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AM3_TADAM.py
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AM3_TADAM.py
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#!/usr/bin/env python3
"""Training and evaluation entry point."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import argparse
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.framework import dtypes
from scipy.spatial import KDTree
from datasets.data import _load_mini_imagenet
from common.util import Dataset
from common.util import ACTIVATION_MAP
from tqdm import trange
import pathlib
import logging
from common.util import summary_writer
from common.gen_experiments import load_and_save_params
import time
import pickle as pkl
tf.logging.set_verbosity(tf.logging.INFO)
logging.basicConfig(level=logging.INFO)
def get_image_size(data_dir):
if 'mini-imagenet' or 'tiered' in data_dir:
image_size = 84
elif 'cifar' in data_dir:
image_size = 32
else:
raise Exception('Unknown dataset: %s' % data_dir)
return image_size
class Namespace(object):
def __init__(self, adict):
self.__dict__.update(adict)
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='train',
choices=['train', 'eval', 'test', 'train_classifier', 'create_embedding'])
# Dataset parameters
parser.add_argument('--data_dir', type=str, default=None, help='Path to the data.')
parser.add_argument('--data_split', type=str, default='sources', choices=['sources', 'target_val', 'target_tst'],
help='Split of the data to be used to perform operation.')
# Training parameters
parser.add_argument('--number_of_steps', type=int, default=int(30000),
help="Number of training steps (number of Epochs in Hugo's paper)")
parser.add_argument('--number_of_steps_to_early_stop', type=int, default=int(1000000),
help="Number of training steps after half way to early stop the training")
parser.add_argument('--log_dir', type=str, default='', help='dir saving all the models and logs')
parser.add_argument('--num_classes_train', type=int, default=5,
help='Number of classes in the train phase, this is coming from the prototypical networks')
parser.add_argument('--num_shots_train', type=int, default=5,
help='Number of shots in a few shot meta-train scenario')
parser.add_argument('--train_batch_size', type=int, default=32, help='Training batch size.')
parser.add_argument('--pre_train_batch_size', type=int, default=64,
help='Batch size to pretrain feature extractor.')
parser.add_argument('--num_tasks_per_batch', type=int, default=2,
help='Number of few shot tasks per batch, so the task encoding batch is num_tasks_per_batch x num_classes_test x num_shots_train .')
parser.add_argument('--init_learning_rate', type=float, default=0.1, help='Initial learning rate.')
parser.add_argument('--save_summaries_secs', type=int, default=60, help='Time between saving summaries')
parser.add_argument('--save_interval_secs', type=int, default=60, help='Time between saving model?')
parser.add_argument('--num_classes_pretrain', type=int, default=64,
help='number of classes when jointly training on the entire train dataset')
parser.add_argument('--optimizer', type=str, default='sgd', choices=['sgd', 'adam'])
parser.add_argument('--augment', type=bool, default=False)
# Learning rate paramteres
parser.add_argument('--lr_anneal', type=str, default='pwc', choices=['const', 'pwc', 'cos', 'exp'])
parser.add_argument('--n_lr_decay', type=int, default=3)
parser.add_argument('--lr_decay_rate', type=float, default=10.0)
parser.add_argument('--num_steps_decay_pwc', type=int, default=2500,
help='Decay learning rate every num_steps_decay_pwc')
parser.add_argument('--clip_gradient_norm', type=float, default=1.0, help='gradient clip norm.')
parser.add_argument('--weights_initializer_factor', type=float, default=0.1,
help='multiplier in the variance of the initialization noise.')
# Evaluation parameters
parser.add_argument('--max_number_of_evaluations', type=float, default=float('inf'))
parser.add_argument('--eval_interval_secs', type=int, default=120, help='Time between evaluating model')
parser.add_argument('--eval_interval_steps', type=int, default=1000,
help='Number of train steps between evaluating model in the training loop')
parser.add_argument('--eval_interval_fine_steps', type=int, default=250,
help='Number of train steps between evaluating model in the training loop in the final phase')
parser.add_argument('--eval_batch_size', type=int, default=100, help='Evaluation batch size?')
# Test parameters
parser.add_argument('--num_classes_test', type=int, default=5, help='Number of classes in the test phase')
parser.add_argument('--num_shots_test', type=int, default=5,
help='Number of shots in a few shot meta-test scenario')
parser.add_argument('--num_cases_test', type=int, default=50000,
help='Number of few-shot cases to compute test accuracy')
parser.add_argument('--pretrained_model_dir', type=str,
default='',
help='Path to the pretrained model.')
