Hi,

I have writedown the center loss in tensorflow. I want to implement in pytorch. As some function are different between tensorflow and pytorch, anyone help me to sort out how to implement this code in pytorch. Thanks in advanced.

```
import tensorflow as tf
def get_center_loss(features, labels, alpha, num_classes):
len_features = features.get_shape()[1]
centers = tf.get_variable('centers', [num_classes, len_features], dtype=tf.float32,
initializer=tf.constant_initializer(0), trainable=False)
labels = tf.reshape(labels, [-1])
centers0=tf.unsorted_segment_mean(features,labels,num_classes)
EdgeWeights=tf.ones((num_classes,num_classes))-tf.eye(num_classes)
margin=tf.constant(100,dtype="float32")
norm = lambda x: tf.reduce_sum(tf.square(x), 1)
center_pairwise_dist = tf.transpose(norm(tf.expand_dims(centers0, 2) - tf.transpose(centers0)))
loss_0= tf.reduce_sum(tf.multiply(tf.maximum(0.0, margin-center_pairwise_dist),EdgeWeights))
centers_batch = tf.gather(centers, labels)
diff = centers_batch - features
unique_label, unique_idx, unique_count = tf.unique_with_counts(labels)
appear_times = tf.gather(unique_count, unique_idx)
appear_times = tf.reshape(appear_times, [-1, 1])
diff = diff / tf.cast((1 + appear_times), tf.float32)
diff = alpha * diff
loss_1 = tf.nn.l2_loss(features - centers_batch)
centers_update_op= tf.scatter_sub(centers, labels, diff)
return loss_0, loss_1, centers_update_op```
```