train.py 文件源码

def build_finetunable_model(inputs, cfg):

  with slim.arg_scope([slim.conv2d], 
                      activation_fn=tf.nn.relu,
                      normalizer_fn=slim.batch_norm,
                      weights_regularizer=slim.l2_regularizer(0.00004),
                      biases_regularizer=slim.l2_regularizer(0.00004)) as scope:

      batch_norm_params = {
        'decay': cfg.BATCHNORM_MOVING_AVERAGE_DECAY,
        'epsilon': 0.001,
        'variables_collections' : [],
        'is_training' : False
      }
      with slim.arg_scope([slim.conv2d], normalizer_params=batch_norm_params):
        features, _ = model.inception_resnet_v2(inputs, reuse=False, scope='InceptionResnetV2')

      # Save off the original variables (for ease of restoring)
      model_variables = slim.get_model_variables()
      inception_vars = {var.op.name:var for var in model_variables}

      batch_norm_params = {
        'decay': cfg.BATCHNORM_MOVING_AVERAGE_DECAY,
        'epsilon': 0.001,
        'variables_collections' : [tf.GraphKeys.MOVING_AVERAGE_VARIABLES],
        'is_training' : True
      }
      with slim.arg_scope([slim.conv2d], normalizer_params=batch_norm_params):

        # Add on the detection heads
        locs, confs, _ = model.build_detection_heads(features, cfg.NUM_BBOXES_PER_CELL)
        model_variables = slim.get_model_variables()
        detection_vars = {var.op.name:var for var in model_variables if var.op.name not in inception_vars}

  return locs, confs, inception_vars, detection_vars