/usr/lib/python2.7/dist-packages/pkg_resources.py:1031: UserWarning: /home/ubuntu/.python-eggs is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).
  warnings.warn(msg, UserWarning)
Opts: {
  prototxt = "",
  format = "lua",
  input = "tmodels/resnet-34.t7",
  caffemodel = "",
  preprocessing = "",
  input_dims = {
    0
  },
  verify = "",
  input_tensor = ""
}	
Parsed opts: %s	{
  prototxt = "",
  inputs = {
    {
      name = "data",
      input_dims = {
        0
      }
    }
  },
  input = "tmodels/resnet-34.t7",
  format = "lua",
  preprocessing = "",
  verify = "",
  input_dims = {
    0
  },
  caffemodel = ""
}	
Running with model: 	
nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> output]
  (1): cudnn.SpatialConvolution(3 -> 64, 7x7, 2,2, 3,3)
  (2): nn.SpatialBatchNormalization
  (3): cudnn.ReLU
  (4): nn.SpatialMaxPooling(3x3, 2,2, 1,1)
  (5): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> output]
    (1): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (2): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (3): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
  }
  (6): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> (4) -> output]
    (1): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(64 -> 128, 3x3, 2,2, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): cudnn.SpatialConvolution(64 -> 128, 1x1, 2,2)
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (2): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (3): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (4): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
  }
  (7): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
    (1): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(128 -> 256, 3x3, 2,2, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): cudnn.SpatialConvolution(128 -> 256, 1x1, 2,2)
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (2): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (3): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (4): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (5): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (6): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
  }
  (8): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> output]
    (1): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(256 -> 512, 3x3, 2,2, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): cudnn.SpatialConvolution(256 -> 512, 1x1, 2,2)
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (2): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
    (3): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
          |      (2): nn.SpatialBatchNormalization
          |      (3): cudnn.ReLU
          |      (4): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
          |      (5): nn.SpatialBatchNormalization
          |    }
          |`-> (2): nn.Identity
           ... -> output
      }
      (2): nn.CAddTable
      (3): cudnn.ReLU
    }
  }
  (9): cudnn.SpatialAveragePooling(7x7, 1,1)
  (10): nn.View(512)
  (11): nn.Linear(512 -> 1000)
}
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_0: bottom=[data], top=[caffe.SpatialConvolution_0]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_1: bottom=[caffe.SpatialConvolution_0], top=[caffe.BatchNorm_1]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_2: bottom=[caffe.BatchNorm_1], top=[caffe.BatchNorm_1]
INFO:torch2caffe.lib_py:added layer caffe.Pooling_3: bottom=[caffe.BatchNorm_1], top=[caffe.Pooling_3]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_4: bottom=[caffe.Pooling_3], top=[caffe.SpatialConvolution_4]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_5: bottom=[caffe.SpatialConvolution_4], top=[caffe.BatchNorm_5]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_6: bottom=[caffe.BatchNorm_5], top=[caffe.BatchNorm_5]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_7: bottom=[caffe.BatchNorm_5], top=[caffe.SpatialConvolution_7]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_8: bottom=[caffe.SpatialConvolution_7], top=[caffe.BatchNorm_8]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_9: bottom=[caffe.BatchNorm_8,caffe.Pooling_3], top=[caffe.Eltwise_9]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_10: bottom=[caffe.Eltwise_9], top=[caffe.Eltwise_9]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_11: bottom=[caffe.Eltwise_9], top=[caffe.SpatialConvolution_11]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_12: bottom=[caffe.SpatialConvolution_11], top=[caffe.BatchNorm_12]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_13: bottom=[caffe.BatchNorm_12], top=[caffe.BatchNorm_12]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_14: bottom=[caffe.BatchNorm_12], top=[caffe.SpatialConvolution_14]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_15: bottom=[caffe.SpatialConvolution_14], top=[caffe.BatchNorm_15]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_16: bottom=[caffe.BatchNorm_15,caffe.Eltwise_9], top=[caffe.Eltwise_16]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_17: bottom=[caffe.Eltwise_16], top=[caffe.Eltwise_16]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_18: bottom=[caffe.Eltwise_16], top=[caffe.SpatialConvolution_18]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_19: bottom=[caffe.SpatialConvolution_18], top=[caffe.BatchNorm_19]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_20: bottom=[caffe.BatchNorm_19], top=[caffe.BatchNorm_19]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_21: bottom=[caffe.BatchNorm_19], top=[caffe.SpatialConvolution_21]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_22: bottom=[caffe.SpatialConvolution_21], top=[caffe.BatchNorm_22]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_23: bottom=[caffe.BatchNorm_22,caffe.Eltwise_16], top=[caffe.Eltwise_23]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_24: bottom=[caffe.Eltwise_23], top=[caffe.Eltwise_23]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_25: bottom=[caffe.Eltwise_23], top=[caffe.SpatialConvolution_25]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_26: bottom=[caffe.SpatialConvolution_25], top=[caffe.BatchNorm_26]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_27: bottom=[caffe.BatchNorm_26], top=[caffe.BatchNorm_26]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_28: bottom=[caffe.BatchNorm_26], top=[caffe.SpatialConvolution_28]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_29: bottom=[caffe.SpatialConvolution_28], top=[caffe.BatchNorm_29]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_30: bottom=[caffe.Eltwise_23], top=[caffe.SpatialConvolution_30]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_31: bottom=[caffe.BatchNorm_29,caffe.SpatialConvolution_30], top=[caffe.Eltwise_31]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_32: bottom=[caffe.Eltwise_31], top=[caffe.Eltwise_31]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_33: bottom=[caffe.Eltwise_31], top=[caffe.SpatialConvolution_33]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_34: bottom=[caffe.SpatialConvolution_33], top=[caffe.BatchNorm_34]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_35: bottom=[caffe.BatchNorm_34], top=[caffe.BatchNorm_34]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_36: bottom=[caffe.BatchNorm_34], top=[caffe.SpatialConvolution_36]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_37: bottom=[caffe.SpatialConvolution_36], top=[caffe.BatchNorm_37]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_38: bottom=[caffe.BatchNorm_37,caffe.Eltwise_31], top=[caffe.Eltwise_38]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_39: bottom=[caffe.Eltwise_38], top=[caffe.Eltwise_38]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_40: bottom=[caffe.Eltwise_38], top=[caffe.SpatialConvolution_40]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_41: bottom=[caffe.SpatialConvolution_40], top=[caffe.BatchNorm_41]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_42: bottom=[caffe.BatchNorm_41], top=[caffe.BatchNorm_41]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_43: bottom=[caffe.BatchNorm_41], top=[caffe.SpatialConvolution_43]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_44: bottom=[caffe.SpatialConvolution_43], top=[caffe.BatchNorm_44]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_45: bottom=[caffe.BatchNorm_44,caffe.Eltwise_38], top=[caffe.Eltwise_45]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_46: bottom=[caffe.Eltwise_45], top=[caffe.Eltwise_45]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_47: bottom=[caffe.Eltwise_45], top=[caffe.SpatialConvolution_47]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_48: bottom=[caffe.SpatialConvolution_47], top=[caffe.BatchNorm_48]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_49: bottom=[caffe.BatchNorm_48], top=[caffe.BatchNorm_48]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_50: bottom=[caffe.BatchNorm_48], top=[caffe.SpatialConvolution_50]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_51: bottom=[caffe.SpatialConvolution_50], top=[caffe.BatchNorm_51]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_52: bottom=[caffe.BatchNorm_51,caffe.Eltwise_45], top=[caffe.Eltwise_52]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_53: bottom=[caffe.Eltwise_52], top=[caffe.Eltwise_52]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_54: bottom=[caffe.Eltwise_52], top=[caffe.SpatialConvolution_54]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_55: bottom=[caffe.SpatialConvolution_54], top=[caffe.BatchNorm_55]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_56: bottom=[caffe.BatchNorm_55], top=[caffe.BatchNorm_55]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_57: bottom=[caffe.BatchNorm_55], top=[caffe.SpatialConvolution_57]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_58: bottom=[caffe.SpatialConvolution_57], top=[caffe.BatchNorm_58]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_59: bottom=[caffe.Eltwise_52], top=[caffe.SpatialConvolution_59]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_60: bottom=[caffe.BatchNorm_58,caffe.SpatialConvolution_59], top=[caffe.Eltwise_60]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_61: bottom=[caffe.Eltwise_60], top=[caffe.Eltwise_60]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_62: bottom=[caffe.Eltwise_60], top=[caffe.SpatialConvolution_62]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_63: bottom=[caffe.SpatialConvolution_62], top=[caffe.BatchNorm_63]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_64: bottom=[caffe.BatchNorm_63], top=[caffe.BatchNorm_63]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_65: bottom=[caffe.BatchNorm_63], top=[caffe.SpatialConvolution_65]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_66: bottom=[caffe.SpatialConvolution_65], top=[caffe.BatchNorm_66]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_67: bottom=[caffe.BatchNorm_66,caffe.Eltwise_60], top=[caffe.Eltwise_67]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_68: bottom=[caffe.Eltwise_67], top=[caffe.Eltwise_67]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_69: bottom=[caffe.Eltwise_67], top=[caffe.SpatialConvolution_69]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_70: bottom=[caffe.SpatialConvolution_69], top=[caffe.BatchNorm_70]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_71: bottom=[caffe.BatchNorm_70], top=[caffe.BatchNorm_70]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_72: bottom=[caffe.BatchNorm_70], top=[caffe.SpatialConvolution_72]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_73: bottom=[caffe.SpatialConvolution_72], top=[caffe.BatchNorm_73]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_74: bottom=[caffe.BatchNorm_73,caffe.Eltwise_67], top=[caffe.Eltwise_74]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_75: bottom=[caffe.Eltwise_74], top=[caffe.Eltwise_74]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_76: bottom=[caffe.Eltwise_74], top=[caffe.SpatialConvolution_76]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_77: bottom=[caffe.SpatialConvolution_76], top=[caffe.BatchNorm_77]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_78: bottom=[caffe.BatchNorm_77], top=[caffe.BatchNorm_77]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_79: bottom=[caffe.BatchNorm_77], top=[caffe.SpatialConvolution_79]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_80: bottom=[caffe.SpatialConvolution_79], top=[caffe.BatchNorm_80]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_81: bottom=[caffe.BatchNorm_80,caffe.Eltwise_74], top=[caffe.Eltwise_81]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_82: bottom=[caffe.Eltwise_81], top=[caffe.Eltwise_81]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_83: bottom=[caffe.Eltwise_81], top=[caffe.SpatialConvolution_83]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_84: bottom=[caffe.SpatialConvolution_83], top=[caffe.BatchNorm_84]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_85: bottom=[caffe.BatchNorm_84], top=[caffe.BatchNorm_84]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_86: bottom=[caffe.BatchNorm_84], top=[caffe.SpatialConvolution_86]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_87: bottom=[caffe.SpatialConvolution_86], top=[caffe.BatchNorm_87]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_88: bottom=[caffe.BatchNorm_87,caffe.Eltwise_81], top=[caffe.Eltwise_88]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_89: bottom=[caffe.Eltwise_88], top=[caffe.Eltwise_88]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_90: bottom=[caffe.Eltwise_88], top=[caffe.SpatialConvolution_90]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_91: bottom=[caffe.SpatialConvolution_90], top=[caffe.BatchNorm_91]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_92: bottom=[caffe.BatchNorm_91], top=[caffe.BatchNorm_91]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_93: bottom=[caffe.BatchNorm_91], top=[caffe.SpatialConvolution_93]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_94: bottom=[caffe.SpatialConvolution_93], top=[caffe.BatchNorm_94]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_95: bottom=[caffe.BatchNorm_94,caffe.Eltwise_88], top=[caffe.Eltwise_95]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_96: bottom=[caffe.Eltwise_95], top=[caffe.Eltwise_95]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_97: bottom=[caffe.Eltwise_95], top=[caffe.SpatialConvolution_97]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_98: bottom=[caffe.SpatialConvolution_97], top=[caffe.BatchNorm_98]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_99: bottom=[caffe.BatchNorm_98], top=[caffe.BatchNorm_98]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_100: bottom=[caffe.BatchNorm_98], top=[caffe.SpatialConvolution_100]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_101: bottom=[caffe.SpatialConvolution_100], top=[caffe.BatchNorm_101]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_102: bottom=[caffe.Eltwise_95], top=[caffe.SpatialConvolution_102]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_103: bottom=[caffe.BatchNorm_101,caffe.SpatialConvolution_102], top=[caffe.Eltwise_103]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_104: bottom=[caffe.Eltwise_103], top=[caffe.Eltwise_103]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_105: bottom=[caffe.Eltwise_103], top=[caffe.SpatialConvolution_105]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_106: bottom=[caffe.SpatialConvolution_105], top=[caffe.BatchNorm_106]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_107: bottom=[caffe.BatchNorm_106], top=[caffe.BatchNorm_106]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_108: bottom=[caffe.BatchNorm_106], top=[caffe.SpatialConvolution_108]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_109: bottom=[caffe.SpatialConvolution_108], top=[caffe.BatchNorm_109]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_110: bottom=[caffe.BatchNorm_109,caffe.Eltwise_103], top=[caffe.Eltwise_110]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_111: bottom=[caffe.Eltwise_110], top=[caffe.Eltwise_110]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_112: bottom=[caffe.Eltwise_110], top=[caffe.SpatialConvolution_112]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_113: bottom=[caffe.SpatialConvolution_112], top=[caffe.BatchNorm_113]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_114: bottom=[caffe.BatchNorm_113], top=[caffe.BatchNorm_113]
INFO:torch2caffe.lib_py:added layer caffe.SpatialConvolution_115: bottom=[caffe.BatchNorm_113], top=[caffe.SpatialConvolution_115]
INFO:torch2caffe.lib_py:added layer caffe.BatchNorm_116: bottom=[caffe.SpatialConvolution_115], top=[caffe.BatchNorm_116]
INFO:torch2caffe.lib_py:added layer caffe.Eltwise_117: bottom=[caffe.BatchNorm_116,caffe.Eltwise_110], top=[caffe.Eltwise_117]
INFO:torch2caffe.lib_py:added layer caffe.ReLU_118: bottom=[caffe.Eltwise_117], top=[caffe.Eltwise_117]
INFO:torch2caffe.lib_py:added layer caffe.Pooling_119: bottom=[caffe.Eltwise_117], top=[caffe.Pooling_119]
INFO:torch2caffe.lib_py:added layer caffe.Flatten_120: bottom=[caffe.Pooling_119], top=[caffe.Flatten_120]
INFO:torch2caffe.lib_py:added layer caffe.InnerProduct_121: bottom=[caffe.Flatten_120], top=[caffe.InnerProduct_121]
ERROR:torch2caffe.caffe_layers:Exception on converting caffe.SpatialConvolution, {'scaleT': array([ 1.], dtype=float32), '_type': 'torch.CudaTensor', 'weight_offset': 9408.0, 'padW': 3.0, 'dH': 2.0, 'benchmarked': True, 'padH': 3.0, 'kH': 7.0, 'output_offset': 802816.0, 'nOutputPlane': 64.0, 'input_offset': 150528.0, 'train': False, 'kW': 7.0, 'groups': 1.0, 'dW': 2.0, 'nInputPlane': 3.0}
Traceback (most recent call last):
  File "/mnt/fb-caffe-exts/torch2caffe/caffe_layers.py", line 344, in convert
    return converter[typename](torch_layer)
  File "/mnt/fb-caffe-exts/torch2caffe/caffe_layers.py", line 128, in spatial_convolution
    weight = torch_layer["weight"]
KeyError: u'weight'
/mnt/download/torch/install/bin/luajit: ./torch2caffe/lib.lua:162: Python error: opaque ref: call
Traceback (most recent call last):
  File "/mnt/fb-caffe-exts/torch2caffe/lib_py.py", line 135, in finalize
    opts)
  File "/mnt/fb-caffe-exts/torch2caffe/caffe_builder.py", line 27, in to_caffe
    opts, layer.typename, layer.torch_layer)
  File "/mnt/fb-caffe-exts/torch2caffe/caffe_layers.py", line 344, in convert
    return converter[typename](torch_layer)
  File "/mnt/fb-caffe-exts/torch2caffe/caffe_layers.py", line 128, in spatial_convolution
    weight = torch_layer["weight"]
KeyError: u'weight'

stack traceback:
	[C]: in function 'finalize'
	./torch2caffe/lib.lua:162: in function 'convert'
	./torch2caffe/lib.lua:168: in function 'main'
	torch2caffe/torch2caffe.lua:23: in main chunk
	[C]: in function 'dofile'
	...load/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
	[C]: at 0x00406670