def build_graph(reader,
model,
input_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
distill_reader=None,
transformer_class=feature_transform.DefaultTransformer):
video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
input_data_pattern,
batch_size=batch_size))
feature_transformer = transformer_class()
model_input, num_frames = feature_transformer.transform(
model_input_raw, num_frames=num_frames)
with tf.name_scope("model"):
if FLAGS.noise_level > 0:
noise_level_tensor = tf.placeholder_with_default(
0.0, shape=[], name="noise_level")
else:
noise_level_tensor = None
if FLAGS.dropout:
keep_prob_tensor = tf.placeholder_with_default(1.0,
shape=[],
name="keep_prob")
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
dropout=FLAGS.dropout,
keep_prob=keep_prob_tensor,
noise_level=noise_level_tensor,
is_training=False)
else:
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
noise_level=noise_level_tensor,
is_training=False)
print "result", result
predictions = result["predictions"]
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("video_id_batch", video_id)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
if FLAGS.dropout:
tf.add_to_collection("keep_prob", keep_prob_tensor)
if FLAGS.noise_level > 0:
tf.add_to_collection("noise_level", noise_level_tensor)
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
is_training=True,
**unused_params):
with tf.variable_scope(scope, tf.AUTO_REUSE):
with tf.variable_scope('lstm1', tf.AUTO_REUSE):
lstm1 = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)])
outputs1, _ = tf.nn.dynamic_rnn(lstm1,
model_input,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True)
with tf.variable_scope('lstm2', tf.AUTO_REUSE):
lstm2 = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)])
outputs2, _ = tf.nn.dynamic_rnn(lstm2,
outputs1,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True)
with tf.variable_scope('lstm3', tf.AUTO_REUSE):
lstm3 = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)])
outputs, state = tf.nn.dynamic_rnn(lstm3,
outputs2 + outputs1,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True)
if FLAGS.lstm_pooling_method == 'last':
inp = state[-1].h
else:
inp = utils.FramePooling(outputs, FLAGS.lstm_pooling_method)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
results = aggregated_model().create_model(model_input=inp,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
results['features'] = inp
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
is_training=True,
**unused_params):
"""Creates a model which uses a stack of LSTMs to represent the video.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
lstm_size = FLAGS.lstm_cells
number_of_layers = FLAGS.lstm_layers
with tf.variable_scope(scope, tf.AUTO_REUSE):
stacked_lstm = tf.contrib.rnn.MultiRNNCell([
tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)
for _ in range(number_of_layers)
])
outputs, state = tf.nn.dynamic_rnn(stacked_lstm,
model_input,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
if FLAGS.lstm_pooling_method == 'last':
inp = state[-1].h
else:
inp = utils.FramePooling(outputs, FLAGS.lstm_pooling_method)
results = aggregated_model().create_model(model_input=inp,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
results['features'] = inp
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
is_training=True,
**unused_params):
lstm_size = FLAGS.lstm_cells
number_of_layers = FLAGS.lstm_layers
with tf.variable_scope(scope, tf.AUTO_REUSE):
stacked_lstm_fw = tf.contrib.rnn.MultiRNNCell([
tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)
for _ in range(number_of_layers)
])
stacked_lstm_bw = tf.contrib.rnn.MultiRNNCell([
tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)
for _ in range(number_of_layers)
])
outputs, state = tf.nn.bidirectional_dynamic_rnn(
stacked_lstm_fw,
stacked_lstm_bw,
model_input,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True)
if FLAGS.lstm_pooling_method == 'last':
l = [state[i][-1].h for i in range(2)]
else:
l = [
utils.FramePooling(outputs[0], FLAGS.lstm_pooling_method),
utils.FramePooling(outputs[1], FLAGS.lstm_pooling_method)
]
output = tf.concat(l, 1)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
results = aggregated_model().create_model(model_input=output,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
results['features'] = output
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
labels,
scope='default',
is_training=True,
**unused_params):
X = FLAGS.residualcnn_x
with tf.variable_scope(scope, tf.AUTO_REUSE):
fc = slim.fully_connected(
model_input,
X,
weights_regularizer=tf.contrib.layers.l2_regularizer(0.01))
reshaped_input = tf.expand_dims(fc, -1)
reshaped_input = tf.expand_dims(reshaped_input, -1)
conv1 = slim.convolution(reshaped_input, 64, [49, 1])
conv1_norm = slim.batch_norm(conv1, is_training=is_training)
module1 = self.residual_module([128, 192, 64], conv1_norm,
'module1')
module1_norm = slim.batch_norm(module1, is_training=is_training)
conv2 = slim.convolution(module1_norm, 128, 1)
conv2_norm = slim.batch_norm(conv2, is_training=is_training)
module2 = self.residual_module([256, 512, 128], conv2_norm,
'module2')
module2_norm = slim.batch_norm(module2, is_training=is_training)
conv3 = slim.convolution(module2_norm, 256, 1)
conv3_norm = slim.batch_norm(conv3, is_training=is_training)
module3 = self.residual_module([512, 256], conv3_norm, 'module3')
module3_norm = slim.batch_norm(module3, is_training=is_training)
conv4 = slim.convolution(module3_norm, X, 1)
conv4_norm = slim.batch_norm(conv4, is_training=is_training)
module4 = self.residual_module([512, X], conv4_norm, 'module4')
features = tf.squeeze(module4, [2])
features = model_utils.FramePooling(features,
FLAGS.residualcnn_pooling) + fc
results = MoeModel().create_model(features, vocab_size)
results['features'] = features
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
**unused_params):
lstm_size = FLAGS.lstm_cells
with tf.variable_scope(scope, tf.AUTO_REUSE):
cells = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)])
outputs1, _ = tf.nn.dynamic_rnn(cells,
model_input,
sequence_length=num_frames,
dtype=tf.float32,
swap_memory=True,
scope='first')
cells1 = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)])
outputs2, state2 = tf.nn.dynamic_rnn(cells1,
outputs1[:, 0:300:2, :],
sequence_length=num_frames /
2,
dtype=tf.float32,
swap_memory=True,
scope='second')
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
if FLAGS.lstm_pooling_method == 'last':
output = state2[-1].h
else:
output = utils.FramePooling(outputs2,
FLAGS.lstm_pooling_method)
results = aggregated_model().create_model(model_input=output,
vocab_size=vocab_size,
**unused_params)
results['features'] = output
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
is_training=True,
**unused_params):
results = {}
with tf.variable_scope(scope, tf.AUTO_REUSE):
rgb_input = tf.slice(model_input, [0, 0, 0], [-1, -1, 1024])
audio_input = tf.slice(model_input, [0, 0, 1024], [-1, -1, 128])
rgb_model = globals()[FLAGS.rgb_frame_level_model]
audio_model = globals()[FLAGS.audio_frame_level_model]
rgb_results = rgb_model().create_model(model_input=rgb_input,
vocab_size=vocab_size,
num_frames=num_frames,
labels=labels,
scope='rgb',
is_training=is_training,
**unused_params)
audio_results = audio_model().create_model(
model_input=audio_input,
vocab_size=vocab_size,
num_frames=num_frames,
labels=labels,
scope='audio',
is_training=is_training**unused_params)
if labels != None:
results['loss'] = rgb_results['loss'] + audio_results['loss']
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
features = rgb_results['features'] + audio_results['features']
output = aggregated_model().create_model(model_input=features,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
if labels != None:
results['loss'] += 6 * losses.CrossEntropyLoss(
).calculate_loss(output['predictions'], labels)
results['predictions'] = output['predictions']
return results
def create_model(self,
model_input,
vocab_size,
num_frames,
labels,
scope='default',
**unused_params):
"""Creates a model which uses a logistic classifier over the average of the
frame-level features.
This class is intended to be an example for implementors of frame level
models. If you want to train a model over averaged features it is more
efficient to average them beforehand rather than on the fly.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
feature_size = model_input.get_shape().as_list()[2]
denominators = tf.reshape(tf.tile(num_frames, [1, feature_size]),
[-1, feature_size])
avg_pooled = tf.reduce_sum(model_input, axis=[1]) / denominators
with tf.variable_scope(scope, tf.AUTO_REUSE):
output = slim.fully_connected(
avg_pooled,
vocab_size,
activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(1e-8))
return {
"predictions": output,
"features": avg_pooled,
"loss": losses.CrossEntropyLoss().calculate_loss(output, labels)
}
def build_model(self, model, reader):
"""Find the model and build the graph."""
label_loss_fn = losses.CrossEntropyLoss()
optimizer_class = tf.train.AdamOptimizer
build_graph(
reader=reader,
model=model,
optimizer_class=optimizer_class,
clip_gradient_norm=FLAGS.clip_gradient_norm,
train_data_pattern=FLAGS.train_data_pattern,
label_loss_fn=label_loss_fn,
base_learning_rate=FLAGS.base_learning_rate,
learning_rate_decay=FLAGS.learning_rate_decay,
learning_rate_decay_examples=FLAGS.learning_rate_decay_examples,
regularization_penalty=FLAGS.regularization_penalty,
num_readers=FLAGS.num_readers,
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
return tf.train.Saver(max_to_keep=0,
keep_checkpoint_every_n_hours=0.25)
def build_graph(model,
reader,
train_data_pattern,
test_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
base_learning_rate=0.01,
learning_rate_decay_steps=10000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""
Creates the Tensorflow graph.
:param model: The core model (e.g. logistic or neural net). It should inherit from BaseModel.
:param reader: The data file reader. It should inherit from BaseReader.
:param train_data_pattern: path to the train data files.
:param test_data_pattern: path to the test data files.
:param label_loss_fn: loss to apply to the model. It should inherit from BaseLoss.
:param base_learning_rate: learning rate to initialize the optimizer with.
:param learning_rate_decay_steps:
:param learning_rate_decay:
:param optimizer_class: Which optimization algorithm to use.
:param clip_gradient_norm: Magnitude of the gradient to clip to.
:param regularization_penalty: How much weight to give the regularization loss compared to the label loss.
