warmspringwinds
ValueError: Variable fcn_8s/vgg_16/conv1/conv1_1/weights already exists, disallowed
Hi, @warmspringwinds ,
I followed the steps from README.md. And start to run the first cell from fully_convolutional_networks.ipynb . However I got the following ValueError, could you suggest me how to fix this error? (my environment: TF 0.12.1, python 2.7.13)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-4efcd9f3827b> in <module>()
47 pred, fcn_16s_variables_mapping = FCN_8s(image_batch_tensor=image_batch_tensor,
48 number_of_classes=number_of_classes,
---> 49 is_training=False)
50
51 # The op for initializing the variables.
/data/code/Dan_segment/tf-image-segmentation/tf_image_segmentation/utils/inference.pyc in new_network_definition(*args, **kwargs)
51 kwargs['image_batch_tensor'] = resized_images_batch
52
---> 53 all_outputs = network_definition(*args, **kwargs)
54
55 all_outputs = list(all_outputs)
/data/code/Dan_segment/tf-image-segmentation/tf_image_segmentation/models/fcn_8s.pyc in FCN_8s(image_batch_tensor, number_of_classes, is_training)
77 is_training=is_training,
78 spatial_squeeze=False,
---> 79 fc_conv_padding='SAME')
80
81
/data/code/Dan_segment/models/slim/nets/vgg.pyc in vgg_16(inputs, num_classes, is_training, dropout_keep_prob, spatial_squeeze, scope, fc_conv_padding)
164 with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
165 outputs_collections=end_points_collection):
--> 166 net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
167 net = slim.max_pool2d(net, [2, 2], scope='pool1')
168 net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.pyc in repeat(inputs, repetitions, layer, *args, **kwargs)
1668 for i in range(repetitions):
1669 kwargs['scope'] = scope + '_' + str(i+1)
-> 1670 outputs = layer(outputs, *args, **kwargs)
1671 return outputs
1672
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
175 current_args = current_scope[key_func].copy()
176 current_args.update(kwargs)
--> 177 return func(*args, **current_args)
178 _add_op(func)
179 setattr(func_with_args, '_key_op', _key_op(func))
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/layers/python/layers/layers.pyc in convolution(inputs, num_outputs, kernel_size, stride, padding, data_format, rate, activation_fn, normalizer_fn, normalizer_params, weights_initializer, weights_regularizer, biases_initializer, biases_regularizer, reuse, variables_collections, outputs_collections, trainable, scope)
838 regularizer=weights_regularizer,
839 collections=weights_collections,
--> 840 trainable=trainable)
841 outputs = nn.convolution(input=inputs,
842 filter=weights,
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
175 current_args = current_scope[key_func].copy()
176 current_args.update(kwargs)
--> 177 return func(*args, **current_args)
178 _add_op(func)
179 setattr(func_with_args, '_key_op', _key_op(func))
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.pyc in model_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device)
242 initializer=initializer, regularizer=regularizer,
243 trainable=trainable, collections=collections,
--> 244 caching_device=caching_device, device=device)
245
246
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
175 current_args = current_scope[key_func].copy()
176 current_args.update(kwargs)
--> 177 return func(*args, **current_args)
178 _add_op(func)
179 setattr(func_with_args, '_key_op', _key_op(func))
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.pyc in variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device)
206 trainable=trainable,
207 collections=collections,
--> 208 caching_device=caching_device)
209
210
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
1022 collections=collections, caching_device=caching_device,
1023 partitioner=partitioner, validate_shape=validate_shape,
-> 1024 custom_getter=custom_getter)
1025
1026
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
848 collections=collections, caching_device=caching_device,
849 partitioner=partitioner, validate_shape=validate_shape,
--> 850 custom_getter=custom_getter)
851
852 def _get_partitioned_variable(self,
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
344 reuse=reuse, trainable=trainable, collections=collections,
345 caching_device=caching_device, partitioner=partitioner,
--> 346 validate_shape=validate_shape)
347
348 def _get_partitioned_variable(
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape)
329 initializer=initializer, regularizer=regularizer, reuse=reuse,
330 trainable=trainable, collections=collections,
--> 331 caching_device=caching_device, validate_shape=validate_shape)
332
333 if custom_getter is not None:
/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape)
630 " Did you mean to set reuse=True in VarScope? "
631 "Originally defined at:\n\n%s" % (
--> 632 name, "".join(traceback.format_list(tb))))
633 found_var = self._vars[name]
634 if not shape.is_compatible_with(found_var.get_shape()):
ValueError: Variable fcn_8s/vgg_16/conv1/conv1_1/weights already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
File "/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 208, in variable
caching_device=caching_device)
File "/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 177, in func_with_args
return func(*args, **current_args)
File "/root/anaconda2/envs/python_2.7_tf_0.12/lib/python2.7/site-packages/tensorflow/contrib/framework/python/ops/variables.py", line 244, in model_variable
caching_device=caching_device, device=device)
@amiltonwong @xilaili
Thank you for your issue.
I guess there might be two reasons for that:
- You didn't change the global variables according to your setup here:
sys.path.append("tf-image-segmentation/")
sys.path.append("/home/dpakhom1/workspace/my_models/slim/")
fcn_16s_checkpoint_path = '/home/dpakhom1/tf_projects/segmentation/model_fcn8s_final.ckpt'
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
- Or you try to run the same cell twice. Restart the notebook or restart the kernel of the current notebook.
Let me know if it helps
Hi @warmspringwinds, I also followed the instructions on the repository and got an error.
Is this global variable setup correct? I cloned tf-image-segmentation and models/slim into my workspace, then:
sys.path.append("/Users/username/Desktop/FCN Segmentation/tf-image-segmentation/") sys.path.append("/Users/username/Desktop/FCN Segmentation/models/slim")
fcn_16s_checkpoint_path = '/Users/username/Desktop/FCN Segmentation/FCN Checkpoints/model_fcn8s_final.ckpt'
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
I am getting this error: name 'adapt_network_for_any_size_input' is not defined
Global variables are not pointing correctly the files fcn_8s.py, inference.py and pascal_voc.py.
Hope you can review this problem I'm having. Thank you.
Hi, @warmspringwinds i get this error after carefully running as explained.
AttributeError Traceback (most recent call last)
AttributeError: 'module' object has no attribute 'pack'
But i am using TF1.1.0. So, this could be a version problem?
Hi,@warmspringwinds. I get this error:"logits and labels must be same size: logits_size=[168960,2] labels_size=[187500,2]".
Hope you can review this problem I'm having. Thank you!!!!!
@sharib-ms yes it a version problem:
Analogously to tf.pack and tf.unpack, we're renamed TensorArray.pack and TensorArray.unpack to TensorArray.stack and TensorArray.unstack. However, TensorArray.pack and TensorArray.unpack cannot be detected lexically since they are indirectly related to the tf namespace e.g. foo = tf.TensorArray(); foo.unpack()
All information is found: https://www.tensorflow.org/install/migration
You can change it to tf.stack and it should work.
maybe you ran the code once , try to restart the kernel,it would clear the weights you have set before
