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train_bier.py
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train_bier.py
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from __future__ import print_function
from __future__ import division
import argparse
import dataset
import collections
import numpy as np
import random
import flip_gradient
import code.deep_inception as models
import tensorflow as tf
from tensorflow.contrib import slim
TrainingData = collections.namedtuple(
'TrainingData', ('crop_size', 'channels', 'mean'))
LEARNING_RATE = 1e-5
WEIGHT_DECAY = 0.0002
BATCH_SIZE = 32
LR_DECAY = 0.1
NUM_HIDDENS_ADVERSARIAL = 2
HIDDEN_ADVERSARIAL_SIZE = 512
LAMBDA_WEIGHT = 100.0
REGULARIZATION_CHOICES = ['activation', 'adversarial']
EMBEDDING_SCOPE_NAME = 'embedding_tower'
VERBOSE = False
def do_print(*args, **kwargs):
"""
Wrapper around tf.Print to enable/disable verbose printing.
"""
if VERBOSE:
return tf.Print(*args, **kwargs)
else:
return args[0]
def embedding_tower(hidden_layer, embedding_sizes, reuse=False):
"""
Creates the embedding tower on top of a feature extractor.
Args:
hidden_layer: Last hidden layer of the feature extractor.
embedding_sizes: array indicating the sizes of our
embedding, e.g. [96, 160, 256]
reuse: tensorflow reuse flag for the variable scope.
Returns: A tuple consisting of the embedding and end_points.
"""
end_points = {}
final_layers = []
with tf.variable_scope(EMBEDDING_SCOPE_NAME, reuse=reuse) as scope:
hidden_layer = slim.flatten(hidden_layer)
for idx, embedding_size in enumerate(embedding_sizes):
scope_name = 'embedding/fc_{}'.format(idx)
embedding = slim.fully_connected(
hidden_layer, embedding_size, activation_fn=None,
scope=scope_name)
regul_out = slim.fully_connected(tf.stop_gradient(
hidden_layer), embedding_size, scope=scope_name,
reuse=True, activation_fn=None, biases_initializer=None)
end_points['{}/embedding/fc_{}'.format(
EMBEDDING_SCOPE_NAME, idx)] = embedding
end_points['{}/embedding/fc_{}_regularizer'.format(
EMBEDDING_SCOPE_NAME, idx)] = regul_out
final_layers.append(embedding)
embedding = tf.concat(final_layers, axis=1)
end_points['{}/embedding'.format(EMBEDDING_SCOPE_NAME)] = embedding
weight_variables = slim.get_variables_by_name('weights', scope=scope)
for w in weight_variables:
tf.add_to_collection('weights', w)
return embedding, end_points
def evaluate2(fvecs, labels, tag='Hidden'):
"""
Evaluation of a single embedding.
Args:
fvecs: numpy array of feature vectors
labels: labels
Returns:
The recall @1
"""
fvecs /= np.maximum(1e-5, np.linalg.norm(fvecs, axis=1, keepdims=True))
D = fvecs.dot(fvecs.T)
# Remove the diagonal for evalution! This is the same sample as the query.
I = np.eye(D.shape[0]) * abs(D).max() * 10.0
D -= I
predictions = D.argmax(axis=1)
pred_labels = labels[predictions]
recall = (pred_labels == labels).sum() / float(len(labels))
print('R@1 ({}): '.format(tag), recall)
return recall
def evaluate(fvecs, labels, embedding_sizes):
"""
Evaluation of a bier embedding.
Args:
fvecs: numpy array of feature vectors
labels: labels
Returns:
The recall @1
"""
embedding_scales = [float(e) / sum(embedding_sizes)
for e in embedding_sizes]
start_idx = 0
for e, s in zip(embedding_sizes, embedding_scales):
stop_idx = start_idx + e
evaluate2(np.array(fvecs[:, start_idx:stop_idx].copy(
)), labels, tag='Embedding-{}'.format(e))