:param num_readers:
:param num_epochs:
:return:
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(
base_learning_rate,
global_step,
learning_rate_decay_steps,
learning_rate_decay,
staircase=True)
optimizer = optimizer_class(learning_rate)
_, train_stk_input, train_stk_label = get_input_train_tensors(
reader, train_data_pattern, batch_size=FLAGS.train_batch_size, num_readers=num_readers, num_epochs=num_epochs)
_, test_stk_input, test_stk_label = get_input_test_tensors(
reader, test_data_pattern, batch_size=FLAGS.test_batch_size, num_readers=1)
train_stk_feature_dim = len(train_stk_input.get_shape()) - 1
test_stk_feature_dim = len(test_stk_input.get_shape()) - 1
assert train_stk_feature_dim == test_stk_feature_dim
train_stk_model_input = tf.nn.l2_normalize(train_stk_input, train_stk_feature_dim)
test_stk_model_input = tf.nn.l2_normalize(test_stk_input, test_stk_feature_dim)
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
train_result = model.create_model(train_stk_model_input)
test_result = model.create_model(test_stk_model_input)
train_predictions = train_result["predictions"]
test_predictions = test_result["predictions"]
stk_embedding = test_result["stk_embedding"]
if "loss" in train_result.keys():
train_loss = train_result["loss"]
else:
train_loss = label_loss_fn.calculate_loss(train_predictions, train_stk_label)
train_aux_loss = tf.constant(0.0)
if "aux_predictions" in train_result.keys():
for pred in train_result["aux_predictions"]:
train_aux_loss += label_loss_fn.calculate_loss(pred, test_stk_label)
if "regularization_loss" in train_result.keys():
train_reg_loss = train_result["regularization_loss"]
else:
train_reg_loss = tf.constant(0.0)
train_reg_losses = tf.losses.get_regularization_losses()
if train_reg_losses:
train_reg_loss += tf.add_n(train_reg_losses)
if "loss" in test_result.keys():
test_loss = test_result["loss"]
else:
test_loss = label_loss_fn.calculate_loss(test_predictions, test_stk_label)
# A dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in train_result.keys():
update_ops += train_result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
train_loss = tf.identity(train_loss)
train_aux_loss = tf.identity(train_aux_loss)
# Incorporate the L2 weight penalties etc.
train_final_loss = regularization_penalty * train_reg_loss + train_loss + train_aux_loss
train_op = slim.learning.create_train_op(
train_final_loss,
optimizer,
global_step=global_step,
clip_gradient_norm=clip_gradient_norm)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("train_loss", train_loss)
tf.add_to_collection("test_top_loss", test_loss)
tf.add_to_collection("train_predictions", train_predictions)
tf.add_to_collection("test_predictions", test_predictions)
tf.add_to_collection("train_stk_input", train_stk_input)
tf.add_to_collection("train_stk_model_input", train_stk_model_input)
tf.add_to_collection("test_stk_input", test_stk_input)
tf.add_to_collection("test_stk_model_input", test_stk_model_input)
tf.add_to_collection("train_stk_label", tf.cast(train_stk_label, tf.float32))
tf.add_to_collection("test_stk_label", tf.cast(test_stk_label, tf.float32))
tf.add_to_collection("stk_embedding", stk_embedding)
tf.add_to_collection("train_op", train_op)
def build_model(self):
"""Builds the model.
Inputs:
self.image_embeddings
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
Outputs:
self.total_loss (training and eval only)
self.target_cross_entropy_losses (training and eval only)
self.target_cross_entropy_loss_weights (training and eval only)
"""
caption_model_fn = find_class_by_name(FLAGS.model, [im2txt_models])
caption_model = caption_model_fn()
# model
outputs = caption_model.create_model(
input_seqs=self.input_seqs,
image_model_output=self.image_model_output,
initializer=self.initializer,
mode=self.mode,
target_seqs=self.target_seqs,
global_step=self.global_step,
input_mask=self.input_mask,
target_lengths=self.target_lengths)
# loss
if self.mode == "inference":
if "logits" in outputs:
tf.nn.softmax(outputs["logits"], name="softmax")
elif "bs_results" in outputs:
self.predicted_ids = outputs["bs_results"].predicted_ids
self.scores = outputs[
"bs_results"].beam_search_decoder_output.scores
if "bs_results_lengths" in outputs:
self.predicted_ids_lengths = outputs["bs_results_lengths"]
if "top_n_attributes" in outputs:
self.top_n_attributes = outputs["top_n_attributes"]
else:
if "mle_caption_logits" in outputs:
logits = tf.reshape(outputs["mle_caption_logits"],
[-1, FLAGS.vocab_size])
targets = tf.reshape(self.target_seqs, [-1])
weights = tf.to_float(tf.reshape(self.input_mask, [-1]))
# Compute losses.
mle_loss_fn = losses.SparseSoftmaxCrossEntropyLoss()
mle_loss = mle_loss_fn.calculate_loss(logits, targets, weights)
# Logging losses.
tf.summary.scalar("losses/mle_loss", mle_loss)
tf.losses.add_loss(mle_loss)
# caption loss
if FLAGS.rl_training == True:
# rl loss
# load greed caption and sample caption to calculate reward
target_caption_words = self.target_seqs
target_caption_lengths = self.target_lengths
greedy_caption_words = outputs["greedy_caption_words"]
greedy_caption_lengths = outputs["greedy_caption_lengths"]
sample_caption_logits = outputs["sample_caption_logits"]
sample_caption_words = outputs["sample_caption_words"]
sample_caption_lengths = outputs["sample_caption_lengths"]
if get_rank(target_caption_words) == 2:
target_caption_words = tf.expand_dims(
target_caption_words, 1)
if get_rank(target_caption_lengths) == 1:
target_caption_lengths = tf.expand_dims(
target_caption_lengths, 1)
if get_shape_as_list(target_caption_words)[-1] is None:
target_caption_words, target_caption_lengths = \
pad_or_truncate(target_caption_words, target_caption_lengths,
axis = -1, max_length = FLAGS.max_ref_length)
if get_shape_as_list(greedy_caption_words)[-1] is None:
greedy_caption_words, greedy_caption_lengths = \
pad_or_truncate(greedy_caption_words, greedy_caption_lengths,
axis = -1, max_length = FLAGS.max_caption_length)
if get_shape_as_list(sample_caption_logits)[1] is None:
sample_caption_logits, _ = \
pad_or_truncate(sample_caption_logits, sample_caption_lengths,
axis = 1, max_length = FLAGS.max_caption_length)
if get_shape_as_list(sample_caption_words)[-1] is None:
sample_caption_words, sample_caption_lengths = \
pad_or_truncate(sample_caption_words, sample_caption_lengths,
axis = -1, max_length = FLAGS.max_caption_length)
if FLAGS.rl_beam_search_approximation:
target_caption_words = tf.contrib.seq2seq.tile_batch(
target_caption_words, multiplier=FLAGS.beam_width)
target_caption_lengths = tf.contrib.seq2seq.tile_batch(
target_caption_lengths, multiplier=FLAGS.beam_width)
rl_loss_cls = find_class_by_name(FLAGS.rl_training_loss,
[losses])
rl_loss_fn = rl_loss_cls()
rl_loss = rl_loss_fn.calculate_loss(
target_caption_words=target_caption_words,
target_caption_lengths=target_caption_lengths,
greedy_caption_words=greedy_caption_words,
greedy_caption_lengths=greedy_caption_lengths,
sample_caption_words=sample_caption_words,
sample_caption_lengths=sample_caption_lengths,
sample_caption_logits=sample_caption_logits)
tf.losses.add_loss(rl_loss)
else:
if "logits" in outputs:
# prepare logits, targets and weight
logits = outputs["logits"]
logits = tf.reshape(
logits, [FLAGS.batch_size, -1, FLAGS.vocab_size])
logits, _ = pad_or_truncate(
logits, None, axis=1, max_length=FLAGS.max_ref_length)
logits = tf.reshape(logits, [-1, FLAGS.vocab_size])
targets = tf.reshape(self.target_seqs, [-1])
weights = tf.to_float(tf.reshape(self.input_mask, [-1]))
# Compute losses.
loss_fn = losses.SparseSoftmaxCrossEntropyLoss()
batch_loss = loss_fn.calculate_loss(
logits, targets, weights)
# Logging losses.
tf.summary.scalar("losses/batch_loss", batch_loss)
tf.losses.add_loss(batch_loss)
self.target_cross_entropy_losses = batch_loss # Used in evaluation.
self.target_cross_entropy_loss_weights = weights # Used in evaluation.
# multi-label-loss
if "attributes_logits" in outputs and "attributes_mask" in outputs:
attributes_logits = outputs["attributes_logits"]
attributes_targets = get_attributes_target(
self.target_seqs, attributes_mask)
if FLAGS.use_idf_weighted_attribute_loss:
attributes_mask = outputs["idf_weighted_mask"]
else:
attributes_mask = outputs["attributes_mask"]
attributes_loss_fn = losses.CrossEntropyLoss()
attributes_loss = attributes_loss_fn.calculate_loss(
attributes_logits, attributes_targets, attributes_mask)
tf.losses.add_loss(attributes_loss)
tf.summary.scalar("losses/attributes_loss",
attributes_loss)
self.attributes_loss = attributes_loss
# discriminative loss
# should be multi-label margin loss, but the loss below is a little different
if "discriminative_logits" in outputs:
word_labels = caption_to_multi_labels(self.target_seqs)
discriminative_loss = tf.losses.hinge_loss(
labels=word_labels,
logits=outputs["discriminative_logits"],
weights=FLAGS.discriminative_loss_weights)
tf.summary.scalar("losses/discriminative_loss",
discriminative_loss)
self.discriminative_loss = discriminative_loss
# word weighted cross entropy loss
if "word_predictions" in outputs:
word_loss_fn = losses.CrossEntropyLoss()
word_loss = word_loss_fn.calculate_loss(
outputs["word_predictions"],
caption_to_multi_labels(self.target_seqs))
tf.summary.scalar("losses/word_loss", word_loss)
tf.losses.add_loss(word_loss)
self.word_loss = word_loss
total_loss = tf.losses.get_total_loss()
