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Can not squeeze dim[1], expected a dimension of 1, got 2 for 'FeatureExtractor/MobilenetV1/Logits/SpatialSqueeze' #4556

Closed stevenson32 closed 6 years ago

stevenson32 commented 6 years ago

System information

InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'FeatureExtractor/MobilenetV1/Logits/SpatialSqueeze' (op: 'Squeeze') with input shapes: [250,2,2,8].

I get this error using model.ckpt from ssd_mobilenet_v1_coco_2017_11_17 and my own mobilenet models that have been fine tuned with no network modification.

I can run the eval script from the object detection API but am looking to add to slim's eval at a lower level for better metrics.

In order to get the object detection checkpointed model to run, the scope in mobilenet_v1.py has to be prepended with 'FeatureExtractor/'. From what I can gather, it seems this issue may be related to slim building the dataset different from the object detection API, because all model parameters as inspected via the 'inspect_checkpoint' tool are correct.

Source code / logs

Traceback
 (most recent call last)
~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
   1566   try:
-> 1567     c_op = c_api.TF_FinishOperation(op_desc)
   1568   except errors.InvalidArgumentError as e:

InvalidArgumentError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'FeatureExtractor/MobilenetV1/Logits/SpatialSqueeze' (op: 'Squeeze') with input shapes: [250,2,2,8].

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-1-b2f90a361522> in <module>()
    150 
    151 
--> 152 tf.app.run(main)

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\platform\app.py in run(main, argv)
    124   # Call the main function, passing through any arguments
    125   # to the final program.
--> 126   _sys.exit(main(argv))
    127 
    128 

<ipython-input-1-b2f90a361522> in main(unused_arg)
    147 
    148 def main(unused_arg):
--> 149   eval_model()
    150 
    151 

<ipython-input-1-b2f90a361522> in eval_model()
    135 def eval_model():
    136   """Evaluates mobilenet_v1."""
--> 137   g, eval_ops = build_model()
    138   with g.as_default():
    139     num_batches = math.ceil(FLAGS.num_examples / float(FLAGS.batch_size))

<ipython-input-1-b2f90a361522> in build_model()
    123           is_training=False,
    124           depth_multiplier=FLAGS.depth_multiplier,
--> 125           num_classes=FLAGS.num_classes)
    126 
    127     if FLAGS.quantize:

~\Anaconda3\envs\tensorflow2\Lib\site-packages\tensorflow\models\research\slim\nets\mobilenet_v1.py in mobilenet_v1(inputs, num_classes, dropout_keep_prob, is_training, min_depth, depth_multiplier, conv_defs, prediction_fn, spatial_squeeze, reuse, scope, global_pool)
    384                              normalizer_fn=None, scope='Conv2d_1c_1x1')
    385         if spatial_squeeze:
--> 386           logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
    387       end_points['Logits'] = logits
    388       if prediction_fn:

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\ops\array_ops.py in squeeze(input, axis, name, squeeze_dims)
   2628   if np.isscalar(axis):
   2629     axis = [axis]
-> 2630   return gen_array_ops.squeeze(input, axis, name)
   2631 
   2632 

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\ops\gen_array_ops.py in squeeze(input, axis, name)
   9662     axis = [_execute.make_int(_i, "axis") for _i in axis]
   9663     _, _, _op = _op_def_lib._apply_op_helper(
-> 9664         "Squeeze", input=input, squeeze_dims=axis, name=name)
   9665     _result = _op.outputs[:]
   9666     _inputs_flat = _op.inputs

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
    785         op = g.create_op(op_type_name, inputs, output_types, name=scope,
    786                          input_types=input_types, attrs=attr_protos,
--> 787                          op_def=op_def)
    788       return output_structure, op_def.is_stateful, op
    789 

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\ops.py in create_op(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_shapes, compute_device)
   3390           input_types=input_types,
   3391           original_op=self._default_original_op,
-> 3392           op_def=op_def)
   3393 
   3394       # Note: shapes are lazily computed with the C API enabled.

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
   1732           op_def, inputs, node_def.attr)
   1733       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1734                                 control_input_ops)
   1735     else:
   1736       self._c_op = None

~\Anaconda3\envs\tensorflow2\lib\site-packages\tensorflow\python\framework\ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
   1568   except errors.InvalidArgumentError as e:
   1569     # Convert to ValueError for backwards compatibility.
-> 1570     raise ValueError(str(e))
   1571 
   1572   return c_op

ValueError: Can not squeeze dim[1], expected a dimension of 1, got 2 for 'FeatureExtractor/MobilenetV1/Logits/SpatialSqueeze' (op: 'Squeeze') with input shapes: [250,2,2,8].
bitfort commented 6 years ago

Adding someone from slim who may be able to help.