我正在尝试基于Tensorflow Object Detection API构建多尺度对象检测推理代码。但是，当在一个会话中预测同一图像的差异比例上的框时，我不太清楚如何解决变量重用问题。这就是我所做的以及我不明白的地方：
在https://github.com/tensorflow/models/blob/master/research/object_detection/evaluator.py中，我再次复制'prediction_dict = model.predict（preprocessed_image，true_image_shapes）'这一行，如下所示。
def _extract_predictions_and_losses(model,
create_input_dict_fn,
ignore_groundtruth=False):
"""Constructs tensorflow detection graph and returns output tensors.
Args:
model: model to perform predictions with.
create_input_dict_fn: function to create input tensor dictionaries.
ignore_groundtruth: whether groundtruth should be ignored.
Returns:
prediction_groundtruth_dict: A dictionary with postprocessed tensors (keyed
by standard_fields.DetectionResultsFields) and optional groundtruth
tensors (keyed by standard_fields.InputDataFields).
losses_dict: A dictionary containing detection losses. This is empty when
ignore_groundtruth is true.
"""
input_dict = create_input_dict_fn()
prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
input_dict = prefetch_queue.dequeue()
original_image = tf.expand_dims(input_dict[fields.InputDataFields.image], 0)
preprocessed_image, true_image_shapes = model.preprocess(
tf.to_float(original_image))
prediction_dict1 = model.predict(preprocessed_image, true_image_shapes)
/****Some code to resize preprocessed_image****/
prediction_dict2 = model.predict(preprocessed_image, true_image_shapes)
detections = model.postprocess(prediction_dict, true_image_shapes)
groundtruth = None
losses_dict = {}
if not ignore_groundtruth:
这给了我以下错误：
Traceback (most recent call last):
File "object_detection/eval_fddb.py", line 167, in <module>
tf.app.run()
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "object_detection/eval_fddb.py", line 163, in main
FLAGS.checkpoint_dir, FLAGS.eval_dir)
File "/local/mnt/workspace/chris/projects/models/object_detection/evaluator_fddb.py", line 261, in evaluate
create_input_dict_fn=create_input_dict_fn)
File "/local/mnt/workspace/chris/projects/models/object_detection/evaluator_fddb.py", line 187, in _extract_prediction_tensors
prediction_dict = model.predict(preprocessed_image)
File "/local/mnt/workspace/chris/projects/models/object_detection/meta_architectures/umd_meta_arch.py", line 362, in predict
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
File "/local/mnt/workspace/chris/projects/models/object_detection/meta_architectures/umd_meta_arch.py", line 278, in _extract_rpn_feature_maps
preprocessed_inputs, scope=self.first_stage_feature_extractor_scope)
File "/local/mnt/workspace/chris/projects/models/object_detection/meta_architectures/faster_rcnn_meta_arch.py", line 154, in extract_proposal_features_w_internal_layers
return self._extract_proposal_features_w_internal_layers(preprocessed_inputs, scope)
File "/local/mnt/workspace/chris/projects/models/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py", line 173, in _extract_proposal_features_w_internal_layers
scope=var_scope)
File "/local/mnt/workspace/chris/projects/models/slim/nets/resnet_v1.py", line 300, in resnet_v1_101
reuse=reuse, scope=scope)
File "/local/mnt/workspace/chris/projects/models/slim/nets/resnet_v1.py", line 214, in resnet_v1
net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1')
File "/local/mnt/workspace/chris/projects/models/slim/nets/resnet_utils.py", line 122, in conv2d_same
rate=rate, padding='VALID', scope=scope)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1027, in convolution
outputs = layer.apply(inputs)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 503, in apply
return self.__call__(inputs, *args, **kwargs)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 443, in __call__
self.build(input_shapes[0])
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/convolutional.py", line 137, in build
dtype=self.dtype)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/layers/base.py", line 383, in add_variable
trainable=trainable and self.trainable)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 1065, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 962, in get_variable
use_resource=use_resource, custom_getter=custom_getter)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 360, in get_variable
validate_shape=validate_shape, use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1561, in layer_variable_getter
return _model_variable_getter(getter, *args, **kwargs)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1553, in _model_variable_getter
custom_getter=getter, use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 261, in model_variable
use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 216, in variable
use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 352, in _true_getter
use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.py", line 664, in _get_single_variable
name, "".join(traceback.format_list(tb))))
ValueError: Variable FirstStageFeatureExtractor/resnet_v1_101/conv1/weights already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 216, in variable
use_resource=use_resource)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/local/mnt/workspace/chris/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 261, in model_variable
use_resource=use_resource)
我的理解是，每次调用model.predict（）时，它都会创建一个包含所有变量的模型。但是在第二次，我无法创建所有变量，因为它们都存在同名。我尝试为第二个model.predict（）添加一个带有'reuse = True'的变量作用域，但由于名称不匹配，它根本不会加载变量。
我的问题是：
如何通过在一个sess.run（）中的两个不同比例图像上运行相同的模型来解决这个问题？
非常感谢。
答案 0 :(得分：0)
在构建模型之前，您是否尝试过使用tf.variable_scope（）？
with tf.variable_scope('first_prediction'):
prediction_dict1 = model.predict(preprocessed_image, true_image_shapes)
with tf.variable_scope('second_prediction'):
prediction_dict2 = model.predict(preprocessed_image, true_image_shapes)
这样，两个模型的原始变量名称应具有不同的前缀，从而避免了重用问题。