# Add summaries.
tf.summary.scalar("losses/total_loss", total_loss)
for var in tf.trainable_variables():
tf.summary.histogram("parameters/" + var.op.name, var)
self.total_loss = total_loss
def create_model(self,
model_input,
vocab_size,
labels,
scope='default',
is_training=True,
**unused_params):
with tf.variable_scope(scope, tf.AUTO_REUSE):
reshaped_input = tf.expand_dims(model_input, -1)
reshaped_input = tf.expand_dims(reshaped_input, -1)
conv1 = slim.convolution(reshaped_input,
64, [49, 1],
stride=(4, 1))
max_pool1 = slim.max_pool2d(conv1, (9, 1), (2, 1), padding='SAME')
norm1 = tf.nn.local_response_normalization(max_pool1)
conv2 = slim.convolution(norm1, 64, 1, 1)
conv3 = slim.convolution(conv2, 192, (9, 1), 1)
norm2 = tf.nn.local_response_normalization(conv3)
max_pool2 = slim.max_pool2d(norm2, (9, 1), (2, 1), padding='SAME')
inception3a = self.inception_module(max_pool2,
[64, 96, 128, 16, 32, 32],
'3a')
inception3b = self.inception_module(inception3a,
[128, 128, 192, 32, 96, 64],
'3b')
max_pool3 = slim.max_pool2d(inception3b, (9, 1), (2, 1),
padding='SAME')
inception4a = self.inception_module(max_pool3,
[192, 96, 208, 16, 48, 64],
'4a')
inception4b = self.inception_module(inception4a,
[160, 112, 224, 24, 64, 64],
'4b')
inception4c = self.inception_module(inception4b,
[128, 128, 256, 24, 64, 64],
'4c')
inception4d = self.inception_module(inception4c,
[112, 144, 288, 32, 64, 64],
'4d')
inception4e = self.inception_module(inception4d,
[256, 160, 320, 32, 128, 128],
'4e')
max_pool4 = slim.max_pool2d(inception4e, (9, 1), (2, 1),
padding='SAME')
inception5a = self.inception_module(max_pool4,
[256, 160, 320, 32, 128, 128],
'5a')
inception5b = self.inception_module(inception5a,
[384, 192, 384, 48, 128, 128],
'5b')
inter1 = tf.squeeze(inception4a, axis=[2])
inter2 = tf.squeeze(inception4d, axis=[2])
output = tf.squeeze(inception5b, axis=[2])
inter1 = model_utils.FramePooling(inter1, FLAGS.googlenet_pooling)
inter2 = model_utils.FramePooling(inter2, FLAGS.googlenet_pooling)
output = model_utils.FramePooling(output, FLAGS.googlenet_pooling)
inter_results1 = MoeModel().create_model(inter1, vocab_size,
'inter1')
inter_results2 = MoeModel().create_model(inter2, vocab_size,
'inter2')
results = MoeModel().create_model(output, vocab_size, 'final')
results['features'] = output
if labels != None:
results['loss'] = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
results['loss'] += losses.CrossEntropyLoss().calculate_loss(
inter_results1['predictions'], labels)
results['loss'] += losses.CrossEntropyLoss().calculate_loss(
inter_results2['predictions'], labels)
return results
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
with tf.name_scope("model"):
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(predictions,
labels_batch)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
train_op = slim.learning.create_train_op(
final_loss,
optimizer,
global_step=global_step,
clip_gradient_norm=clip_gradient_norm)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
def create_model(self,
model_input,
vocab_size,
labels=None,
scope='default',
l2_penalty=1e-8,
is_training=True,
**unused_params):
reshaped_input = tf.expand_dims(model_input, -1)
reshaped_input = tf.expand_dims(reshaped_input, -1)
out1 = tf.layers.conv2d(
reshaped_input,
128, (32, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
out1_norm = tf.layers.batch_normalization(out1, training=is_training)
out1_pool = tf.layers.max_pooling2d(out1_norm, (8, 1),
2,
padding='same')
out2 = tf.layers.conv2d(
out1_pool,
256, (32, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
out2_norm = tf.layers.batch_normalization(out2, training=is_training)
out2_pool = tf.layers.max_pooling2d(out2_norm, (8, 1),
2,
padding='same')
out3 = tf.layers.conv2d(
out2_pool,
256, (32, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
out3_norm = tf.layers.batch_normalization(out3, training=is_training)
out3_pool = tf.layers.max_pooling2d(out3_norm, (8, 1),
2,
padding='same')
encoded = tf.reduce_max(out3_pool, axis=[2, 3])
decode = tf.expand_dims(encoded, -1)
decode = tf.expand_dims(decode, -1)
decode1 = tf.layers.conv2d(
decode,
64, (4, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
decode1_batch = tf.layers.batch_normalization(decode1,
training=is_training)
decode1_upsample = tf.layers.conv2d_transpose(
decode1_batch,
256, (8, 1),
strides=(2, 1),
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer())
decode2 = tf.layers.conv2d(
decode1_upsample,
64,
1,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer())
decode2 = tf.layers.conv2d(
decode2,
64, (4, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
decode2_batch = tf.layers.batch_normalization(decode2,
training=is_training)
decode2_upsample = tf.layers.conv2d_transpose(
decode2_batch,
256, (8, 1),
strides=(2, 1),
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer())
decode3 = tf.layers.conv2d(
decode2_upsample,
64,
1,
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer())
decode3 = tf.layers.conv2d(
decode3,
64, (4, 1),
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
padding='same')
decode3_batch = tf.layers.batch_normalization(decode3,
training=is_training)
decode3_upsample = tf.layers.conv2d_transpose(
decode3_batch,
256, (8, 1),
strides=(2, 1),
padding='same',
activation=tf.nn.relu,
kernel_initializer=tf.contrib.layers.xavier_initializer())
decoded = tf.reduce_max(decode3_upsample, axis=[2, 3])
results = {}
results['loss'] = 500 * tf.losses.mean_squared_error(
model_input, decoded)
output = MoeModel().create_model(encoded, vocab_size)
results['predictions'] = output['predictions']
if labels is not None:
results['loss'] += losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
return results
def create_model(self,
model_input,
vocab_size,
labels,
scope='default',
l2_penalty=1e-8,
is_training=True,
**unused_params):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
out1 = slim.fully_connected(
model_input,
768,
weights_regularizer=slim.l2_regularizer(l2_penalty))
out1_drop = slim.dropout(out1, is_training=is_training)
out2 = slim.fully_connected(
out1_drop,
512,
weights_regularizer=slim.l2_regularizer(l2_penalty))
out2_drop = slim.dropout(out2, is_training=is_training)
encoded = slim.fully_connected(
out2_drop,
384,
weights_regularizer=slim.l2_regularizer(l2_penalty))
out4 = slim.fully_connected(
encoded,
640,
weights_regularizer=slim.l2_regularizer(l2_penalty))
out4_drop = slim.dropout(out4, is_training=is_training)
out5 = slim.fully_connected(
out4_drop,
768,
weights_regularizer=slim.l2_regularizer(l2_penalty))
decoded = slim.fully_connected(
out5,
int(model_input.shape[1]),
weights_regularizer=slim.l2_regularizer(l2_penalty))
results = {}
encoder_loss = 500 * tf.losses.mean_squared_error(
model_input, decoded)
tf.summary.scalar("encoder_loss", encoder_loss)
results['loss'] = encoder_loss
output = MoeModel().create_model(encoded,
vocab_size,
scope="final_layer")
output1 = MoeModel().create_model(out4,
vocab_size,
scope="intermediate_layer")
results['predictions'] = (output['predictions'] +
output1['predictions']) / 2
if labels is not None:
prediction_loss = losses.CrossEntropyLoss().calculate_loss(
results['predictions'], labels)
tf.summary.scalar("prediction_loss", prediction_loss)
results['loss'] += prediction_loss
return results
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
with tf.name_scope("model"):
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(predictions,
labels_batch)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
train_op = slim.learning.create_train_op(
final_loss,
optimizer,
global_step=global_step,
clip_gradient_norm=clip_gradient_norm)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
#data_pattern = ['/Users/super/yt8m_videofeature/train*.tfrecord'] data_pattern = ['I:\\yt8m_video\\train*.tfrecord'] num_epochs = 5 reader_batch_size = 1024 num_readers = 1 num_classes = 4716 mini_batch_size = 1024 label_loss_fn = losses.CrossEntropyLoss() regularization_penalty = 1 base_learning_rate = 0.01 learning_rate_decay_examples = 4000000 learning_rate_decay = 0.95 optimizer_class = tf.train.AdamOptimizer clip_gradient_norm = 1.0 regularization_penalty = 1
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
transformer_class=feature_transform.DefaultTransformer,
augmenter_class=data_augmentation.DefaultAugmenter,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
if FLAGS.distillation_features:
video_id, model_input_raw, labels_batch, num_frames, distill_labels_batch = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
if FLAGS.distillation_features and FLAGS.distillation_type == 2:
p = FLAGS.distillation_percent
print "distillation_percent =", p, "reforming labels"
float_labels = tf.cast(labels_batch, dtype=tf.float32)
sum_float_labels = tf.reduce_sum(float_labels,
axis=1,
keep_dims=True)
sum_distill_labels = tf.reduce_sum(
distill_labels_batch, axis=1, keep_dims=True) + 1e-6
distill_labels_batch = float_labels + distill_labels_batch * (
sum_float_labels / sum_distill_labels * p)
distill_labels_batch = tf.clip_by_value(distill_labels_batch,
clip_value_min=0.0,
clip_value_max=1.0)
else:
video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
# data augmentation, will not persist in inference
data_augmenter = augmenter_class()
model_input_raw, labels_batch, num_frames = data_augmenter.augment(
model_input_raw, num_frames=num_frames, labels_batch=labels_batch)
tf.summary.histogram("model/input_raw", model_input_raw)
feature_transformer = transformer_class()
model_input, num_frames = feature_transformer.transform(
model_input_raw, num_frames=num_frames)
tf.summary.histogram("model/input", model_input)
with tf.name_scope("model"):
if FLAGS.noise_level > 0:
noise_level_tensor = tf.placeholder_with_default(
0.0, shape=[], name="noise_level")
else:
noise_level_tensor = None
if FLAGS.distillation_as_input:
distillation_predictions = distill_labels_batch
else:
distillation_predictions = None
if FLAGS.dropout:
keep_prob_tensor = tf.placeholder_with_default(1.0,
shape=[],
name="keep_prob")
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
dropout=FLAGS.dropout,
keep_prob=keep_prob_tensor,
distillation_predictions=distillation_predictions,
noise_level=noise_level_tensor)
else:
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
distillation_predictions=distillation_predictions,
noise_level=noise_level_tensor)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
print "result", result
predictions = result["predictions"]
if "loss" in result.keys():
label_loss = result["loss"]
else:
video_weights_batch = None
if FLAGS.reweight:
video_weights_batch = get_video_weights(video_id)
if FLAGS.distillation_as_boosting:
video_weights_batch = get_weights_by_predictions(
labels_batch, distillation_predictions)
if FLAGS.multitask:
support_predictions = result["support_predictions"]
tf.summary.histogram("model/support_predictions",
support_predictions)
print "support_predictions", support_predictions
if FLAGS.distillation_features and FLAGS.distillation_type == 1:
p = FLAGS.distillation_percent
print "distillation_percent =", p
if p <= 0:
label_loss = label_loss_fn.calculate_loss(
predictions,
support_predictions,
labels_batch,
weights=video_weights_batch)
elif p >= 1:
label_loss = label_loss_fn.calculate_loss(
predictions,
support_predictions,
distill_labels_batch,
weights=video_weights_batch)
else:
label_loss = label_loss_fn.calculate_loss(predictions, support_predictions, labels_batch, weights=video_weights_batch) * (1.0 - p) \
+ label_loss_fn.calculate_loss(predictions, support_predictions, distill_labels_batch, weights=video_weights_batch) * p
elif FLAGS.distillation_features and FLAGS.distillation_type == 2:
print "using pure distillation loss"
label_loss = label_loss_fn.calculate_loss(
predictions,
support_predictions,
distill_labels_batch,
weights=video_weights_batch)
else:
print "using original loss"
label_loss = label_loss_fn.calculate_loss(
predictions,
support_predictions,
labels_batch,
weights=video_weights_batch)
else:
if FLAGS.distillation_features and FLAGS.distillation_type == 1:
p = FLAGS.distillation_percent
print "distillation_percent =", p
if p <= 0:
label_loss = label_loss_fn.calculate_loss(
predictions,
labels_batch,
weights=video_weights_batch)
elif p >= 1:
label_loss = label_loss_fn.calculate_loss(
predictions,
distill_labels_batch,
weights=video_weights_batch)
else:
label_loss = label_loss_fn.calculate_loss(predictions, labels_batch, weights=video_weights_batch) * (1.0 - p) \
+ label_loss_fn.calculate_loss(predictions, distill_labels_batch, weights=video_weights_batch) * p
elif FLAGS.distillation_features and FLAGS.distillation_type == 2:
print "using pure distillation loss"
label_loss = label_loss_fn.calculate_loss(
predictions,
distill_labels_batch,
weights=video_weights_batch)
else:
print "using original loss"
label_loss = label_loss_fn.calculate_loss(
predictions, labels_batch, weights=video_weights_batch)
tf.summary.histogram("model/predictions", predictions)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
gradients = utils.clip_gradient_norms(gradients,
clip_gradient_norm)
train_op = optimizer.apply_gradients(gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
if FLAGS.dropout:
tf.add_to_collection("keep_prob", keep_prob_tensor)
if FLAGS.noise_level > 0:
tf.add_to_collection("noise_level", noise_level_tensor)
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.compat.v1.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit from
BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an unlimited
number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
local_device_protos = device_lib.list_local_devices()
gpus = [x.name for x in local_device_protos if x.device_type == "GPU"]
gpus = gpus[:FLAGS.num_gpu]
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = "/gpu:%d"
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = "/cpu:%d"
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size *
num_towers,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar("learning_rate", learning_rate)
optimizer = optimizer_class(learning_rate)
input_data_dict = (get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size *
num_towers,
num_readers=num_readers,
num_epochs=num_epochs))
print('input_data_dict', input_data_dict)
model_input_raw = input_data_dict["video_matrix"]
labels_batch = input_data_dict["labels"]
num_frames = input_data_dict["num_frames"]
print("model_input_shape, ", model_input_raw.shape)
print("labels_batch, ", labels_batch)
import csv
import urllib3
import numpy as np
import pandas as pd
whitelisted_cls_mask = np.zeros((3862, ), dtype=np.float32)
url = pd.read_csv('segment_label_ids.csv')
# response = urllib2.urlopen(url)
for line in url:
try:
cls_id = int(line[0])
whitelisted_cls_mask[cls_id] = 1.
