def _main_():
# #################
# Setup export path
###################
version = 1
output_dir = os.path.join(output_loc, model_name)
export_path = os.path.join(output_dir, str(version))
# ######################
# Interference Pipeline
# ######################
input_names = 'image_tensor'
output_names = ['detection_boxes', 'detection_classes', 'detection_scores', 'num_detections']
with tf.Session() as sess:
input_tensor = tf.placeholder(dtype=tf.uint8, shape=(None, None, None, 3), name=input_names)
# ###################
# load frozen graph
# ###################
graph_def = load_graph_from_pb(frozen_graph)
outputs = tf.import_graph_def(graph_def,
input_map={'image_tensor': input_tensor},
return_elements=output_names,
name='')
outputs = [sess.graph.get_tensor_by_name(ops.name +':0')for ops in outputs]
outputs = dict(zip(output_names, outputs))
# #####################
# Quantize Frozen Model
# #####################
transforms = ["add_default_attributes",
"quantize_weights", "round_weights",
"fold_batch_norms", "fold_old_batch_norms"]
quantized_graph = TransformGraph(input_graph_def=graph_def,
inputs=input_names,
outputs=output_names,
transforms=transforms)
# #####################
# Export to TF Serving#
# #####################
# Reference: https://github.com/tensorflow/models/tree/master/research/object_detection
with tf.Graph().as_default():
tf.import_graph_def(quantized_graph, name='')
# Optimizing graph
rewrite_options = rewriter_config_pb2.RewriterConfig()
rewrite_options.optimizers.append('pruning')
rewrite_options.optimizers.append('constfold')
rewrite_options.optimizers.append('layout')
graph_options = tf.GraphOptions(rewrite_options=rewrite_options, infer_shapes=True)
# Build model for TF Serving
config = tf.ConfigProto(graph_options=graph_options)
# @TODO: add XLA for higher performance (AOT for ARM, JIT for x86/GPUs)
# https://www.tensorflow.org/performance/xla/
# config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
# Reference:
# https://www.tensorflow.org/guide/saved_model
with session.Session(config=config) as sess:
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
tensor_info_inputs = {'inputs': tf.saved_model.utils.build_tensor_info(input_tensor)}
tensor_info_outputs = {}
for k, v in outputs.items():
tensor_info_outputs[k] = tf.saved_model.utils.build_tensor_info(v)
detection_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs = tensor_info_inputs,
outputs = tensor_info_outputs,
method_name= signature_constants.PREDICT_METHOD_NAME))
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING], #tag_constants.SERVING is IMP to specify as this indicates the saved graph is meant for serving
signature_def_map={'predict_images': detection_signature,
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: detection_signature,
},
)
builder.save()
print("\n\nModel is ready for TF Serving. (saved at {}/saved_model.pb)".format(export_path))
X, Z1, _X = [], [], []
X.append(tf.random_uniform([dim, dim], 0, 10, name='X' + str(0)))
_X.append(tf.placeholder(dtype=tf.float32, shape=[dim, dim]))
Z1.append(tf.matmul(_X[0], _X[0]))
with tf.device(dev2):
Y, Z2, _Y = [], [], []
Y.append(tf.random_uniform([dim, dim], 0, 10, name='Y' + str(0)))
_Y.append(tf.placeholder(dtype=tf.float32, shape=[dim, dim]))
Z2.append(tf.matmul(_Y[0], _Y[0]))
with tf.device(dev3):
Z3 = []
Z3.append(tf.add(Z2[0], Z1[0]))
config_proto = tf.ConfigProto(graph_options=tf.GraphOptions(
build_cost_model=1))
config_proto.intra_op_parallelism_threads = 1
config_proto.inter_op_parallelism_threads = 1
config_proto.graph_options.optimizer_options.opt_level = -1
config_proto.graph_options.rewrite_options.constant_folding = (
rewriter_config_pb2.RewriterConfig.OFF)
config_proto.graph_options.rewrite_options.arithmetic_optimization = (
rewriter_config_pb2.RewriterConfig.OFF)
config_proto.graph_options.rewrite_options.dependency_optimization = (
rewriter_config_pb2.RewriterConfig.OFF)
config_proto.graph_options.rewrite_options.layout_optimizer = (
rewriter_config_pb2.RewriterConfig.OFF)
sess = tf.Session(config=config_proto)
sess.run(tf.global_variables_initializer())
def _add_infer_shapes(graph_def):
with tf.Graph().as_default():
with tf.Session(config=tf.ConfigProto(
graph_options=tf.GraphOptions(infer_shapes=True))) as sess:
tf.import_graph_def(graph_def, name="")
return sess.graph_def
def create_session(): optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0) config = tf.ConfigProto(operation_timeout_in_ms=150000, graph_options=tf.GraphOptions(optimizer_options=optimizer_options)) # config.graph_options.rewrite_options.constant_folding = rewriter_config_pb2.RewriterConfig.OFF config.graph_options.place_pruned_graph = True return tf.Session(config=config)
def main(unused_argv):
params = hyperparameters.get_hyperparameters(FLAGS.default_hparams_file,
FLAGS.hparams_file, FLAGS,
FLAGS.hparams)
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu if (FLAGS.tpu or params['use_tpu']) else '',
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
if params['use_async_checkpointing']:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(2500, params['iterations_per_loop'])
config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=get_model_dir(params),
save_checkpoints_steps=save_checkpoints_steps,
keep_checkpoint_max=None, # Keep all checkpoints.
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=params['iterations_per_loop'],
num_shards=params['num_cores'],
# copybara:strip_begin
tpu_job_name=FLAGS.tpu_job_name,
# copybara:strip_end
per_host_input_for_training=contrib_tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
resnet_classifier = contrib_tpu.TPUEstimator(
use_tpu=params['use_tpu'],
model_fn=resnet_model_fn,
config=config,
params=params,
train_batch_size=params['train_batch_size'],
eval_batch_size=params['eval_batch_size'],
export_to_tpu=FLAGS.export_to_tpu)
# copybara:strip_begin
if FLAGS.xla_compile:
resnet_classifier = contrib_tpu.TPUEstimator(
use_tpu=params['use_tpu'],
model_fn=xla.estimator_model_fn(resnet_model_fn),
config=config,
params=params,
train_batch_size=params['train_batch_size'],
eval_batch_size=params['eval_batch_size'],
export_to_tpu=FLAGS.export_to_tpu)
# copybara:strip_end
assert (params['precision'] == 'bfloat16' or
params['precision'] == 'float32'), (
'Invalid value for precision parameter; '
'must be bfloat16 or float32.')
tf.logging.info('Precision: %s', params['precision'])
use_bfloat16 = params['precision'] == 'bfloat16'
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s', FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train = imagenet_input.ImageNetBigtableInput(
is_training=True,
use_bfloat16=use_bfloat16,
transpose_input=params['transpose_input'],
selection=select_train)
imagenet_eval = imagenet_input.ImageNetBigtableInput(
is_training=False,
use_bfloat16=use_bfloat16,
transpose_input=params['transpose_input'],
selection=select_eval)
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=params['transpose_input'],
cache=params['use_cache'] and is_training,
image_size=params['image_size'],
num_parallel_calls=params['num_parallel_calls'],
use_bfloat16=use_bfloat16) for is_training in [True, False]
]
steps_per_epoch = params['num_train_images'] // params['train_batch_size']
eval_steps = params['num_eval_images'] // params['eval_batch_size']
if FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
get_model_dir(params), timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time() # This time will include compilation time
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d', eval_results,
elapsed_time)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= params['train_steps']:
tf.logging.info('Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info('Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
elif FLAGS.mode == 'eval_igt':
# IGT evaluation mode. Evaluate metrics for the desired parameters
# (true or shifted) on the desired dataset (train or eval). Note that
# train is still with data augmentation.
# Get checkpoint file names.
index_files = tf.gfile.Glob(
os.path.join(get_model_dir(params), 'model.ckpt-*.index'))
checkpoints = [fn[:-len('.index')] for fn in index_files]
# Need to sort them to get proper tensorboard plotting (increasing event
# timestamps correspond to increasing steps).
checkpoint_steps = []
for ckpt in checkpoints:
tf.logging.info(ckpt)
step_match = re.match(r'.*model.ckpt-([0-9]*)', ckpt)
checkpoint_steps.append(int(step_match.group(1)))
checkpoints = [
ckpt for _, ckpt in sorted(zip(checkpoint_steps, checkpoints))
]
tf.logging.info('There are {} checkpoints'.format(len(checkpoints)))
tf.logging.info(', '.join(checkpoints))
# Keep track of the last processed checkpoint (fault tolerance).
analysis_state_path = os.path.join(
get_model_dir(params),
'analysis_state_' + FLAGS.igt_eval_set + '_' + FLAGS.igt_eval_mode)
next_analysis_index = 0
if tf.gfile.Exists(analysis_state_path):
with tf.gfile.Open(analysis_state_path) as fd:
next_analysis_index = int(fd.read())
# Process each checkpoint.
while next_analysis_index < len(checkpoints):
tf.logging.info('Next analysis index: {}'.format(next_analysis_index))
ckpt_path = checkpoints[next_analysis_index]
tf.logging.info('Starting to evaluate: {}.'.format(ckpt_path))
start_timestamp = time.time() # This time will include compilation time
if FLAGS.igt_eval_set == 'train':
the_input_fn = imagenet_train.input_fn
the_steps = steps_per_epoch
elif FLAGS.igt_eval_set == 'eval':
the_input_fn = imagenet_eval.input_fn
the_steps = eval_steps
else:
raise ValueError('Unsupported igt_eval_set')
eval_results = resnet_classifier.evaluate(
input_fn=the_input_fn,
steps=the_steps,
checkpoint_path=ckpt_path,
name=FLAGS.igt_eval_set + '_' + FLAGS.igt_eval_mode)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d', eval_results,
elapsed_time)
next_analysis_index += 1
file_io.atomic_write_string_to_file(analysis_state_path,
str(next_analysis_index))
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir(get_model_dir(params)) # pylint:disable=protected-access,g-line-too-long
steps_per_epoch = params['num_train_images'] // params['train_batch_size']
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', params['train_steps'],
params['train_steps'] / steps_per_epoch, current_step)
start_timestamp = time.time() # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if params['use_async_checkpointing']:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=get_model_dir(params),
save_steps=max(2500, params['iterations_per_loop'])))
resnet_classifier.train(
input_fn=imagenet_train.input_fn,
max_steps=params['train_steps'],
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < params['train_steps']:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
params['train_steps'])
resnet_classifier.train(
input_fn=imagenet_train.input_fn, max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info('Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=params['num_eval_images'] // params['eval_batch_size'])
tf.logging.info('Eval results at step %d: %s', next_checkpoint,
eval_results)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Finished training up to step %d. Elapsed seconds %d.',
params['train_steps'], elapsed_time)
if FLAGS.export_dir is not None:
# The guide to serve a exported TensorFlow model is at:
# https://www.tensorflow.org/serving/serving_basic
tf.logging.info('Starting to export model.')
