MobileNet SSD目标检测维度错误：能否用1024x33图像训练？

尝试使用TensorFlow Object Detection API实现ssd_mobilenet_v2_fpnlite_320x320目标检测算法，输入图像尺寸设置为1024x33，运行训练脚本时触发如下ValueError：

ValueError: Dimensions must be equal, but are 4 and 3 for '{{node ssd_mobile_net_v2_fpn_keras_feature_extractor/FeatureMaps/top_down/add}} = AddV2[T=DT_FLOAT](ssd_mobile_net_v2_fpn_keras_feature_extractor/FeatureMaps/top_down/nearest_neighbor_upsampling/nearest_neighbor_upsampling/Reshape_1, ssd_mobile_net_v2_fpn_keras_feature_extractor/FeatureMaps/top_down/projection_2/BiasAdd)' with input shapes: [16,4,64,128], [16,3,64,128].
        
        Call arguments received:
          • image_features=[("'layer_7'", 'tf.Tensor(shape=(16, 5, 128, 32), dtype=float32)'), ("'layer_14'", 'tf.Tensor(shape=(16, 3, 64, 96), dtype=float32)'), ("'layer_19'", 'tf.Tensor(shape=(16, 2, 32, 1280), dtype=float32)')]
    
    Call arguments received:
      • inputs=tf.Tensor(shape=(16, 33, 1024, 3), dtype=float32)
      • kwargs={'training': 'False'}

对应的pipeline.config配置如下：

# SSD with Mobilenet v2 FPN-lite (go/fpn-lite) feature extractor, shared box
# predictor and focal loss (a mobile version of Retinanet).
# Retinanet: see Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 22.2 mAP on COCO17 Val

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: false
    num_classes: 1
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 2
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 33
        width: 1024
      }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 128
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true,
            decay: 0.997,
            epsilon: 0.001,
          }
        }
        num_layers_before_predictor: 4
        share_prediction_tower: true
        use_depthwise: true
        kernel_size: 3
      }
    }
    feature_extractor {
      type: 'ssd_mobilenet_v2_fpn_keras'
      use_depthwise: true
      fpn {
        min_level: 3
        max_level: 7
        additional_layer_depth: 128
      }
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          random_normal_initializer {
            stddev: 0.01
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.997,
          epsilon: 0.001,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 2.0
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "/content/models/mymodel/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  batch_size: 16
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 8
  num_steps: 100
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_object_covered: 0.0
      min_aspect_ratio: 0.75
      max_aspect_ratio: 3.0
      min_area: 0.75
      max_area: 1.0
      overlap_thresh: 0.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .08
          total_steps: 50000
          warmup_learning_rate: .026666
          warmup_steps: 1000
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
}

train_input_reader: {
  label_map_path: "/content/labelmap.pbtxt"
  tf_record_input_reader {
    input_path: "/content/train.tfrecord"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
}

eval_input_reader: {
  label_map_path: "/content/labelmap.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "/content/val.tfrecord"
  }
}

核心疑问：是否可以使用1024x33这个图像尺寸进行该模型的训练？

错误原因 #

报错源于FPN（特征金字塔网络）的top-down特征融合环节：上层特征图经过上采样后，高度维度为4，而对应下层特征图的高度维度为3，两者无法进行逐元素相加操作。

这是因为输入图像高度33无法被多次2倍下采样整除：33经过第一次下采样变为16（取整），第二次8，第三次4，第四次2；但部分中间层特征（比如layer_14）的高度是3，和上采样后的特征高度4不匹配，导致维度冲突。原模型ssd_mobilenet_v2_fpnlite_320x320的输入尺寸320是2的幂次，下采样后所有层级特征图的尺寸都是整数，不会出现这类问题。

是否能用该尺寸训练？ #

不是完全不可行，但不推荐直接使用33作为高度，原因是需要修改API源码调整特征融合逻辑，成本很高。更高效的解决方式如下：

1. 修改输入高度为可被多次下采样整除的数值 #

把输入高度从33改为32（宽度1024保持不变），32是2的5次方，经过多次下采样后所有层级的特征图尺寸都会是整数，FPN融合时维度能完美对齐，直接解决报错问题。

修改pipeline.config中image_resizer的配置：

image_resizer {
  fixed_shape_resizer {
    height: 32
    width: 1024
  }
}

2. 若必须保留33高度（不推荐） #

需要修改TensorFlow Object Detection API中ssd_mobilenet_v2_fpn_keras_feature_extractor.py里的特征融合逻辑，手动调整上采样或特征图裁剪的尺寸，让对应层级的特征图维度匹配。这种方式需要对模型结构有深入理解，且后续维护成本高，仅适合有定制化需求的开发者。

内容的提问来源于stack exchange，提问作者mj01