def main(*args):
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
steps_per_epoch = int(
round(
math.ceil(
float(NUM_SAMPLES_TRAIN) /
(flags.batch_size * num_gpus * num_workers))))
if flags.is_training:
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=mox.get_optimizer_fn(name='adam',
learning_rate=0.001),
run_mode=mox.ModeKeys.TRAIN,
batch_size=flags.batch_size,
log_dir=flags.train_url,
max_number_of_steps=steps_per_epoch * 150,
log_every_n_steps=20,
save_summary_steps=50,
save_model_secs=120,
export_model=mox.ExportKeys.TF_SERVING)
else:
mox.run(input_fn=input_fn,
model_fn=model_fn,
run_mode=mox.ModeKeys.EVAL,
batch_size=5,
log_every_n_steps=1,
max_number_of_steps=int(NUM_SAMPLES_EVAL / 5),
checkpoint_path=flags.train_url)
mox.run(input_fn=input_fn,
output_fn=output_fn,
model_fn=model_fn,
run_mode=mox.ModeKeys.PREDICT,
batch_size=24,
max_number_of_steps=int(NUM_SAMPLES_TEST / 24),
log_every_n_steps=50,
output_every_n_steps=int(NUM_SAMPLES_TEST / 24),
checkpoint_path=flags.train_url)
# Write results to file. tf.gfile allow writing file to EBS/s3
submission_file = os.path.join(flags.train_url, 'submission.csv')
result = submission.to_csv(path_or_buf=None, index=False)
with tf.gfile.Open(submission_file, 'w') as f:
f.write(result)
def config_bs_ims(strategy):
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
res = []
if ":" not in strategy:
image_size, batch_size = strategy.split('-')
return [(float(image_size), float(batch_size))]
else:
stags = strategy.split(",")
last_steps, last_epoch = 0, 0
for i in range(len(stags)):
cur_epoch, value = stags[i].strip().split(':')
image_size, batch_size = value.strip().split('-')
cur_epoch, image_size, batch_size = float(cur_epoch), float(
image_size), float(batch_size)
cur_batch_tot = batch_size * num_gpus * num_workers
cur_steps = int(
round(math.ceil(flags.num_samples / float(cur_batch_tot)))) * (
cur_epoch - last_epoch) + last_steps
res.append((int(cur_steps), int(image_size), int(batch_size)))
last_steps, last_epoch = cur_steps, cur_epoch
return res
def convert_ps_to_controller():
# ps0 -> worker0
# ps1 -> worker1
# worker0 -> controller
# worker1 -> sleep
job_name = mox.get_flag('job_name')
task_index = mox.get_flag('task_index')
ps_hosts = mox.get_flag('ps_hosts')
worker_hosts = mox.get_flag('worker_hosts')
mox.set_flag('ps_hosts', '')
mox.set_flag('worker_hosts', ps_hosts)
mox.set_flag('controller_host', worker_hosts.split(',')[0])
if job_name == 'ps':
tf.logging.info('convert ps to worker')
mox.set_flag('job_name', 'worker')
elif job_name == 'worker' and task_index == 0:
tf.logging.info('convert worker-0 to controller')
mox.set_flag('job_name', 'controller')
os.environ['CUDA_VISIBLE_DEVICES'] = ''
else:
tf.logging.info('sleep unused server')
time.sleep(9999999)
def main(*args):
_data_url = flags.data_url
_train_url = flags.train_url
if not mox.file.is_directory(_train_url):
mox.file.make_dirs(_train_url)
mox.file.make_dirs('/cache/data_url')
mox.file.make_dirs('/cache/train_url')
mox.file.copy_parallel(_data_url, '/cache/data_url')
mox.file.copy_parallel(_train_url, '/cache/train_url')
flags.data_url = '/cache/data_url'
flags.train_url = '/cache/train_url'
atexit.register(
lambda: mox.file.copy_parallel('/cache/train_url', _train_url))
logger = logging.getLogger()
while logger.handlers:
logger.handlers.pop()
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
steps_per_epoch = int(
math.ceil(
float(NUM_SAMPLES_TRAIN) /
(flags.batch_size * num_gpus * num_workers)))
submission = pd.DataFrame(columns=['id', 'is_iceberg'])
def input_fn(run_mode, **kwargs):
if run_mode == mox.ModeKeys.TRAIN:
num_samples = NUM_SAMPLES_TRAIN
num_epochs = None
shuffle = True
file_pattern = 'iceberg-train-*.tfrecord'
else:
num_epochs = 1
shuffle = False
if run_mode == mox.ModeKeys.EVAL:
num_samples = NUM_SAMPLES_EVAL
file_pattern = 'iceberg-eval-*.tfrecord'
else:
num_samples = NUM_SAMPLES_TEST
file_pattern = 'iceberg-test-*.tfrecord'
keys_to_features = {
'band_1':
tf.FixedLenFeature((75 * 75, ), tf.float32, default_value=None),
'band_2':
tf.FixedLenFeature((75 * 75, ), tf.float32, default_value=None),
'angle':
tf.FixedLenFeature([1], tf.float32, default_value=None),
}
items_to_handlers = {
'band_1': slim.tfexample_decoder.Tensor('band_1', shape=[75, 75]),
'band_2': slim.tfexample_decoder.Tensor('band_2', shape=[75, 75]),
'angle': slim.tfexample_decoder.Tensor('angle', shape=[])
}
if run_mode == mox.ModeKeys.PREDICT:
keys_to_features['id'] = tf.FixedLenFeature([1],
tf.string,
default_value=None)
items_to_handlers['id'] = slim.tfexample_decoder.Tensor('id',
shape=[])
else:
keys_to_features['label'] = tf.FixedLenFeature([1],
tf.int64,
default_value=None)
items_to_handlers['label'] = slim.tfexample_decoder.Tensor(
'label', shape=[])
dataset = mox.get_tfrecord(dataset_dir=flags.data_url,
file_pattern=file_pattern,
num_samples=num_samples,
keys_to_features=keys_to_features,
items_to_handlers=items_to_handlers,
num_epochs=num_epochs,
shuffle=shuffle)
if run_mode == mox.ModeKeys.PREDICT:
band_1, band_2, id_or_label, angle = dataset.get(
['band_1', 'band_2', 'id', 'angle'])
# Non-DMA safe string cannot tensor may not be copied to a GPU.
# So we encode string to a list of integer.
id_or_label = tf.py_func(
lambda str: np.array([ord(ch) for ch in str]), [id_or_label],
tf.int64)
# We know `id` is a string of 8 alphabets.
id_or_label = tf.reshape(id_or_label, shape=(8, ))
else:
band_1, band_2, id_or_label, angle = dataset.get(
['band_1', 'band_2', 'label', 'angle'])
band_3 = band_1 + band_2
# Rescale the input image to [0, 1]
def rescale(*args):
ret_images = []
for image in args:
image = tf.cast(image, tf.float32)
image_min = tf.reduce_min(image)
image_max = tf.reduce_max(image)
image = (image - image_min) / (image_max - image_min)
ret_images.append(image)
return ret_images
band_1, band_2, band_3 = rescale(band_1, band_2, band_3)
image = tf.stack([band_1, band_2, band_3], axis=2)
# Data augementation
if run_mode == mox.ModeKeys.TRAIN:
image = tf.image.random_flip_left_right(image)
image = tf.image.random_flip_up_down(image)
image = tf.image.rot90(image,
k=tf.random_uniform(shape=(),
maxval=3,
minval=0,
dtype=tf.int32))
return image, id_or_label, angle
def model_v1(images, angles, run_mode):
is_training = (run_mode == mox.ModeKeys.TRAIN)
# Conv Layer 1
x = Conv2D(64,
kernel_size=(3, 3),
activation='relu',
input_shape=(75, 75, 3))(images)
x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(x)
x = Dropout(0.2)(x, training=is_training)
# Conv Layer 2
x = Conv2D(128, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.2)(x, training=is_training)
# Conv Layer 3
x = Conv2D(128, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.2)(x, training=is_training)
# Conv Layer 4
x = Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(x)
x = Dropout(0.2)(x, training=is_training)
# Flatten the data for upcoming dense layers
x = Flatten()(x)
x = Concatenate()([x, angles])
# Dense Layers
x = Dense(512)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x, training=is_training)
# Dense Layer 2
x = Dense(256)(x)
x = Activation('relu')(x)
x = Dropout(0.2)(x, training=is_training)
# Sigmoid Layer
logits = Dense(2)(x)
return logits
def model_fn(inputs, run_mode, **kwargs):