unused_export_path = resnet_classifier.export_saved_model(
export_dir_base=FLAGS.export_dir,
serving_input_receiver_fn=imagenet_input.image_serving_input_fn)
def main():
# Prepare training and testing data
loadpath = "./"
src_file = loadpath + "Pairs2M.src.num"
tgt_file = loadpath + "Pairs2M.tgt.num"
dic_file = loadpath + "Pairs2M.reddit.dic"
opt = Options()
opt_t = Options()
train, val, test, wordtoix, ixtoword = read_pair_data_full(
src_file,
tgt_file,
dic_file,
max_num=opt.data_size,
p_f=loadpath + 'demo.p')
train = [
x for x in train
if 2 < len(x[1]) < opt.maxlen - 4 and 2 < len(x[0]) < opt_t.maxlen - 4
]
val = [
x for x in val
if 2 < len(x[1]) < opt.maxlen - 4 and 2 < len(x[0]) < opt_t.maxlen - 4
]
if TEST_FLAG:
test = test + val + train
opt.test_freq = 1
opt.n_words = len(ixtoword)
opt_t.n_words = len(ixtoword)
print dict(opt)
if opt.model == 'cnn_rnn':
opt_t.maxlen = opt_t.maxlen - opt_t.filter_shape + 1
opt_t.update_params()
print dict(opt_t)
print('Total words: %d' % opt.n_words)
# load w2v
if os.path.exists(opt.embedding_path_lime):
with open(opt.embedding_path_lime, 'rb') as pfile:
embedding = cPickle.load(pfile)
else:
w2v = gensim.models.KeyedVectors.load_word2vec_format(
opt.embedding_path, binary=True)
embedding = {
i: copy.deepcopy(w2v[ixtoword[i]])
for i in range(opt.n_words) if ixtoword[i] in w2v
}
with open(opt.embedding_path_lime, 'wb') as pfile:
cPickle.dump(embedding, pfile, protocol=cPickle.HIGHEST_PROTOCOL)
for d in ['/gpu:0']:
with tf.device(d):
src_ = tf.placeholder(tf.int32,
shape=[opt.batch_size, opt.sent_len])
tgt_ = tf.placeholder(tf.int32,
shape=[opt_t.batch_size, opt_t.sent_len])
res_, gan_cost_d_, train_op_d, gan_cost_g_, train_op_g = dialog_gan(
src_, tgt_, opt, opt_t)
merged = tf.summary.merge_all()
uidx = 0
graph_options = tf.GraphOptions(build_cost_model=1)
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True,
graph_options=graph_options)
config.gpu_options.per_process_gpu_memory_fraction = 0.95
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
run_metadata = tf.RunMetadata()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES) #tf.trainable_variables()
if opt.load_from_ae:
save_keys = tensors_key_in_file(
opt.load_path) #t_var g_W:0 key: W
ss = [
var for var in t_vars
if var.name[2:][:-2] in save_keys.keys()
]
ss = [
var.name[2:] for var in ss
if var.get_shape() == save_keys[var.name[2:][:-2]]
]
cc = {
var.name[2:][:-2]: var
for var in t_vars if var.name[2:] in ss
}
loader = tf.train.Saver(var_list=cc)
loader.restore(sess, opt.load_path)
print("Loading variables from '%s'." % opt.load_path)
print(
"Loaded variables:" + " ".join(
[var.name
for var in t_vars if var.name[2:] in ss]))
else:
save_keys = tensors_key_in_file(opt.load_path)
ss = [
var for var in t_vars
if var.name[:-2] in save_keys.keys()
]
ss = [
var.name for var in ss
if var.get_shape() == save_keys[var.name[:-2]]
]
loader = tf.train.Saver(
var_list=[var for var in t_vars if var.name in ss])
loader.restore(sess, opt.load_path)
print("Loading variables from '%s'." % opt.load_path)
print("Loaded variables:" + str(ss))
# load reverse model
try:
save_keys = tensors_key_in_file('./save/rev_model')
ss = [
var for var in t_vars
if var.name[:-2] in save_keys.keys()
and 'g_rev_' in var.name
]
ss = [
var.name for var in ss
if var.get_shape() == save_keys[var.name[:-2]]
]
loader = tf.train.Saver(
var_list=[var for var in t_vars if var.name in ss])
loader.restore(sess, './save/rev_model')
print(
"Loading reverse variables from ./save/rev_model")
print("Loaded variables:" + str(ss))
except Exception as e:
print("No reverse model loaded")
except Exception as e:
print 'Error: ' + str(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
loss_d, loss_g = 0, 0
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
tgt, src = zip(*[train[t] for t in train_index])
x_batch = prepare_data_for_cnn(src, opt) # Batch L
y_batch = prepare_data_for_rnn(
tgt, opt_t, is_add_GO=False
) if opt.model == 'cnn_rnn' else prepare_data_for_cnn(
tgt, opt_t)
feed = {src_: x_batch, tgt_: y_batch}
if uidx % opt.d_freq == 0:
if profile:
_, loss_d = sess.run(
[train_op_d, gan_cost_d_],
feed_dict=feed,
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
else:
_, loss_d = sess.run([train_op_d, gan_cost_d_],
feed_dict=feed)
if uidx % opt.g_freq == 0:
if profile:
_, loss_g = sess.run(
[train_op_g, gan_cost_g_],
feed_dict=feed,
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
else:
_, loss_g = sess.run([train_op_g, gan_cost_g_],
feed_dict=feed)
if profile:
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.
PRINT_ALL_TIMING_MEMORY)
exit(0)
if uidx % opt.valid_freq == 0:
VALID_SIZE = 1024
valid_multiplier = np.int(
np.floor(VALID_SIZE / opt.batch_size))
res_all, val_tgt_all, loss_val_d_all, loss_val_g_all = [], [], [], []
for val_step in range(valid_multiplier):
valid_index = np.random.choice(len(val),
opt.batch_size)
val_tgt, val_src = zip(*[val[t] for t in valid_index])
val_tgt_all.extend(val_tgt)
x_val_batch = prepare_data_for_cnn(val_src,
opt) # Batch L
y_val_batch = prepare_data_for_rnn(
val_tgt, opt_t, is_add_GO=False
) if opt.model == 'cnn_rnn' else prepare_data_for_cnn(
val_tgt, opt_t)
feed_val = {src_: x_val_batch, tgt_: y_val_batch}
loss_val_d, loss_val_g = sess.run(
[gan_cost_d_, gan_cost_g_], feed_dict=feed_val)
loss_val_d_all.append(loss_val_d)
loss_val_g_all.append(loss_val_g)
res = sess.run(res_, feed_dict=feed_val)
res_all.extend(res['syn_sent'])
print("Validation: loss D %f loss G %f " %
(np.mean(loss_val_d_all), np.mean(loss_val_g_all)))
#print "Val Perm :" + " ".join([ixtoword[x] for x in val_src_permutated[0] if x != 0])
print "Val Source:" + u' '.join([
ixtoword[x] for x in val_src[0] if x != 0
]).encode('utf-8').strip()
print "Val Target :" + u' '.join([
ixtoword[x] for x in val_tgt[0] if x != 0
]).encode('utf-8').strip()
print "Val Generated:" + u' '.join([
ixtoword[x] for x in res['syn_sent'][0] if x != 0
]).encode('utf-8').strip()
print ""
val_set = [prepare_for_bleu(s) for s in val_tgt_all]
gen = [prepare_for_bleu(s) for s in res_all]
[bleu1s, bleu2s, bleu3s,
bleu4s] = cal_BLEU_4(gen, {0: val_set},
is_corpus=opt.is_corpus)
[rouge1, rouge2, rouge3, rouge4, rougeL,
rouges] = cal_ROUGE(gen, {0: val_set},
is_corpus=opt.is_corpus)
etp_score, dist_score = cal_entropy(gen)
bleu_nltk = cal_BLEU_4_nltk(gen,
val_set,
is_corpus=opt.is_corpus)
rel_score = cal_relevance(gen, val_set, embedding)
print 'Val BLEU: ' + ' '.join([
str(round(it, 3))
for it in (bleu_nltk, bleu1s, bleu2s, bleu3s, bleu4s)
])
print 'Val Rouge: ' + ' '.join([
str(round(it, 3))
for it in (rouge1, rouge2, rouge3, rouge4)
])
print 'Val Entropy: ' + ' '.join([
str(round(it, 3))
for it in (etp_score[0], etp_score[1], etp_score[2],
etp_score[3])
])
print 'Val Diversity: ' + ' '.join([
str(round(it, 3))
for it in (dist_score[0], dist_score[1], dist_score[2],
dist_score[3])
])
print 'Val Relevance(G,A,E): ' + ' '.join([
str(round(it, 3))
for it in (rel_score[0], rel_score[1], rel_score[2])
])
print 'Val Avg. length: ' + str(
round(
np.mean([
len([y for y in x if y != 0]) for x in res_all
]), 3))
print ""
summary = sess.run(merged, feed_dict=feed_val)
summary2 = tf.Summary(value=[
tf.Summary.Value(tag="bleu-2", simple_value=bleu2s),
tf.Summary.Value(tag="rouge-2", simple_value=rouge2),
tf.Summary.Value(tag="etp-4",
simple_value=etp_score[3])
])
test_writer.add_summary(summary, uidx)
test_writer.add_summary(summary2, uidx)
if uidx % opt.test_freq == 0:
iter_num = np.int(np.floor(len(test) / opt.batch_size)) + 1
res_all, test_tgt_all = [], []
for i in range(iter_num):
test_index = range(i * opt.batch_size,
(i + 1) * opt.batch_size)
test_tgt, test_src = zip(
*[test[t % len(test)] for t in test_index])
test_tgt_all.extend(test_tgt)
x_batch = prepare_data_for_cnn(test_src, opt)
y_batch = prepare_data_for_rnn(
test_tgt, opt_t, is_add_GO=False
) if opt.model == 'cnn_rnn' else prepare_data_for_cnn(
test_tgt, opt_t)
feed = {src_: x_batch, tgt_: y_batch}
res = sess.run(res_, feed_dict=feed)
res_all.extend(res['syn_sent'])
test_set = [prepare_for_bleu(s) for s in test_tgt_all]
gen = [prepare_for_bleu(s) for s in res_all]
[bleu1s, bleu2s, bleu3s,
bleu4s] = cal_BLEU_4(gen, {0: test_set},
is_corpus=opt.is_corpus)
[rouge1, rouge2, rouge3, rouge4, rougeL,
rouges] = cal_ROUGE(gen, {0: test_set},
is_corpus=opt.is_corpus)
etp_score, dist_score = cal_entropy(gen)
bleu_nltk = cal_BLEU_4_nltk(gen,
test_set,
is_corpus=opt.is_corpus)
rel_score = cal_relevance(gen, test_set, embedding)
print 'Test BLEU: ' + ' '.join([
str(round(it, 3))
for it in (bleu_nltk, bleu1s, bleu2s, bleu3s, bleu4s)
])
print 'Test Rouge: ' + ' '.join([
str(round(it, 3))
for it in (rouge1, rouge2, rouge3, rouge4)
])
print 'Test Entropy: ' + ' '.join([
str(round(it, 3))
for it in (etp_score[0], etp_score[1], etp_score[2],
etp_score[3])
])
print 'Test Diversity: ' + ' '.join([
str(round(it, 3))
for it in (dist_score[0], dist_score[1], dist_score[2],
dist_score[3])
])
print 'Test Relevance(G,A,E): ' + ' '.join([
str(round(it, 3))
for it in (rel_score[0], rel_score[1], rel_score[2])
])
print 'Test Avg. length: ' + str(
round(
np.mean([
len([y for y in x if y != 0]) for x in res_all
]), 3))
print ''
if TEST_FLAG:
exit()
if uidx % opt.print_freq == 0:
print("Iteration %d: loss D %f loss G %f" %
(uidx, loss_d, loss_g))
res = sess.run(res_, feed_dict=feed)
if opt.grad_penalty:
print "grad_penalty: " + str(res['gp'])
print "Source:" + u' '.join([
ixtoword[x] for x in x_batch[0] if x != 0
]).encode('utf-8').strip()
print "Target:" + u' '.join([
ixtoword[x] for x in y_batch[0] if x != 0
]).encode('utf-8').strip()
print "Generated:" + u' '.join([
ixtoword[x] for x in res['syn_sent'][0] if x != 0
]).encode('utf-8').strip()
print ""
sys.stdout.flush()
summary = sess.run(merged, feed_dict=feed)
train_writer.add_summary(summary, uidx)
if uidx % opt.save_freq == 0:
saver.save(sess, opt.save_path)
def load(self,
ckpt_path,
hparams,
master='local',
batch_timeout_micros=80 * 1000,
buckets=None):
self.hparams = hparams
self.buckets = buckets
self.tpu_graph = tf.Graph()
tpu_config = tf.ConfigProto(
operation_timeout_in_ms=600 * 1000,
allow_soft_placement=True,
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True)),
isolate_session_state=True)
# Find tpu master.
print('master value set to:', master)
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
master, zone=None, project=None)
master = tpu_cluster_resolver.get_master()
self.sess = tf.Session(master, graph=self.tpu_graph, config=tpu_config)
with self.tpu_graph.as_default():
self.vocab_table = tf.contrib.lookup.index_to_string_table_from_file(
self.vocab_prefix, default_value=vocab_utils.UNK)
if self.scenario == 'Offline':
with self.tpu_graph.as_default():
self.source = tf.placeholder(shape=(hparams.infer_batch_size,
hparams.src_max_len_infer),
dtype=tf.int32)
self.source_sequence_length = tf.placeholder(
shape=(hparams.infer_batch_size), dtype=tf.int32)
inputs = [[self.source, self.source_sequence_length]]
self.predict_ops.append(self.offline_op(inputs))
else:
with self.tpu_graph.as_default():
self.source = tf.placeholder(
shape=[None, hparams.src_max_len_infer], dtype=tf.int32)
self.source_sequence_length = tf.placeholder(shape=[None],
dtype=tf.int32)
inputs = [self.source, self.source_sequence_length]
for _ in buckets:
self.predict_ops.append(
self.server_op(
inputs,
num_batch_threads=16,
max_batch_size=hparams.infer_batch_size,
batch_timeout_micros=batch_timeout_micros,
allowed_batch_sizes=[hparams.infer_batch_size],
max_enqueued_batches=10000))