fvecs[:, start_idx:stop_idx] /= np.maximum(1e-5, np.linalg.norm(
fvecs[:, start_idx:stop_idx], axis=1, keepdims=True)) / s
start_idx = stop_idx
# Compute distance matrix.
D = fvecs.dot(fvecs.T)
I = np.eye(D.shape[0]) * abs(D).max() * 10.0
D -= I
# compute R@1
predictions = D.argmax(axis=1)
pred_labels = labels[predictions]
recall = (pred_labels == labels).sum() / float(len(labels))
print('R@1 (Embedding): ', (pred_labels == labels).sum() /
float(len(labels)))
return recall
def build_train(predictions, end_points, y, embedding_sizes,
shrinkage=0.06,
lambda_div=0.0, C=25, alpha=2.0, beta=0.5, initial_acts=0.5,
eta_style=False, dtype=tf.float32, regularization=None):
"""
Builds the boosting based training.
Args:
predictions: tensor of the embedding predictions
end_points: dictionary of endpoints of the embedding tower
y: tensor class labels
embedding_sizes: list, which indicates the size of the sub-embedding
(e.g. [96, 160, 256])
shrinkage: if you use eta_style = True, set to 1.0, otherwise keep it
small (e.g. 0.06).
lambda_div: regularization parameter.
C: parameter for binomial deviance.
alpha: parameter for binomial deviance.
dtype: data type for computations, typically tf.float32
initial_acts: 0.5 if eta_style is false, 0.0 if eta_style is true
regularization: regularization method (either activation or
adversarial)
Returns:
The training loss.
"""
shape = predictions.get_shape().as_list()
num_learners = len(embedding_sizes)
# Pairwise labels.
pairs = tf.reshape(
tf.cast(tf.equal(y[:, tf.newaxis], y[tf.newaxis, :]), dtype), [-1])
m = 1.0 * pairs + (-C * (1.0 - pairs))
W = tf.reshape((1.0 - tf.eye(shape[0], dtype=dtype)), [-1])
W = W * pairs / tf.reduce_sum(pairs) + W * \
(1.0 - pairs) / tf.reduce_sum(1.0 - pairs)
# * boosting_weights_init
boosting_weights = tf.ones(shape=(shape[0] * shape[0],), dtype=dtype)
normed_fvecs = []
regular_fvecs = []
# L2 normalize fvecs
for i in xrange(len(embedding_sizes)):
start = int(sum(embedding_sizes[:i]))
stop = int(start + embedding_sizes[i])
fvec = tf.cast(predictions[:, start:stop], dtype)
regular_fvecs.append(fvec)
fvec = do_print(fvec, [tf.norm(fvec, axis=1)],
'fvecs_{}_norms'.format(i))
tf.summary.histogram('fvecs_{}'.format(i), fvec)
tf.summary.histogram('fvecs_{}_norm'.format(i), tf.norm(fvec, axis=1))
normed_fvecs.append(
fvec / tf.maximum(tf.constant(1e-5, dtype=dtype),
tf.norm(fvec, axis=1, keep_dims=True)))
alpha = tf.constant(alpha, dtype=dtype)
beta = tf.constant(beta, dtype=dtype)
C = tf.constant(C, dtype=dtype)
shrinkage = tf.constant(shrinkage, dtype=dtype)
loss = tf.constant(0.0, dtype=dtype)
acts = tf.constant(initial_acts, dtype=dtype)
tf.summary.histogram('boosting_weights_0', boosting_weights)
tf.summary.histogram('boosting_weights_0_pos', tf.boolean_mask(
boosting_weights, tf.equal(pairs, 1.0)))
tf.summary.histogram('boosting_weights_0_neg', tf.boolean_mask(
boosting_weights, tf.equal(pairs, 0.0)))
Ds = []
for i in xrange(len(embedding_sizes)):
fvec = normed_fvecs[i]
Ds.append(tf.matmul(fvec, tf.transpose(fvec)))
D = tf.reshape(Ds[-1], [-1])
my_act = alpha * (D - beta) * m
my_loss = tf.log(tf.exp(-my_act) + tf.constant(1.0, dtype=dtype))
tmp = (tf.reduce_sum(my_loss * boosting_weights * W) /
tf.constant(num_learners, dtype=dtype))
loss += tmp
tf.summary.scalar('learner_loss_{}'.format(i), tmp)
if eta_style:
nu = 2.0 / (1.0 + 1.0 + i)
if shrinkage != 1.0:
acts = (1.0 - nu) * acts + nu * shrinkage * D
inputs = alpha * (acts - beta) * m
booster_loss = tf.log(tf.exp(-(inputs)) + 1.0)
boosting_weights = tf.stop_gradient(
-tf.gradients(tf.reduce_sum(booster_loss), inputs)[0])
else:
acts = (1.0 - nu) * acts + nu * shrinkage * my_act
booster_loss = tf.log(tf.exp(-acts) + 1.0)
boosting_weights = tf.stop_gradient(
-tf.gradients(tf.reduce_sum(booster_loss), acts)[0])
else:
# simpler variant of the boosting algorithm.
acts += shrinkage * (D - beta) * alpha * m
booster_loss = tf.log(tf.exp(-acts) + 1.0)
cls_weight = tf.cast(1.0 * pairs + (1.0 - pairs) * 2.0,
dtype=dtype)
boosting_weights = tf.stop_gradient(-tf.gradients(
tf.reduce_sum(booster_loss), acts)[0] * cls_weight)
tf.summary.histogram(
'boosting_weights_{}'.format(i + 1), boosting_weights)
pos_weights = tf.boolean_mask(
boosting_weights, tf.equal(pairs, 1.0))
neg_weights = tf.boolean_mask(
boosting_weights, tf.equal(pairs, 0.0))
pos_bins = tf.histogram_fixed_width(pos_weights, (tf.constant(
0.0, dtype=dtype), tf.constant(1.0, dtype=dtype)), nbins=10)
neg_bins = tf.histogram_fixed_width(neg_weights, (tf.constant(
0.0, dtype=dtype), tf.constant(1.0, dtype=dtype)), nbins=10)
loss = do_print(loss, [tf.reduce_mean(
booster_loss)], 'Booster loss {}'.format(i + 1))
loss = do_print(loss, [pos_bins, neg_bins],
'Positive and negative boosting weights {}'.format(
i + 1), summarize=100)
tf.summary.histogram(
'boosting_weights_{}_pos'.format(i + 1), pos_weights)
tf.summary.histogram(
'boosting_weights_{}_neg'.format(i + 1), neg_weights)
tf.summary.scalar('booster_loss_{}'.format(
i + 1), tf.reduce_mean(booster_loss))
# add the independence loss
tf.summary.scalar('discriminative_loss', loss)
embedding_weights = [v for v in tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES) if 'embedding' in v.name and
'weight' in v.name]
if lambda_div > 0.0:
loss += REGULARIZATION_FUNCTIONS[regularization](
fvecs=normed_fvecs, end_points=end_points,
embedding_weights=embedding_weights,
embedding_sizes=embedding_sizes,
lambda_weight=LAMBDA_WEIGHT) * lambda_div
tf.summary.scalar('loss', loss)
return loss
def build_pairwise_tower_loss(fvecs_i, fvecs_j, scope=None,
lambda_weight=LAMBDA_WEIGHT):
"""
Builds an adversarial regressor from fvecs_j to fvecs_i.
Args:
fvecs_i: the target embedding (i.e. the smaller embedding)
fvecs_j: the source embedding (i.e. the begger embedding)
scope: scope name of the regressor.
lambda_weight: the regularization parameter for the weights.
Returns:
An adversarial regressor loss function.
