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#609 Ci fix

Merged
Ghost merged 1 commits into Deci-AI:master from deci-ai:bugfix/infra-000_ci
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  1. from super_gradients.training.models import ResNeXt50, ResNeXt101, GoogleNetV1
  2. from super_gradients.training.models.classification_models import repvgg, efficientnet, densenet, resnet, regnet
  3. from super_gradients.training.models.classification_models.mobilenetv2 import MobileNetV2Base, MobileNetV2_135, CustomMobileNetV2
  4. from super_gradients.training.models.classification_models.mobilenetv3 import mobilenetv3_large, mobilenetv3_small, mobilenetv3_custom
  5. from super_gradients.training.models.classification_models.shufflenetv2 import (
  6. ShufflenetV2_x0_5,
  7. ShufflenetV2_x1_0,
  8. ShufflenetV2_x1_5,
  9. ShufflenetV2_x2_0,
  10. CustomizedShuffleNetV2,
  11. )
  12. from super_gradients.training.models.classification_models.vit import ViTBase, ViTLarge, ViTHuge
  13. from super_gradients.training.models.detection_models.csp_darknet53 import CSPDarknet53
  14. from super_gradients.training.models.detection_models.darknet53 import Darknet53
  15. from super_gradients.training.models.detection_models.ssd import SSDMobileNetV1, SSDLiteMobileNetV2
  16. from super_gradients.training.models.detection_models.yolox import YoloX_N, YoloX_T, YoloX_S, YoloX_M, YoloX_L, YoloX_X, CustomYoloX
  17. from super_gradients.training.models.segmentation_models.ddrnet import DDRNet23, DDRNet23Slim, AnyBackBoneDDRNet23
  18. from super_gradients.training.models.segmentation_models.regseg import RegSeg48
  19. from super_gradients.training.models.segmentation_models.shelfnet import ShelfNet18_LW, ShelfNet34_LW, ShelfNet50, ShelfNet503343, ShelfNet101
  20. from super_gradients.training.models.segmentation_models.stdc import STDC1Classification, STDC2Classification, STDC1Seg, STDC2Seg, STDCSegmentationBase
  21. from super_gradients.training.models.kd_modules.kd_module import KDModule
  22. from super_gradients.training.models.classification_models.beit import BeitBasePatch16_224, BeitLargePatch16_224
  23. from super_gradients.training.models.segmentation_models.ppliteseg import PPLiteSegT, PPLiteSegB
  24. from super_gradients.training.models.segmentation_models.unet import UNetCustom, UnetClassification
  25. from super_gradients.common.object_names import Models
  26. ARCHITECTURES = {
  27. Models.RESNET18: resnet.ResNet18,
  28. Models.RESNET34: resnet.ResNet34,
  29. Models.RESNET50_3343: resnet.ResNet50_3343,
  30. Models.RESNET50: resnet.ResNet50,
  31. Models.RESNET101: resnet.ResNet101,
  32. Models.RESNET152: resnet.ResNet152,
  33. Models.RESNET18_CIFAR: resnet.ResNet18Cifar,
  34. Models.CUSTOM_RESNET: resnet.CustomizedResnet,
  35. Models.CUSTOM_RESNET50: resnet.CustomizedResnet50,
  36. Models.CUSTOM_RESNET_CIFAR: resnet.CustomizedResnetCifar,
  37. Models.CUSTOM_RESNET50_CIFAR: resnet.CustomizedResnet50Cifar,
  38. Models.MOBILENET_V2: MobileNetV2Base,
  39. Models.MOBILE_NET_V2_135: MobileNetV2_135,
  40. Models.CUSTOM_MOBILENET_V2: CustomMobileNetV2,
  41. Models.MOBILENET_V3_LARGE: mobilenetv3_large,
  42. Models.MOBILENET_V3_SMALL: mobilenetv3_small,
  43. Models.MOBILENET_V3_CUSTOM: mobilenetv3_custom,
  44. Models.CUSTOM_DENSENET: densenet.CustomizedDensnet,
  45. Models.DENSENET121: densenet.DenseNet121,
  46. Models.DENSENET161: densenet.DenseNet161,
  47. Models.DENSENET169: densenet.DenseNet169,
  48. Models.DENSENET201: densenet.DenseNet201,
  49. Models.SHELFNET18_LW: ShelfNet18_LW,
