I am working on using yolov3 for end application. Since YOLOv3 is very huge I thought of using dynamic quantization. I have loaded the pre-trained weight and the model looks like below. Please help me with this problem.
Darknet(
(module_list): ModuleList(
(0): Sequential(
(conv_0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_0): LeakyReLU(negative_slope=0.1)
)
(1): Sequential(
(conv_1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(batch_norm_1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_1): LeakyReLU(negative_slope=0.1)
)
(2): Sequential(
(conv_2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_2): LeakyReLU(negative_slope=0.1)
)
(3): Sequential(
(conv_3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_3): LeakyReLU(negative_slope=0.1)
)
(4): Sequential(
(shortcut_4): EmptyLayer()
)
(5): Sequential(
(conv_5): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(batch_norm_5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_5): LeakyReLU(negative_slope=0.1)
)
(6): Sequential(
(conv_6): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_6): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_6): LeakyReLU(negative_slope=0.1)
)
(7): Sequential(
(conv_7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_7): LeakyReLU(negative_slope=0.1)
)
(8): Sequential(
(shortcut_8): EmptyLayer()
)
(9): Sequential(
(conv_9): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_9): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_9): LeakyReLU(negative_slope=0.1)
)
(10): Sequential(
(conv_10): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_10): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_10): LeakyReLU(negative_slope=0.1)
)
(11): Sequential(
(shortcut_11): EmptyLayer()
)
(12): Sequential(
(conv_12): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(batch_norm_12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_12): LeakyReLU(negative_slope=0.1)
)
(13): Sequential(
(conv_13): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_13): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_13): LeakyReLU(negative_slope=0.1)
)
(14): Sequential(
(conv_14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_14): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_14): LeakyReLU(negative_slope=0.1)
)
(15): Sequential(
(shortcut_15): EmptyLayer()
)
(16): Sequential(
(conv_16): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_16): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_16): LeakyReLU(negative_slope=0.1)
)
(17): Sequential(
(conv_17): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_17): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_17): LeakyReLU(negative_slope=0.1)
)
(18): Sequential(
(shortcut_18): EmptyLayer()
)
(19): Sequential(
(conv_19): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_19): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_19): LeakyReLU(negative_slope=0.1)
)
(20): Sequential(
(conv_20): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_20): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_20): LeakyReLU(negative_slope=0.1)
)
(21): Sequential(
(shortcut_21): EmptyLayer()
)
(22): Sequential(
(conv_22): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_22): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_22): LeakyReLU(negative_slope=0.1)
)
(23): Sequential(
(conv_23): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_23): LeakyReLU(negative_slope=0.1)
)
(24): Sequential(
(shortcut_24): EmptyLayer()
)
(25): Sequential(
(conv_25): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_25): LeakyReLU(negative_slope=0.1)
)
(26): Sequential(
(conv_26): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_26): LeakyReLU(negative_slope=0.1)
)
(27): Sequential(
(shortcut_27): EmptyLayer()
)
(28): Sequential(
(conv_28): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_28): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_28): LeakyReLU(negative_slope=0.1)
)
(29): Sequential(
(conv_29): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_29): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_29): LeakyReLU(negative_slope=0.1)
)
(30): Sequential(
(shortcut_30): EmptyLayer()
)
(31): Sequential(
(conv_31): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_31): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_31): LeakyReLU(negative_slope=0.1)
)
(32): Sequential(
(conv_32): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_32): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_32): LeakyReLU(negative_slope=0.1)
)
(33): Sequential(
(shortcut_33): EmptyLayer()
)
(34): Sequential(
(conv_34): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_34): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_34): LeakyReLU(negative_slope=0.1)
)
(35): Sequential(
(conv_35): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_35): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_35): LeakyReLU(negative_slope=0.1)
)
(36): Sequential(
(shortcut_36): EmptyLayer()
)
(37): Sequential(
(conv_37): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(batch_norm_37): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_37): LeakyReLU(negative_slope=0.1)
)
(38): Sequential(
(conv_38): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_38): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_38): LeakyReLU(negative_slope=0.1)
)
(39): Sequential(
(conv_39): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_39): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_39): LeakyReLU(negative_slope=0.1)
)
(40): Sequential(
(shortcut_40): EmptyLayer()
)
(41): Sequential(
(conv_41): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_41): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_41): LeakyReLU(negative_slope=0.1)
)
(42): Sequential(
(conv_42): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_42): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_42): LeakyReLU(negative_slope=0.1)
)
(43): Sequential(
(shortcut_43): EmptyLayer()
)
(44): Sequential(
(conv_44): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_44): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_44): LeakyReLU(negative_slope=0.1)
)
(45): Sequential(
(conv_45): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_45): LeakyReLU(negative_slope=0.1)
)
(46): Sequential(
(shortcut_46): EmptyLayer()
)
(47): Sequential(
(conv_47): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_47): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_47): LeakyReLU(negative_slope=0.1)
)
(48): Sequential(
(conv_48): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_48): LeakyReLU(negative_slope=0.1)
)
(49): Sequential(
(shortcut_49): EmptyLayer()
)
(50): Sequential(
(conv_50): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_50): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_50): LeakyReLU(negative_slope=0.1)
)
(51): Sequential(
(conv_51): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_51): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_51): LeakyReLU(negative_slope=0.1)
)
(52): Sequential(
(shortcut_52): EmptyLayer()
)
(53): Sequential(
(conv_53): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_53): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_53): LeakyReLU(negative_slope=0.1)
)
(54): Sequential(