"""
# build a regressor from fvecs_j to fvecs_i
fvecs_i = flip_gradient.flip_gradient(fvecs_i)
fvecs_j = flip_gradient.flip_gradient(fvecs_j)
net = fvecs_j
bias_loss = 0.0
weight_loss = 0.0
adversarial_loss = 0.0
with tf.variable_scope(scope):
for i in xrange(NUM_HIDDENS_ADVERSARIAL):
if i < NUM_HIDDENS_ADVERSARIAL - 1:
net = slim.fully_connected(
net, HIDDEN_ADVERSARIAL_SIZE, scope='fc_{}'.format(i),
activation_fn=tf.nn.relu)
else:
net = slim.fully_connected(net, fvecs_i.get_shape().as_list(
)[-1], scope='fc_{}'.format(i), activation_fn=None)
b = slim.get_variables(
scope=tf.get_variable_scope().name + '/fc_{}/biases'.format(i)
)[0]
W = slim.get_variables(
scope=tf.get_variable_scope().name + '/fc_{}/weights'.format(i)
)[0]
weight_loss += tf.reduce_mean(
tf.square(tf.reduce_sum(W * W, axis=1) - 1)) * lambda_weight
if b is not None:
bias_loss += tf.maximum(
0.0,
tf.reduce_sum(b * b) - 1.0) * lambda_weight
adversarial_loss += -tf.reduce_mean(tf.square(fvecs_i * net))
tf.summary.scalar('adversarial loss', adversarial_loss)
tf.summary.scalar('weight loss', weight_loss)
tf.summary.scalar('bias loss', bias_loss)
return adversarial_loss + weight_loss + bias_loss
def adversarial_loss(fvecs, end_points, embedding_weights, embedding_sizes,
lambda_weight=LAMBDA_WEIGHT):
"""
Applies the adversarial loss on our embedding.
Args:
fvecs: tensor of the embedding feature vectors.
end_points: dictionary of end_points of the embedding tower.
embedding_weights: weight matrices of the embedding.
embedding_sizes: list of embedding sizes, e.g. [96, 160, 256]
lambda_weight: weight regularization parameter.
Returns:
The regularization loss.
"""
loss = 0.0
with tf.variable_scope('pws'):
for layer_idx, fvecs in enumerate(iterate_regularization_acts(
end_points, embedding_sizes)):
for i in xrange(len(fvecs)):
for j in xrange(i + 1, len(fvecs)):
name = 'pw_tower_loss_layer_{}_from_{}_to_{}'.format(
layer_idx, i, j)
loss += build_pairwise_tower_loss(
fvecs[i], fvecs[j],
name,
lambda_weight=lambda_weight)
weight_loss = 0.0
for W in embedding_weights:
weight_loss += tf.reduce_mean(
tf.square(tf.reduce_sum(W * W, axis=1) - 1))
weight_loss = do_print(weight_loss, [weight_loss], 'weight loss')
loss = do_print(loss, [loss], 'adversarial correlation dann hidden loss')
tf.summary.scalar('adversarial correlation dann hidden losss', loss)
tf.summary.scalar('weight loss', weight_loss)
return loss + lambda_weight * weight_loss
def iterate_regularization_acts(end_points, embedding_sizes):
"""
Iterates through the regularization activations.
Args:
end_points: Dictionary of end_points.
embedding_sizes: List of embedding sizes, e.g. [96, 160, 256].
Yields:
All iteration endpoints
"""
num_embeddings = len(embedding_sizes)
fvecs = []
# yield the output layer.
for i in xrange(num_embeddings):
fvecs.append(end_points[EMBEDDING_SCOPE_NAME +
'/embedding/fc_{}_regularizer'.format(i)])
yield fvecs
def activation_loss(fvecs, end_points, embedding_weights, embedding_sizes,
lambda_weight=LAMBDA_WEIGHT):
"""
Applies the activation loss on our embedding.
Args:
fvecs: embedding tensors.
end_points: dictionary of end_points from embedding_tower.
embedding_weights: weight matrices of embeddings
embedding_sizes: list of embedding sizes, e.g. [96, 160, 256].
lambda_weight: Weight regularization parameter.
Returns:
The activation loss.