# Add longest sequence predict op.
self.predict_ops.append(
self.server_op(
inputs,
num_batch_threads=16,
max_batch_size=hparams.infer_batch_size,
batch_timeout_micros=5000 * 1000,
allowed_batch_sizes=[hparams.infer_batch_size],
max_enqueued_batches=10000))
with self.tpu_graph.as_default():
vs = tf.global_variables()
assign_ops = []
var_map = {}
with tf.variable_scope('f32', dtype=tf.float32):
for i in vs:
if 'output_projection' in i.name:
new_var = tf.get_variable(
i.name[:-2], [i.shape[0], hparams.tgt_vocab_size])
assign_ops.append(
tf.assign(
i,
tf.pad(
tf.cast(new_var, i.dtype),
[[0, 0],
[
0, 128 *
(hparams.tgt_vocab_size // 128 + 1) -
hparams.tgt_vocab_size
]])))
else:
new_var = tf.get_variable(i.name[:-2], i.shape)
assign_ops.append(
tf.assign(i, tf.cast(new_var, i.dtype)))
var_map[i.name[:-2]] = new_var.name[:-2]
self.sess.run(tpu.initialize_system())
tf.train.init_from_checkpoint(ckpt_path, var_map)
self.sess.run(tf.initializers.global_variables())
self.sess.run(tf.tables_initializer())
self.sess.run(assign_ops)
return self
def run_model(opt, train, val, ixtoword):
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:'+ str(params['Wemb'].shape) + ' opt: ' + str((opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
x_org_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
# is_train_ = tf.placeholder(tf.bool, name='is_train_')
res_, g_loss_, d_loss_, gen_op, dis_op = textGAN(x_, x_org_, opt)
merged = tf.summary.merge_all()
# opt.is_train = False
# res_val_, loss_val_, _ = auto_encoder(x_, x_org_, opt)
# merged_val = tf.summary.merge_all()
#tensorboard --logdir=run1:/tmp/tensorflow/ --port 6006
#writer = tf.train.SummaryWriter(opt.log_path, graph=tf.get_default_graph())
uidx = 0
config = tf.ConfigProto(log_device_placement = False, allow_soft_placement=True, graph_options=tf.GraphOptions(build_cost_model=1))
#config = tf.ConfigProto(device_count={'GPU':0})
config.gpu_options.per_process_gpu_memory_fraction = 0.8
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
run_metadata = tf.RunMetadata()
with tf.Session(config = config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train', sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
#pdb.set_trace()
t_vars = tf.trainable_variables()
#print([var.name[:-2] for var in t_vars])
loader = restore_from_save(t_vars, sess, opt)
print('\nload successfully\n')
except Exception as e:
print(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
# for i in range(34):
# valid_index = np.random.choice(
# len(val), opt.batch_size)
# val_sents = [val[t] for t in valid_index]
# val_sents_permutated = add_noise(val_sents, opt)
# x_val_batch = prepare_data_for_cnn(
# val_sents_permutated, opt)
# x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
# res = sess.run(res_, feed_dict={
# x_: x_val_batch, x_org_: x_val_batch_org})
# if i == 0:
# valid_text = res['syn_sent']
# else:
# valid_text = np.concatenate(
# (valid_text, res['syn_sent']), 0)
# np.savetxt('./text_news/vae_words.txt', valid_text, fmt='%i', delimiter=' ')
# pdb.set_trace()
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
# if epoch >= 10:
# print("Relax embedding ")
# opt.fix_emb = False
# opt.batch_size = 2
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
sents_permutated = add_noise(sents, opt)
#sents[0] = np.random.permutation(sents[0])
x_batch = prepare_data_for_cnn(sents_permutated, opt) # Batch L
x_batch_org = prepare_data_for_rnn(sents, opt)
d_loss = 0
g_loss = 0
if profile:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run([dis_op, d_loss_], feed_dict={x_: x_batch, x_org_: x_batch_org},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),run_metadata=run_metadata)
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run([gen_op, g_loss_], feed_dict={x_: x_batch, x_org_: x_batch_org},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),run_metadata=run_metadata)
else:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run([dis_op, d_loss_], feed_dict={x_: x_batch, x_org_: x_batch_org})
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run([gen_op, g_loss_], feed_dict={x_: x_batch, x_org_: x_batch_org})
''' validation '''
if uidx % opt.valid_freq == 0:
valid_index = np.random.choice(len(val), opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
x_val_batch = prepare_data_for_cnn(val_sents_permutated, opt)
x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
d_loss_val = sess.run(d_loss_, feed_dict={x_: x_val_batch, x_org_: x_val_batch_org})
g_loss_val = sess.run(g_loss_, feed_dict={x_: x_val_batch, x_org_: x_val_batch_org})
res = sess.run(res_, feed_dict={x_: x_val_batch, x_org_: x_val_batch_org})
print("Validation d_loss %f, g_loss %f mean_dist %f" % (d_loss_val, g_loss_val, res['mean_dist']))
print("Sent:" + u' '.join([ixtoword[x] for x in res['syn_sent']
[0] if x != 0]).encode('utf-8', 'ignore').decode("utf8").strip())
print("MMD loss %f, GAN loss %f" % (res['mmd'], res['gan']))
# np.savetxt('./text_arxiv/syn_val_words.txt', res['syn_sent'], fmt='%i', delimiter=' ')
if opt.discrimination:
print ("Real Prob %f Fake Prob %f" % (res['prob_r'], res['prob_f']))
for i in range(4):
valid_index = np.random.choice(
len(val), opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
x_val_batch = prepare_data_for_cnn(
val_sents_permutated, opt)
x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
res = sess.run(res_, feed_dict={
x_: x_val_batch, x_org_: x_val_batch_org})
if i == 0:
valid_text = res['syn_sent']
else:
valid_text = np.concatenate(
(valid_text, res['syn_sent']), 0)
np.savetxt('./text_news/syn_val_words.txt',valid_text, fmt='%i', delimiter=' ')
val_set = [prepare_for_bleu(s) for s in val_sents]
[bleu2s, bleu3s, bleu4s] = cal_BLEU([prepare_for_bleu(s) for s in res['syn_sent']], {0: val_set})
print('Val BLEU (2,3,4): ' + ' '.join([str(round(it, 3)) for it in (bleu2s, bleu3s, bleu4s)]))
summary = sess.run(merged, feed_dict={x_: x_val_batch, x_org_: x_val_batch_org})
test_writer.add_summary(summary, uidx)
def main(unused_argv):
input_image_size = FLAGS.input_image_size
if not input_image_size:
if FLAGS.model_name.startswith('efficientnet-edgetpu'):
_, _, input_image_size, _ = efficientnet_edgetpu_builder.efficientnet_edgetpu_params(
FLAGS.model_name)
elif FLAGS.model_name.startswith('efficientnet-tpu'):
_, _, input_image_size, _ = efficientnet_tpu_builder.efficientnet_tpu_params(
FLAGS.model_name)
elif FLAGS.model_name.startswith('efficientnet'):
_, _, input_image_size, _ = efficientnet_builder.efficientnet_params(
FLAGS.model_name)
else:
raise ValueError(
'input_image_size must be set except for EfficientNet')
# For imagenet dataset, include background label if number of output classes
# is 1001
include_background_label = False #(FLAGS.num_label_classes == 1001)
if FLAGS.tpu or FLAGS.use_tpu:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
else:
tpu_cluster_resolver = None
if FLAGS.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(100, FLAGS.iterations_per_loop)
config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
# Initializes model parameters.
params = dict(steps_per_epoch=FLAGS.num_train_images /
FLAGS.train_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
est = tf.contrib.tpu.TPUEstimator(use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu,
params=params)
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
def build_imagenet_input(is_training):
"""Generate ImageNetInput for training and eval."""
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s',
FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
return imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=FLAGS.use_bfloat16,
transpose_input=FLAGS.transpose_input,
selection=select_train if is_training else select_eval,
include_background_label=include_background_label,
autoaugment_name=FLAGS.autoaugment_name,
mixup_alpha=FLAGS.mixup_alpha)
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
return imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=FLAGS.transpose_input,
cache=FLAGS.use_cache and is_training,
image_size=input_image_size,
num_parallel_calls=FLAGS.num_parallel_calls,
use_bfloat16=FLAGS.use_bfloat16,
include_background_label=include_background_label,
autoaugment_name=FLAGS.autoaugment_name,
mixup_alpha=FLAGS.mixup_alpha,
num_classes=FLAGS.num_label_classes)
imagenet_train = build_imagenet_input(is_training=True)
imagenet_eval = build_imagenet_input(is_training=False)
if FLAGS.mode == 'eval':
eval_steps = FLAGS.num_eval_images // FLAGS.eval_batch_size
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time(
) # This time will include compilation time
eval_results = est.evaluate(input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= FLAGS.train_steps:
tf.logging.info(
'Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir(
FLAGS.model_dir) # pylint: disable=protected-access,line-too-long
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', FLAGS.train_steps,
FLAGS.train_steps / params['steps_per_epoch'], current_step)
start_timestamp = time.time(
) # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if FLAGS.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, FLAGS.iterations_per_loop)))
est.train(input_fn=imagenet_train.input_fn,
max_steps=FLAGS.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < FLAGS.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
FLAGS.train_steps)
est.train(input_fn=imagenet_train.input_fn,
max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = est.evaluate(input_fn=imagenet_eval.input_fn,
steps=FLAGS.num_eval_images //
FLAGS.eval_batch_size)
tf.logging.info('Eval results at step %d: %s', next_checkpoint,
eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
FLAGS.train_steps, elapsed_time)
if FLAGS.export_dir:
export(est, FLAGS.export_dir, input_image_size)
def cfg():
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0,
do_constant_folding=False)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
return tf.ConfigProto(log_device_placement=True,
graph_options=graph_options)
def main(unused_argv):
# Mnas optimize - set the proper image data format
tf.keras.backend.set_image_data_format(FLAGS.data_format)
# Mnas optimize - optimization flags
# gpu_thread_count = 2
# os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
# os.environ['TF_GPU_THREAD_COUNT'] = str(gpu_thread_count)
# os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
# os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
# enable mixed precision? -> Not much benefits seen yet
# os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
node0 = "172.31.11.9:6060"
node1 = "172.31.1.33:6060"
strategy = tf.distribute.MirroredStrategy()
if FLAGS.total_nodes > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(tf.distribute.experimental.CollectiveCommunication.NCCL)
if not FLAGS.is_evaluator:
if FLAGS.node_num == 0:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': [node0, node1]
},
'task': {'type': 'worker', 'index': 0}
})
else:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': [node0, node1]
},
'task': {'type': 'worker', 'index': 1}
})
else:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'evaluator': ["localhost:6060"]
},
'task': {'type': 'evaluator', 'index': 0}
})
if FLAGS.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(100, FLAGS.iterations_per_loop)
gconfig = tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True)))
if FLAGS.use_xla:
gconfig.session_config.graph_options.optimizer_options.global_jit_level = (tf.OptimizerOptions.ON_1)
# mnasnet opt - check if this is required!
gconfig.gpu_options.allow_growth = True
#gconfig.session_config.gpu_options.visible_device_list = str(hvd.local_rank())
config = tf.estimator.RunConfig(
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
train_distribute=strategy,
session_config=gconfig) # pylint: disable=line-too-long
print('mnasnet opt - config cluster spec', config.cluster_spec)
# Initializes model parameters.
params = dict(
steps_per_epoch=FLAGS.num_train_images / FLAGS.train_batch_size,
batch_size=FLAGS.train_batch_size,
dtype = tf.float32,
use_bfloat16=FLAGS.use_bfloat16,
quantized_training=FLAGS.quantized_training)
mnasnet_est = tf.estimator.Estimator(
model_fn=mnasnet_model_fn,
model_dir=FLAGS.model_dir,
config=config,
params=params)
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s', FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=False,
transpose_input=FLAGS.transpose_input,
selection=selection) for (is_training, selection) in
[(True, select_train),
(False, select_eval)]]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=FLAGS.transpose_input,
cache=FLAGS.use_cache and is_training,
image_size=FLAGS.input_image_size,
num_parallel_calls=FLAGS.num_parallel_calls,
use_bfloat16=FLAGS.use_bfloat16) for is_training in [True, False]
]
if FLAGS.mode == 'eval':
eval_steps = FLAGS.num_eval_images // FLAGS.eval_batch_size
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time() # This time will include compilation time
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d', eval_results,
elapsed_time)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= FLAGS.train_steps:
tf.logging.info('Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info('Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, FLAGS.post_quantize)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir( # pylint: disable=protected-access
FLAGS.model_dir)
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', FLAGS.train_steps,
FLAGS.train_steps / params['steps_per_epoch'], current_step)
start_timestamp = time.time() # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if FLAGS.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, FLAGS.iterations_per_loop)))
mnasnet_est.train(
input_fn=imagenet_train.input_fn,
max_steps=FLAGS.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
train_spec = tf.estimator.TrainSpec(input_fn=imagenet_train.input_fn, max_steps=FLAGS.train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=imagenet_eval.input_fn, steps=FLAGS.num_eval_images // FLAGS.eval_batch_size, throttle_secs=600)
tf.estimator.train_and_evaluate(mnasnet_est, train_spec, eval_spec)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Finished training up to step %d. Elapsed seconds %d.',
FLAGS.train_steps, elapsed_time)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, FLAGS.post_quantize)
def main(unused_argv):
params = params_dict.ParamsDict(mnasnet_config.MNASNET_CFG,
mnasnet_config.MNASNET_RESTRICTIONS)
params = params_dict.override_params_dict(params,
FLAGS.config_file,
is_strict=True)
params = params_dict.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
additional_params = {
'steps_per_epoch': params.num_train_images / params.train_batch_size,
'quantized_training': FLAGS.quantized_training,
}
params = params_dict.override_params_dict(params,
additional_params,
is_strict=False)
params.validate()
params.lock()
if FLAGS.tpu or params.use_tpu:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
else:
tpu_cluster_resolver = None
if params.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(100, params.iterations_per_loop)
config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=params.iterations_per_loop,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
# Validates Flags.
if params.precision == 'bfloat16' and params.use_keras:
raise ValueError(
'Keras layers do not have full support to bfloat16 activation training.'
