Python tensorflow.scatter_mul() Examples
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code examples of tensorflow.scatter_mul().
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Example #1
Source File: memory.py From DOTA_models with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #2
Source File: memory.py From yolo_v2 with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #3
Source File: memory.py From Gun-Detector with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #4
Source File: scatter_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testVariableRankMul(self): self._VariableRankTests(tf.scatter_mul)
Example #5
Source File: scatter_ops_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def testRepeatIndicesMul(self): self._VariableRankTests(tf.scatter_mul, True)
Example #6
Source File: memory.py From hands-detection with MIT License | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #7
Source File: memory.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #8
Source File: memory.py From object_detection_with_tensorflow with MIT License | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #9
Source File: memory.py From HumanRecognition with MIT License | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #10
Source File: memory.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #11
Source File: memory.py From models with Apache License 2.0 | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)
Example #12
Source File: memory.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def make_update_op(self, upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output): """Function that creates all the update ops.""" base_update_op = super(LSHMemory, self).make_update_op( upd_idxs, upd_keys, upd_vals, batch_size, use_recent_idx, intended_output) # compute hash slots to be updated hash_slot_idxs = self.get_hash_slots(upd_keys) # make updates update_ops = [] with tf.control_dependencies([base_update_op]): for i, slot_idxs in enumerate(hash_slot_idxs): # for each slot, choose which entry to replace entry_idx = tf.random_uniform([batch_size], maxval=self.num_per_hash_slot, dtype=tf.int32) entry_mul = 1 - tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32) entry_add = (tf.expand_dims(upd_idxs, 1) * tf.one_hot(entry_idx, self.num_per_hash_slot, dtype=tf.int32)) mul_op = tf.scatter_mul(self.hash_slots[i], slot_idxs, entry_mul) with tf.control_dependencies([mul_op]): add_op = tf.scatter_add(self.hash_slots[i], slot_idxs, entry_add) update_ops.append(add_op) return tf.group(*update_ops)