object_detection张量流api是否特定图像尺寸？

Question

我使用来自tensorflow模型存储库的object_detection 。

我想在我自己的数据集中训练非常具体的图像。 我所拥有的图像没有特定的尺寸，并且变化很大。

我得到的错误是：

InvalidArgumentError (see above for traceback): ConcatOp : Dimensions of inputs should match: shape[0] = [1,1446,1024,3] vs. shape[1] = [1,1449,1024,3]
     [[Node: concat_1 = ConcatV2[N=8, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](Preprocessor/sub, Preprocessor_1/sub, Preprocessor_2/sub, Preprocessor_3/sub, Preprocessor_4/sub, Preprocessor_5/sub, Preprocessor_6/sub, Preprocessor_7/sub, concat_1/axis)]]
     [[Node: MultiClassNonMaxSuppression_1/Equal/_3597 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_17245_MultiClassNonMaxSuppression_1/Equal", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

完整的输出可以在pastebin中找到。

下面是我使用的配置。

# Faster R-CNN with Resnet-50 (v1), configured for Oxford-IIT Pets Dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  faster_rcnn {
    num_classes: 16
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 600
        max_dimension: 1024
      }
    }
    feature_extractor {
      type: 'faster_rcnn_resnet50'
      first_stage_features_stride: 16
    }
    first_stage_anchor_generator {
      grid_anchor_generator {
        scales: [0.25, 0.5, 1.0, 2.0]
        aspect_ratios: [0.5, 1.0, 2.0]
        height_stride: 16
        width_stride: 16
      }
    }
    first_stage_box_predictor_conv_hyperparams {
      op: CONV
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        truncated_normal_initializer {
          stddev: 0.01
        }
      }
    }
    first_stage_nms_score_threshold: 0.0
    first_stage_nms_iou_threshold: 0.7
    first_stage_max_proposals: 300
    first_stage_localization_loss_weight: 2.0
    first_stage_objectness_loss_weight: 1.0
    initial_crop_size: 14
    maxpool_kernel_size: 2
    maxpool_stride: 2
    second_stage_box_predictor {
      mask_rcnn_box_predictor {
        use_dropout: false
        dropout_keep_probability: 1.0
        fc_hyperparams {
          op: FC
          regularizer {
            l2_regularizer {
              weight: 0.0
            }
          }
          initializer {
            variance_scaling_initializer {
              factor: 1.0
              uniform: true
              mode: FAN_AVG
            }
          }
        }
      }
    }
    second_stage_post_processing {
      batch_non_max_suppression {
        score_threshold: 0.0
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 300
      }
      score_converter: SOFTMAX
    }
    second_stage_localization_loss_weight: 2.0
    second_stage_classification_loss_weight: 1.0
  }
}

train_config: {
  batch_size: 8
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        manual_step_learning_rate {
          initial_learning_rate: 0.0003
          schedule {
            step: 0
            learning_rate: .0003
          }
          schedule {
            step: 900000
            learning_rate: .00003
          }
          schedule {
            step: 1200000
            learning_rate: .000003
          }
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  gradient_clipping_by_norm: 10.0
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
}

train_input_reader: {
  tf_record_input_reader {
    input_path: "train.record"
  }
  label_map_path: "label_map.pbtxt"
}

eval_config: {
  num_examples: 200
}

eval_input_reader: {
  tf_record_input_reader {
    input_path: "val.record"
  }
  label_map_path: "label_map.pbtxt"
}

问题1 ：检测API是否需要输入图像的特定尺寸？

问题2 ：出现此错误的原因是什么？ 如何解决错误或需要从哪里开始？

我已经尝试过的是给每个图像一个1024px和500px的宽度。

我采取的步骤：

1. 我创建了一个create_record.py文件，并创建了train.record和val.record文件。
2. 我运行了train.py，但由于上面的错误而失败了。

我在带有一个Nvidia GPU的Ubuntu 16.04上使用python 3.5.2。

Answer 1

我通过将batch_size更改为1解决了该问题。

问题在于每个图像的张量大小不同。 如果具有相同尺寸的图像，则可以将batch_size设置为更高。 由于不是这种情况，您必须将batch_size设置为1。

因此答案是，只要batch_size为1，api就可以处理不同的尺寸。