32], %v1040: Tensor[(32), float32], %v1042: Tensor[(192, 32, 1, 1), float32], %v1043: Tensor[(192), float32], %v1045: Tensor[(192, 1, 3, 3), float32], %v1046: Tensor[(192), float32], %v1048: Tensor[(32, 192, 1, 1), float32], %v1049: Tensor[(32), float32], %v1051: Tensor[(192, 32, 1, 1), float32], %v1052: Tensor[(192), float32], %v1054: Tensor[(192, 1, 5, 5), float32], %v1055: Tensor[(192), float32], %v1057: Tensor[(48, 192, 1, 1), float32], %v1058: Tensor[(48), float32], %v1060: Tensor[(288, 48, 1, 1), float32], %v1061: Tensor[(288), float32], %v1063: Tensor[(288, 1, 5, 5), float32], %v1064: Tensor[(288), float32], %v1066: Tensor[(48, 288, 1, 1), float32], %v1067: Tensor[(48), float32], %v1069: Tensor[(288, 48, 1, 1), float32], %v1070: Tensor[(288), float32], %v1072: Tensor[(288, 1, 5, 5), float32], %v1073: Tensor[(288), float32], %v1075: Tensor[(48, 288, 1, 1), float32], %v1076: Tensor[(48), float32], %v1078: Tensor[(288, 48, 1, 1), float32], %v1079: Tensor[(288), float32], %v1081: Tensor[(288, 1, 3, 3), float32], %v1082: Tensor[(288), float32], %v1084: Tensor[(96, 288, 1, 1), float32], %v1085: Tensor[(96), float32], %v1087: Tensor[(576, 96, 1, 1), float32], %v1088: Tensor[(576), float32], %v1090: Tensor[(576, 1, 3, 3), float32], %v1091: Tensor[(576), float32], %v1093: Tensor[(96, 576, 1, 1), float32], %v1094: Tensor[(96), float32], %v1096: Tensor[(576, 96, 1, 1), float32], %v1097: Tensor[(576), float32], %v1099: Tensor[(576, 1, 3, 3), float32], %v1100: Tensor[(576), float32], %v1102: Tensor[(96, 576, 1, 1), float32], %v1103: Tensor[(96), float32], %v1105: Tensor[(576, 96, 1, 1), float32], %v1106: Tensor[(576), float32], %v1108: Tensor[(576, 1, 3, 3), float32], %v1109: Tensor[(576), float32], %v1111: Tensor[(96, 576, 1, 1), float32], %v1112: Tensor[(96), float32], %v1114: Tensor[(576, 96, 1, 1), float32], %v1115: Tensor[(576), float32], %v1117: Tensor[(576, 1, 3, 3), float32], %v1118: Tensor[(576), float32], %v1120: Tensor[(96, 576, 1, 1), float32], %v1121: Tensor[(96), float32], %v1123: Tensor[(576, 96, 1, 1), float32], %v1124: Tensor[(576), float32], %v1126: Tensor[(576, 1, 5, 5), float32], %v1127: Tensor[(576), float32], %v1129: Tensor[(136, 576, 1, 1), float32], %v1130: Tensor[(136), float32], %v1132: Tensor[(816, 136, 1, 1), float32], %v1133: Tensor[(816), float32], %v1135: Tensor[(816, 1, 5, 5), float32], %v1136: Tensor[(816), float32], %v1138: Tensor[(136, 816, 1, 1), float32], %v1139: Tensor[(136), float32], %v1141: Tensor[(816, 136, 1, 1), float32], %v1142: Tensor[(816), float32], %v1144: Tensor[(816, 1, 5, 5), float32], %v1145: Tensor[(816), float32], %v1147: Tensor[(136, 816, 1, 1), float32], %v1148: Tensor[(136), float32], %v1150: Tensor[(816, 136, 1, 1), float32], %v1151: Tensor[(816), float32], %v1153: Tensor[(816, 1, 5, 5), float32], %v1154: Tensor[(816), float32], %v1156: Tensor[(136, 816, 1, 1), float32], %v1157: Tensor[(136), float32], %v1159: Tensor[(816, 136, 1, 1), float32], %v1160: Tensor[(816), float32], %v1162: Tensor[(816, 1, 5, 5), float32], %v1163: Tensor[(816), float32], %v1165: Tensor[(136, 816, 1, 1), float32], %v1166: Tensor[(136), float32], %v1168: Tensor[(816, 136, 1, 1), float32], %v1169: Tensor[(816), float32], %v1171: Tensor[(816, 1, 5, 5), float32], %v1172: Tensor[(816), float32], %v1174: Tensor[(232, 816, 1, 1), float32], %v1175: Tensor[(232), float32], %v1177: Tensor[(1392, 232, 1, 1), float32], %v1178: Tensor[(1392), float32], %v1180: Tensor[(1392, 1, 5, 5), float32], %v1181: Tensor[(1392), float32], %v1183: Tensor[(232, 1392, 