# Architecture parameters
parser.add_argument('--dropout', type=float, default=1.0)
parser.add_argument('--fc_dropout', type=float, default=None, help='Dropout before the final fully connected layer')
parser.add_argument('--weight_decay', type=float, default=0.0005)
parser.add_argument('--weight_decay_cbn', type=float, default=0.01)
parser.add_argument('--num_filters', type=int, default=64)
parser.add_argument('--num_units_in_block', type=int, default=3)
parser.add_argument('--num_blocks', type=int, default=4)
parser.add_argument('--num_max_pools', type=int, default=3)
parser.add_argument('--block_size_growth', type=float, default=2.0)
parser.add_argument('--activation', type=str, default='swish-1', choices=['relu', 'selu', 'swish-1'])
parser.add_argument('--feature_dropout_p', type=float, default=None)
parser.add_argument('--feature_expansion_size', type=int, default=None)
parser.add_argument('--feature_bottleneck_size', type=int, default=None)
parser.add_argument('--class_embed_size', type=int, default=None)
parser.add_argument('--task_encoder_sharing', default=None, choices=['global', 'layer', None])
parser.add_argument('--feature_extractor', type=str, default='simple_res_net',
choices=['simple_res_net'], help='Which feature extractor to use')
# Feature extractor pretraining parameters (auxiliary 64-classification task)
parser.add_argument('--feat_extract_pretrain', type=str, default=None,
choices=[None, 'finetune', 'freeze', 'multitask'],
help='Whether or not pretrain the feature extractor')
parser.add_argument('--feat_extract_pretrain_offset', type=int, default=15000)
parser.add_argument('--feat_extract_pretrain_decay_rate', type=float, default=0.9,
help='rate at which 64 way task selection probability decays in multitask mode')
parser.add_argument('--feat_extract_pretrain_decay_n', type=int, default=20,
help='number of times 64 way task selection probability decays in multitask mode')
parser.add_argument('--feat_extract_pretrain_lr_decay_rate', type=float, default=10.0,
help='rate at which 64 way task learning rate decays')
parser.add_argument('--feat_extract_pretrain_lr_decay_n', type=float, default=2.0,
help='number of times 64 way task learning rate decays')
parser.add_argument('--encoder_sharing', type=str, default='shared',
choices=['shared', 'siamese'],
help='How to link fetaure extractors in task encoder and classifier')
parser.add_argument('--encoder_classifier_link', type=str, default='cbn',
choices=['attention', 'cbn', 'prototypical', 'std_normalized_euc_head',
'self_attention_euclidian',
'cosine', 'polynomial', 'perceptron', 'cbn_cos'],
help='How to link fetaure extractors in task encoder and classifier')
parser.add_argument('--embedding_pooled', type=bool, default=True,
help='Whether to use avg pooling to create embedding')
parser.add_argument('--task_encoder', type=str, default='self_att_mlp',
choices=['fixed_alpha', 'fixed_alpha_mlp', 'self_att_mlp'])
parser.add_argument('--metric_multiplier_init', type=float, default=10.0, help='multiplier of cosine metric')
parser.add_argument('--metric_multiplier_trainable', type=bool, default=False,
help='multiplier of cosine metric trainability')
parser.add_argument('--polynomial_metric_order', type=int, default=1)
parser.add_argument('--cbn_premultiplier', type=str, default='var', choices=['var', 'projection'])
parser.add_argument('--cbn_num_layers', type=int, default=3)
parser.add_argument('--cbn_per_block', type=bool, default=False)
parser.add_argument('--cbn_per_network', type=bool, default=False)
parser.add_argument('--cbn_after_shortcut', type=bool, default=False)
parser.add_argument('--conv_dropout', type=float, default=None)
parser.add_argument('--alpha', type=float, default=1.0)
parser.add_argument('--mlp_weight_decay', type=float, default=0.0)
parser.add_argument('--mlp_dropout', type=float, default=0.0)
parser.add_argument('--mlp_type', type=str, default='non-linear')
parser.add_argument('--att_input', type=str, default='word')
parser.add_argument('--att_weight_decay', type=float, default=0.0)
parser.add_argument('--att_dropout', type=float, default=0.0)
parser.add_argument('--att_type', type=str, default='non-linear')
parser.add_argument('--activation_mlp', type=str, default='relu', choices=['relu', 'selu', 'swish-1'])
args = parser.parse_args()
print(args)
return args
def get_logdir_name(flags):
logdir=flags.log_dir
return logdir
class ScaledVarianceRandomNormal(init_ops.Initializer):
"""Initializer that generates tensors with a normal distribution scaled as per https://arxiv.org/pdf/1502.01852.pdf.
Args:
mean: a python scalar or a scalar tensor. Mean of the random values
to generate.
stddev: a python scalar or a scalar tensor. Standard deviation of the
random values to generate.
seed: A Python integer. Used to create random seeds. See
@{tf.set_random_seed}
for behavior.
dtype: The data type. Only floating point types are supported.