except ValueError:
# Simply skip the non-integer line.
continue
#response.close()
# url2 = 'http://storage.googleapis.com/youtube8m-lijun-mlengine/classCount.csv'
# response2 = urllib2.urlopen(url2)
# fobj2 = csv.reader(response2)
# for line in fobj2:
# try:
# cls_id = int(line[0])
# whitelisted_cls_mask[cls_id] = (15-np.log(int(line[1])))**2
# except ValueError:
# # Simply skip the non-integer line.
# continue
# response2.close()
# select=tf.matmul(tf.cast(labels_batch, tf.float32),tf.reshape(whitelisted_cls_mask,[3862,1]))>0
# select=tf.squeeze(select)
# model_input_raw = model_input_raw[select,:,:]
# labels_batch = labels_batch[select,:]
# num_frames = num_frames[select]
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
tower_inputs = tf.split(model_input, num_towers)
tower_labels = tf.split(labels_batch, num_towers)
tower_num_frames = tf.split(num_frames, num_towers)
tower_gradients = []
tower_predictions = []
tower_label_losses = []
tower_reg_losses = []
# import csv
# import urllib2
# import numpy as np
# whitelisted_cls_mask = np.zeros((3862,),
# dtype=np.float32)
# url = 'http://storage.googleapis.com/youtube8m-lijun-mlengine/segment_label_ids.csv'
# response = urllib2.urlopen(url)
# fobj = csv.reader(response)
# for line in fobj:
# try:
# cls_id = int(line[0])
# whitelisted_cls_mask[cls_id] = 1.
# except ValueError:
# # Simply skip the non-integer line.
# continue
# response.close()
# whitelisted_cls_mask=whitelisted_cls_mask+np.ones((3862,),dtype=np.float32)
whitelisted_cls_mask = whitelisted_cls_mask * 4 + np.ones(
(3862, ), dtype=np.float32)
# whitelisted_cls_mask=0.05*(whitelisted_cls_mask*99+np.ones((3862,),dtype=np.float32))
# print('whitelisted_cls_mask',np.amin(whitelisted_cls_mask))
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.f
with tf.device(device_string % i):
with (tf.variable_scope(("tower"), reuse=True if i > 0 else
None)): # reuse=True if i > 0 else None
with (slim.arg_scope(
[slim.model_variable, slim.variable],
device="/cpu:0" if num_gpus != 1 else "/gpu:0")):
result = model.create_model(tower_inputs[i],
num_frames=tower_num_frames[i],
vocab_size=reader.num_classes,
labels=tower_labels[i])
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
# print('result predictions',result["predictions"])
predictions = result["predictions"]
tower_predictions.append(predictions)
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(
predictions,
tower_labels[i],
label_weights=whitelisted_cls_mask)
if "aux_predictions" in result.keys():
for pred in result["aux_predictions"]:
label_loss += label_loss_fn.calculate_loss(
pred,
tower_labels[i],
label_weights=whitelisted_cls_mask)
# print('label_loss',label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
tower_reg_losses.append(reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
tower_label_losses.append(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
tower_gradients.append(gradients)
label_loss = tf.reduce_mean(tf.stack(tower_label_losses))
tf.summary.scalar("label_loss", label_loss)
if regularization_penalty != 0:
reg_loss = tf.reduce_mean(tf.stack(tower_reg_losses))
tf.summary.scalar("reg_loss", reg_loss)
merged_gradients = utils.combine_gradients(tower_gradients)
if clip_gradient_norm > 0:
with tf.name_scope("clip_grads"):
merged_gradients = utils.clip_gradient_norms(
merged_gradients, clip_gradient_norm)
train_op = optimizer.apply_gradients(merged_gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", tf.concat(tower_predictions, 0))
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
def build_graph(reader,
model,
train_data_pattern,
train_data_pattern2,
train_data_pattern3,
eval_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None,
l2_penalty=1e-8,
gpu_only=1):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
# data files
files1 = gfile.Glob(train_data_pattern)
files2 = gfile.Glob(train_data_pattern2)
files3 = gfile.Glob(train_data_pattern3)
files = files1 + files2 + files3
if not files:
raise IOError("Unable to find training files. data_pattern='" +
data_pattern + "'.")
logging.info("Total number of training files: %s + %s + %s = %s.",
str(len(files1)), str(len(files2)), str(len(files3)),
str(len(files)))
files4 = gfile.Glob(eval_data_pattern)
logging.info("Total number of eval files: %s.", str(len(files4)))
if FLAGS.fold == -1:
validate_files = files4
train_files = files
else:
validate_files = files[FLAGS.fold::5]
train_files = [x for x in files if x not in validate_files]
logging.info("train files: {}, first is: {}.".format(
len(train_files), train_files[0].split('/')[-1]))
logging.info("eval files: {}, first is: {}.".format(
len(validate_files), validate_files[0].split('/')[-1]))
# label weights for loss function. ugly hard coded for now.
wgts_np = np.ones(FLAGS.truncated_num_classes)
over_weight_labels = False
if over_weight_labels:
labels_to_overwgt = [
38, 47, 49, 55, 72, 76, 86, 89, 93, 94, 95, 98, 99, 101, 102, 110,
111, 113, 114, 115, 120, 121
]
wgts_np[labels_to_overwgt] = 2.0
wgts_4_lossfn = tf.constant(wgts_np, dtype=tf.float32)
global_step = tf.Variable(0, trainable=False, name="global_step")
restart_learning_rate = tf.Variable(base_learning_rate,
trainable=False,
name="restart_learning_rate")
local_device_protos = device_lib.list_local_devices()
gpus = [x.name for x in local_device_protos if x.device_type == 'GPU']
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = '/gpu:%d'
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = '/cpu:%d'
learning_rate = tf.train.exponential_decay(restart_learning_rate,
global_step * batch_size *
num_towers,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_files,
batch_size=batch_size * num_towers,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
# model params
# probabilities for keeping a neuron in a layer, assuming max 10 layers, below default value
with tf.variable_scope("tower", reuse=True) as scope:
layers_keep_probs = tf.Variable(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
trainable=False,
name="layers_keep_probs")
model_input = model_input_raw
if FLAGS.apply_global_normalization:
g_mean, g_std = model_utils.load_global_moments()
g_inv_std = 1.0 / g_std
global_mean = tf.constant(g_mean, dtype=tf.float32)
# expand global mean to match new dimension and fill rest with zeros
new_dim = tf.cast(model_input_raw.shape[1], tf.int32)
zero_padding = tf.zeros(new_dim - tf.shape(global_mean), tf.float32)
global_mean_padded = tf.concat([global_mean, zero_padding], 0)
# expand global inv std to match new dimension and fill rest with ones
global_inv_std = tf.constant(g_inv_std, dtype=tf.float32)
one_padding = tf.ones(new_dim - tf.shape(global_inv_std), tf.float32)
global_inv_std_padded = tf.concat([global_inv_std, one_padding], 0)
# apply normalizations (can do both) if requested
# global L2 normalization
model_input = tf.multiply(tf.subtract(model_input, global_mean_padded),
global_inv_std_padded)
# regular L2 normalization
if FLAGS.apply_batch_l2_normalization:
feature_dim = len(model_input.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input, feature_dim)
tower_inputs = tf.split(model_input, num_towers)
tower_labels = tf.split(labels_batch, num_towers)
tower_num_frames = tf.split(num_frames, num_towers)
tower_gradients = []
tower_predictions = []
tower_label_losses = []
tower_reg_losses = []
# eval graph - to monitor performance out of sample during training
e_video_id, e_input_raw, e_labels_batch, e_num_frames = (
get_input_data_tensors(reader,
validate_files,
batch_size=batch_size * num_towers,
num_readers=num_readers,
num_epochs=2 * num_epochs))
e_input = e_input_raw
if FLAGS.apply_global_normalization:
e_input = tf.multiply(tf.subtract(e_input, global_mean_padded),
global_inv_std_padded)
if FLAGS.apply_batch_l2_normalization:
feature_dim = len(model_input.get_shape()) - 1
e_input = tf.nn.l2_normalize(e_input, feature_dim)
e_tower_inputs = tf.split(e_input, num_towers)
e_tower_labels = tf.split(e_labels_batch, num_towers)
e_tower_num_frames = tf.split(e_num_frames, num_towers)
e_tower_predictions = []
e_tower_layers_keep_probs = tf.Variable(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
trainable=False,
name="layers_keep_probs")
logging.info(e_tower_inputs)
# end eval
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.