  50. Models.SHELFNET34_LW: ShelfNet34_LW,
  51. Models.SHELFNET50_3343: ShelfNet503343,
  52. Models.SHELFNET50: ShelfNet50,
  53. Models.SHELFNET101: ShelfNet101,
  54. Models.SHUFFLENET_V2_X0_5: ShufflenetV2_x0_5,
  55. Models.SHUFFLENET_V2_X1_0: ShufflenetV2_x1_0,
  56. Models.SHUFFLENET_V2_X1_5: ShufflenetV2_x1_5,
  57. Models.SHUFFLENET_V2_X2_0: ShufflenetV2_x2_0,
  58. Models.SHUFFLENET_V2_CUSTOM5: CustomizedShuffleNetV2,
  59. Models.DARKNET53: Darknet53,
  60. Models.CSP_DARKNET53: CSPDarknet53,
  61. Models.RESNEXT50: ResNeXt50,
  62. Models.RESNEXT101: ResNeXt101,
  63. Models.GOOGLENET_V1: GoogleNetV1,
  64. Models.EFFICIENTNET_B0: efficientnet.EfficientNetB0,
  65. Models.EFFICIENTNET_B1: efficientnet.EfficientNetB1,
  66. Models.EFFICIENTNET_B2: efficientnet.EfficientNetB2,
  67. Models.EFFICIENTNET_B3: efficientnet.EfficientNetB3,
  68. Models.EFFICIENTNET_B4: efficientnet.EfficientNetB4,
  69. Models.EFFICIENTNET_B5: efficientnet.EfficientNetB5,
  70. Models.EFFICIENTNET_B6: efficientnet.EfficientNetB6,
  71. Models.EFFICIENTNET_B7: efficientnet.EfficientNetB7,
  72. Models.EFFICIENTNET_B8: efficientnet.EfficientNetB8,
  73. Models.EFFICIENTNET_L2: efficientnet.EfficientNetL2,
  74. Models.CUSTOMIZEDEFFICIENTNET: efficientnet.CustomizedEfficientnet,
  75. Models.REGNETY200: regnet.RegNetY200,
  76. Models.REGNETY400: regnet.RegNetY400,
  77. Models.REGNETY600: regnet.RegNetY600,
  78. Models.REGNETY800: regnet.RegNetY800,
  79. Models.CUSTOM_REGNET: regnet.CustomRegNet,
  80. Models.NAS_REGNET: regnet.NASRegNet,
  81. Models.YOLOX_N: YoloX_N,
  82. Models.YOLOX_T: YoloX_T,
  83. Models.YOLOX_S: YoloX_S,
  84. Models.YOLOX_M: YoloX_M,
  85. Models.YOLOX_L: YoloX_L,
  86. Models.YOLOX_X: YoloX_X,
  87. Models.CUSTOM_YOLO_X: CustomYoloX,
  88. Models.SSD_MOBILENET_V1: SSDMobileNetV1,
  89. Models.SSD_LITE_MOBILENET_V2: SSDLiteMobileNetV2,
  90. Models.REPVGG_A0: repvgg.RepVggA0,
  91. Models.REPVGG_A1: repvgg.RepVggA1,
  92. Models.REPVGG_A2: repvgg.RepVggA2,
  93. Models.REPVGG_B0: repvgg.RepVggB0,
  94. Models.REPVGG_B1: repvgg.RepVggB1,
  95. Models.REPVGG_B2: repvgg.RepVggB2,
  96. Models.REPVGG_B3: repvgg.RepVggB3,
  97. Models.REPVGG_D2SE: repvgg.RepVggD2SE,
  98. Models.REPVGG_CUSTOM: repvgg.RepVggCustom,
  99. Models.DDRNET_23: DDRNet23,
  100. Models.DDRNET_23_SLIM: DDRNet23Slim,
  101. Models.CUSTOM_DDRNET_23: AnyBackBoneDDRNet23,
  102. Models.STDC1_CLASSIFICATION: STDC1Classification,
  103. Models.STDC2_CLASSIFICATION: STDC2Classification,
  104. Models.STDC1_SEG: STDC1Seg,
  105. Models.STDC1_SEG50: STDC1Seg,
  106. Models.STDC1_SEG75: STDC1Seg,
  107. Models.STDC2_SEG: STDC2Seg,
  108. Models.STDC2_SEG50: STDC2Seg,
  109. Models.STDC2_SEG75: STDC2Seg,
  110. Models.CUSTOM_STDC: STDCSegmentationBase,
  111. Models.REGSEG48: RegSeg48,
  112. Models.KD_MODULE: KDModule,
  113. Models.VIT_BASE: ViTBase,
  114. Models.VIT_LARGE: ViTLarge,
  115. Models.VIT_HUGE: ViTHuge,
  116. Models.BEIT_BASE_PATCH16_224: BeitBasePatch16_224,
  117. Models.BEIT_LARGE_PATCH16_224: BeitLargePatch16_224,
  118. Models.PP_LITE_T_SEG: PPLiteSegT,
  119. Models.PP_LITE_T_SEG50: PPLiteSegT,
  120. Models.PP_LITE_T_SEG75: PPLiteSegT,
  121. Models.PP_LITE_B_SEG: PPLiteSegB,
  122. Models.PP_LITE_B_SEG50: PPLiteSegB,
  123. Models.PP_LITE_B_SEG75: PPLiteSegB,
  124. Models.CUSTOM_ANYNET: regnet.CustomAnyNet,
  125. Models.UNET_CUSTOM: UNetCustom,
  126. Models.UNET_CUSTOM_CLS: UnetClassification,
  127. }
  128. KD_ARCHITECTURES = {Models.KD_MODULE: KDModule}
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