"""
def __init__(self, mean=0.0, factor=1.0, seed=None, dtype=dtypes.float32):
self.mean = mean
self.factor = factor
self.seed = seed
self.dtype = dtypes.as_dtype(dtype)
def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
if shape:
n = float(shape[-1])
else:
n = 1.0
for dim in shape[:-2]:
n *= float(dim)
self.stddev = np.sqrt(self.factor * 2.0 / n)
return random_ops.random_normal(shape, self.mean, self.stddev,
dtype, seed=self.seed)
def _get_scope(is_training, flags):
normalizer_params = {
'epsilon': 0.001,
'momentum': .95,
'trainable': is_training,
'training': is_training,
}
conv2d_arg_scope = slim.arg_scope(
[slim.conv2d, slim.fully_connected],
activation_fn=ACTIVATION_MAP[flags.activation],
normalizer_fn=tf.layers.batch_normalization,
normalizer_params=normalizer_params,
# padding='SAME',
trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=flags.weight_decay),
weights_initializer=ScaledVarianceRandomNormal(factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0)
)
dropout_arg_scope = slim.arg_scope(
[slim.dropout],
keep_prob=flags.dropout,
is_training=is_training)
return conv2d_arg_scope, dropout_arg_scope
def _get_scope_cbn(is_training, flags):
normalizer_params = {
'epsilon': 0.001,
'momentum': .95,
'trainable': is_training,
'training': is_training,
'center': False,
'scale': False
}
conv2d_arg_scope = slim.arg_scope(
[slim.conv2d, slim.fully_connected],
activation_fn=ACTIVATION_MAP[flags.activation],
normalizer_fn=tf.layers.batch_normalization,
normalizer_params=normalizer_params,
# padding='SAME',
trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=flags.weight_decay),
weights_initializer=ScaledVarianceRandomNormal(factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0)
)
dropout_arg_scope = slim.arg_scope(
[slim.dropout],
keep_prob=flags.dropout,
is_training=is_training)
return conv2d_arg_scope, dropout_arg_scope
def leaky_relu(x, alpha=0.1, name=None):
return tf.maximum(x, alpha * x, name=name)
def get_cbn_layer(h, beta, gamma):
"""
:param h: input layer
:param beta: additive conditional batch norm paraemeter
:param gamma: multiplicative conditional batch norm parameter in the delta form
:return: conditional batch norm in the form (gamma + 1.0) * h + beta
"""
if beta is None or gamma is None:
return h
beta = tf.expand_dims(beta, axis=1)
gamma = tf.expand_dims(gamma, axis=1)
beta = tf.tile(beta, multiples=[1, h.shape.as_list()[0] // beta.shape.as_list()[0], 1])
beta = tf.reshape(beta, [-1, beta.shape.as_list()[-1]])
beta = tf.reshape(beta, [-1, 1, 1, beta.shape.as_list()[-1]])
gamma = tf.tile(gamma, multiples=[1, h.shape.as_list()[0] // gamma.shape.as_list()[0], 1])
gamma = tf.reshape(gamma, [-1, beta.shape.as_list()[-1]])
gamma = tf.reshape(gamma, [-1, 1, 1, beta.shape.as_list()[-1]])
h = (gamma + 1.0) * h + beta
return h
def build_simple_res_net(images, flags, num_filters, beta=None, gamma=None, is_training=False, reuse=None, scope=None):
conv2d_arg_scope, dropout_arg_scope = _get_scope(is_training, flags)
activation_fn = ACTIVATION_MAP[flags.activation]
with conv2d_arg_scope, dropout_arg_scope:
with tf.variable_scope(scope or 'feature_extractor', reuse=reuse):
# h = slim.conv2d(images, num_outputs=num_filters[0], kernel_size=6, stride=1,
# scope='conv_input', padding='SAME')
# h = slim.max_pool2d(h, kernel_size=2, stride=2, padding='SAME', scope='max_pool_input')
h = images
for i in range(len(num_filters)):
# make shortcut
shortcut = slim.conv2d(h, num_outputs=num_filters[i], kernel_size=1, stride=1,
activation_fn=None,
scope='shortcut' + str(i), padding='SAME')
for j in range(flags.num_units_in_block):
h = slim.conv2d(h, num_outputs=num_filters[i], kernel_size=3, stride=1,
scope='conv' + str(i) + '_' + str(j), padding='SAME', activation_fn=None)
if flags.conv_dropout:
h = slim.dropout(h, keep_prob=1.0 - flags.conv_dropout)
if beta is not None and gamma is not None and not flags.cbn_after_shortcut:
with tf.variable_scope('conditional_batch_norm' + str(i) + '_' + str(j), reuse=reuse):
h = get_cbn_layer(h, beta=beta[i, j], gamma=gamma[i, j])
if j < (flags.num_units_in_block - 1):
h = activation_fn(h, name='activation_' + str(i) + '_' + str(j))
h = h + shortcut
if beta is not None and gamma is not None and flags.cbn_after_shortcut:
with tf.variable_scope('conditional_batch_norm' + str(i) + '_' + str(j), reuse=reuse):
h = get_cbn_layer(h, beta=beta[i, j], gamma=gamma[i, j])
h = activation_fn(h, name='activation_' + str(i) + '_' + str(flags.num_units_in_block - 1))
if i < flags.num_max_pools:
h = slim.max_pool2d(h, kernel_size=2, stride=2, padding='SAME', scope='max_pool' + str(i))
if flags.feature_expansion_size:
if flags.feature_dropout_p:
h = slim.dropout(h, scope='feature_expansion_dropout', keep_prob=1.0 - flags.feature_dropout_p)
h = slim.conv2d(slim.dropout(h), num_outputs=flags.feature_expansion_size, kernel_size=1, stride=1,
scope='feature_expansion', padding='SAME')
if flags.embedding_pooled == True:
kernel_size = h.shape.as_list()[-2]
h = slim.avg_pool2d(h, kernel_size=kernel_size, scope='avg_pool')
h = slim.flatten(h)
if flags.feature_dropout_p:
h = slim.dropout(h, scope='feature_bottleneck_dropout', keep_prob=1.0 - flags.feature_dropout_p)
# Bottleneck layer
if flags.feature_bottleneck_size:
h = slim.fully_connected(h, num_outputs=flags.feature_bottleneck_size,
activation_fn=activation_fn, normalizer_fn=None,
scope='feature_bottleneck')
return h
def build_feature_extractor_graph(images, flags, num_filters, beta=None, gamma=None, is_training=False,