logging.info('For tower: ' + str(i))
with tf.device(device_string % i):
with (tf.variable_scope(("tower"), reuse=True if i > 0 else None)):
with (slim.arg_scope(
[slim.model_variable, slim.variable],
device="/cpu:0" if num_gpus != 1 else "/gpu:0")):
logging.info(layers_keep_probs)
result = model.create_model(
tower_inputs[i],
num_frames=tower_num_frames[i],
vocab_size=reader.num_classes,
labels=tower_labels[i],
layers_keep_probs=layers_keep_probs,
l2_penalty=l2_penalty,
is_training=True)
for variable in slim.get_model_variables():
logging.info(variable)
tf.summary.histogram(variable.op.name, variable)
# create shadow moving average model variables
if FLAGS.use_ema == True:
model_vars = [x for x in slim.get_model_variables()]
ema = tf.train.ExponentialMovingAverage(
decay=1.0 - 1.0 / FLAGS.ema_halflife)
ema_op = ema.apply(model_vars)
logging.info("model_vars:")
logging.info(" || ".join([str(x) for x in model_vars]))
ema_vars = [ema.average(x) for x in model_vars]
ema_vars_pair_dict = {
ema.average_name(x): x.op.name
for x in model_vars
}
logging.info("ema_vars_pair_dict:")
for x, y in ema_vars_pair_dict.items():
logging.info(x + ': ' + y)
for v in ema_vars:
tf.summary.histogram(v.op.name, v)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, ema_op)
tf.add_to_collection("ema_op", ema_op)
predictions = result["predictions"]
tower_predictions.append(predictions)
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(
predictions, tower_labels[i], FLAGS.loss_epsilon)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
tower_reg_losses.append(reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
tower_label_losses.append(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
tower_gradients.append(gradients)
# eval ops
logging.info("eval ops")
e_result = model.create_model(
e_tower_inputs[i],
num_frames=e_tower_num_frames[i],
vocab_size=reader.num_classes,
labels=e_tower_labels[i],
layers_keep_probs=
e_tower_layers_keep_probs, #tf.Variable([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], tf.float32, name="layers_keep_probs")
l2_penalty=l2_penalty,
is_training=False)
e_predictions = e_result["predictions"]
e_tower_predictions.append(e_predictions)
# end eval ops
label_loss = tf.reduce_mean(tf.stack(tower_label_losses))
tf.summary.scalar("label_loss", label_loss)
if regularization_penalty != 0:
reg_loss = tf.reduce_mean(tf.stack(tower_reg_losses))
tf.summary.scalar("reg_loss", reg_loss)
merged_gradients = utils.combine_gradients(tower_gradients)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
merged_gradients = utils.clip_gradient_norms(
merged_gradients, clip_gradient_norm)
train_op = optimizer.apply_gradients(merged_gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("restart_learning_rate", restart_learning_rate)
tf.add_to_collection("layers_keep_probs", layers_keep_probs)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", tf.concat(tower_predictions, 0))
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
#tf.add_to_collection("ema_op", ema_op)
# add eval graph
e_label_loss = label_loss_fn.calculate_loss(
tf.concat(e_tower_predictions, 0), e_labels_batch, FLAGS.loss_epsilon)
tf.summary.scalar("e_label_loss", e_label_loss)
tf.add_to_collection("e_predictions", tf.concat(e_tower_predictions, 0))
tf.add_to_collection("e_labels", tf.cast(e_labels_batch, tf.float32))
tf.add_to_collection("e_loss", e_label_loss)
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit from
BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an unlimited
number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
local_device_protos = device_lib.list_local_devices()
gpus = [x.name for x in local_device_protos if x.device_type == "GPU"]
print(gpus)
gpus = gpus[:FLAGS.num_gpu]
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = "/gpu:%d"
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = "/cpu:%d"
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar("learning_rate", learning_rate)
if clip_gradient_norm > 0.:
optimizer = optimizer_class(learning_rate)
else:
optimizer = optimizer_class(learning_rate)
input_data_dict = (get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
model_input_raw = input_data_dict["video_matrix"]
labels_batch = input_data_dict["labels"]
num_frames = input_data_dict["num_frames"]
print("model_input_shape, ", model_input_raw.shape)
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
offset = np.array([4. / 512] * 1024 + [0] * 128)
offset = tf.constant(offset, dtype=tf.float32)
eigen_val = tf.constant(np.sqrt(
np.load("yt8m_pca/eigenvals.npy")[:1024, 0]),
dtype=tf.float32)
model_input = tf.multiply(
model_input_raw - offset,
tf.pad(eigen_val + 1e-4, [[0, 128]], constant_values=1.))
# model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
if FLAGS.segment_labels:
label_weights = input_data_dict["label_weights"]
else:
label_weights = None
tower_logits = []
tower_predictions = []
tower_label_losses = []
tower_reg_losses = []
print("flag1!!!!", device_string)
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.
with tf.device(device_string % i):
with tf.variable_scope("tower_%d" % i, reuse=False):
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
is_training=True)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
tower_predictions.append(predictions)
logits = result["logits"]
tower_logits.append(logits)
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(
predictions, labels_batch, label_weights=label_weights)
if "aux_predictions" in result.keys():
for pred in result["aux_predictions"]:
label_loss += label_loss_fn.calculate_loss(
pred,
labels_batch,
label_weights=label_weights)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
tower_reg_losses.append(reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
tower_label_losses.append(label_loss)
with tf.device("/gpu:%d" % 0):
with tf.variable_scope("ensemble"):
ftr_mean = tf.reduce_mean(model_input, axis=1)
print("ftr mean shape: ", ftr_mean.get_shape().as_list())
ftr_mean = slim.batch_norm(ftr_mean,
center=True,
scale=True,
fused=False,
is_training=True,
scope="mix_weights_bn")
mix_weights = slim.fully_connected(
ftr_mean,
num_towers,
activation_fn=None,
weights_initializer=slim.variance_scaling_initializer(),
scope="mix_weights")
mix_weights = tf.nn.softmax(mix_weights, axis=-1)
tf.summary.histogram("mix_weights", mix_weights)
logits = tf.stack(tower_logits, axis=1)
final_logit = tf.reduce_sum(tf.multiply(
logits, tf.expand_dims(mix_weights, axis=-1)),
axis=1,
keepdims=False)
final_predictions = tf.nn.sigmoid(final_logit)
print("flag2!!!", FLAGS.final_temperature, FLAGS.final_lambda)
rank_pred = tf.expand_dims(tf.nn.softmax(tf.div(
final_logit, FLAGS.final_temperature),
axis=-1),
axis=1)
aux_rank_preds = tf.nn.softmax(tf.div(logits, FLAGS.final_temperature),
axis=-1)
epsilon = 1e-8
kl_loss = tf.reduce_sum(
rank_pred *
(tf.log(rank_pred + epsilon) - tf.log(aux_rank_preds + epsilon)),
axis=-1)
regularization_loss = FLAGS.final_lambda * tf.reduce_mean(
tf.reduce_sum(kl_loss, axis=-1), axis=-1)
final_label_loss = label_loss_fn.calculate_loss(
final_predictions, labels_batch, label_weights=label_weights)
label_loss = tf.reduce_sum(
tf.stack(tower_label_losses)) + final_label_loss
tf.summary.scalar("label_loss", label_loss)
reg_loss = tf.reduce_sum(
tf.stack(tower_reg_losses)) + regularization_loss
tf.summary.scalar("reg_loss", reg_loss)
final_loss = label_loss + regularization_penalty * reg_loss
gradients = optimizer.compute_gradients(final_loss,
colocate_gradients_with_ops=True)
if clip_gradient_norm > 0:
gradients = utils.clip_gradient_norms(gradients, clip_gradient_norm)
final_train_op = optimizer.apply_gradients(gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", final_predictions)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", final_train_op)
def build_graph(reader,
input_data_pattern,
model,
distill_readers=None,
distill_data_patterns=None,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
transformer_class=feature_transform.DefaultTransformer):
video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
input_data_pattern,
batch_size=batch_size))
if distill_readers is not None:
all_distill_labels = []
for dreader, dpattern in zip(distill_readers, distill_data_patterns):
distill_video_id, distill_labels_batch, unused_labels_batch, unused_num_frames = (
get_input_data_tensors(dreader,
dpattern,
batch_size=batch_size))
all_distill_labels.append(distill_labels_batch)
all_distill_labels = tf.stack(all_distill_labels, axis=2)
distill_weight_var = tf.get_variable("distill_weight",
[len(distill_readers)])
distill_weight = tf.nn.softmax(distill_weight_var)
final_distill_labels = tf.einsum("ijk,k->ij", all_distill_labels,
distill_weight)
feature_transformer = transformer_class()
model_input, num_frames = feature_transformer.transform(
model_input_raw, num_frames=num_frames)
with tf.name_scope("model"):
if FLAGS.noise_level > 0:
noise_level_tensor = tf.placeholder_with_default(
0.0, shape=[], name="noise_level")
else:
noise_level_tensor = None
if distill_readers is not None:
distillation_predictions = final_distill_labels
else:
distillation_predictions = None
if FLAGS.dropout:
keep_prob_tensor = tf.placeholder_with_default(1.0,
shape=[],
name="keep_prob")
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
dropout=FLAGS.dropout,
keep_prob=keep_prob_tensor,
noise_level=noise_level_tensor,
distillation_predictions=distillation_predictions,
is_training=False)
else:
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
noise_level=noise_level_tensor,
distillation_predictions=distillation_predictions,
is_training=False)
print "result", result
predictions = result["predictions"]
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("video_id_batch", video_id)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
if FLAGS.dropout:
tf.add_to_collection("keep_prob", keep_prob_tensor)
if FLAGS.noise_level > 0:
tf.add_to_collection("noise_level", noise_level_tensor)
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
if FLAGS.distillation_features:
unused_video_id, model_input_raw, labels_batch, num_frames, distill_labels_batch = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
if FLAGS.distillation_features and FLAGS.distillation_type == 2:
p = FLAGS.distillation_percent
print("distillation_percent =", p, "reforming labels")