' You have set precision as %s and use_keras as %s' %
(params.precision, params.use_keras))
# Initializes model parameters.
mnasnet_est = tf.contrib.tpu.TPUEstimator(
use_tpu=params.use_tpu,
model_fn=build_model_fn,
config=config,
train_batch_size=params.train_batch_size,
eval_batch_size=params.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu,
params=params.as_dict())
if FLAGS.mode == 'export_only':
export(mnasnet_est, FLAGS.export_dir, params, FLAGS.post_quantize)
return
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s',
FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=False,
transpose_input=params.transpose_input,
selection=selection)
for (is_training,
selection) in [(True, select_train), (False, select_eval)]
]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=params.transpose_input,
cache=params.use_cache and is_training,
image_size=params.input_image_size,
num_parallel_calls=params.num_parallel_calls,
use_bfloat16=(params.precision == 'bfloat16'))
for is_training in [True, False]
]
if FLAGS.mode == 'eval':
eval_steps = params.num_eval_images // params.eval_batch_size
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time(
) # This time will include compilation time
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
mnas_utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= params.train_steps:
tf.logging.info(
'Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, params, FLAGS.post_quantize)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir( # pylint: disable=protected-access
FLAGS.model_dir)
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', params.train_steps,
params.train_steps / params.steps_per_epoch, current_step)
start_timestamp = time.time(
) # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if params.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, params.iterations_per_loop)))
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=params.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < params.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
params.train_steps)
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=params.num_eval_images // params.eval_batch_size)
tf.logging.info('Eval results at step %d: %s', next_checkpoint,
eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
mnas_utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
params.train_steps, elapsed_time)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, params,
FLAGS.post_quantize)
def main(__):
print(f"Tensorflow Version is {tf.__version__}")
# mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
if FLAGS.job_name is None or FLAGS.job_name == '':
raise ValueError('Must specify an explicit job_name !')
else:
print('job_name : %s' % FLAGS.job_name)
if FLAGS.task_index is None or FLAGS.task_index == '':
raise ValueError('Must specify an explicit task_index!')
else:
print('task_index : %d' % FLAGS.task_index)
ps_spec = FLAGS.ps_hosts.split(',')
worker_spec = FLAGS.worker_hosts.split(',')
# 创建集群
num_worker = len(worker_spec)
print("Cluster num is {}".format(num_worker))
cluster = tf.train.ClusterSpec({'ps': ps_spec, 'worker': worker_spec})
server = tf.train.Server(cluster,
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
if FLAGS.job_name == 'ps':
server.join()
is_chief = (FLAGS.task_index == 0)
worker_device = '/job:worker/task:%d/cpu:0' % FLAGS.task_index
with tf.device(
tf.train.replica_device_setter(cluster=cluster,
worker_device=worker_device)):
# with tf.device("/cpu:0"):
# --------[PART 01] build model --------------
# ----[0] init (1 V 2 placehold)
# 0.1 Variable
# 0.2 placeholder
global_step = tf.Variable(0, name='global_step',
trainable=False) # 创建纪录全局训练步数变量
hid_w = tf.Variable(tf.truncated_normal(
[IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],
stddev=1.0 / IMAGE_PIXELS),
name='hid_w')
hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name='hid_b')
sm_w = tf.Variable(tf.truncated_normal([FLAGS.hidden_units, 10],
stddev=1.0 /
math.sqrt(FLAGS.hidden_units)),
name='sm_w')
sm_b = tf.Variable(tf.zeros([10]), name='sm_b')
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10]) # real_y
# ----[1] Forward
hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)
y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
#----[2].BackProp (loss ,opt )
cross_entropy = - \
tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
train_step = opt.minimize(cross_entropy, global_step=global_step)
# 生成本地的参数初始化操作init_op
init_op = tf.global_variables_initializer()
#train_dir = tempfile.mkdtemp()
sv = tf.train.Supervisor(is_chief=is_chief,
logdir=FLAGS.log_file,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
if is_chief:
print('Worker %d: Initailizing session...' % FLAGS.task_index)
else:
print('Worker %d: Waiting for session to be initaialized...' %
FLAGS.task_index)
# --------[PART 02 ] Train model --------------
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0)))
sess = sv.prepare_or_wait_for_session(server.target)
print('Worker %d: Session initialization complete.' %
FLAGS.task_index)
time_begin = time.time()
print('Traing begins @ %f' % time_begin)
local_step = 0
while True:
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
train_feed = {x: batch_xs, y_: batch_ys}
"""
feed_dict 输入数据是最慢的
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
"""
local_step += 1
now = time.time()
print('%f: Worker %d: traing step %d dome (global step:%d)' %
(now, FLAGS.task_index, local_step, step))
if step >= FLAGS.train_steps:
break
time_end = time.time()
print('Training ends @ %f' % time_end)
train_time = time_end - time_begin
print('Training elapsed time:%f s' % train_time)
val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print('After %d training step(s), validation cross entropy = %g' %
(FLAGS.train_steps, val_xent))
sess.close()
# ------------[Part 4] Output ----------------
if FLAGS.record_type == "record_parameters":
par_str = "lr_%g_b1_%g_b2_%g_bsize_%g" % (
FLAGS.learning_rate, FLAGS.beta_1, FLAGS.beta_2, FLAGS.batch_size)
FLAGS.model_save_path = os.path.join(FLAGS.current_path, "model",
par_str + ".h5")
FLAGS.history_output_path = os.path.join(FLAGS.current_path,
"train_process",
par_str + ".csv")
FLAGS.predict_label_output_path = os.path.join(FLAGS.current_path,
"submit",
par_str + ".csv")
# 1 . model save
model.save(FLAGS.model_save_path)
history_df = pd.DataFrame(history.history)
history_df.to_csv(FLAGS.history_output_path)
# 2. predict
predict_data = pd.read_csv(FLAGS.predict_x_path)
predict_x = predict_data.values.reshape([len(predict_data)] + [28, 28, 1])
predict_y = model.predict(predict_x)
# label
predict_label = np.argmax(predict_y, axis=1)
df_predict_label = pd.DataFrame({
"ImageId": range(1,
len(predict_label) + 1),
"Label": predict_label
})
# export
df_predict_label.to_csv(FLAGS.predict_label_output_path, index=False)
def export(model_params, checkpoint_file, config=None):
# Input data
batch_size = 1
im_size = model_params.im_size
guide_image = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
gb_image = tf.placeholder(tf.float32,
[batch_size, im_size[1], im_size[0], 1])
input_image = tf.placeholder(tf.float32,
[batch_size, im_size[1], im_size[0], 3])
# Create model
model_func = get_model_func(model_params.base_model)
# split the model into visual modulator and other parts, visual modulator only need to run once
if model_params.use_visual_modulator:
if model_params.base_model == 'lite':
v_m_params = visual_modulator_lite(guide_image,
model_params,
is_training=False)
else:
v_m_params = visual_modulator(guide_image,
model_params,
is_training=False)
else:
v_m_params = None
net, end_points = model_func([guide_image, gb_image, input_image],
model_params,
visual_modulator_params=v_m_params,
is_training=False)
probabilities = tf.nn.sigmoid(net, name='prob')
global_step = tf.Variable(0, name='global_step', trainable=False)
rewrite_options = rewriter_config_pb2.RewriterConfig()
rewrite_options.optimizers.append('pruning')
rewrite_options.optimizers.append('constfold')
rewrite_options.optimizers.append('layout')
graph_options = tf.GraphOptions(rewrite_options=rewrite_options,
infer_shapes=True)
config = tf.ConfigProto(
graph_options=graph_options,
allow_soft_placement=True,
)
output_names = ['prob']
for i, v_m_param in enumerate(v_m_params):
visual_mod_name = 'visual_mod_params_%d' % (i + 1)
tf.identity(v_m_param, name=visual_mod_name)
output_names.append(visual_mod_name)
# Create a saver to load the network
saver = tf.train.Saver([
v for v in tf.global_variables()
]) #if '-up' not in v.name and '-cr' not in v.name])
save_name = checkpoint_file + '.graph.pb'
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, checkpoint_file)
if not model_params.base_model == 'lite':
sess.run(interp_surgery(tf.global_variables()))
output_graph_def = graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_names)
with open(save_name, 'wb') as writer:
writer.write(output_graph_def.SerializeToString())
model_params.output_names = output_names
with open(save_name + '.json', 'w') as writer:
json.dump(vars(model_params), writer)
print 'Model saved in', save_name
# from https://github.com/tensorflow/tensorflow/issues/7251
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
from tensorflow.python.client.timeline import Timeline
with tf.device("/gpu:0"):
x = tf.ones(100, name="x")
idxs = tf.range(100)
for i in range(10):
y = tf.identity(x, name="identity-" + str(i))
x = tf.dynamic_stitch([idxs, idxs], [x, y], name="stitch-" + str(i))
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))
sess = tf.InteractiveSession(config=config)
metadata = tf.RunMetadata()
sess.run(x,
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE,
output_partition_graphs=True),
run_metadata=metadata)
timeline = Timeline(metadata.step_stats)
with open("dynamic_stitch_gpu_profile.json", "w") as f:
f.write(timeline.generate_chrome_trace_format())
with open("dynamic_stitch_gpu_profile.pbtxt", "w") as f:
f.write(str(metadata))
def main():
args = parse_args()
model_dir = args.model[0] if args.model else None
if not model_dir:
if args.start:
model_dir = 'models/' + datetime.now().strftime('%Y%m%d.%H%M')
if args.name:
model_dir += '-' + args.name[0] + '/'
else:
model_dir += '/'
else:
model_dir = most_recent_model()
params = {
'steps': args.steps[0] if args.steps else MAX_STEPS,
'batch_size': args.batch_size[0] if args.batch_size else BATCH_SIZE,
'learning_rate': 1e-4 if args.warm_start else 3e-4,
'num_channels': get_num_channels(args, model_dir) or 128,
'num_blocks': get_num_blocks(args, model_dir) or 9
}
config = tf.estimator.RunConfig(
tf_random_seed=0xfde6885f if args.deterministic else None,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
do_common_subexpression_elimination=not args.debug,
do_constant_folding=not args.debug,
do_function_inlining=not args.debug)),
gpu_options=tf.GPUOptions(allow_growth=True)))
if args.warm_start:
steps_to_skip = 10000
warm_start_from = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from=args.warm_start[0],
vars_to_warm_start='[0-9x].*' # only layers
)
else:
steps_to_skip = 0
warm_start_from = None
if args.deterministic:
# since 16-bit floating point is not accurate enough for deterministic output, fix
# it to `f32` instead.
set_compute_type(tf.float32)
if args.mask:
features_mask = list(map(lambda x: float(x), args.mask[0].split(';')))
else:
features_mask = None
hooks = [tf_debug.LocalCLIDebugHook()] if args.debug else []
nn = tf.estimator.Estimator(config=config,
model_fn=model_fn,
model_dir=model_dir,
params=params,
warm_start_from=warm_start_from)
if args.start or args.resume:
nn.train(hooks=hooks + [LearningRateScheduler(steps_to_skip)],
input_fn=lambda: input_fn(args.files, params[
'batch_size'], features_mask, True, args.deterministic),
steps=params['steps'] // params['batch_size'])
elif args.verify:
# iterate over the entire dataset and collect the metric, which we will
# then pretty-print as a JSON object to standard output
results = nn.evaluate(
hooks=hooks,
input_fn=lambda: input_fn(args.files, params[
'batch_size'], features_mask, False, args.deterministic),
steps=params['steps'] // params['batch_size'])
print(
json.dumps(
results,
default=lambda x: float(x)
if x != int(x) else int(x), # handle `Decimal` types
sort_keys=True,
separators=(',', ': '),
indent=4))
elif args.dump:
predictor = nn.predict(input_fn=lambda: input_fn([], params[
'batch_size'], None, False, False),
hooks=[DumpHook()])
for _ in predictor:
pass
elif args.features_map > 0:
predictor = nn.predict(input_fn=lambda: input_fn(
args.files, 1, features_mask, False, args.deterministic))
count = 0
print('(;GM[1]FF[4]SZ[19]')
for results in predictor:
board_state = to_sgf_heat_map(results['features'],
results['tower'])
print('(;{})'.format(board_state))
count += 1
if count > 100:
break
print(')')
elif args.print:
# tensors are given then print all available tensors with some statistics.
if not args.files:
out = {}
for var in nn.get_variable_names():
var_value = np.asarray(nn.get_variable_value(var))
out[var] = {
'mean': float(np.average(var_value)),
'std': float(np.std(var_value))
}
print(
json.dumps(
out,
default=lambda x: float(x)
if x != int(x) else int(x), # handle `Decimal` types
sort_keys=True,
separators=(',', ': '),
indent=4))
else:
for var in args.files:
print(var, nn.get_variable_value(var).tolist())
def main():
np.random.seed(0)
tf.set_random_seed(0)
dtype = np.float32
train_images = u.get_mnist_images()
dsize = 10000
patches = train_images[:, :dsize].astype(dtype)
fs = [dsize, 28 * 28, 196, 28 * 28]
# values from deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
X0 = patches
lambda_ = 3e-3
rho = tf.constant(0.1, dtype=dtype)
beta = 3
W0_0 = u.ng_init(fs[2], fs[3])
W1_0 = u.ng_init(fs[3], fs[2])
W0f = u.flatten([W0_0.flatten(), W1_0.flatten()])
def f(i):
return fs[i + 1] # W[i] has shape f[i] x f[i-1]
dsize = f(-1)
n = len(fs) - 2
# helper to create variables with numpy or TF initial value
init_dict = {} # {var_placeholder: init_value}
vard = {} # {var: u.VarInfo}
def init_var(val, name, trainable=False, noinit=False):
if isinstance(val, tf.Tensor):
collections = [] if noinit else None
var = tf.Variable(val, name=name, collections=collections)
else:
val = np.array(val)
assert u.is_numeric, "Unknown type"
holder = tf.placeholder(dtype,
shape=val.shape,
name=name + "_holder")
var = tf.Variable(holder, name=name, trainable=trainable)
init_dict[holder] = val
var_p = tf.placeholder(var.dtype, var.shape)
var_setter = var.assign(var_p)
vard[var] = u.VarInfo(var_setter, var_p)
return var
lr = init_var(0.2, "lr")
Wf = init_var(W0f, "Wf", True)
Wf_copy = init_var(W0f, "Wf_copy", True)
W = u.unflatten(Wf, fs[1:]) # perftodo: this creates transposes
X = init_var(X0, "X")
W.insert(0, X)
def sigmoid(x):
return tf.sigmoid(x)
def d_sigmoid(y):
return y * (1 - y)
def kl(x, y):
return x * tf.log(x / y) + (1 - x) * tf.log((1 - x) / (1 - y))
def d_kl(x, y):
return (1 - x) / (1 - y) - x / y
# A[i] = activations needed to compute gradient of W[i]
# A[n+1] = network output
A = [None] * (n + 2)
fail_node = tf.Print(0, [0], "fail, this must never run")
with tf.control_dependencies([fail_node]):
A[0] = u.Identity(dsize, dtype=dtype)
A[1] = W[0]
for i in range(1, n + 1):
A[i + 1] = sigmoid(W[i] @ A[i])
# reconstruction error and sparsity error
err = (A[3] - A[1])
rho_hat = tf.reduce_sum(A[2], axis=1, keep_dims=True) / dsize
# B[i] = backprops needed to compute gradient of W[i]
# B2[i] = backprops from sampled labels needed for natural gradient
B = [None] * (n + 1)
B2 = [None] * (n + 1)
B[n] = err * d_sigmoid(A[n + 1])
sampled_labels_live = tf.random_normal((f(n), f(-1)), dtype=dtype, seed=0)
sampled_labels = init_var(sampled_labels_live,
"sampled_labels",
noinit=True)
B2[n] = sampled_labels * d_sigmoid(A[n + 1])
for i in range(n - 1, -1, -1):
backprop = t(W[i + 1]) @ B[i + 1]
backprop2 = t(W[i + 1]) @ B2[i + 1]
B[i] = backprop * d_sigmoid(A[i + 1])
B2[i] = backprop2 * d_sigmoid(A[i + 1])
# dW[i] = gradient of W[i]
dW = [None] * (n + 1)
pre_dW = [None] * (n + 1) # preconditioned dW
pre_dW_stable = [None] * (n + 1) # preconditioned stable dW
cov_A = [None] * (n + 1) # covariance of activations[i]
cov_B2 = [None] * (n + 1) # covariance of synthetic backprops[i]
vars_svd_A = [None] * (n + 1)
vars_svd_B2 = [None] * (n + 1)
for i in range(1, n + 1):
cov_op = A[i] @ t(A[i]) / dsize + lambda_ * u.Identity(A[i].shape[0])
cov_A[i] = init_var(cov_op, "cov_A%d" % (i, ))
cov_op = B2[i] @ t(B2[i]) / dsize + lambda_ * u.Identity(
B2[i].shape[0])
cov_B2[i] = init_var(cov_op, "cov_B2%d" % (i, ))
vars_svd_A[i] = u.SvdWrapper(cov_A[i],
"svd_A_%d" % (i, ),
do_inverses=True)
vars_svd_B2[i] = u.SvdWrapper(cov_B2[i],
"svd_B2_%d" % (i, ),
do_inverses=True)
whitened_A = vars_svd_A[i].inv @ A[i]
whitened_B = vars_svd_B2[i].inv @ B[i]
pre_dW[i] = (whitened_B @ t(whitened_A)) / dsize
dW[i] = (B[i] @ t(A[i])) / dsize
# Loss function
reconstruction = u.L2(err) / (2 * dsize)
loss = reconstruction
grad_live = u.flatten(dW[1:])
pre_grad_live = u.flatten(pre_dW[1:]) # fisher preconditioned gradient
grad = init_var(grad_live, "grad")
pre_grad = init_var(pre_grad_live, "pre_grad")
update_params_op = Wf.assign(Wf - lr * pre_grad).op
save_params_op = Wf_copy.assign(Wf).op
pre_grad_dot_grad = tf.reduce_sum(pre_grad * grad)
grad_norm = tf.reduce_sum(grad * grad)
pre_grad_norm = u.L2(pre_grad)
def dump_svd_info(step):
"""Dump singular values and gradient values in those coordinates."""
for i in range(1, n + 1):
svd = vars_svd_A[i]
s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])
u.dump(s0, "A_%d_%d" % (i, step))
A0 = A[i].eval()
At0 = v0.T @ A0
u.dump(A0 @ A0.T, "Acov_%d_%d" % (i, step))
u.dump(At0 @ At0.T, "Atcov_%d_%d" % (i, step))
u.dump(s0, "As_%d_%d" % (i, step))
for i in range(1, n + 1):
svd = vars_svd_B2[i]
s0, u0, v0 = sess.run([svd.s, svd.u, svd.v])
u.dump(s0, "B2_%d_%d" % (i, step))
B0 = B[i].eval()
Bt0 = v0.T @ B0
u.dump(B0 @ B0.T, "Bcov_%d_%d" % (i, step))
u.dump(Bt0 @ Bt0.T, "Btcov_%d_%d" % (i, step))
u.dump(s0, "Bs_%d_%d" % (i, step))
def advance_batch():
sess.run(sampled_labels.initializer) # new labels for next call
def update_covariances():
ops_A = [cov_A[i].initializer for i in range(1, n + 1)]
ops_B2 = [cov_B2[i].initializer for i in range(1, n + 1)]
sess.run(ops_A + ops_B2)
def update_svds():
vars_svd_A[2].update()
vars_svd_B2[2].update()
vars_svd_B2[1].update()
def init_svds():
"""Initialize our SVD to identity matrices."""
ops = []
for i in range(1, n + 1):
ops.extend(vars_svd_A[i].init_ops)
ops.extend(vars_svd_B2[i].init_ops)
sess = tf.get_default_session()
sess.run(ops)
init_op = tf.global_variables_initializer()
from tensorflow.core.protobuf import rewriter_config_pb2
rewrite_options = rewriter_config_pb2.RewriterConfig(
disable_model_pruning=True,
constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
memory_optimization=rewriter_config_pb2.RewriterConfig.MANUAL)
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options,
rewrite_options=rewrite_options)
config = tf.ConfigProto(graph_options=graph_options)
sess = tf.InteractiveSession(config=config)
sess.run(Wf.initializer, feed_dict=init_dict)
sess.run(X.initializer, feed_dict=init_dict)
advance_batch()
update_covariances()
init_svds()
sess.run(init_op, feed_dict=init_dict) # initialize everything else
print("Running training.")
u.reset_time()
step_lengths = [] # keep track of learning rates
losses = []
# adaptive line search parameters
alpha = 0.3 # acceptable fraction of predicted decrease
beta = 0.8 # how much to shrink when violation
growth_rate = 1.05 # how much to grow when too conservative
def update_cov_A(i):
sess.run(cov_A[i].initializer)
def update_cov_B2(i):
sess.run(cov_B2[i].initializer)
# only update whitening matrix of input activations in the beginning
vars_svd_A[1].update()
for step in range(40):
update_covariances()
update_svds()
sess.run(grad.initializer)
sess.run(pre_grad.initializer)
lr0, loss0 = sess.run([lr, loss])
update_params_op.run()
advance_batch()
losses.append(loss0)
step_lengths.append(lr0)
print("Step %d loss %.2f" % (step, loss0))
u.record_time()
assert losses[-1] < 0.59
assert losses[-1] > 0.57
assert 20e-3 < min(
u.global_time_list) < 50e-3, "Time should be 40ms on 1080"
u.summarize_time()
print("Test passed")
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
if FLAGS.job_name == "ps":
ps_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.00001))
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index,
config=ps_config)
server.join()
elif FLAGS.job_name == "worker":
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
local_worker_device = "/job:worker/task:%d" % FLAGS.task_index
with tf.device(
tf.train.replica_device_setter(
ps_device='/job:ps/cpu:0',
worker_device=local_worker_device,
cluster=cluster)):
if FLAGS.network == 'lstm':
from models.lstm import KitModel
elif FLAGS.network == 'gru':
from models.gru import KitModel
elif FLAGS.network == 'fc':
from models.fullyconnect import KitModel
elif FLAGS.network == 'alexnet':
from models.alexnet import KitModel
elif FLAGS.network == 'vgg16':
from models.vgg16 import KitModel
elif FLAGS.network == 'vgg19' or FLAGS.network == 'vgg_e':
from models.vgg19 import KitModel
elif FLAGS.network == 'inception_v3':
from models.inception_v3 import KitModel
elif FLAGS.network == 'resnet':
from models.resnet import KitModel
elif FLAGS.network == 'seq2seq':
import models.translate.translate
from models.translate.translate import dist_train
dist_train(FLAGS, server, cluster)
sys.exit()
else:
sys.exit("Invalid network [%s]" % args.network)
this_model = KitModel(FLAGS)
this_model.build_model()
train_dir = tempfile.mkdtemp()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
],
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L1)),
gpu_options=tf.GPUOptions(visible_device_list=""))
if FLAGS.infer_shapes == True:
sess_config.graph_options.infer_shapes = FLAGS.infer_shapes
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir=train_dir,
init_op=tf.global_variables_initializer(),
global_step=this_model.global_step,
summary_writer=None,
saver=None)
if FLAGS.task_index == 0:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target,
config=sess_config,
start_standard_services=True)
print_model()
print("Start warmup %d epoch." % FLAGS.warmup)
for _ in range(FLAGS.warmup):
this_model.get_data()
sess.run(this_model.train_op, feed_dict=this_model.get_feed_dict())
current_step = 0
duration = 0
while current_step < FLAGS.epoch:
current_step += 1
this_model.get_data()
print("Start step %d" % current_step)
start_time = time.time()
_, step_loss = sess.run([this_model.train_op, this_model.cost],
feed_dict=this_model.get_feed_dict())
end_time = time.time()
print(
"Finish step %d, loss = %f, speed = %f sampes/s, duration = %f seconds"
% (current_step, step_loss, FLAGS.batch_size /
(end_time - start_time), end_time - start_time))
duration += end_time - start_time
print("Total Time = %f s." % duration)
#writer.close()
else:
sys.exit("Invalid job role name [%s]!" % args.job_name)
def main(_):
ps_hosts = FLAGS.ps_hosts.split(",")
worker_hosts = FLAGS.worker_hosts.split(",")
# Create a cluster from the parameter server and worker hosts.
cluster = tf.train.ClusterSpec({"ps": ps_hosts, "worker": worker_hosts})
if FLAGS.job_name == "ps":
ps_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.01))
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc_rdma",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index,
config=ps_config)
server.join()
elif FLAGS.job_name == "worker":
