deeplabv3+ 代码解析去除decoder 部分

心已赠人 2024-04-19 09:10 66阅读 0赞

只需要训练时不提供参数“

--decoder_output_stride=4 \

”

就可以直接去掉decode部分，那么取而代之的就是下面要讨论的。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNDk4NTM_size_16_color_FFFFFF_t_70][]

可以看出来，直接将16倍的特征图上采样到了原始图片大小。并且使用的是21通道的小特征图。

从代码来看：

def _get_logits(images,
                    model_options,
                    weight_decay=0.0001,
                    reuse=None,
                    is_training=False,
                    fine_tune_batch_norm=False,
                    nas_training_hyper_parameters=None):
      """Gets the logits by atrous/image spatial pyramid pooling.
    
      Args:
        images: A tensor of size [batch, height, width, channels].
        model_options: A ModelOptions instance to configure models.
        weight_decay: The weight decay for model variables.
        reuse: Reuse the model variables or not.
        is_training: Is training or not.
        fine_tune_batch_norm: Fine-tune the batch norm parameters or not.
        nas_training_hyper_parameters: A dictionary storing hyper-parameters for
          training nas models. Its keys are:
          - `drop_path_keep_prob`: Probability to keep each path in the cell when
            training.
          - `total_training_steps`: Total training steps to help drop path
            probability calculation.
    
      Returns:
        outputs_to_logits: A map from output_type to logits.
      """
      features, end_points = extract_features(
          images,
          model_options,
          weight_decay=weight_decay,
          reuse=reuse,
          is_training=is_training,
          fine_tune_batch_norm=fine_tune_batch_norm,
          nas_training_hyper_parameters=nas_training_hyper_parameters)
    
      if model_options.decoder_output_stride is not None:
        features = refine_by_decoder(
            features,
            end_points,
            crop_size=model_options.crop_size,
            decoder_output_stride=model_options.decoder_output_stride,
            decoder_use_separable_conv=model_options.decoder_use_separable_conv,
            model_variant=model_options.model_variant,
            weight_decay=weight_decay,
            reuse=reuse,
            is_training=is_training,
            fine_tune_batch_norm=fine_tune_batch_norm,
            use_bounded_activation=model_options.use_bounded_activation)
    
      outputs_to_logits = {}
      for output in sorted(model_options.outputs_to_num_classes):
        outputs_to_logits[output] = get_branch_logits(
            features,
            model_options.outputs_to_num_classes[output],
            model_options.atrous_rates,
            aspp_with_batch_norm=model_options.aspp_with_batch_norm,
            kernel_size=model_options.logits_kernel_size,
            weight_decay=weight_decay,
            reuse=reuse,
            scope_suffix=output)
    
      return outputs_to_logits

直接跳过decoder，将(33,33,256)特征图传入get\_branch\_logits. 处理过后得到(33,33,21)特征图。

之后直接在train\_utils.py还原到原图大小。

if upsample_logits:
          # Label is not downsampled, and instead we upsample logits.
          logits = tf.image.resize_bilinear(
              logits,
              preprocess_utils.resolve_shape(labels, 4)[1:3],
              align_corners=True)
          scaled_labels = labels

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNDk4NTM_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/18/94bb48cc41924811a5ebb92ac82aa26f.png