float_labels = tf.cast(labels_batch, dtype=tf.float32)
sum_float_labels = tf.reduce_sum(float_labels,
axis=1,
keep_dims=True)
sum_distill_labels = tf.reduce_sum(
distill_labels_batch, axis=1, keep_dims=True) + 1e-6
distill_labels_batch = float_labels + distill_labels_batch * (
sum_float_labels / sum_distill_labels * p)
distill_labels_batch = tf.clip_by_value(distill_labels_batch,
clip_value_min=0.0,
clip_value_max=1.0)
else:
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
if FLAGS.norm:
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
else:
model_input = model_input_raw
with tf.name_scope("model"):
if FLAGS.distillation_features and FLAGS.distillation_type == 0:
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
distill_labels=distill_labels_batch)
else:
result = model.create_model(model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
if "predictions_negative" in result.keys():
predictions_negative = result["predictions_negative"]
else:
predictions_negative = 1 - predictions
if "predictions_positive" in result.keys():
predictions_positive = result["predictions_positive"]
else:
predictions_positive = predictions
if predictions.get_shape().ndims == 3:
predictions = tf.reshape(
predictions, [-1, predictions.get_shape().as_list()[2]])
labels_batch = tf.reshape(
labels_batch, [-1, labels_batch.get_shape().as_list()[2]])
if "bottleneck" in result.keys():
bottle_neck = result["bottleneck"]
else:
bottle_neck = tf.constant(0.0)
if "predictions_class" in result.keys():
predictions_class = result["predictions_class"]
else:
predictions_class = predictions
if "predictions_encoder" in result.keys():
predictions_encoder = result["predictions_encoder"]
else:
predictions_encoder = predictions
if "predictions_experts" in result.keys():
predictions_experts = result["predictions_experts"]
else:
predictions_experts = predictions
if "predictions_postprocess" in result.keys():
predictions_postprocess = result["predictions_postprocess"]
else:
predictions_postprocess = predictions
if "loss" in result.keys():
append_loss = result["loss"]
else:
append_loss = tf.constant(0.0)
if "predictions_encoder" in result.keys():
label_loss, float_encoders = label_loss_fn.calculate_loss_mix2(
predictions, predictions_class, predictions_encoder,
labels_batch)
tf.summary.histogram("model/float_encoders", float_encoders)
elif FLAGS.distillation_features and FLAGS.distillation_type == 1:
label_loss = label_loss_fn.calculate_loss_distill_boost(
predictions, distill_labels_batch, labels_batch)
elif FLAGS.distillation_features and FLAGS.distillation_type == 2:
label_loss = label_loss_fn.calculate_loss_distill(
predictions, distill_labels_batch, labels_batch)
elif FLAGS.distillation_features and FLAGS.distillation_type == 3:
label_loss = label_loss_fn.calculate_loss_distill_relabel(
predictions, distill_labels_batch, labels_batch)
elif "predictions_class" in result.keys():
label_loss = label_loss_fn.calculate_loss_mix(
predictions, predictions_class, labels_batch)
elif "predictions_experts" in result.keys():
label_loss = label_loss_fn.calculate_loss_max(
predictions, predictions_experts, labels_batch)
elif "predictions_postprocess" in result.keys():
label_loss = label_loss_fn.calculate_loss_postprocess(
predictions_postprocess, labels_batch)
elif "predictions_negative" in result.keys():
label_loss = label_loss_fn.calculate_loss_negative(
predictions_positive, predictions_negative, labels_batch)
else:
label_loss = label_loss_fn.calculate_loss(predictions,
labels_batch)
if "prediction_frames" in result.keys():
predictions_frames = result["prediction_frames"]
labels_frames = tf.tile(
tf.reshape(labels_batch, [-1, 1, reader.num_classes]),
[1, FLAGS.moe_num_extend, 1])
labels_frames = tf.cast(
tf.reshape(labels_frames, [-1, reader.num_classes]),
tf.float32)
frame_loss = label_loss_fn.calculate_loss(predictions_frames,
labels_frames)
if "prediction_prepare_frames" in result.keys():
prediction_prepare_frames = result["prediction_prepare_frames"]
prediction_prepare_video = result["prediction_prepare_video"]
max_frames = model_input.get_shape().as_list()[1]
frames_sum = tf.reduce_sum(tf.abs(model_input), axis=2)
frames_true = tf.ones(tf.shape(frames_sum))
frames_false = tf.zeros(tf.shape(frames_sum))
frames_bool = tf.where(tf.greater(frames_sum, frames_false),
frames_true, frames_false)
frames_bool = tf.reshape(
frames_bool[:, 0:max_frames:FLAGS.stride_size], [-1, 1])
labels_prepare_frames = tf.tile(
tf.reshape(labels_batch, [-1, 1, reader.num_classes]),
[1, max_frames // FLAGS.stride_size, 1])
labels_prepare_frames = tf.cast(
tf.reshape(labels_prepare_frames,
[-1, reader.num_classes]),
tf.float32) * frames_bool
prediction_prepare_frames = prediction_prepare_frames * frames_bool
label_loss = 0.1*label_loss_fn.calculate_loss(prediction_prepare_frames, labels_prepare_frames) + \
0.1*label_loss_fn.calculate_loss(prediction_prepare_video, labels_batch)
else:
label_loss = label_loss * 0.0
elif "prediction_minmax" in result.keys():
predictions_minmax = result["prediction_minmax"]
predictions_min = tf.reduce_min(predictions_minmax, axis=1)
predictions_max = tf.reduce_max(predictions_minmax, axis=1)
epsilon = 10e-6
float_labels = tf.cast(labels_batch, tf.float32)
cross_entropy_loss = float_labels * tf.log(
predictions_min + epsilon) + (
1 - float_labels) * tf.log(1 - predictions_max + epsilon)
frame_loss = tf.reduce_mean(
tf.reduce_sum(tf.negative(cross_entropy_loss), 1))
label_loss = label_loss * 0.0
else:
frame_loss = tf.constant(0.0)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss + frame_loss + append_loss
if FLAGS.gradient == "my":
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
variables_to_train = tf_variables.trainable_variables()
top_grads, top_vars = mygradients(final_loss,
variables_to_train,
global_step=global_step,
name="mygradients_net")
grads_and_vars = list(zip(top_grads, top_vars))
train_op = opt.apply_gradients(grads_and_vars,
global_step=global_step)
else:
optimizer = optimizer_class(learning_rate)
train_op = slim.learning.create_train_op(
final_loss,
optimizer,
global_step=global_step,
clip_gradient_norm=clip_gradient_norm)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", final_loss)
tf.add_to_collection("reg_loss", reg_loss)
tf.add_to_collection("bottleneck", bottle_neck)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
def main():
batch_size = 256
num_classes = 10
""" load dataset """
dataset = loaders.Cifar10Loader('./datasets/CIFAR-10').load()
train_dataset, valid_dataset = dataset
""" processor """
train_processor = processors.Cifar10ClassificationProcessor(
batch_size,
num_classes=num_classes,
enable_augmentation=True,
image_size=(32, 32))
valid_processor = processors.Cifar10ClassificationProcessor(
batch_size,
num_classes=num_classes,
enable_augmentation=False,
image_size=(32, 32))
""" iterator """
train_iterator = iterators.MultiprocessIterator(train_dataset,
train_processor,
num_workers=1)
valid_iterator = iterators.MultiprocessIterator(valid_dataset,
valid_processor,
num_workers=1)
""" device """
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
""" model """
model = models.RiriverceCifar10Net9(input_channels=3,
num_classes=num_classes).to(device)
""" loss """
loss_function = losses.CrossEntropyLoss().to(device)
""" optimizer """
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=20)
""" logger """
logger = loggers.SimpleLogger()
""" learning """
for epoch in range(100):
print(f"-" * 64)
print(f"[epoch {epoch:>4d}]")
phase = 'train'
torch.set_grad_enabled(True)
for batch_data in tqdm.tqdm(train_iterator, desc=phase):
optimizer.zero_grad()
batch_image = torch.from_numpy(batch_data['image']).to(device)
batch_target = torch.from_numpy(batch_data['target']).to(device)
batch_output = model(batch_image)
batch_loss = loss_function(batch_output, batch_target)
batch_loss.sum().backward()
optimizer.step()
batch_loss = batch_loss.data.cpu().numpy()
batch_label = np.argmax(batch_target.data.cpu().numpy(),
axis=-1).flatten()
batch_pred = np.argmax(batch_output.data.cpu().numpy(),
axis=-1).flatten()
logger.add_batch_loss(batch_loss, phase=phase)
logger.add_batch_pred(batch_pred, phase=phase)
logger.add_batch_label(batch_label, phase=phase)
loss = logger.get_loss(phase)
accuracy = logger.get_accuracy(phase)
print(f"loss : {loss}")
print(f"accuracy : {accuracy}")
phase = 'valid'
torch.set_grad_enabled(False)
for batch_data in tqdm.tqdm(valid_iterator, desc=phase):
optimizer.zero_grad()
batch_image = torch.from_numpy(batch_data['image']).to(device)
batch_target = torch.from_numpy(batch_data['target']).to(device)
batch_output = model(batch_image)
batch_loss = loss_function(batch_output, batch_target)
batch_loss = batch_loss.data.cpu().numpy()
batch_label = np.argmax(batch_target.data.cpu().numpy(),
axis=-1).flatten()
batch_pred = np.argmax(batch_output.data.cpu().numpy(),
axis=-1).flatten()
logger.add_batch_loss(batch_loss, phase=phase)
logger.add_batch_pred(batch_pred, phase=phase)
logger.add_batch_label(batch_label, phase=phase)
loss = logger.get_loss(phase)
accuracy = logger.get_accuracy(phase)
print(f"loss : {loss:.4f}")
print(f"accuracy : {accuracy:.4f}")
logger.step()
def evaluate():
tf.set_random_seed(0) # for reproducibility
# Write json of flags
model_flags_path = os.path.join(FLAGS.train_dir, "model_flags.json")
if not file_io.file_exists(model_flags_path):
raise IOError(("Cannot find file %s. Did you run train.py on the same "
"--train_dir?") % model_flags_path)
flags_dict = json.loads(file_io.FileIO(model_flags_path, "r").read())
with tf.Graph().as_default():
# convert feature_names and feature_sizes to lists of values
feature_names, feature_sizes = utils.GetListOfFeatureNamesAndSizes(
flags_dict["feature_names"], flags_dict["feature_sizes"])
if flags_dict["frame_features"]:
reader = readers.YT8MFrameFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
else:
reader = readers.YT8MAggregatedFeatureReader(
feature_names=feature_names, feature_sizes=feature_sizes)
model = frame_level_models.NetVLADModelLF()
model_lst = [model]
if FLAGS.ensemble_num > 1:
for ensemble_idx in range(1, FLAGS.ensemble_num):
model2 = frame_level_models.NetVLADModelLF()
model_lst.append(model2)
label_loss_fn = losses.CrossEntropyLoss()
if FLAGS.eval_data_pattern is "":
raise IOError("'eval_data_pattern' was not specified. " +
"Nothing to evaluate.")