scope='feature_extractor_task_encoder', reuse=None, is_64way=False):
if flags.feature_extractor == 'simple_res_net':
h = build_simple_res_net(images, flags=flags, num_filters=num_filters, beta=beta, gamma=gamma,
is_training=is_training, reuse=reuse, scope=scope)
else:
h = None
embedding_shape = h.get_shape().as_list()
if is_training and is_64way is False:
h = tf.reshape(h, shape=(flags.num_tasks_per_batch, embedding_shape[0] // flags.num_tasks_per_batch, -1),
name='reshape_to_separate_tasks_generic_features')
else:
h = tf.reshape(h, shape=(1, embedding_shape[0], -1),
name='reshape_to_separate_tasks_generic_features')
return h
def build_wordemb_transformer(embeddings, flags, is_training=False, reuse=None, scope=None):
with tf.variable_scope(scope or 'mlp_transformer', reuse=reuse):
# h = slim.conv2d(images, num_outputs=num_filters[0], kernel_size=6, stride=1,
# scope='conv_input', padding='SAME')
# h = slim.max_pool2d(h, kernel_size=2, stride=2, padding='SAME', scope='max_pool_input')
h = embeddings
if flags.mlp_type=='linear':
h = slim.fully_connected(h, 512, reuse=False, scope='mlp_layer',
activation_fn=None, trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=flags.mlp_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
elif flags.mlp_type=='non-linear':
h = slim.fully_connected(h, 300, reuse=False, scope='mlp_layer',
activation_fn=ACTIVATION_MAP[flags.activation_mlp], trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.mlp_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(
factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
h = slim.dropout(h, scope='mlp_dropout', keep_prob=1.0 - flags.mlp_dropout, is_training=is_training)
h = slim.fully_connected(h, 512, reuse=False, scope='mlp_layer_1',
activation_fn=None, trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.mlp_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(
factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
return h
def build_self_attention(embeddings, flags, is_training=False, reuse=None, scope=None):
with tf.variable_scope(scope or 'self_attention', reuse=reuse):
# h = slim.conv2d(images, num_outputs=num_filters[0], kernel_size=6, stride=1,
# scope='conv_input', padding='SAME')
# h = slim.max_pool2d(h, kernel_size=2, stride=2, padding='SAME', scope='max_pool_input')
h = embeddings
if flags.att_type=='linear':
h = slim.fully_connected(h, 1, reuse=False, scope='self_att_layer',
activation_fn=None, trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=flags.att_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
elif flags.att_type=='non-linear':
h = slim.fully_connected(h, 300, reuse=False, scope='self_att_layer',
activation_fn=ACTIVATION_MAP[flags.activation_mlp], trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.att_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(
factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
h = slim.dropout(h, scope='self_att_dropout', keep_prob=1.0 - flags.att_dropout, is_training=is_training)
h = slim.fully_connected(h, 1, reuse=False, scope='self_att_layer_1',
activation_fn=None, trainable=is_training,
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.att_weight_decay),
weights_initializer=ScaledVarianceRandomNormal(
factor=flags.weights_initializer_factor),
biases_initializer=tf.constant_initializer(0.0))
h = tf.sigmoid(h)
return h
def build_task_encoder_cbn(embeddings, flags, is_training, reuse=None, scope='class_encoder_cbn'):
conv2d_arg_scope, dropout_arg_scope = _get_scope(is_training, flags)
with conv2d_arg_scope, dropout_arg_scope:
with tf.variable_scope(scope, reuse=reuse):
task_encoding = embeddings
if is_training:
task_encoding = tf.reshape(task_encoding, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, flags.num_shots_train, -1),
name='reshape_to_separate_tasks_task_encoding')
else:
task_encoding = tf.reshape(task_encoding,
shape=(1, flags.num_classes_test, flags.num_shots_test, -1),
name='reshape_to_separate_tasks_task_encoding')
task_encoding = tf.reduce_mean(task_encoding, axis=2, keep_dims=False)
return task_encoding
def get_polynomial(input, flags, is_training, scope='polynomial_metric', reuse=False):
with tf.variable_scope(scope, reuse=reuse):
output = 0.0
for p in range(1, flags.polynomial_metric_order + 1):
if p == 1:
init_val = -flags.metric_multiplier_init
else:
init_val = 0.0
weight = tf.Variable(init_val, trainable=(is_training and flags.metric_multiplier_trainable),
name='power_weight' + str(p), dtype=tf.float32)
tf.summary.scalar('power_weight' + str(p), weight)
output = output + tf.multiply(weight, tf.pow(input, p))
return output
def build_polynomial_head(features_generic, task_encoding, flags, is_training, scope='polynomial_head'):
"""
Implements the head using feature normalization by std before the Euclidian distance
:param features_generic:
:param task_encoding:
:param flags:
:param is_training:
:param reuse:
:param scope:
:return:
"""
with tf.variable_scope(scope):
# features_generic_norm = tf.norm(features_generic, axis=-1, keep_dims=True)
# task_encoding_norm = tf.norm(task_encoding, axis=-1, keep_dims=True)
#
# features_generic = tf.div(features_generic, 1e-6 + features_generic_norm, name='feature_generic_normalized')
# task_encoding = tf.div(task_encoding, 1e-6 + task_encoding_norm, name='feature_generic_normalized')
if len(features_generic.get_shape().as_list()) == 2:
features_generic = tf.expand_dims(features_generic, axis=0)
if len(task_encoding.get_shape().as_list()) == 2:
task_encoding = tf.expand_dims(task_encoding, axis=0)
# i is the number of steps in the task_encoding sequence
# j is the number of steps in the features_generic sequence
j = task_encoding.get_shape().as_list()[1]
i = features_generic.get_shape().as_list()[1]
# tile to be able to produce weight matrix alpha in (i,j) space
features_generic = tf.expand_dims(features_generic, axis=2)
task_encoding = tf.expand_dims(task_encoding, axis=1)
# features_generic changes over i and is constant over j
# task_encoding changes over j and is constant over i
task_encoding_tile = tf.tile(task_encoding, (1, i, 1, 1))
features_generic_tile = tf.tile(features_generic, (1, 1, j, 1))
# implement equation (4)
euclidian = tf.norm(task_encoding_tile - features_generic_tile, name='euclidian_distance', axis=-1)
polynomial_metric = get_polynomial(euclidian, flags, is_training=is_training)
if is_training:
polynomial_metric = tf.reshape(polynomial_metric,
shape=(flags.num_tasks_per_batch * flags.train_batch_size, -1))
else:
polynomial_metric_shape = polynomial_metric.get_shape().as_list()
polynomial_metric = tf.reshape(polynomial_metric, shape=(polynomial_metric_shape[1], -1))
return polynomial_metric
def build_polynomial_queryproto_head(features_generic, task_encoding, flags, is_training, scope='polynomial_head'):
"""
Implements the head using feature normalization by std before the Euclidian distance
:param features_generic:
:param task_encoding:
:param flags:
:param is_training:
:param reuse:
:param scope:
:return:
"""
# the shape of task_encoding is [num_tasks, batch_size, num_classes, ]
with tf.variable_scope(scope):
if len(features_generic.get_shape().as_list()) == 2:
features_generic = tf.expand_dims(features_generic, axis=0)
if len(task_encoding.get_shape().as_list()) == 2:
task_encoding = tf.expand_dims(task_encoding, axis=0)
# i is the number of steps in the task_encoding sequence
# j is the number of steps in the features_generic sequence
j = task_encoding.get_shape().as_list()[2]
i = features_generic.get_shape().as_list()[1]
# tile to be able to produce weight matrix alpha in (i,j) space
features_generic = tf.expand_dims(features_generic, axis=2)
# task_encoding = tf.expand_dims(task_encoding, axis=1)
# features_generic changes over i and is constant over j
# task_encoding changes over j and is constant over i
features_generic_tile = tf.tile(features_generic, (1, 1, j, 1))
# implement equation (4)
euclidian = -tf.norm(task_encoding - features_generic_tile, name='neg_euclidian_distance', axis=-1)
polynomial_metric = get_polynomial(euclidian, flags, is_training=is_training)
if is_training:
polynomial_metric = tf.reshape(polynomial_metric,
shape=(flags.num_tasks_per_batch * flags.train_batch_size, -1))
else:
polynomial_metric_shape = polynomial_metric.get_shape().as_list()
polynomial_metric = tf.reshape(polynomial_metric, shape=(polynomial_metric_shape[1], -1))
return polynomial_metric
def placeholder_inputs(batch_size, image_size, scope):
"""
:param batch_size:
:return: placeholders for images and
"""
with tf.variable_scope(scope):
images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, 3), name='images')
labels_placeholder = tf.placeholder(tf.int64, shape=(batch_size), name='labels')
return images_placeholder, labels_placeholder
def get_batch(data_set, images_placeholder, labels_placeholder, batch_size):
"""
:param data_set:
:param images_placeholder:
:param labels_placeholder:
:return:
"""
images_feed, labels_feed = data_set.next_batch(batch_size)
feed_dict = {
images_placeholder: images_feed.astype(dtype=np.float32),
labels_placeholder: labels_feed,
}
return feed_dict
def preprocess(images):
# mean = tf.constant(np.asarray([127.5, 127.5, 127.5]).reshape([1, 1, 3]), dtype=tf.float32, name='image_mean')
# std = tf.constant(np.asarray([127.5, 127.5, 127.5]).reshape([1, 1, 3]), dtype=tf.float32, name='image_std')
# return tf.div(tf.subtract(images, mean), std)
std = tf.constant(np.asarray([0.5, 0.5, 0.5]).reshape([1, 1, 3]), dtype=tf.float32, name='image_std')
return tf.div(images, std)
def get_nearest_neighbour_acc(flags, embeddings, labels):
num_correct = 0
num_tot = 0
for i in trange(flags.num_cases_test):
test_classes = np.random.choice(np.unique(labels), size=flags.num_classes_test, replace=False)
train_idxs, test_idxs = get_few_shot_idxs(labels=labels, classes=test_classes, num_shots=flags.num_shots_test)
# TODO: this is to fix the OOM error, this can be removed when embed() supports batch processing
test_idxs = np.random.choice(test_idxs, size=100, replace=False)
np_embedding_train = embeddings[train_idxs]
# Using the np.std instead of np.linalg.norm improves results by around 1-1.5%
np_embedding_train = np_embedding_train / np.std(np_embedding_train, axis=1, keepdims=True)
# np_embedding_train = np_embedding_train / np.linalg.norm(np_embedding_train, axis=1, keepdims=True)
labels_train = labels[train_idxs]
np_embedding_test = embeddings[test_idxs]
np_embedding_test = np_embedding_test / np.std(np_embedding_test, axis=1, keepdims=True)
# np_embedding_test = np_embedding_test / np.linalg.norm(np_embedding_test, axis=1, keepdims=True)
labels_test = labels[test_idxs]
kdtree = KDTree(np_embedding_train)
nns, nn_idxs = kdtree.query(np_embedding_test, k=1)
labels_predicted = labels_train[nn_idxs]
num_matches = sum(labels_predicted == labels_test)
num_correct += num_matches
num_tot += len(labels_predicted)
# print("Accuracy: ", (100.0 * num_correct) / num_tot)
return (100.0 * num_correct) / num_tot