# Create and start a server for the local task.
server = tf.train.Server(
cluster,
# protocol = "grpc+verbs",
job_name=FLAGS.job_name,
task_index=FLAGS.task_index)
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.data_dir)
#####################################
# Select the preprocessing function #
#####################################
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
FLAGS.network, is_training=True)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(FLAGS.network,
dataset.num_classes,
is_training=True)
if FLAGS.dataset_name != "synthetic":
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size,
train_image_size)
images, labels = tf.train.batch([image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
else:
images = random_ops.random_uniform(
(FLAGS.batch_size, network_fn.default_image_size,
network_fn.default_image_size, 3),
maxval=1)
labels = random_ops.random_uniform((FLAGS.batch_size, ),
maxval=FLAGS.num_classes - 1,
dtype=tf.int32)
with tf.device(
tf.train.replica_device_setter(
ps_device='/job:ps/cpu:0',
worker_device=("/job:worker/task:%d" % FLAGS.task_index),
cluster=cluster)):
global_step = tf.contrib.framework.get_or_create_global_step()
#images, labels = cifar.distorted_inputs(FLAGS)
logits, end_points = network_fn(images)
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels)
cost = tf.reduce_mean(loss)
train_op = tf.train.AdagradOptimizer(0.01).minimize(
cost, global_step=global_step)
saver = tf.train.Saver()
print_model()
train_dir = tempfile.mkdtemp()
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=[
"/job:ps", "/job:worker/task:%d" % FLAGS.task_index
],
graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L1)),
gpu_options=tf.GPUOptions(visible_device_list=""))
if FLAGS.infer_shapes == True:
sess_config.graph_options.infer_shapes = FLAGS.infer_shapes
sv = tf.train.Supervisor(is_chief=(FLAGS.task_index == 0),
logdir=train_dir,
init_op=tf.global_variables_initializer(),
global_step=global_step,
summary_writer=None,
saver=saver)
if FLAGS.task_index == 0:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
sess = sv.prepare_or_wait_for_session(server.target,
config=sess_config,
start_standard_services=True)
writer = tf.summary.FileWriter('./graphs', sess.graph)
writer.close()
tf.train.export_meta_graph(filename='kit_meta_graph.txt',
graph=sess.graph,
as_text=True)
print("Graph Saved.")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print("Start warmup %d epoch." % FLAGS.warmup)
for _ in range(FLAGS.warmup):
sess.run(train_op)
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
current_step = 0
duration = 0
while current_step < FLAGS.epoch:
current_step += 1
start_time = time.time()
_, step_loss = sess.run([train_op, cost],
options=options,
run_metadata=run_metadata)
end_time = time.time()
print(
"Finish step %d, loss = %f, speed = %f sampes/s, duration = %f seconds"
% (current_step, step_loss, FLAGS.batch_size /
(end_time - start_time), end_time - start_time))
duration += end_time - start_time
if current_step == 3:
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open('timeline.json', 'w') as f:
f.write(chrome_trace)
print("Total Time = %f s." % duration)
saver.save(sess, "kit_alexnet")
else:
sys.exit("Invalid job role name [%s]!" % args.job_name)
def tf_run_frozen_graph(self, file, xla, parallel, warmup, num_iter):
print("run frozen graph----------------------------")
graph_def, graph = self.import_graph(file)
if (self.debug):
print()
print('Operations:')
assert graph is not None
ops = graph.get_operations() # type: Iterable[tf.Operation]
input_nodes = []
variables_nodes = []
last_nodes = []
for op in ops:
if (self.debug):
print('- {0:20s} "{1}" ({2} outputs)'.format(
op.type, op.name, len(op.outputs)))
last_nodes = op.outputs
if op.type == 'Placeholder':
for node in op.outputs:
input_nodes.append(node)
if "Variable" in op.type:
variables_nodes.append(op)
if (self.debug):
print()
print('Sources (operations without inputs):')
for op in ops:
if len(op.inputs) > 0:
continue
print('- {0}'.format(op.name))
print()
print('Operation inputs:')
for op in ops:
if len(op.inputs) == 0:
continue
print('- {0:20}'.format(op.name))
print(' {0}'.format(', '.join(i.name for i in op.inputs)))
print()
print('Tensors:')
for op in ops:
for out in op.outputs:
print('- {0:20} {1:10} "{2}"'.format(
str(out.shape), out.dtype.name, out.name))
with tf.Session(graph=graph) as sess:
var_inits = []
g_def = graph.as_graph_def()
for var in variables_nodes:
vt = graph.get_tensor_by_name(var.outputs[0].name)
# v = tf.get_variable(name = var.name, shape = vt.shape, initializer = tf.ones_initializer)
# v = tf.get_variable(name = var.name, shape = vt.shape, initializer = tf.ones_initializer)
# Ones initializer
dt = tf.as_dtype(vt.dtype.base_dtype).as_datatype_enum
dt_int32 = tf.as_dtype(tf.int32).as_datatype_enum
init = tf.NodeDef(
name=var.name + "/ones",
op="Fill",
input=[var.name + "/ones/shape", var.name + "/ones/const"],
attr={
'T': tf.AttrValue(type=dt),
'index_type': tf.AttrValue(type=dt_int32)
})
shape = tf.NodeDef(
name=var.name + "/ones/shape",
op="Const",
attr={
"dtype":
tf.AttrValue(type=dt_int32),
"value":
tf.AttrValue(tensor=tf.make_tensor_proto(
vt.get_shape().as_list()))
})
const = tf.NodeDef(
name=var.name + "/ones/const",
op="Const",
#dtype =tf.AttrValue(type=dt),
attr={
"dtype": tf.AttrValue(type=dt),
"value":
tf.AttrValue(tensor=tf.make_tensor_proto(1.0, dt))
})
node = tf.NodeDef(name=var.name + "/assign",
op='Assign',
input=[var.name, var.name + "/ones"],
attr={
'use_locking': tf.AttrValue(b=False),
'validate_shape': tf.AttrValue(b=True),
'T': tf.AttrValue(type=dt)
})
g_def.node.extend([shape, const, init, node])
var_inits.append("^" + var.name + "/assign")
noop_assign = tf.NodeDef(name="init_all_var",
op="NoOp",
input=var_inits)
g_def.node.extend([noop_assign])
tf.reset_default_graph()
tf.import_graph_def(g_def)
session_conf = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
graph_options=tf.GraphOptions(infer_shapes=True),
inter_op_parallelism_threads=parallel)
if xla:
session_conf.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
with tf.Session(config=session_conf) as sess:
init = tf.get_default_graph().get_operation_by_name(
"import/init_all_var")
input_nodes = []
varlist = []
feed_dict = {}
aps = []
ops = tf.get_default_graph().get_operations()
for op in ops:
if op.type == 'Placeholder':
for node in op.outputs:
feed_dict[node] = np.ones(
node.shape, dtype=node.dtype.as_numpy_dtype())
# Get result of applygradient
for op in ops:
if "ApplyGradient" in str(op.type):
aps.append(op)
varlist.append(op.inputs[0])
last_outputs = []
num_nodes = len(ops)
name2nodeIdx_map = {}
for i in range(num_nodes):
name2nodeIdx_map[ops[i].name] = i
node_outputs_ = [[] for i in range(num_nodes)]
for n in range(num_nodes):
op = ops[n]
pending_count = len(op.inputs)
for i in range(pending_count):
input_name_id = op.inputs[i].name.split(':')
node_outputs_[name2nodeIdx_map[input_name_id[0]]].append(n)
for n in range(num_nodes):
if len(node_outputs_[n]) == 0 and ops[n].type != 'NoOp':
print('- {0:20s} {1}'.format(ops[n].type, ops[n].name))
for m in range(len(ops[n].inputs)):
print('<-in-- {0:20s}'.format(ops[n].inputs[m].name))
last_outputs.append(ops[n].inputs[m])
# Init as Ones
sess.run(init)
# Get vals before apply_gradients
for i in range(warmup):
ret = sess.run(last_outputs + varlist, feed_dict)
for i in range(0, len(last_outputs)):
out_flat = ret[i].flat
if (len(out_flat) > 0):
max_len = min(10, len(out_flat))
print(last_outputs[i].name)
print(out_flat[:max_len], "...(size=", len(out_flat),
"end with", out_flat[-1], ")")
# Do the apply_gradient
sess.run(init)
ret1 = sess.run(varlist + aps, feed_dict)
print("Updated:")
for i in range(0, len(varlist)):
print(varlist[i].name, ret1[i])
iter_times = []
for i in range(num_iter):
start_time = time.time()
ret = sess.run(last_outputs + varlist, feed_dict)
ret1 = sess.run(varlist + aps, feed_dict)
iter_time = (time.time() - start_time) * 1000
iter_times.append(iter_time)
print("Iteration time %f ms" % (iter_time))
print("Summary: [min, max, mean] = [%f, %f, %f] ms" %
(min(iter_times), max(iter_times),
sum(iter_times) / len(iter_times)))
def main(unused_argv):
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu if (FLAGS.tpu or FLAGS.use_tpu) else '',
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
if FLAGS.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(100, FLAGS.iterations_per_loop)
config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_cores,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
resnet_classifier = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=resnet_model_fn,
config=config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu)
assert FLAGS.precision == 'bfloat16' or FLAGS.precision == 'float32', (
'Invalid value for --precision flag; must be bfloat16 or float32.')
tf.logging.info('Precision: %s', FLAGS.precision)
use_bfloat16 = FLAGS.precision == 'bfloat16'
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s',
FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=use_bfloat16,
transpose_input=FLAGS.transpose_input,
selection=selection)
for (is_training,
selection) in [(True, select_train), (False, select_eval)]
]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=FLAGS.transpose_input,
cache=FLAGS.use_cache and is_training,
num_parallel_calls=FLAGS.num_parallel_calls,
use_bfloat16=use_bfloat16) for is_training in [True, False]
]
steps_per_epoch = FLAGS.num_train_images // FLAGS.train_batch_size
eval_steps = FLAGS.num_eval_images // FLAGS.eval_batch_size
if FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time(
) # This time will include compilation time
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= FLAGS.train_steps:
tf.logging.info(
'Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir(
FLAGS.model_dir) # pylint: disable=protected-access,line-too-long
steps_per_epoch = FLAGS.num_train_images // FLAGS.train_batch_size
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', FLAGS.train_steps,
FLAGS.train_steps / steps_per_epoch, current_step)
start_timestamp = time.time(
) # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if FLAGS.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, FLAGS.iterations_per_loop)))
if FLAGS.profile_every_n_steps > 0:
hooks.append(
tpu_profiler_hook.TPUProfilerHook(
save_steps=FLAGS.profile_every_n_steps,
output_dir=FLAGS.model_dir,
tpu=FLAGS.tpu))
resnet_classifier.train(input_fn=imagenet_train.input_fn,
max_steps=FLAGS.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < FLAGS.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
FLAGS.train_steps)
resnet_classifier.train(input_fn=imagenet_train.input_fn,
max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=FLAGS.num_eval_images // FLAGS.eval_batch_size)
tf.logging.info('Eval results at step %d: %s', next_checkpoint,
eval_results)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
FLAGS.train_steps, elapsed_time)
if FLAGS.export_dir is not None:
# The guide to serve a exported TensorFlow model is at:
# https://www.tensorflow.org/serving/serving_basic
tf.logging.info('Starting to export model.')
resnet_classifier.export_saved_model(
export_dir_base=FLAGS.export_dir,
serving_input_receiver_fn=imagenet_input.image_serving_input_fn
)
def facs_model(learning_rate, scale_class_weight, use_two_fc, use_three_fc,
use_four_fc, use_five_fc, use_six_fc, use_seven_fc, hparam):
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
opt_level=tf.OptimizerOptions.L0)))
tf.reset_default_graph()
sess = tf.Session("", config=config)
# Setup placeholders, and reshape the data
x = tf.placeholder(tf.float32, [None, n_input], name="x")
y = tf.placeholder(tf.float32, [None, n_output], name="labels")
sw = tf.placeholder(tf.float32, [None, n_output], name='intensity_weights')