video_id_batch = build_graph(reader=reader,
model=model_lst,
eval_data_pattern=FLAGS.eval_data_pattern,
label_loss_fn=label_loss_fn,
num_readers=FLAGS.num_readers,
batch_size=FLAGS.batch_size)
logging.info("built evaluation graph")
if not FLAGS.force_output_model_name:
video_id_batch = tf.get_collection("video_id_batch")[0]
prediction_batch = tf.get_collection("predictions")[0]
label_batch = tf.get_collection("labels")[0]
loss = tf.get_collection("loss")[0]
summary_op = tf.get_collection("summary_op")[0]
saver = tf.train.Saver(tf.global_variables())
summary_writer = tf.summary.FileWriter(FLAGS.train_dir,
graph=tf.get_default_graph())
evl_metrics = eval_util.EvaluationMetrics(reader.num_classes,
FLAGS.top_k)
last_global_step_val = -1
#with tf.device("/gpu:0"):
while True:
last_global_step_val = evaluation_loop(video_id_batch,
prediction_batch,
label_batch, loss,
summary_op, saver,
summary_writer, evl_metrics,
last_global_step_val)
if FLAGS.run_once:
break
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
global_step = tf.Variable(0, trainable=False, name="global_step")
local_device_protos = device_lib.list_local_devices()
gpus = [x.name for x in local_device_protos if x.device_type == 'GPU']
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = '/gpu:%d'
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = '/cpu:%d'
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size *
num_towers,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size * num_towers,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
tower_inputs = tf.split(model_input, num_towers)
tower_labels = tf.split(labels_batch, num_towers)
tower_num_frames = tf.split(num_frames, num_towers)
tower_gradients = []
tower_predictions = []
tower_label_losses = []
tower_reg_losses = []
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.
with tf.device(device_string % i):
with (tf.variable_scope(("tower"), reuse=True if i > 0 else None)):
with (slim.arg_scope(
[slim.model_variable, slim.variable],
device="/cpu:0" if num_gpus != 1 else "/gpu:0")):
result = model.create_model(tower_inputs[i],
num_frames=tower_num_frames[i],
vocab_size=reader.num_classes,
labels=tower_labels[i])
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
tower_predictions.append(predictions)
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(
predictions, tower_labels[i])
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
tower_reg_losses.append(reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
tower_label_losses.append(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
tower_gradients.append(gradients)
label_loss = tf.reduce_mean(tf.stack(tower_label_losses))
tf.summary.scalar("label_loss", label_loss)
if regularization_penalty != 0:
reg_loss = tf.reduce_mean(tf.stack(tower_reg_losses))
tf.summary.scalar("reg_loss", reg_loss)
merged_gradients = utils.combine_gradients(tower_gradients)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
merged_gradients = utils.clip_gradient_norms(
merged_gradients, clip_gradient_norm)
train_op = optimizer.apply_gradients(merged_gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", tf.concat(tower_predictions, 0))
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
def build_graph(all_readers,
all_train_data_patterns,
input_reader,
input_data_pattern,
model,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=256,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
all_readers: The data file readers. Every element in it should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_patterns: glob paths to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
original_input = None
if input_data_pattern is not None:
original_video_id, original_input, unused_labels_batch, unused_num_frames = (
get_input_data_tensors(input_reader,
input_data_pattern,
batch_size=batch_size,
num_epochs=num_epochs))
optimizer = optimizer_class(learning_rate)
model_input_raw_tensors = []
labels_batch_tensor = None
for reader, data_pattern in zip(all_readers, all_train_data_patterns):
video_id, model_input_raw, labels_batch, unused_num_frames = (
get_input_data_tensors(reader,
data_pattern,
batch_size=batch_size,
num_epochs=num_epochs))
if labels_batch_tensor is None:
labels_batch_tensor = labels_batch
model_input_raw_tensors.append(tf.expand_dims(model_input_raw, axis=2))
if original_input is not None:
id_match = tf.ones_like(original_video_id, dtype=tf.float32)
id_match = id_match * tf.cast(
tf.equal(original_video_id, video_id), dtype=tf.float32)
tf.summary.scalar("model/id_match", tf.reduce_mean(id_match))
model_input = tf.concat(model_input_raw_tensors, axis=2)
labels_batch = labels_batch_tensor
tf.summary.histogram("model/input", model_input)
with tf.name_scope("model"):
if FLAGS.noise_level > 0:
noise_level_tensor = tf.placeholder_with_default(
0.0, shape=[], name="noise_level")
else:
noise_level_tensor = None
if FLAGS.dropout:
keep_prob_tensor = tf.placeholder_with_default(1.0,
shape=[],
name="keep_prob")
result = model.create_model(model_input,
labels=labels_batch,
vocab_size=reader.num_classes,
original_input=original_input,
dropout=FLAGS.dropout,
keep_prob=keep_prob_tensor,
noise_level=noise_level_tensor)
else:
result = model.create_model(model_input,
labels=labels_batch,
vocab_size=reader.num_classes,
original_input=original_input,
noise_level=noise_level_tensor)
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
if "loss" in result.keys():
label_loss = result["loss"]
else:
video_weights_batch = None
if FLAGS.reweight:
video_weights_batch = get_video_weights(video_id)
else:
video_weights_batch = None
if FLAGS.multitask:
print "using multitask loss"
support_predictions = result["support_predictions"]
tf.summary.histogram("model/support_predictions",
support_predictions)
print "support_predictions", support_predictions
label_loss = label_loss_fn.calculate_loss(
predictions,
support_predictions,
labels_batch,
weights=video_weights_batch)
else:
print "using original loss"
label_loss = label_loss_fn.calculate_loss(
predictions, labels_batch, weights=video_weights_batch)
tf.summary.histogram("model/predictions", predictions)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
if FLAGS.training:
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
gradients = utils.clip_gradient_norms(
gradients, clip_gradient_norm)
train_op = optimizer.apply_gradients(gradients,
global_step=global_step)
else:
train_op = tf.no_op()
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("input_batch_raw", model_input)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
if FLAGS.dropout:
tf.add_to_collection("keep_prob", keep_prob_tensor)
if FLAGS.noise_level > 0:
tf.add_to_collection("noise_level", noise_level_tensor)
def build_graph(refiner_model,
discriminator_model,
train_data_reader,
train_data_pattern,
true_label_reader,
true_label_pattern,
refiner_loss_fn=losses.CrossEntropyLoss(),
similarity_loss_fn=losses.MeanSquareErrorLoss(),
discriminator_loss_fn=losses.CrossEntropyLoss(),
batch_size=4,
base_learning_rate=0.01,
learning_rate_decay_examples=4000,
learning_rate_decay=0.99,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
prediction_threshold=0.5,
regularization_penalty=1,
num_readers=2,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
if FLAGS.accumulate_gradients:
actual_batch_size = batch_size * FLAGS.apply_every_n_batches
else:
actual_batch_size = batch_size
learning_rate = tf.train.exponential_decay(
base_learning_rate,
global_step * actual_batch_size,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
image_id, image_data, image_mask = (
get_input_data_tensors(
train_data_reader,
train_data_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
_, _, true_mask = (
get_input_data_tensors(
true_label_reader,
true_label_pattern,
batch_size=batch_size,
num_readers=num_readers,
num_epochs=num_epochs))
model_input = image_data
tf.summary.histogram("model/input", model_input)
with tf.name_scope("refiner_model"):
result = refiner_model.create_model(
model_input,
scope="refiner_model",
l2_penalty=FLAGS.l2_penalty)
print "result", result
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
if "loss" in result.keys():
label_loss = result["loss"]
else:
refiner_loss = refiner_loss_fn.calculate_loss(predictions, image_mask)
image_input = tf.cast(tf.squeeze(image_data, axis=3) > 127, tf.int32)
similarity_loss = similarity_loss_fn.calculate_loss(predictions, image_input)
tf.summary.scalar("refiner_loss", refiner_loss)
tf.summary.scalar("similarity_loss", similarity_loss)
label_loss = refiner_loss + similarity_loss
tf.summary.scalar("label_loss", label_loss)
tf.add_to_collection("refiner_loss", refiner_loss)
tf.add_to_collection("similarity_loss", similarity_loss)
tf.summary.histogram("model/predictions", predictions)
tf.summary.scalar("label_loss", label_loss)
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses(scope="refiner_model")
if reg_losses:
reg_loss += tf.add_n(reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("reg_loss", reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope="refiner_model")
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
bar_label_loss = tf.identity(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + bar_label_loss
optimizing(optimizer, final_loss, clip_gradient_norm, global_step, prefix="refiner", scope="refiner_model")
labels = tf.cast(image_mask, tf.int32)
float_labels = tf.cast(image_mask, tf.float32)
bool_predictions = tf.greater(predictions, prediction_threshold)
true_pos = tf.cast(tf.reduce_sum(tf.cast(labels > 0, tf.int32) * tf.cast(predictions > prediction_threshold, tf.int32)), tf.float32)
false_pos = tf.cast(tf.reduce_sum(tf.cast(labels <= 0, tf.int32) * tf.cast(predictions > prediction_threshold, tf.int32)), tf.float32)
false_neg = tf.cast(tf.reduce_sum(tf.cast(labels > 0, tf.int32) * tf.cast(predictions <= prediction_threshold, tf.int32)), tf.float32)
mean_iou = (2.0 * true_pos + 1e-7) / (2.0 * true_pos + false_pos + false_neg + 1e-7)
print mean_iou
num_examples = tf.shape(labels)[0]
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("id_batch", image_id)
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("model_input", model_input)
tf.add_to_collection("num_examples", num_examples)
tf.add_to_collection("labels", labels)
tf.add_to_collection("float_labels", float_labels)
tf.add_to_collection("bool_predictions", bool_predictions)
tf.add_to_collection("mean_iou", mean_iou)
def split_into_small_patches(masks, label_value):
masks = tf.expand_dims(masks, axis=3)
masks = tf.pad(masks, paddings=[[0,0], [0,0], [1,1], [0,0]])
PATCH_SIZE = [1, 320, 320, 1]
HALF_PATCH_SIZE = [1, 160, 160, 1]
patches = tf.extract_image_patches(masks, PATCH_SIZE, HALF_PATCH_SIZE, [1,1,1,1], "VALID")
patches = tf.reshape(patches, [-1, 320, 320, 1])
if label_value == 0:
labels = tf.zeros([tf.shape(patches)[0],1])
else:
labels = tf.ones([tf.shape(patches)[0],1])
return patches, labels
with tf.name_scope("discriminator_model"):
p_patches, p_labels = split_into_small_patches(predictions, 0)
t_patches, t_labels = split_into_small_patches(tf.cast(true_mask, tf.float32), 1)
disc_batch = tf.concat([p_patches, t_patches], axis=0)
disc_labels = tf.concat([p_labels, t_labels], axis=0)
print "disc_batch", disc_batch
print "disc_labels", disc_labels
disc_result = discriminator_model.create_model(
disc_batch,
scope="discriminator_model",
l2_penalty=FLAGS.l2_penalty)
print "disc_result", disc_result
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
disc_predictions = disc_result["predictions"]
if "loss" in disc_result.keys():
disc_label_loss = disc_result["loss"]
else:
disc_label_loss = discriminator_loss_fn.calculate_loss(disc_predictions, disc_labels) * 20000
tf.summary.scalar("discriminator_loss", disc_label_loss)
tf.summary.histogram("model/disc_predictions", disc_predictions)
if "regularization_loss" in disc_result.keys():
disc_reg_loss = result["regularization_loss"]
else:
disc_reg_loss = tf.constant(0.0)
disc_reg_losses = tf.losses.get_regularization_losses(scope="discriminator_model")
if disc_reg_losses:
disc_reg_loss += tf.add_n(disc_reg_losses)
if regularization_penalty != 0:
tf.summary.scalar("disc_reg_loss", disc_reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
disc_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope="discriminator_model")
if "update_ops" in disc_result.keys():
disc_update_ops += disc_result["update_ops"]
if disc_update_ops:
with tf.control_dependencies(disc_update_ops):
disc_barrier = tf.no_op(name="disc_gradient_barrier")
with tf.control_dependencies([disc_barrier]):
bar_disc_label_loss = tf.identity(disc_label_loss)