1, 1), float32], %v1184: Tensor[(232), float32], %v1186: Tensor[(1392, 232, 1, 1), float32], %v1187: Tensor[(1392), float32], %v1189: Tensor[(1392, 1, 5, 5), float32], %v1190: Tensor[(1392), float32], %v1192: Tensor[(232, 1392, 1, 1), float32], %v1193: Tensor[(232), float32], %v1195: Tensor[(1392, 232, 1, 1), float32], %v1196: Tensor[(1392), float32], %v1198: Tensor[(1392, 1, 5, 5), float32], %v1199: Tensor[(1392), float32], %v1201: Tensor[(232, 1392, 1, 1), float32], %v1202: Tensor[(232), float32], %v1204: Tensor[(1392, 232, 1, 1), float32], %v1205: Tensor[(1392), float32], %v1207: Tensor[(1392, 1, 5, 5), float32], %v1208: Tensor[(1392), float32], %v1210: Tensor[(232, 1392, 1, 1), float32], %v1211: Tensor[(232), float32], %v1213: Tensor[(1392, 232, 1, 1), float32], %v1214: Tensor[(1392), float32], %v1216: Tensor[(1392, 1, 5, 5), float32], %v1217: Tensor[(1392), float32], %v1219: Tensor[(232, 1392, 1, 1), float32], %v1220: Tensor[(232), float32], %v1222: Tensor[(1392, 232, 1, 1), float32], %v1223: Tensor[(1392), float32], %v1225: Tensor[(1392, 1, 3, 3), float32], %v1226: Tensor[(1392), float32], %v1228: Tensor[(384, 1392, 1, 1), float32], %v1229: Tensor[(384), float32], %v1233: Tensor[(1), int64], %v1234: Tensor[(4), int64], %v1238: Tensor[(1), int64], %v1239: Tensor[(4), int64], %v1243: Tensor[(1), int64], %v1244: Tensor[(4), int64], %v1248: Tensor[(1), int64], %v1249: Tensor[(4), int64], %v1253: Tensor[(1), int64], %v1254: Tensor[(4), int64], %v1259: Tensor[(4), float32], %v1264: Tensor[(4), float32], %v1269: Tensor[(4), float32], %v1274: Tensor[(4), float32], %v1279: Tensor[(4), float32], %scratch.layer1_rn.weight: Tensor[(64, 32, 3, 3), float32], %scratch.layer2_rn.weight: Tensor[(128, 48, 3, 3), float32], %scratch.layer3_rn.weight: Tensor[(256, 136, 3, 3), float32], %scratch.layer4_rn.weight: Tensor[(512, 384, 3, 3), float32], %scratch.output_conv.0.bias: Tensor[(32), float32], %scratch.output_conv.0.weight: Tensor[(32, 64, 3, 3), float32], %scratch.output_conv.2.bias: Tensor[(32), float32], %scratch.output_conv.2.weight: Tensor[(32, 32, 3, 3), float32], %scratch.output_conv.4.bias: Tensor[(1), float32], %scratch.output_conv.4.weight: Tensor[(1, 32, 1, 1), float32], %scratch.refinenet1.out_conv.bias: Tensor[(64), float32], %scratch.refinenet1.out_conv.weight: Tensor[(64, 64, 1, 1), float32], %scratch.refinenet1.resConfUnit1.conv1.bias: Tensor[(64), float32], %scratch.refinenet1.resConfUnit1.conv1.weight: Tensor[(64, 64, 3, 3), float32], %scratch.refinenet1.resConfUnit1.conv2.bias: Tensor[(64), float32], %scratch.refinenet1.resConfUnit1.conv2.weight: Tensor[(64, 64, 3, 3), float32], %scratch.refinenet1.resConfUnit2.conv1.bias: Tensor[(64), float32], %scratch.refinenet1.resConfUnit2.conv1.weight: Tensor[(64, 64, 3, 3), float32], %scratch.refinenet1.resConfUnit2.conv2.bias: Tensor[(64), float32], %scratch.refinenet1.resConfUnit2.conv2.weight: Tensor[(64, 64, 3, 3), float32], %scratch.refinenet2.out_conv.bias: Tensor[(64), float32], %scratch.refinenet2.out_conv.weight: Tensor[(64, 128, 1, 1), float32], %scratch.refinenet2.resConfUnit1.conv1.bias: Tensor[(128), float32], %scratch.refinenet2.resConfUnit1.conv1.weight: Tensor[(128, 128, 3, 3), float32], %scratch.refinenet2.resConfUnit1.conv2.bias: Tensor[(128), float32], %scratch.refinenet2.resConfUnit1.conv2.weight: Tensor[(128, 128, 3, 3), float32], %scratch.refinenet2.resConfUnit2.conv1.bias: Tensor[(128), float32], %scratch.refinenet2.resConfUnit2.conv1.weight: Tensor[(128, 128, 3, 3), float32], %scratch.refinenet2.resConfUnit2.conv2.bias: Tensor[(128), float32], %scratch.refinenet2.resConfUnit2.conv2.weight: Tensor[(128, 128, 3, 3), float32], %scratch.refinenet3.out_conv.bias: Tensor[(128), float32], %scratch.refinenet3.out_conv.weight: Tensor[(128, 256, 1, 1), float32], %scratch.refinenet3.resConfUnit1.conv1.bias: Tensor[(256), float32], %scratch.refinenet3.resConfUnit1.conv1.weight: Tensor[(256, 256, 3, 3), float32], %scratch.refinenet3.resConfUnit1.conv2.bias: Tensor[(256), float32], %scratch.refinenet3.resConfUnit1.conv2.weight: Tensor[(256, 256, 3, 3), float32], %scratch.refinenet3.resConfUnit2.conv1.bias: Tensor[(256), float32], %scratch.refinenet3.resConfUnit2.conv1.weight: Tensor[(256, 256, 3, 3), float32], %scratch.refinenet3.resConfUnit2.conv2.bias: Tensor[(256), float32], %scratch.refinenet3.resConfUnit2.conv2.weight: Tensor[(256, 256, 3, 3), float32], %scratch.refinenet4.out_conv.bias: Tensor[(256), float32], %scratch.refinenet4.out_conv.weight: Tensor[(256, 512, 1, 1), float32], %scratch.refinenet4.resConfUnit2.conv1.bias: Tensor[(512), float32], %scratch.refinenet4.resConfUnit2.conv1.weight: Tensor[(512, 512, 3, 3), float32], %scratch.refinenet4.resConfUnit2.conv2.bias: Tensor[(512), float32], %scratch.refinenet4.resConfUnit2.conv2.weight: Tensor[(512, 512, 3, 3), float32], %v483: Tensor[(1, 3, 1, 1), float32], %v485: Tensor[(1, 3, 1, 1), float32], %v500: Tensor[(1), int64], %v501: Tensor[(1), int64], %v502: Tensor[(1), int64], %v503: Tensor[(1), int64], %v509: float32, %v513: float32, %v514: float32, %v518: float32, %v519: float32, %v525: float32, %v526: float32, %v541: Tensor[(1), int64], %v542: Tensor[(1), int64], %v543: Tensor[(1), int64], %v544: Tensor[(1), int64], %v550: float32, %v554: float32, %v555: float32, %v561: float32, %v562: float32, %v566: float32, %v567: float32, %v574: float32, %v575: float32, %v579: float32, %v580: float32, %v587: float32, %v588: float32, %v603: Tensor[(1), int64], %v604: Tensor[(1), int64], %v605: Tensor[(1), int64], %v606: Tensor[(1), int64], %v612: float32, %v616: float32, %v617: float32, %v623: float32, %v624: float32, %v628: float32, %v629: float32, %v636: float32, %v637: float32, %v641: float32, %v642: float32, %v649: float32, %v650: float32, %v665: Tensor[(1), int64], %v666: Tensor[(1), int64], %v667: Tensor[(1), int64], %v668: Tensor[(1), int64], %v674: float32, %v678: float32, %v679: float32, %v685: float32, %v686: float32, %v690: float32, %v691: float32, %v698: float32, %v699: float32, %v703: float32, %v704: float32, %v711: float32, %v712: float32, %v716: float32, %v717: float32, %v724: float32, %v725: float32, %v729: float32, %v730: float32, %v737: float32, %v738: float32, %v742: float32, %v743: float32, %v749: float32, %v750: float32, %v754: float32, %v755: float32, %v762: float32, %v763: float32, %v767: float32, %v768: float32, %v775: float32, %v776: float32, %v780: float32, %v781: float32, %v788: float32, %v789: float32, %v793: float32, %v794: float32, %v801: float32, %v802: float32, %v817: Tensor[(1), int64], %v818: Tensor[(1), int64], %v819: Tensor[(1), int64], %v820: Tensor[(1), int64], %v826: float32, %v830: float32, %v831: float32, %v837: float32, %v838: float32, %v842: float32, %v843: float32, %v850: float32, %v851: float32, %v855: float32, %v856: float32, %v863: float32, %v864: float32, %v868: float32, %v869: float32, %v876: float32, %v877: float32, %v881: float32, %v882: float32, %v889: float32, %v890: float32, %v894: float32, %v895: float32, %v902: float32, %v903: float32, %v907: float32, %v908: float32) {