def get_cbn_premultiplier(task_encoding, i, j, flags, is_training, reuse):
"""
:param task_encoding:
:param i:
:param j:
:param flags:
:param is_training:
:param reuse:
:return:
"""
if flags.cbn_premultiplier == 'var':
beta_weight = tf.get_variable(name='beta_weight' + str(i) + str(j), dtype=tf.float32, initializer=0.0,
trainable=is_training,
regularizer=tf.contrib.layers.l2_regularizer(scale=flags.weight_decay_cbn,
scope='penalize_beta' + str(i) + str(
j)))
gamma_weight = tf.get_variable(name='gamma_weight' + str(i) + str(j), dtype=tf.float32, initializer=0.0,
trainable=is_training,
regularizer=tf.contrib.layers.l2_regularizer(scale=flags.weight_decay_cbn,
scope='penalize_gamma' + str(
i) + str(
j)))
#tf.summary.scalar('beta_weight' + str(i) + str(j), beta_weight)
#tf.summary.scalar('gamma_weight' + str(i) + str(j), gamma_weight)
elif flags.cbn_premultiplier == 'projection':
beta_weight_projection = slim.fully_connected(task_encoding, num_outputs=1,
activation_fn=None, normalizer_fn=None, reuse=reuse,
weights_initializer=init_ops.zeros_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay),
scope='beta_weight' + str(i) + str(j), trainable=is_training)
gamma_weight_projection = slim.fully_connected(task_encoding, num_outputs=1,
activation_fn=None, normalizer_fn=None, reuse=reuse,
weights_initializer=init_ops.zeros_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay),
scope='gamma_weight' + str(i) + str(j), trainable=is_training)
beta_weight = tf.get_variable(name='beta_weight' + str(i) + str(j), dtype=tf.float32,
shape=beta_weight_projection.shape,
trainable=is_training,
regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay_cbn,
scope='penalize_beta' + str(i) + str(j)))
gamma_weight = tf.get_variable(name='gamma_weight' + str(i) + str(j), dtype=tf.float32,
shape=gamma_weight_projection.shape,
trainable=is_training,
regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay_cbn,
scope='penalize_gamma' + str(i) + str(j)))
beta_weight = tf.assign(beta_weight, beta_weight_projection,
name='assign_beta_weight_projection' + str(i) + str(j))
gamma_weight = tf.assign(gamma_weight, gamma_weight_projection,
name='assign_gamma_weight_projection' + str(i) + str(j))
return beta_weight, gamma_weight
def get_cbn_gamma_beta_net(h, i, j, num_filters, flags, is_training, reuse):
"""
:param h:
:param i:
:param j:
:param flags:
:param is_training:
:param reuse:
:return:
"""
activation_fn = ACTIVATION_MAP[flags.activation]
beta, gamma = h, h
for l in range(flags.cbn_num_layers):
beta_old, gamma_old = beta, gamma
beta = slim.fully_connected(beta, num_outputs=num_filters,
activation_fn=None, normalizer_fn=None, reuse=reuse,
weights_initializer=ScaledVarianceRandomNormal(factor=0.1),
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay),
scope='projection_beta' + str(i) + str(j) + str(l), trainable=is_training)
gamma = slim.fully_connected(gamma, num_outputs=num_filters,
activation_fn=None, normalizer_fn=None, reuse=reuse,
weights_initializer=ScaledVarianceRandomNormal(factor=0.1),
weights_regularizer=tf.contrib.layers.l2_regularizer(
scale=flags.weight_decay),
scope='projection_gamma' + str(i) + str(j) + str(l), trainable=is_training)
if l > 0:
beta = tf.add(beta, beta_old, name='shortcut_beta' + str(i) + str(j) + str(l))
gamma = tf.add(gamma, gamma_old, name='shortcut_gamma' + str(i) + str(j) + str(l))
if l < flags.cbn_num_layers - 1:
beta = activation_fn(beta, name='activate_beta' + str(i) + str(j) + str(l))
gamma = activation_fn(gamma, name='activate_gamma' + str(i) + str(j) + str(l))
beta_weight, gamma_weight = get_cbn_premultiplier(h, i, j, flags, is_training, reuse)
beta = tf.multiply(beta, beta_weight, name='premultiply_cbn_beta' + str(i) + str(j))
gamma = tf.multiply(gamma, gamma_weight, name='premultiply_cbn_gamma' + str(i) + str(j))
return beta, gamma
def get_cbn_params(features_task_encode, num_filters_list, flags, reuse=False, is_training=False,
scope='cbn_params_raw'):
"""
:param features_task_encode:
:param num_filters_list:
:param flags:
:param reuse:
:param is_training:
:param scope:
:return:
"""
if flags.feature_extractor == 'res_net':
num_filters_modifier = 4
else:
num_filters_modifier = 1
with tf.variable_scope(scope, reuse=reuse):
h = tf.reduce_mean(features_task_encode, axis=1, keep_dims=False)
beta_reshape = [[None] * flags.num_units_in_block for _ in range(len(num_filters_list))]
gamma_reshape = [[None] * flags.num_units_in_block for _ in range(len(num_filters_list))]
for i, num_filters in enumerate(num_filters_list):
for j in range(flags.num_units_in_block):
if flags.cbn_per_block and j < (flags.num_units_in_block - 1):
beta, gamma = None, None
elif flags.cbn_per_network and (
j < (flags.num_units_in_block - 1) or i < (len(num_filters_list) - 1)):
beta, gamma = None, None
else:
beta, gamma = get_cbn_gamma_beta_net(h, i, j, num_filters=num_filters * num_filters_modifier,
flags=flags, is_training=is_training, reuse=reuse)
beta_reshape[i][j] = beta
gamma_reshape[i][j] = gamma
return np.asarray(gamma_reshape), np.asarray(beta_reshape)
def build_task_encoder(embeddings, label_embeddings, flags, is_training, querys=None, reuse=None, scope='class_encoder'):
conv2d_arg_scope, dropout_arg_scope = _get_scope(is_training, flags)
alpha=None
with conv2d_arg_scope, dropout_arg_scope:
with tf.variable_scope(scope, reuse=reuse):
if flags.task_encoder == 'fixed_alpha_mlp':
task_encoding = embeddings
print("entered the word embedding task encoder...")