# Main function compares the number of FCs for performance.
if use_two_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc1/relu", relu)
logits = fc_layer(relu, n_hidden_1, n_output, "fc2")
elif use_three_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc3/relu", relu)
fc2 = fc_layer(relu, n_hidden_1, n_hidden_2, "fc2")
relu_2 = tf.nn.relu(fc2)
tf.summary.histogram("fc3/relu", relu_2)
logits = fc_layer(relu_2, n_hidden_2, n_output, "fc3")
elif use_four_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc4/relu", relu)
fc2 = fc_layer(relu, n_hidden_1, n_hidden_2, "fc2")
relu_2 = tf.nn.relu(fc2)
tf.summary.histogram("fc4/relu", relu_2)
fc3 = fc_layer(relu_2, n_hidden_2, n_hidden_3, "fc3")
relu_3 = tf.nn.relu(fc3)
tf.summary.histogram("fc4/relu", relu_3)
logits = fc_layer(relu_3, n_hidden_3, n_output, "fc4")
elif use_five_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc5/relu", relu)
fc2 = fc_layer(relu, n_hidden_1, n_hidden_2, "fc2")
relu_2 = tf.nn.relu(fc2)
tf.summary.histogram("fc5/relu", relu_2)
fc3 = fc_layer(relu_2, n_hidden_2, n_hidden_3, "fc3")
relu_3 = tf.nn.relu(fc3)
tf.summary.histogram("fc5/relu", relu_3)
fc4 = fc_layer(relu_3, n_hidden_3, n_hidden_4, "fc4")
relu_4 = tf.nn.relu(fc4)
tf.summary.histogram("fc5/relu", relu_4)
logits = fc_layer(relu_4, n_hidden_4, n_output, "fc5")
elif use_six_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc6/relu", relu)
fc2 = fc_layer(relu, n_hidden_1, n_hidden_2, "fc2")
relu_2 = tf.nn.relu(fc2)
tf.summary.histogram("fc6/relu", relu_2)
fc3 = fc_layer(relu_2, n_hidden_2, n_hidden_3, "fc3")
relu_3 = tf.nn.relu(fc3)
tf.summary.histogram("fc6/relu", relu_3)
fc4 = fc_layer(relu_3, n_hidden_3, n_hidden_4, "fc4")
relu_4 = tf.nn.relu(fc4)
tf.summary.histogram("fc6/relu", relu_4)
fc5 = fc_layer(relu_4, n_hidden_4, n_hidden_5, "fc5")
relu_5 = tf.nn.relu(fc5)
tf.summary.histogram("fc6/relu", relu_5)
logits = fc_layer(relu_5, n_hidden_5, n_output, "fc6")
elif use_seven_fc:
fc1 = fc_layer(x, n_input, n_hidden_1, "fc1")
relu = tf.nn.relu(fc1)
tf.summary.histogram("fc7/relu", relu)
fc2 = fc_layer(relu, n_hidden_1, n_hidden_2, "fc2")
relu_2 = tf.nn.relu(fc2)
tf.summary.histogram("fc7/relu", relu_2)
fc3 = fc_layer(relu_2, n_hidden_2, n_hidden_3, "fc3")
relu_3 = tf.nn.relu(fc3)
tf.summary.histogram("fc7/relu", relu_3)
fc4 = fc_layer(relu_3, n_hidden_3, n_hidden_4, "fc4")
relu_4 = tf.nn.relu(fc4)
tf.summary.histogram("fc7/relu", relu_4)
fc5 = fc_layer(relu_4, n_hidden_4, n_hidden_5, "fc5")
relu_5 = tf.nn.relu(fc5)
tf.summary.histogram("fc7/relu", relu_5)
fc6 = fc_layer(relu_5, n_hidden_5, n_hidden_6, "fc6")
relu_6 = tf.nn.relu(fc6)
tf.summary.histogram("fc7/relu", relu_6)
logits = fc_layer(relu_6, n_hidden_6, n_output, "fc7")
else:
logits = fc_layer(x, n_input, n_output, "fc")
# Loss function
with tf.name_scope("xent"):
# The positive and negative samples in the data are unbalanced.
# To push the algorithm to focus on fitting positives, I weighted the
# positive values more than the negative.
maxY = tf.reduce_sum(y, 1) * scale_class_weight
class_weights = (maxY + 1) / 6
# Some expressions are more intense than others in the CK+ database and
# and that is weighted in the loss function by sample weights, sw.
# However, I got better results with just weighting all AUs
# with equal intensity.
# mult_w = tf.multiply(y, sw)
# sum_w = tf.reduce_sum(mult_w,1)
#
# class_weights = ( sum_w + 1) / 6
print(class_weights.get_shape())
xent = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,
labels=y,
name="xent"))
xent = tf.reduce_mean(xent * class_weights)
tf.summary.scalar("xent", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
zero = tf.constant(0, dtype=tf.float32)
onesMat = tf.ones_like(logits)
zerosMat = tf.zeros_like(logits)
onesY = tf.ones_like(y, dtype=tf.float32)
yFloat = tf.cast(y, dtype=tf.float32)
yFlipped = onesY - yFloat
# PREDICTION - If logits >= 0, logits = 1, else logits = 0.
logitsBin = tf.cast(tf.where(logits >= zero, onesMat, zerosMat),
dtype=tf.float32,
name="op_to_restore")
tf.add_to_collection("coll", logitsBin)
tf.add_to_collection("coll", x)
print('logitsBin', logitsBin.get_shape())
print('y', y.get_shape())
print('where_logitsBin', tf.where(logitsBin)[:, 1].get_shape())
print('where_y', tf.where(y)[:, 1].get_shape())
time_steps = tf.cast(tf.shape(y)[0], dtype='int32')
print(time_steps.get_shape())
nFacs = tf.count_nonzero(y, 1, dtype=tf.float32)
onesFacs = tf.ones_like(nFacs)
nFacs_Zeros = onesFacs * numFacs - nFacs
nFacs = tf.where(tf.equal(nFacs, zero), onesFacs, nFacs)
nFacs_Zeros = tf.where(tf.equal(nFacs_Zeros, zero), onesFacs,
nFacs_Zeros)
# Find TPR, TNR, FPR, FNR.
matrix_positive = tf.cast(
tf.equal(logitsBin, y)
& tf.equal(yFloat, tf.constant(1, dtype=tf.float32)),
dtype=tf.float32)
correct_pos = tf.reduce_sum(matrix_positive) / tf.reduce_sum(yFloat)
tf.summary.scalar("TruePosRate", correct_pos)
matrix_negative = tf.cast(tf.equal(logitsBin, y)
& tf.equal(yFloat, zero),
dtype=tf.float32)
correct_neg = tf.reduce_sum(matrix_negative) / tf.reduce_sum(yFlipped)
tf.summary.scalar("TrueNegRate", correct_neg)
matrix_falsePos = tf.cast(tf.not_equal(logitsBin, y)
& tf.equal(y, zero),
dtype=tf.float32) #or yFlipped = 1
falsePos = tf.reduce_sum(matrix_falsePos) / tf.reduce_sum(yFlipped)
tf.summary.scalar("falsePosRate", falsePos)
matrix_falseNeg = tf.cast(
tf.not_equal(logitsBin, y)
& tf.equal(yFloat, tf.constant(1, dtype=tf.float32)),
dtype=tf.float32)
falseNeg = tf.reduce_sum(matrix_falseNeg) / tf.reduce_sum(yFloat)
tf.summary.scalar("falseNegRate", falseNeg)
tp_sum = tf.reduce_sum(matrix_positive, 0)
tp_sum_append = tf.concat([tf.constant([0], dtype=tf.float32), tp_sum],
0)
tf_sum = tf.reduce_sum(matrix_negative, 0)
fp_sum = tf.reduce_sum(matrix_falsePos, 0)
fn_sum = tf.reduce_sum(matrix_falseNeg, 0)
# Get Matrix of Confusion for multiclass binary classifier.
confusion = tf.Variable(initial_value=tf.zeros(
[n_output + 1, n_output + 1]),
name='confusion')
confusion1 = tf.Variable(initial_value=tf.cast(tf.diag(
np.repeat(1, n_output + 1)),
dtype=tf.float32),
name='confusion1')
confusion2 = tf.Variable(initial_value=tf.zeros(
[n_output + 1, n_output + 1]),
name='confusion2')
confusion3 = tf.Variable(initial_value=tf.zeros(
[n_output + 1, n_output + 1]),
name='confusion3')
confusion4 = tf.Variable(initial_value=tf.zeros(
[n_output + 1, n_output + 1]),
name='confusion4')
confusion1 = confusion1[0, 0].assign(5)
confusion1 = confusion1 * tp_sum_append
confusion2 = confusion2[0, 0].assign(tf.reduce_sum(tf_sum))
confusion3 = tf.assign(confusion3[0, 1:n_output + 1], fp_sum)
confusion4 = confusion4[1:n_output + 1, 0].assign(fn_sum)
confusion = confusion1 + confusion2 + confusion3 + confusion4
txtConfusion = tf.as_string(confusion,
precision=0,
name='txtConfusion')
tf.summary.text('txtConfusion', txtConfusion)
correct_prediction = tf.cast(tf.equal(logitsBin, y),
dtype=tf.float32,
name="correct_prediction")
accuracy = tf.reduce_mean(correct_prediction, name="accuracy")
tf.summary.scalar("accuracy", accuracy)
# Summary for tensorboard
summ = tf.summary.merge_all()
saver = tf.train.Saver()
init = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init)
writer = tf.summary.FileWriter(LOGDIR + hparam + '/train')
test_writer = tf.summary.FileWriter(LOGDIR + hparam + '/test')
writer.add_graph(sess.graph)
for i in range(3001):
if i % 5 == 0:
[train_accuracy, s] = sess.run([accuracy, summ],
feed_dict={
x: train_x,
y: train_y,
sw: sw_train
})
sess.run([confusion],
feed_dict={
x: test_x,
y: test_y,
sw: sw_test
})
writer.add_summary(s, i)
if i % 50 == 0:
[acc, s] = sess.run([accuracy, summ],
feed_dict={
x: test_x,
y: test_y,
sw: sw_test
})
sess.run([confusion],
feed_dict={
x: test_x,
y: test_y,
sw: sw_test
})
test_writer.add_summary(s, i)
saver.save(sess, os.path.join(savepath, hparam, "model"), i)
sess.run(train_step, feed_dict={x: train_x, y: train_y, sw: sw_train})
print(f"Rank {hvd.rank()}:{hvd.local_rank()} reporting!")
else:
np.random.seed(args.seed)
tf.random.set_random_seed(args.seed)
random.seed(args.seed)
if args.architecture in ('stylegan2'):
assert args.starting_phase == args.ending_phase
if 'OMP_NUM_THREADS' not in os.environ:
print("Warning: OMP_NUM_THREADS not set. Setting it to 1.")
os.environ['OMP_NUM_THREADS'] = str(1)
gopts = tf.GraphOptions(place_pruned_graph=True)
config = tf.ConfigProto(graph_options=gopts, allow_soft_placement=True)
# config = tf.ConfigProto()
if args.gpu:
config.gpu_options.allow_growth = True
# config.inter_op_parallelism_threads = 1
#config.gpu_options.per_process_gpu_memory_fraction = 0.96
if args.horovod:
config.gpu_options.visible_device_list = str(hvd.local_rank())
else:
config = tf.ConfigProto(
graph_options=gopts,
intra_op_parallelism_threads=int(os.environ['OMP_NUM_THREADS']),
inter_op_parallelism_threads=args.num_inter_ops,
def _testDecoderFPropFloatHelper(self,
func_inline=False,
num_decoder_layers=1,
target_seq_len=5,
residual_start=0):
"""Computes decoder from params and computes loss with random inputs."""
cluster = cluster_factory.ForTestingWorker(add_summary=True)
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
do_function_inlining=func_inline)))
with cluster, self.session(graph=tf.Graph(),
use_gpu=False,
config=config) as sess:
tf.set_random_seed(8372749040)
vn_config = py_utils.VariationalNoiseParams(None, False, False)
p = self._DecoderParams(vn_config)
p.rnn_layers = num_decoder_layers
p.residual_start = residual_start
p.target_seq_len = target_seq_len
dec = p.Instantiate()
src_seq_len = 5
src_enc = tf.random_normal([src_seq_len, 2, 8], seed=9283748)
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=tf.float32)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 1]],
dtype=tf.float32))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
metrics = dec.FPropDefaultTheta(encoder_outputs, targets)
loss = metrics['loss'][0]
correct_predicts = metrics['fraction_of_correct_next_step_preds'][
0]
summaries = tf.summary.merge(
tf.get_collection(tf.GraphKeys.SUMMARIES))
tf.global_variables_initializer().run()
loss_v, _ = sess.run([loss, correct_predicts])
summaries.eval()
return loss_v
lms_obj.run(graph=tf.get_default_graph())
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/MNIST_data/data/")
from tensorflow.core.protobuf import rewriter_config_pb2
rewrite_options = rewriter_config_pb2.RewriterConfig(disable_model_pruning=True,
#constant_folding=rewriter_config_pb2.RewriterConfig.OFF,
#dependency_optimization=rewriter_config_pb2.RewriterConfig.OFF,
#layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,
#arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF,
#min_graph_nodes=-1,
memory_optimization=rewriter_config_pb2.RewriterConfig.NO_MEM_OPT)#SCHEDULING_HEURISTICS)
graph_options = tf.GraphOptions(rewrite_options=rewrite_options)#, infer_shapes=True)
config = tf.ConfigProto(graph_options=graph_options, allow_soft_placement=True, log_device_placement=True)
config.gpu_options.allow_growth=True
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
#graph = tf.get_default_graph()
#writer = tf.summary.FileWriter("./rewriter_graph1")
#writer.add_graph(graph=graph)
import numpy as np
picture = np.ones([batch_size, 200 * 200], dtype=np.float32)
picture_label = np.ones([batch_size], dtype=np.float32)
with tf.Session(config=config) as sess:
def _testDecoderFPropGradientCheckerHelper(self, func_inline=False):
config = tf.ConfigProto(graph_options=tf.GraphOptions(
optimizer_options=tf.OptimizerOptions(
do_function_inlining=func_inline)))
with self.session(graph=tf.Graph(), use_gpu=False,
config=config) as sess:
tf.set_random_seed(8372749040)
np.random.seed(274854)
vn_config = py_utils.VariationalNoiseParams(None, False, False)
p = self._DecoderParams(vn_config)
p.dtype = tf.float64
dec = p.Instantiate()
src_seq_len = 5
src_enc = tf.constant(np.random.uniform(size=(src_seq_len, 2, 8)),
tf.float64)
src_enc_padding = tf.constant(
[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [1.0, 1.0]],
dtype=tf.float64)
encoder_outputs = py_utils.NestedMap(encoded=src_enc,
padding=src_enc_padding)
target_ids = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 15],
[5, 6, 7, 8], [10, 5, 2, 5]],
dtype=tf.int32))
target_labels = tf.transpose(
tf.constant([[0, 1, 2, 3], [1, 2, 3, 4], [10, 11, 12, 13],
[5, 7, 8, 10], [10, 5, 2, 4]],
dtype=tf.int32))
target_paddings = tf.transpose(
tf.constant([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0],
[0, 1, 0, 0], [1, 1, 1, 1]],
dtype=tf.float64))
target_transcripts = tf.constant(
['abcd', 'bcde', 'klmp', 'fghi', 'kfcf'])
target_weights = 1.0 - target_paddings
targets = py_utils.NestedMap({
'ids': target_ids,
'labels': target_labels,
'weights': target_weights,
'paddings': target_paddings,
'transcripts': target_transcripts,
})
metrics = dec.FPropDefaultTheta(encoder_outputs, targets)
loss = metrics['loss'][0]
all_vars = tf.trainable_variables()
grads = tf.gradients(loss, all_vars)
def DenseGrad(var, grad):
if isinstance(grad, tf.Tensor):
return grad
elif isinstance(grad, tf.IndexedSlices):
return tf.unsorted_segment_sum(grad.values, grad.indices,
tf.shape(var)[0])
dense_grads = [DenseGrad(x, y) for (x, y) in zip(all_vars, grads)]
tf.global_variables_initializer().run()
test_utils.CompareToGoldenSingleFloat(self, 3.458078, loss.eval())
