ValueError: Shape must be rank 2 but is rank 3 for 'MatMul' (op: 'MatMul') with input shapes: [?,1000,2048], [2048,64].

[zhangxgu]

The error raised when I run this code: ''' import loupe as lp import tensorflow as tf x = tf.placeholder("float", [None,1000,2048]) NetVLAD = lp.NetVLAD(feature_size=2048, max_samples=1000, cluster_size=64, output_dim=2048, gating=True, add_batch_norm=True, is_training=True) NetVLAD.forward(x) ''' I think this x.shape is #batch_size dot #max_sample dot #feature_size. Should I change line 126 in loupe.py into ''' cluster_weights = tf.get_variable("cluster_weights", [1, self.feature_size, self.cluster_size], initializer = tf.random_normal_initializer( stddev=1 / math.sqrt(self.feature_size))) ''' ? But it also lead to other error, can you help me? Thank you very much!

[antoine77340]

Hi, You are right sorry there was an error in my documentation. I am changing that. So in the documentation I wrote: Args: reshaped_input: The input in reshaped in the following form: 'batch_size' x 'max_samples' x 'feature_size'.

But I meant:

   Args:
    reshaped_input: Your input of form:
    'batch_size' x 'max_samples' x 'feature_size'.
    must be reshaped in that form:
     'batch_size*max_samples' x 'feature_size' by performing:
     reshaped_input = tf.reshape(input, [-1, feature_size])

So if you instead do x = tf.placeholder("float", [1,1000,2048]) # you can put any batch size and not only 1 x = tf.reshape(x, [-1,2048]) NetVLAD = lp.NetVLAD(feature_size=2048, max_samples=1000, cluster_size=64, output_dim=2048, gating=True, add_batch_norm=True, is_training=True) NetVLAD.forward(x)

It should work.

Thank you for pointing this.

Antoine