"""
loss = 0.0
for fvecs in iterate_regularization_acts(end_points, embedding_sizes):
print(fvecs)
for i in xrange(len(fvecs)):
for j in xrange(i + 1, len(fvecs)):
loss += tf.reduce_mean(
tf.square(fvecs[i][:, tf.newaxis, :] *
fvecs[j][:, :, tf.newaxis]))
weight_loss = 0.0
for W in embedding_weights:
weight_loss += tf.reduce_mean(
tf.square(tf.reduce_sum(W * W, axis=0) - 1))
weight_loss = do_print(weight_loss, [weight_loss], 'weight loss')
loss = do_print(loss, [loss], 'group loss')
return loss + weight_loss * lambda_weight
def main():
global NUM_HIDDENS_ADVERSARIAL
global HIDDEN_ADVERSARIAL_SIZE
global BATCH_SIZE
global LR_DECAY
global LAMBDA_WEIGHT
parser = argparse.ArgumentParser()
parser.add_argument('--train-images', required=True)
parser.add_argument('--train-labels', required=True)
parser.add_argument('--test-images', required=False)
parser.add_argument('--test-labels', required=False)
parser.add_argument('--batch-size', type=int, default=BATCH_SIZE)
parser.add_argument('--weights', default='data/inception.npy')
parser.add_argument('--lambda-weight', type=float, default=LAMBDA_WEIGHT)
parser.add_argument('--lambda-div', type=float, default=0.0)
parser.add_argument('--shrinkage', type=float, default=0.06)
parser.add_argument('--eta-style', action='store_true')
parser.add_argument('--lr-decay', type=float, default=LR_DECAY)
parser.add_argument('--embedding-sizes', type=str, default='96,160,256')
parser.add_argument('--eval-every', type=int, default=1000)
parser.add_argument('--num-iterations', type=int, default=20000)
parser.add_argument('--logdir', type=str, default='train')
parser.add_argument('--seed', type=int, default=5)
parser.add_argument('--regularization', type=str, default='activation',
choices=REGULARIZATION_CHOICES)
parser.add_argument('--hidden-adversarial-size',
type=int, default=HIDDEN_ADVERSARIAL_SIZE)
parser.add_argument('--num-hidden-adversarial', type=int,
default=NUM_HIDDENS_ADVERSARIAL)
parser.add_argument('--labels-per-batch', type=int, default=6)
parser.add_argument('--images-per-identity', type=int)
parser.add_argument('--embedding-lr-multiplier', type=float, default=10.0)
parser.add_argument('--lr-anneal', type=int)
parser.add_argument('--use-same-learnrate', action='store_true')
parser.add_argument('--skip-test', action='store_true')
dtype = tf.float32
args = parser.parse_args()
LAMBDA_WEIGHT = args.lambda_weight
LR_DECAY = args.lr_decay
BATCH_SIZE = args.batch_size
NUM_HIDDENS_ADVERSARIAL = args.num_hidden_adversarial
HIDDEN_ADVERSARIAL_SIZE = args.hidden_adversarial_size
print(args.logdir)
skip_test = args.skip_test
if args.test_images is None or args.test_labels is None:
skip_test = True
random.seed(args.seed)
np.random.seed(args.seed)
tf.set_random_seed(args.seed)
embedding_sizes = [int(x) for x in args.embedding_sizes.split(',')]
spec = TrainingData(crop_size=224, channels=3, mean=(
104.0, 117.0, 123.0))
print('creating datasets...')
train_provider = dataset.NpyDatasetProvider(
data_spec=spec,
labels_per_batch=args.labels_per_batch,
images_per_identity=args.images_per_identity,
image_file=args.train_images,
label_file=args.train_labels,
batch_size=BATCH_SIZE)
test_provider = None
train_labels, train_data = train_provider.dequeue_op
if not skip_test:
test_provider = dataset.NpyDatasetProvider(
data_spec=spec,
image_file=args.test_images,
label_file=args.test_labels,
batch_size=BATCH_SIZE,
is_training=False)
test_labels, test_data = test_provider.dequeue_op
net = models.GoogleNet({'data': train_data})
hidden_layer = net.get_output()
preds, end_points = embedding_tower(
hidden_layer, embedding_sizes)
end_points['pool5_7x7_s1'] = hidden_layer
if not skip_test:
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
test_net = models.GoogleNet({'data': test_data}, trainable=False)
test_hidden_layer = test_net.get_output()
test_preds, test_endpoints = embedding_tower(
test_hidden_layer,
embedding_sizes,
reuse=True)
loss = build_train(
preds,
end_points,
train_labels,
embedding_sizes,
shrinkage=args.shrinkage,
lambda_div=args.lambda_div,
eta_style=args.eta_style,
dtype=dtype,
regularization=args.regularization)