# Second run to make sure the function is determistic.
test_utils.CompareToGoldenSingleFloat(self, 3.458078, loss.eval())
symbolic_grads = [x.eval() for x in dense_grads if x is not None]
numerical_grads = []
for v in all_vars:
numerical_grads.append(
test_utils.ComputeNumericGradient(sess, loss, v))
for x, y in zip(symbolic_grads, numerical_grads):
self.assertAllClose(x, y)
def freeze_graph_with_def_protos(
input_graph_def,
input_saver_def,
input_checkpoint,
output_node_names,
restore_op_name,
filename_tensor_name,
clear_devices,
initializer_nodes,
optimize_graph=True,
variable_names_blacklist=''):
"""Converts all variables in a graph and checkpoint into constants."""
del restore_op_name, filename_tensor_name # Unused by updated loading code.
# 'input_checkpoint' may be a prefix if we're using Saver V2 format
if not saver_lib.checkpoint_exists(input_checkpoint):
raise ValueError(
'Input checkpoint "' + input_checkpoint + '" does not exist!')
if not output_node_names:
raise ValueError(
'You must supply the name of a node to --output_node_names.')
# Remove all the explicit device specifications for this node. This helps to
# make the graph more portable.
if clear_devices:
for node in input_graph_def.node:
node.device = ''
with tf.Graph().as_default():
tf.import_graph_def(input_graph_def, name='')
if optimize_graph:
logging.info('Graph Rewriter optimizations enabled')
rewrite_options = rewriter_config_pb2.RewriterConfig(
optimize_tensor_layout=True)
rewrite_options.optimizers.append('pruning')
rewrite_options.optimizers.append('constfold')
rewrite_options.optimizers.append('layout')
graph_options = tf.GraphOptions(
rewrite_options=rewrite_options, infer_shapes=True)
else:
logging.info('Graph Rewriter optimizations disabled')
graph_options = tf.GraphOptions()
config = tf.ConfigProto(graph_options=graph_options)
with session.Session(config=config) as sess:
if input_saver_def:
saver = saver_lib.Saver(saver_def=input_saver_def)
saver.restore(sess, input_checkpoint)
else:
var_list = {}
reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in var_to_shape_map:
try:
tensor = sess.graph.get_tensor_by_name(key + ':0')
except KeyError:
# This tensor doesn't exist in the graph (for example it's
# 'global_step' or a similar housekeeping element) so skip it.
continue
var_list[key] = tensor
saver = saver_lib.Saver(var_list=var_list)
saver.restore(sess, input_checkpoint)
if initializer_nodes:
sess.run(initializer_nodes)
variable_names_blacklist = (variable_names_blacklist.split(',') if
variable_names_blacklist else None)
output_graph_def = graph_util.convert_variables_to_constants(
sess,
input_graph_def,
output_node_names.split(','),
variable_names_blacklist=variable_names_blacklist)
return output_graph_def
a1 = tf.ones((n, n))
a2 = tf.ones((n, n))
with tf.device("cpu:1"):
a3 = tf.matmul(a1, a2)
with tf.device("cpu:2"):
a4 = tf.matmul(a1, a2)
with tf.device("cpu:3"):
a5 = tf.matmul(a3, a4)
# Turn off graph optimizations
no_opt = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0,
do_common_subexpression_elimination=False,
do_function_inlining=False,
do_constant_folding=False)
config = tf.ConfigProto(
graph_options=tf.GraphOptions(optimizer_options=no_opt),
log_device_placement=True,
allow_soft_placement=False,
device_count={"CPU": 8},
inter_op_parallelism_threads=3,
intra_op_parallelism_threads=1)
sess = tf.Session(config=config)
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE,
output_partition_graphs=True)
# Run session.
sess.run(a5.op, options=run_options, run_metadata=run_metadata)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
def main(unused_argv):
# Mnas optimize - set the proper image data format
tf.keras.backend.set_image_data_format(FLAGS.data_format)
# Mnas optimize - optimization flags
# gpu_thread_count = 2
# os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
# os.environ['TF_GPU_THREAD_COUNT'] = str(gpu_thread_count)
# os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
# os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
# enable mixed precision? -> Not much benefits seen yet
# os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
# Horovod: initialize Horovod.
if FLAGS.use_horovod:
hvd.init()
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu if (FLAGS.tpu or FLAGS.use_tpu) else '',
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
if FLAGS.use_async_checkpointing:
save_checkpoints_steps = None
else:
if not FLAGS.use_horovod:
save_checkpoints_steps = max(100, FLAGS.iterations_per_loop)
else:
save_checkpoints_steps = max(
100, FLAGS.iterations_per_loop) if hvd.rank() == 0 else None
config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
if FLAGS.use_xla:
config.session_config.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
# Horovod: pin GPU to be used to process local rank (one GPU per process)
if FLAGS.use_horovod:
config.session_config.gpu_options.allow_growth = True
config.session_config.gpu_options.visible_device_list = str(
hvd.local_rank())
# Validates Flags.
if FLAGS.use_bfloat16 and FLAGS.use_keras:
raise ValueError(
'Keras layers do not have full support to bfloat16 activation training.'
' You have set use_bfloat as %s and use_keras as %s' %
(FLAGS.use_bfloat16, FLAGS.use_keras))
# Initializes model parameters.
steps_per_epoch = FLAGS.num_train_images / FLAGS.train_batch_size
steps_per_epoch = steps_per_epoch // hvd.size(
) if FLAGS.use_horovod else steps_per_epoch
params = dict(steps_per_epoch=steps_per_epoch,
use_bfloat16=FLAGS.use_bfloat16,
quantized_training=FLAGS.quantized_training)
if FLAGS.use_horovod:
params['hvd'] = True
params['hvd_curr_host'] = hvd.rank()
params['hvd_num_hosts'] = hvd.size()
mnasnet_est = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=mnasnet_model_fn,
config=config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu,
params=params)
# Horovod: BroadcastGlobalVariablesHook broadcasts initial variable states from
# rank 0 to all other processes. This is necessary to ensure consistent
# initialization of all workers when training is started with random weights or
# restored from a checkpoint.
if FLAGS.use_horovod:
bcast_hook = hvd.BroadcastGlobalVariablesHook(0)
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s',
FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=False,
transpose_input=FLAGS.transpose_input,
selection=selection)
for (is_training,
selection) in [(True, select_train), (False, select_eval)]
]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=FLAGS.transpose_input,
cache=FLAGS.use_cache and is_training,
image_size=FLAGS.input_image_size,
num_parallel_calls=FLAGS.num_parallel_calls,
use_bfloat16=FLAGS.use_bfloat16)
for is_training in [True, False]
]
if FLAGS.mode == 'eval':
eval_steps = FLAGS.num_eval_images // FLAGS.eval_batch_size
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time(
) # This time will include compilation time
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= FLAGS.train_steps:
tf.logging.info(
'Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, FLAGS.post_quantize)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir( # pylint: disable=protected-access
FLAGS.model_dir)
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', FLAGS.train_steps,
FLAGS.train_steps / params['steps_per_epoch'], current_step)
start_timestamp = time.time(
) # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if FLAGS.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, FLAGS.iterations_per_loop)))
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=FLAGS.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
curr_rank = 0
if FLAGS.use_horovod:
curr_rank = hvd.rank()
while current_step < FLAGS.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
FLAGS.train_steps)
if FLAGS.use_horovod:
# try dali pipeline
mnasnet_est.train(input_fn=imagenet_train.train_data_fn,
max_steps=next_checkpoint,
hooks=[bcast_hook])
# this uses the old tf data pipeline
# mnasnet_est.train(
# input_fn=imagenet_train.input_fn, max_steps=next_checkpoint, hooks=[bcast_hook])
else:
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d. Hvd rank %d',
next_checkpoint, int(time.time() - start_timestamp),
curr_rank)
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
eval_on_single_gpu = FLAGS.eval_on_single_gpu
tf.logging.info('Starting to evaluate.')
if eval_on_single_gpu:
if curr_rank == 0:
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.train_data_fn, #input_fn
steps=FLAGS.num_eval_images //
FLAGS.eval_batch_size)
tf.logging.info(
'Eval results at step %d: %s. Hvd rank %d',
next_checkpoint, eval_results, curr_rank)
else:
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.train_data_fn, #input_fn
steps=FLAGS.num_eval_images // FLAGS.eval_batch_size)
tf.logging.info('Eval results at step %d: %s. Hvd rank %d',
next_checkpoint, eval_results, curr_rank)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
FLAGS.train_steps, elapsed_time)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, FLAGS.post_quantize)
def test_avgpool2d():
''' Run tests on the Wave custom avgpool2d operator.
'''
tf.reset_default_graph()
# Turn off graph-rewriting optimizations
config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))
iterations = 100
for i in range(iterations):
tf.reset_default_graph()
# NCHW
t_n = 1
t_h = 64
t_w = 64
t_c = 2
# window
w_n = 1
w_h = 2
w_w = 2
w_c = 1
#strides
s_n = 1
s_h = 2
s_w = 2
s_c = 1
# N H W C
max_in = tf.get_variable("a", [t_n, t_h, t_w, t_c], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1))
t_init = tf.global_variables_initializer()
# SAME variant
with tf.Session('', config=config) as sess:
t_init.run()
# print("Wave Kernel:\n-------------------------------------------------")
z_op = waveflow.wavecomp_ops_module.wave_avg_pool_dfx(
max_in,
ksize=[w_n, w_h, w_w, w_c],
strides=[s_n, s_h, s_w, s_c],
padding='SAME',
data_format='NHWC')
# Base tensorflow. Only supports NHWC.
z2_op = nn_ops.avg_pool(
max_in,
ksize=[w_n, w_h, w_w, w_c],
strides=[s_n, s_h, s_w, s_c],
padding='SAME',
data_format='NHWC')
# z = z_op.eval()
# z2 = z2_op.eval()
z, z2 = sess.run([z_op, z2_op])
# print("\nTF:\n-------------------------------------------------")
assert_str = "Failure on i: %d, mode: SAME" % (i)
if not compare_tensor(z, z2, assert_str):
print("z: shape: %s, %s" % (z.shape, z))
print("z (np): shape: %s, %s" % (z2.shape, z2))
print("\n\n")
assert False
# Valid variant
with tf.Session('', config=config) as sess:
t_init.run()
# print("Wave Kernel:\n-------------------------------------------------")
z_op = waveflow.wavecomp_ops_module.wave_avg_pool_dfx(
max_in,
ksize=[w_n, w_h, w_w, w_c],
strides=[s_n, s_h, s_w, s_c],
padding='VALID',
data_format='NHWC')
# Base tensorflow. Only supports NHWC.
z2_op = nn_ops.avg_pool(
max_in,
ksize= [w_n, w_h, w_w, w_c],
strides=[s_n, s_h, s_w, s_c],
padding='VALID',
data_format='NHWC')
z, z2 = sess.run([z_op, z2_op])
# print("\nTF:\n-------------------------------------------------")
assert_str = "Failure on i: %d, mode: VALID" % (i)
if not compare_tensor(z, z2, assert_str):
print("z: shape: %s, %s" % (z.shape, z))
print("z (np): shape: %s, %s" % (z2.shape, z2))
print("\n\n")
assert False
return True
