models/efficientnet_v2_l.py [9:57]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def __init__(self): super().__init__() self.features_0_0 = torch.nn.modules.conv.Conv2d(3, 24, 3, 2, 1, bias=False) self.features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False) self.features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_3 = torch.nn.modules.conv.Conv2d(24, 32, 1, 1, 0, bias=False) self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(32, 128, 3, 2, 1, bias=False) self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(128, 64, 1, 1, 0, bias=False) self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - models/efficientnet_v2_xl.py [9:57]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def __init__(self): super().__init__() self.features_0_0 = torch.nn.modules.conv.Conv2d(3, 24, 3, 2, 1, bias=False) self.features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_0 = torch.nn.modules.conv.Conv2d(24, 24, 3, 1, 1, bias=False) self.features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(24) self.features_1_conv_3 = torch.nn.modules.conv.Conv2d(24, 32, 1, 1, 0, bias=False) self.features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_2_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_2_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_4_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, bias=False) self.features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_4_conv_3 = torch.nn.modules.conv.Conv2d(32, 32, 1, 1, 0, bias=False) self.features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32) self.features_5_conv_0 = torch.nn.modules.conv.Conv2d(32, 128, 3, 2, 1, bias=False) self.features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(128) self.features_5_conv_3 = torch.nn.modules.conv.Conv2d(128, 64, 1, 1, 0, bias=False) self.features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_6_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_6_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_7_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_7_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_8_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_9_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_10_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) self.features_11_conv_0 = torch.nn.modules.conv.Conv2d(64, 256, 3, 1, 1, bias=False) self.features_11_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(256) self.features_11_conv_3 = torch.nn.modules.conv.Conv2d(256, 64, 1, 1, 0, bias=False) self.features_11_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64) - 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