(conv_54): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_54): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_54): LeakyReLU(negative_slope=0.1)
)
(55): Sequential(
(shortcut_55): EmptyLayer()
)
(56): Sequential(
(conv_56): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_56): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_56): LeakyReLU(negative_slope=0.1)
)
(57): Sequential(
(conv_57): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_57): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_57): LeakyReLU(negative_slope=0.1)
)
(58): Sequential(
(shortcut_58): EmptyLayer()
)
(59): Sequential(
(conv_59): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_59): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_59): LeakyReLU(negative_slope=0.1)
)
(60): Sequential(
(conv_60): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_60): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_60): LeakyReLU(negative_slope=0.1)
)
(61): Sequential(
(shortcut_61): EmptyLayer()
)
(62): Sequential(
(conv_62): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(batch_norm_62): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_62): LeakyReLU(negative_slope=0.1)
)
(63): Sequential(
(conv_63): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_63): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_63): LeakyReLU(negative_slope=0.1)
)
(64): Sequential(
(conv_64): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_64): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_64): LeakyReLU(negative_slope=0.1)
)
(65): Sequential(
(shortcut_65): EmptyLayer()
)
(66): Sequential(
(conv_66): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_66): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_66): LeakyReLU(negative_slope=0.1)
)
(67): Sequential(
(conv_67): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_67): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_67): LeakyReLU(negative_slope=0.1)
)
(68): Sequential(
(shortcut_68): EmptyLayer()
)
(69): Sequential(
(conv_69): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_69): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_69): LeakyReLU(negative_slope=0.1)
)
(70): Sequential(
(conv_70): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_70): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_70): LeakyReLU(negative_slope=0.1)
)
(71): Sequential(
(shortcut_71): EmptyLayer()
)
(72): Sequential(
(conv_72): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_72): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_72): LeakyReLU(negative_slope=0.1)
)
(73): Sequential(
(conv_73): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_73): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_73): LeakyReLU(negative_slope=0.1)
)
(74): Sequential(
(shortcut_74): EmptyLayer()
)
(75): Sequential(
(conv_75): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_75): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_75): LeakyReLU(negative_slope=0.1)
)
(76): Sequential(
(conv_76): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_76): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_76): LeakyReLU(negative_slope=0.1)
)
(77): Sequential(
(conv_77): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_77): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_77): LeakyReLU(negative_slope=0.1)
)
(78): Sequential(
(conv_78): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_78): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_78): LeakyReLU(negative_slope=0.1)
)
(79): Sequential(
(conv_79): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_79): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_79): LeakyReLU(negative_slope=0.1)
)
(80): Sequential(
(conv_80): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_80): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_80): LeakyReLU(negative_slope=0.1)
)
(81): Sequential(
(conv_81): Conv2d(1024, 255, kernel_size=(1, 1), stride=(1, 1))
)
(82): Sequential(
(yolo_82): YOLOLayer(
(mse_loss): MSELoss()
(bce_loss): BCELoss()
(ce_loss): CrossEntropyLoss()
)
)
(83): Sequential(
(route_83): EmptyLayer()
)
(84): Sequential(
(conv_84): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_84): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_84): LeakyReLU(negative_slope=0.1)
)
(85): Sequential(
(upsample_85): Upsample(scale_factor=2.0, mode=nearest)
)
(86): Sequential(
(route_86): EmptyLayer()
)
(87): Sequential(
(conv_87): Conv2d(768, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_87): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_87): LeakyReLU(negative_slope=0.1)
)
(88): Sequential(
(conv_88): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_88): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_88): LeakyReLU(negative_slope=0.1)
)
(89): Sequential(
(conv_89): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_89): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_89): LeakyReLU(negative_slope=0.1)
)
(90): Sequential(
(conv_90): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_90): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_90): LeakyReLU(negative_slope=0.1)
)
(91): Sequential(
(conv_91): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_91): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_91): LeakyReLU(negative_slope=0.1)
)
(92): Sequential(
(conv_92): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_92): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_92): LeakyReLU(negative_slope=0.1)
)
(93): Sequential(
(conv_93): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))
)
(94): Sequential(
(yolo_94): YOLOLayer(
(mse_loss): MSELoss()
(bce_loss): BCELoss()
(ce_loss): CrossEntropyLoss()
)
)
(95): Sequential(
(route_95): EmptyLayer()
)
(96): Sequential(
(conv_96): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_96): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_96): LeakyReLU(negative_slope=0.1)
)
(97): Sequential(
(upsample_97): Upsample(scale_factor=2.0, mode=nearest)
)
(98): Sequential(
(route_98): EmptyLayer()
)
(99): Sequential(
(conv_99): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_99): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_99): LeakyReLU(negative_slope=0.1)
)
(100): Sequential(
(conv_100): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_100): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_100): LeakyReLU(negative_slope=0.1)
)
(101): Sequential(
(conv_101): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_101): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_101): LeakyReLU(negative_slope=0.1)
)
(102): Sequential(
(conv_102): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_102): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_102): LeakyReLU(negative_slope=0.1)
)
(103): Sequential(
(conv_103): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(batch_norm_103): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_103): LeakyReLU(negative_slope=0.1)
)
(104): Sequential(
(conv_104): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(batch_norm_104): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(leaky_104): LeakyReLU(negative_slope=0.1)
)
(105): Sequential(
(conv_105): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))
)
(106): Sequential(
(yolo_106): YOLOLayer(
(mse_loss): MSELoss()
(bce_loss): BCELoss()
(ce_loss): CrossEntropyLoss()
)
)
)
)