# Incorporate the L2 weight penalties etc.
disc_final_loss = regularization_penalty * disc_reg_loss + bar_disc_label_loss
tf.add_to_collection("discriminator_loss", disc_label_loss)
optimizing(optimizer, disc_final_loss, clip_gradient_norm, global_step, prefix="discriminator", scope="discriminator_model")
# refiner 2
refiner2_label_loss = label_loss - disc_label_loss
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
refiner2_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope="refiner_model")
if "update_ops" in result.keys():
refiner2_update_ops += result["update_ops"]
if refiner2_update_ops:
with tf.control_dependencies(refiner2_update_ops):
refiner2_barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([refiner2_barrier]):
bar_refiner2_label_loss = tf.identity(refiner2_label_loss)
refiner2_final_loss = regularization_penalty * reg_loss + bar_refiner2_label_loss
optimizing(optimizer, refiner2_final_loss, clip_gradient_norm, global_step, prefix="refiner2", scope="refiner_model")
tf.add_to_collection("refiner2_loss", refiner2_label_loss)
def build_graph(reader,
model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
model: The core model (e.g. logistic or neural net). It should inherit
from BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
local_device_protos = device_lib.list_local_devices()
gpus = [x.name for x in local_device_protos if x.device_type == 'GPU']
gpus = gpus[:FLAGS.num_gpu]
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = '/gpu:%d'
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = '/cpu:%d'
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size *
num_towers,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
unused_video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size * num_towers,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
feature_dim = len(model_input_raw.get_shape()) - 1
model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
tower_inputs = tf.split(model_input, num_towers)
tower_labels = tf.split(labels_batch, num_towers)
tower_num_frames = tf.split(num_frames, num_towers)
tower_gradients = []
tower_predictions = []
tower_label_losses = []
tower_reg_losses = []
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.
with tf.device(device_string % i):
with (tf.variable_scope(("tower"), reuse=True if i > 0 else None)):
with (slim.arg_scope(
[slim.model_variable, slim.variable],
device="/cpu:0" if num_gpus != 1 else "/gpu:0")):
result = model.create_model(tower_inputs[i],
num_frames=tower_num_frames[i],
vocab_size=reader.num_classes,
labels=tower_labels[i])
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions = result["predictions"]
tower_predictions.append(predictions)
if "loss" in result.keys():
label_loss = result["loss"]
else:
label_loss = label_loss_fn.calculate_loss(
predictions, tower_labels[i])
if "regularization_loss" in result.keys():
reg_loss = result["regularization_loss"]
else:
reg_loss = tf.constant(0.0)
reg_losses = tf.losses.get_regularization_losses()
if reg_losses:
reg_loss += tf.add_n(reg_losses)
tower_reg_losses.append(reg_loss)
# Adds update_ops (e.g., moving average updates in batch normalization) as
# a dependency to the train_op.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if "update_ops" in result.keys():
update_ops += result["update_ops"]
if update_ops:
with tf.control_dependencies(update_ops):
barrier = tf.no_op(name="gradient_barrier")
with tf.control_dependencies([barrier]):
label_loss = tf.identity(label_loss)
tower_label_losses.append(label_loss)
# Incorporate the L2 weight penalties etc.
final_loss = regularization_penalty * reg_loss + label_loss
gradients = optimizer.compute_gradients(
final_loss, colocate_gradients_with_ops=False)
tower_gradients.append(gradients)
label_loss = tf.reduce_mean(tf.stack(tower_label_losses))
tf.summary.scalar("label_loss", label_loss)
if regularization_penalty != 0:
reg_loss = tf.reduce_mean(tf.stack(tower_reg_losses))
tf.summary.scalar("reg_loss", reg_loss)
merged_gradients = utils.combine_gradients(tower_gradients)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
merged_gradients = utils.clip_gradient_norms(
merged_gradients, clip_gradient_norm)
train_op = optimizer.apply_gradients(merged_gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("loss", label_loss)
tf.add_to_collection("predictions", tf.concat(tower_predictions, 0))
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
tf.add_to_collection("train_op", train_op)
def build_graph(reader,
generator_model,
discriminator_model,
train_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
base_learning_rate=0.01,
learning_rate_decay_examples=1000000,
learning_rate_decay=0.95,
optimizer_class=tf.train.AdamOptimizer,
clip_gradient_norm=1.0,
regularization_penalty=1,
num_readers=1,
num_epochs=None):
"""Creates the Tensorflow graph.
This will only be called once in the life of
a training model, because after the graph is created the model will be
restored from a meta graph file rather than being recreated.
Args:
reader: The data file reader. It should inherit from BaseReader.
generator_model: The core model for generator. It should inherit from
BaseModel.
discriminator_model: The core model for discriminator. It should inherit from
BaseModel.
train_data_pattern: glob path to the training data files.
label_loss_fn: What kind of loss to apply to the model. It should inherit
from BaseLoss.
batch_size: How many examples to process at a time.
base_learning_rate: What learning rate to initialize the optimizer with.
optimizer_class: Which optimization algorithm to use.
clip_gradient_norm: Magnitude of the gradient to clip to.
regularization_penalty: How much weight to give the regularization loss
compared to the label loss.
num_readers: How many threads to use for I/O operations.
num_epochs: How many passes to make over the data. 'None' means an
unlimited number of passes.
"""
global_step = tf.Variable(0, trainable=False, name="global_step")
gpus = get_gpus()
num_gpus = len(gpus)
if num_gpus > 0:
logging.info("Using the following GPUs to train: " + str(gpus))
num_towers = num_gpus
device_string = '/gpu:%d'
else:
logging.info("No GPUs found. Training on CPU.")
num_towers = 1
device_string = '/cpu:%d'
learning_rate = tf.train.exponential_decay(base_learning_rate,
global_step * batch_size *
num_towers,
learning_rate_decay_examples,
learning_rate_decay,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = optimizer_class(learning_rate)
model_input_raw, _ = (get_input_data_tensors(reader,
train_data_pattern,
batch_size=batch_size *
num_towers,
num_readers=num_readers,
num_epochs=num_epochs))
tf.summary.histogram("model/input_raw", model_input_raw)
model_input = model_input_raw
noise_input = tf.placeholder(
tf.float32, shape=[None, random_noise_generator.get_dim()])
image_width, image_height = reader.get_image_size()
tower_inputs = tf.split(model_input, num_towers)
tower_noise_input = tf.split(noise_input, num_towers)
tower_D_gradients = []
tower_G_gradients = []
tower_generated_images = []
tower_predictions_for_fake = []
tower_predictions_for_real = []
tower_D_losses = []
tower_G_losses = []
for i in range(num_towers):
# For some reason these 'with' statements can't be combined onto the same
# line. They have to be nested.
with tf.device(device_string % i):
with (tf.variable_scope(("tower"), reuse=True if i > 0 else None)):
with (slim.arg_scope(
[slim.model_variable, slim.variable],
device="/cpu:0" if num_gpus != 1 else "/gpu:0")):
generator_model.create_model(image_width * image_height)
discriminator_model.create_model(image_width *
image_height)
generated_result = generator_model.run_model(
tower_noise_input[i])
generated_images = generated_result["output"]
generated_images_shaped = tf.reshape(
generated_images, [-1, image_height, image_width, 1])
tf.summary.image('generated_images',
generated_images_shaped, 10)
tower_generated_images.append(generated_images)
result_from_fake = discriminator_model.run_model(
generated_images)
result_from_real = discriminator_model.run_model(
tower_inputs[i])
for variable in slim.get_model_variables():
tf.summary.histogram(variable.op.name, variable)
predictions_for_fake = result_from_fake["predictions"]
predictions_for_real = result_from_real["predictions"]
tower_predictions_for_fake.append(predictions_for_fake)
tower_predictions_for_real.append(predictions_for_real)
logits_for_fake = result_from_fake["logits"]
logits_for_real = result_from_real["logits"]
D_loss_fake = label_loss_fn.calculate_loss(
logits_for_fake, tf.zeros_like(logits_for_fake))
D_loss_real = label_loss_fn.calculate_loss(
logits_for_real, tf.ones_like(logits_for_real))
D_loss = D_loss_fake + D_loss_real
tower_D_losses.append(D_loss)
G_loss = label_loss_fn.calculate_loss(
logits_for_fake, tf.ones_like(logits_for_fake))
tower_G_losses.append(G_loss)
D_var = discriminator_model.get_variables()
D_gradients = optimizer.compute_gradients(D_loss,
var_list=D_var)
tower_D_gradients.append(D_gradients)
G_var = generator_model.get_variables()
G_gradients = optimizer.compute_gradients(G_loss,
var_list=G_var)
tower_G_gradients.append(G_gradients)
D_loss = tf.reduce_mean(tf.stack(tower_D_losses))
G_loss = tf.reduce_mean(tf.stack(tower_G_losses))
tf.summary.scalar("D_loss", D_loss)
tf.summary.scalar("G_loss", G_loss)
merged_D_gradients = utils.combine_gradients(tower_D_gradients)
merged_G_gradients = utils.combine_gradients(tower_G_gradients)
if clip_gradient_norm > 0:
with tf.name_scope('clip_grads'):
merged_D_gradients = utils.clip_gradient_norms(
merged_D_gradients, clip_gradient_norm)
merged_G_gradients = utils.clip_gradient_norms(
merged_G_gradients, clip_gradient_norm)
# Attach global_step only once so that it will be increased by 1.
D_train_op = optimizer.apply_gradients(merged_D_gradients)
G_train_op = optimizer.apply_gradients(merged_G_gradients,
global_step=global_step)
tf.add_to_collection("global_step", global_step)
tf.add_to_collection("D_loss", D_loss)
tf.add_to_collection("G_loss", G_loss)
tf.add_to_collection("p_for_fake", tf.concat(tower_predictions_for_fake,
0))
tf.add_to_collection("p_for_data", tf.concat(tower_predictions_for_real,
0))
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("generated_images",
tf.concat(tower_generated_images, 0))
tf.add_to_collection("D_train_op", D_train_op)
tf.add_to_collection("G_train_op", G_train_op)
tf.add_to_collection("noise_input_placeholder", noise_input)
