我已经下载了一个实现VGG16 ConvNet的张量流GraphDef,我用它做了这个:
Pl['images'] = tf.placeholder(tf.float32,
[None, 448, 448, 3],
name="images") #batch x width x height x channels
with open("tensorflow-vgg16/vgg16.tfmodel", mode='rb') as f:
fileContent = f.read()
graph_def = tf.GraphDef()
graph_def.ParseFromString(fileContent)
tf.import_graph_def(graph_def, input_map={"images": Pl['images']})
此外,我的图像特征与"import/pool5/"
的输出是同质的。
如何告诉我的图表不想使用他的输入"images"
,而是将张量"import/pool5/"
作为输入?
谢谢!
修改
好的,我意识到我不是很清楚。情况如下:
我正在尝试使用this implementation的ROI池,使用预先训练的VGG16,我有GraphDef格式。所以这就是我的工作:
首先,我加载模型:
tf.reset_default_graph()
with open("tensorflow-vgg16/vgg16.tfmodel",
mode='rb') as f:
fileContent = f.read()
graph_def = tf.GraphDef()
graph_def.ParseFromString(fileContent)
graph = tf.get_default_graph()
然后,我创建了占位符
images = tf.placeholder(tf.float32,
[None, 448, 448, 3],
name="images") #batch x width x height x channels
boxes = tf.placeholder(tf.float32,
[None,5], # 5 = [batch_id,x1,y1,x2,y2]
name = "boxes")
我将图的第一部分的输出定义为conv5_3 / Relu
tf.import_graph_def(graph_def,
input_map={'images':images})
out_tensor = graph.get_tensor_by_name("import/conv5_3/Relu:0")
因此,out_tensor
的形状为[None,14,14,512]
然后,我进行投资回报汇集:
[out_pool,argmax] = module.roi_pool(out_tensor,
boxes,
7,7,1.0/1)
使用out_pool.shape = N_Boxes_in_batch x 7 x 7 x 512
,与pool5
同质。然后,我想将out_pool
作为pool5
之后的操作的输入,因此它看起来像
tf.import_graph_def(graph.as_graph_def(),
input_map={'import/pool5':out_pool})
但它不起作用,我有这个错误:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-89-527398d7344b> in <module>()
5
6 tf.import_graph_def(graph.as_graph_def(),
----> 7 input_map={'import/pool5':out_pool})
8
9 final_out = graph.get_tensor_by_name("import/Relu_1:0")
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/importer.py in import_graph_def(graph_def, input_map, return_elements, name, op_dict)
333 # NOTE(mrry): If the graph contains a cycle, the full shape information
334 # may not be available for this op's inputs.
--> 335 ops.set_shapes_for_outputs(op)
336
337 # Apply device functions for this op.
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py in set_shapes_for_outputs(op)
1610 raise RuntimeError("No shape function registered for standard op: %s"
1611 % op.type)
-> 1612 shapes = shape_func(op)
1613 if len(op.outputs) != len(shapes):
1614 raise RuntimeError(
/home/hbenyounes/vqa/roi_pooling_op_grad.py in _roi_pool_shape(op)
13 channels = dims_data[3]
14 print(op.inputs[1].name, op.inputs[1].get_shape())
---> 15 dims_rois = op.inputs[1].get_shape().as_list()
16 num_rois = dims_rois[0]
17
/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/tensor_shape.py in as_list(self)
745 A list of integers or None for each dimension.
746 """
--> 747 return [dim.value for dim in self._dims]
748
749 def as_proto(self):
TypeError: 'NoneType' object is not iterable
有任何线索吗?
答案 0 :(得分:1)
我会做的是这样的事情:
- 首先检索张量的名称,这些张量代表VGG16中pool5之后的3个完全连接层的权重和偏差。
要做到这一点,我会检查[n.name for n in graph.as_graph_def().node]
。
(它们可能看起来像import / locali / weight:0,import / locali / bias:0等)
- 将它们放在python列表中:
weights_names=["import/local1/weight:0" ,"import/local2/weight:0" ,"import/local3/weight:0"]
biases_names=["import/local1/bias:0" ,"import/local2/bias:0" ,"import/local3/bias:0"]
- 定义一个类似于:
的函数def pool5_tofcX(input_tensor, layer_number=3):
flatten=tf.reshape(input_tensor,(-1,7*7*512))
tmp=flatten
for i in xrange(layer_number):
tmp=tf.matmul(tmp, graph.get_tensor_by_name(weights_name[i]))
tmp=tf.nn.bias_add(tmp, graph.get_tensor_by_name(biases_name[i]))
tmp=tf.nn.relu(tmp)
return tmp
然后使用函数定义张量:
wanted_output=pool5_tofcX(out_pool)
然后你就完成了!
答案 1 :(得分:1)
使用tf.train.export_meta_graph
来存储整个MetaGraph通常非常方便。然后,在恢复时,您可以使用tf.train.import_meta_graph
,,因为结果是它将所有其他参数传递给具有import_scoped_meta_graph
参数的基础input_map
并使用它当它到达它自己的import_graph_def
调用时。
没有记录,并且花了很多时间才找到它,但它确实有效!
答案 2 :(得分:0)
Jonan Georgiev在这里提供了一个很好的答案。在此git问题的结尾,还描述了相同的方法,但几乎没有大张声调:https://github.com/tensorflow/tensorflow/issues/3389
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return res.json(users);
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tf.data.Dataset
张量切换占位符的复制/粘贴可运行示例。
get_next
输出,我们可以清楚地看到平方运算的输入已更改。
import tensorflow as tf
my_placeholder = tf.placeholder(dtype=tf.float32, shape=1, name='my_placeholder')
my_op = tf.square(my_placeholder, name='my_op')
# Save the graph to memory
graph_def = tf.get_default_graph().as_graph_def()
print('----- my_op before any remapping -----')
print([n for n in graph_def.node if n.name == 'my_op'])
tf.reset_default_graph()
ds = tf.data.Dataset.from_tensors(1.0)
next_tensor = tf.data.make_one_shot_iterator(ds).get_next(name='my_next_tensor')
# Restore the graph with a custom input mapping
tf.graph_util.import_graph_def(graph_def, input_map={'my_placeholder': next_tensor}, name='')
print('----- my_op after remapping -----')
print([n for n in tf.get_default_graph().as_graph_def().node if n.name == 'my_op'])