deeplabv3+ 代码解析 去除decoder 部分

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。

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