label_embeddings = build_wordemb_transformer(label_embeddings,flags,is_training)
if is_training:
task_encoding = tf.reshape(task_encoding, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, flags.num_shots_train, -1),
name='reshape_to_separate_tasks_task_encoding')
label_embeddings = tf.reshape(label_embeddings, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, -1),
name='reshape_to_separate_tasks_label_embedding')
else:
task_encoding = tf.reshape(task_encoding,
shape=(1, flags.num_classes_test, flags.num_shots_test, -1),
name='reshape_to_separate_tasks_task_encoding')
label_embeddings = tf.reshape(label_embeddings,
shape=(1, flags.num_classes_test, -1),
name='reshape_to_separate_tasks_label_embedding')
task_encoding = tf.reduce_mean(task_encoding, axis=2, keep_dims=False)
task_encoding = flags.alpha*task_encoding+(1-flags.alpha)*label_embeddings
elif flags.task_encoder == 'self_att_mlp':
task_encoding = embeddings
print("entered the word embedding task encoder...")
label_embeddings_transformed = build_wordemb_transformer(label_embeddings,flags,is_training)
if is_training:
task_encoding = tf.reshape(task_encoding, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, flags.num_shots_train, -1),
name='reshape_to_separate_tasks_task_encoding')
label_embeddings = tf.reshape(label_embeddings, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, -1),
name='reshape_to_separate_tasks_label_embedding')
label_embeddings_transformed = tf.reshape(label_embeddings_transformed, shape=(
flags.num_tasks_per_batch, flags.num_classes_train, -1),
name='reshape_to_separate_tasks_label_embedding_transformed')
else:
task_encoding = tf.reshape(task_encoding,
shape=(1, flags.num_classes_test, flags.num_shots_test, -1),
name='reshape_to_separate_tasks_task_encoding')
label_embeddings = tf.reshape(label_embeddings,
shape=(1, flags.num_classes_test, -1),
name='reshape_to_separate_tasks_label_embedding')
label_embeddings_transformed = tf.reshape(label_embeddings_transformed,
shape=(1, flags.num_classes_test, -1),
name='reshape_to_separate_tasks_label_embedding')
task_encoding = tf.reduce_mean(task_encoding, axis=2, keep_dims=False)
if flags.att_input=='proto':
alpha = build_self_attention(task_encoding,flags,is_training)
elif flags.att_input=='word':
alpha = build_self_attention(label_embeddings_transformed,flags,is_training)
elif flags.att_input=='word_original':
alpha = build_self_attention(label_embeddings,flags,is_training)
elif flags.att_input=='combined':
embeddings=tf.concat([task_encoding, label_embeddings_transformed], axis=2)
alpha = build_self_attention(embeddings, flags, is_training)
elif flags.att_input=='queryproto':
j = task_encoding.get_shape().as_list()[1]
i = querys.get_shape().as_list()[1]
task_encoding_tile = tf.expand_dims(task_encoding, axis=1)
task_encoding_tile = tf.tile(task_encoding_tile, (1, i, 1, 1))
querys_tile = tf.expand_dims(querys, axis=2)
querys_tile = tf.tile(querys_tile, (1, 1, j, 1))
label_embeddings_tile = tf.expand_dims(label_embeddings_transformed, axis=1)
label_embeddings_tile = tf.tile(label_embeddings_tile, (1, i, 1, 1))
att_input = tf.concat([task_encoding_tile, querys_tile], axis=3)
alpha = build_self_attention(att_input, flags, is_training)
elif flags.att_input=='queryword':
j = task_encoding.get_shape().as_list()[1]
i = querys.get_shape().as_list()[1]
task_encoding_tile = tf.expand_dims(task_encoding, axis=1)
task_encoding_tile = tf.tile(task_encoding_tile, (1, i, 1, 1))
querys_tile = tf.expand_dims(querys, axis=2)
querys_tile = tf.tile(querys_tile, (1, 1, j, 1))
label_embeddings_tile = tf.expand_dims(label_embeddings_transformed, axis=1)
label_embeddings_tile = tf.tile(label_embeddings_tile, (1, i, 1, 1))
att_input = tf.concat([label_embeddings_tile, querys_tile], axis=3)
alpha = build_self_attention(att_input, flags, is_training)
if querys is None:
task_encoding = alpha*task_encoding+(1-alpha)*label_embeddings_transformed
else:
task_encoding = alpha * task_encoding_tile + (1-alpha) * label_embeddings_tile
else:
task_encoding = None
return task_encoding, alpha
def build_inference_graph(images_deploy_pl, images_task_encode_pl, flags, is_training,
is_primary, label_embeddings):
num_filters = [round(flags.num_filters * pow(flags.block_size_growth, i)) for i in range(flags.num_blocks)]