# Add weight decay.
all_weights = tf.get_collection('weights')
all_weights = list(set(all_weights))
for w in all_weights:
loss += tf.cast(tf.reduce_sum(w * w) * WEIGHT_DECAY, dtype=dtype)
all_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
hidden_vars = [v for v in all_vars if 'embedding' not in v.name]
embedding_vars = [v for v in all_vars if 'embedding' in v.name]
global_step = tf.train.get_or_create_global_step()
lr = tf.constant(LEARNING_RATE, dtype=tf.float32,
shape=(), name='learning_rate')
if args.lr_anneal:
lr = tf.train.exponential_decay(
lr, global_step, args.lr_anneal, LR_DECAY, staircase=True)
lr = do_print(lr, [lr], 'learning rate')
opt_hidden = tf.train.AdamOptimizer(learning_rate=lr)
train_op_hidden = opt_hidden.minimize(loss, var_list=hidden_vars)
opt_embedding = tf.train.AdamOptimizer(
learning_rate=lr * args.embedding_lr_multiplier)
train_op_embedding = opt_embedding.minimize(
loss, global_step=global_step, var_list=embedding_vars)
with tf.control_dependencies([train_op_hidden, train_op_embedding]):
train_op = tf.no_op()
init_op = tf.global_variables_initializer()
with tf.control_dependencies([init_op]):
load_train_op = net.create_load_op(args.weights, ignore_missing=True)
if not skip_test:
load_test_op = test_net.create_load_op(
args.weights, ignore_missing=True)
checkpoint_saver = tf.train.CheckpointSaverHook(
args.logdir,
save_steps=args.eval_every,
saver=tf.train.Saver(max_to_keep=100000))
latest_checkpoint = tf.train.latest_checkpoint(args.logdir)
need_init = latest_checkpoint is None
assign_op = None
start_iter = 0
if not need_init:
start_iter = int(latest_checkpoint.split('-')[-1])
assign_op = global_step.assign(start_iter)
with tf.train.MonitoredTrainingSession(
checkpoint_dir=args.logdir,
is_chief=True,
hooks=[checkpoint_saver],
save_checkpoint_secs=None) as sess:
if need_init:
sess.run(init_op)
sess.run(load_train_op)
if not skip_test:
sess.run(load_test_op)
else:
sess.run(assign_op)
if not args.skip_test:
hidden_test_output = test_net.get_output()
writer = tf.summary.FileWriter(args.logdir)
for i in xrange(start_iter, args.num_iterations):
if not args.skip_test and i % args.eval_every == 0:
test_provider.feed_data(sess)
all_fvecs = []
all_fvecs_hidden = []
all_labels = []
#all_caffe_fvecs = []
num_batches = int(
np.ceil(test_provider.num_images / float(BATCH_SIZE)))
print('Evaluating {} batches'.format(num_batches))
for batch_idx in xrange(num_batches):
fvec, fvec_hidden, cls = sess.run(
[test_preds, hidden_test_output, test_labels])
fvec = fvec[cls >= 0, ...]
fvec_hidden = fvec_hidden[cls >= 0, ...]
cls = cls[cls >= 0, ...]
all_fvecs.append(np.array(fvec))
all_fvecs_hidden.append(np.array(fvec_hidden[:, 0, 0, :]))
all_labels.append(np.array(cls))
all_labels = np.concatenate(all_labels)
recall = evaluate2(np.vstack(all_fvecs_hidden), all_labels)
summary = tf.Summary(value=[tf.Summary.Value(
tag='Recall@1_Hidden_Layer', simple_value=recall)])
writer.add_summary(summary, i)
recall = evaluate(np.vstack(all_fvecs),
all_labels, embedding_sizes)
summary = tf.Summary(value=[tf.Summary.Value(
tag='Recall@1_Embedding_Layer', simple_value=recall)])
writer.add_summary(summary, i)
lossval, _ = sess.run([loss, train_op])
if i % 40 == 0:
print('loss: {}@Iteration {}'.format(lossval, i))
REGULARIZATION_FUNCTIONS = {
'activation': activation_loss,
'adversarial': adversarial_loss,
}
if __name__ == '__main__':
main()