reuse = not is_primary
with tf.variable_scope('Model'):
feature_extractor_encoding_scope = 'feature_extractor_encoder'
features_task_encode = build_feature_extractor_graph(images=images_task_encode_pl, flags=flags,
is_training=is_training,
num_filters=num_filters,
scope=feature_extractor_encoding_scope,
reuse=flags.feat_extract_pretrain is not None)
if flags.encoder_sharing == 'shared':
ecoder_reuse = True
feature_extractor_classifier_scope = feature_extractor_encoding_scope
elif flags.encoder_sharing == 'siamese':
# TODO: in the case of pretrained feature extractor this is not good,
# because the classfier part will be randomly initialized
ecoder_reuse = False
feature_extractor_classifier_scope = 'feature_extractor_classifier'
else:
raise Exception('Option not implemented')
if flags.encoder_classifier_link == 'cbn':
task_encoding = build_task_encoder_cbn(embeddings=features_task_encode,
flags=flags,
is_training=is_training, reuse=reuse)
# gamma, beta = None,None
gamma, beta = get_cbn_params(features_task_encode=task_encoding, num_filters_list=num_filters,
is_training=is_training, flags=flags, reuse=reuse)
features_task_encode = build_feature_extractor_graph(images=images_task_encode_pl, flags=flags,
is_training=is_training,
num_filters=num_filters,
gamma=gamma, beta=beta,
scope=feature_extractor_classifier_scope,
reuse=ecoder_reuse)
features_generic = build_feature_extractor_graph(images=images_deploy_pl, flags=flags,
is_training=is_training,
num_filters=num_filters,
gamma=gamma, beta=beta,
scope=feature_extractor_classifier_scope,
reuse=ecoder_reuse)
querys = None
if 'query' in flags.att_input:
querys = features_generic
task_encoding, alpha = build_task_encoder(embeddings=features_task_encode,
label_embeddings=label_embeddings,
flags=flags, is_training=is_training, reuse=reuse, querys=querys)
if 'query' in flags.att_input:
logits = build_polynomial_queryproto_head(features_generic, task_encoding, flags, is_training=is_training)
else:
logits = build_polynomial_head(features_generic, task_encoding, flags, is_training=is_training)
else:
raise Exception('Option not implemented')
return logits, features_task_encode, features_generic, alpha
def build_feat_extract_pretrain_graph(images, flags, is_training):
num_filters = [round(flags.num_filters * pow(flags.block_size_growth, i)) for i in range(flags.num_blocks)]
with tf.variable_scope('Model'):
feature_extractor_encoding_scope = 'feature_extractor_encoder'
features = build_feature_extractor_graph(images=images, flags=flags,
is_training=is_training,
num_filters=num_filters,
scope=feature_extractor_encoding_scope,
reuse=False, is_64way=True)
embedding_shape = features.get_shape().as_list()
features = tf.reshape(features, shape=(embedding_shape[0] * embedding_shape[1], -1))
# Classification loss
logits = slim.fully_connected(features, flags.num_classes_pretrain,
activation_fn=None, normalizer_fn=None, reuse=False,
scope='pretrain_logits', trainable=is_training)
return logits
def cosine_decay(learning_rate, global_step, max_step, name=None):
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.framework import constant_op
with ops.name_scope(name, "CosineDecay",
[learning_rate, global_step, max_step]) as name:
learning_rate = ops.convert_to_tensor(0.5 * learning_rate, name="learning_rate")
dtype = learning_rate.dtype
global_step = math_ops.cast(global_step, dtype)
const = math_ops.cast(constant_op.constant(1), learning_rate.dtype)
freq = math_ops.cast(constant_op.constant(np.pi / max_step), learning_rate.dtype)
osc = math_ops.cos(math_ops.multiply(freq, global_step))
osc = math_ops.add(osc, const)
return math_ops.multiply(osc, learning_rate, name=name)
def get_train_datasets(flags):
mini_imagenet = _load_mini_imagenet(data_dir=flags.data_dir, split='sources')
few_shot_data_train = Dataset(mini_imagenet)
pretrain_data_train, pretrain_data_test = None, None
if flags.feat_extract_pretrain:
train_idx = np.random.choice(range(len(mini_imagenet[0])), size=int(0.9 * len(mini_imagenet[0])),
replace=False)
test_idx = np.setxor1d(range(len(mini_imagenet[0])), train_idx)
new_labels = mini_imagenet[1]
for i, old_class in enumerate(set(mini_imagenet[1])):
new_labels[mini_imagenet[1] == old_class] = i