# In train or eval, id_or_labels represents labels. In predict, id_or_labels represents id.
images, id_or_labels, angles = inputs
# Reshape angles from [batch_size] to [batch_size, 1]
angles = tf.expand_dims(angles, 1)
# Apply your version of model
logits = model_v1(images, angles, run_mode)
if run_mode == mox.ModeKeys.PREDICT:
logits = tf.nn.softmax(logits)
# clip logits to get lower loss value.
logits = tf.clip_by_value(logits,
clip_value_min=0.05,
clip_value_max=0.95)
model_spec = mox.ModelSpec(output_info={
'id': id_or_labels,
'logits': logits
})
else:
labels_one_hot = slim.one_hot_encoding(id_or_labels, 2)
loss = tf.losses.softmax_cross_entropy(
logits=logits,
onehot_labels=labels_one_hot,
label_smoothing=0.0,
weights=1.0)
model_spec = mox.ModelSpec(loss=loss, log_info={'loss': loss})
return model_spec
def output_fn(outputs):
global submission
for output in outputs:
for id, logits in zip(output['id'], output['logits']):
# Decode id from integer list to string.
id = ''.join([chr(ch) for ch in id])
# Get the probability of label==1
is_iceberg = logits[1]
df = pd.DataFrame([[id, is_iceberg]],
columns=['id', 'is_iceberg'])
submission = submission.append(df)
if flags.is_training:
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=mox.get_optimizer_fn(name='adam',
learning_rate=0.001),
run_mode=mox.ModeKeys.TRAIN,
batch_size=flags.batch_size,
log_dir=flags.train_url,
max_number_of_steps=steps_per_epoch * 150,
log_every_n_steps=20,
save_summary_steps=50,
save_model_secs=120)
else:
mox.run(input_fn=input_fn,
model_fn=model_fn,
run_mode=mox.ModeKeys.EVAL,
batch_size=5,
log_every_n_steps=1,
max_number_of_steps=int(NUM_SAMPLES_EVAL / 5),
checkpoint_path=flags.train_url)
mox.run(input_fn=input_fn,
output_fn=output_fn,
model_fn=model_fn,
run_mode=mox.ModeKeys.PREDICT,
batch_size=24,
max_number_of_steps=int(NUM_SAMPLES_TEST / 24),
log_every_n_steps=50,
output_every_n_steps=int(NUM_SAMPLES_TEST / 24),
checkpoint_path=flags.train_url)
# Write results to file. tf.gfile allow writing file to EBS/s3
submission_file = os.path.join(flags.train_url, 'submission.csv')
result = submission.to_csv(path_or_buf=None, index=False)
with tf.gfile.Open(submission_file, 'w') as f:
f.write(result)
_data_url = flags.data_url
_train_url = flags.train_url
if not mox.file.is_directory(_train_url):
mox.file.make_dirs(_train_url)
mox.file.make_dirs('/cache/data_url')
mox.file.make_dirs('/cache/train_url')
mox.file.copy_parallel(_data_url, '/cache/data_url')
mox.file.copy_parallel(_train_url, '/cache/train_url')
flags.data_url = '/cache/data_url'
flags.train_url = '/cache/train_url'
atexit.register(lambda: mox.file.copy_parallel('/cache/train_url', _train_url))
logger = logging.getLogger()
while logger.handlers:
logger.handlers.pop()
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
steps_per_epoch = int(
math.ceil(
float(NUM_SAMPLES_TRAIN) /
(flags.batch_size * num_gpus * num_workers)))
submission = pd.DataFrame(columns=['id', 'is_iceberg'])
def input_fn(run_mode, **kwargs):
if run_mode == mox.ModeKeys.TRAIN:
num_samples = NUM_SAMPLES_TRAIN
num_epochs = None
shuffle = True
file_pattern = 'iceberg-train-*.tfrecord'
else:
def input_fn(mode, **kwargs):
if not flags.synthetic:
ds_strategy_spec = []
ds_switch_steps = []
if flags.split_dataset_like_mxnet and mox.get_flag('job_name'):
if num_workers == 4:
file_pattern = 'train-*-of-*-node-%d-*-*' % task_index
elif num_workers == 8:
file_pattern = 'train-*-of-*-node-*-%d-*' % task_index
elif num_workers == 16:
file_pattern = 'train-*-of-*-node-*-*-%d' % task_index
else:
raise ValueError('num_workers should be 4, 8, 16')
else:
file_pattern = flags.file_pattern
for step, ims, bs in schduler:
# switch to next dataset 2 steps earlier because there are 2 pipeline prefetch queue
ds_switch_steps.append(step - 2)
if ims == 128:
ds_strategy_spec.append(
(os.path.join(imagenet_160_data,
file_pattern), bs, ims, 0.08))
elif ims == 224:
ds_strategy_spec.append(
(os.path.join(imagenet_data,
file_pattern), bs, ims, 0.087))
elif ims == 288:
ds_strategy_spec.append(
(os.path.join(imagenet_data,
file_pattern), bs, ims, 0.5))
else:
raise ValueError('image is not in [128, 224, 288]')
# The last stage of dataset does not need to be switched
ds_switch_steps.pop(-1)
tf.logging.info('Dataset will be switched at step: %s' %
ds_switch_steps)
dataset = ProgressiveImagenetDataset(
num_samples=flags.num_samples,
strategy_spec=ds_strategy_spec,
ds_switch_steps=ds_switch_steps,
shuffle=True,
num_parallel=flags.num_readers,
labels_offset=labels_offset,
private_num_threads=flags.private_num_threads,
shuffle_buffer_size=512 * 8 * 2)
image, label = dataset.get(['image', 'label'])
image_shape = tf.shape(image)[2]
batch_size = tf.shape(label)[0]
tf.summary.scalar(name='image_shape', tensor=image_shape)
tf.summary.scalar(name='batch_size', tensor=batch_size)
else:
import numpy as np
image = tf.constant(
np.random.randint(low=0,
high=255,
size=[flags.batch_size, 128, 128, 3],
dtype=np.uint8))
label = tf.constant(
np.random.randint(low=0,
high=999,
size=[flags.batch_size],
dtype=np.int64))
if flags.split_to_device:
input_spec = mox.InputSpec(split_to_device=True)
input_spec.new_input([image, label])
return input_spec
else:
return image, label
def main(*args, **kwargs):
if flags.use_controller:
convert_ps_to_controller()
job_name = mox.get_flag('job_name')
task_index = mox.get_flag('task_index')
if flags.local_cache == 'hard':
if flags.use_controller:
# In all-reduce mode, worker-0 does not download dataset (controller-0 will download).
imagenet_data, imagenet_160_data = download_dataset(
flags.data_url,
flags.data_url_160,
skip_download=(job_name == 'worker' and task_index == 0))
else:
# PS dose not download dataset.
imagenet_data, imagenet_160_data = download_dataset(
flags.data_url,
flags.data_url_160,
skip_download=(job_name == 'ps'))
log_dir = '/cache/cache-outputs'
else:
imagenet_data = flags.data_url
imagenet_160_data = flags.data_url_160
log_dir = flags.train_url
print('download dataset finish at %s' % time.time())
if (not job_name or
(job_name == 'worker' and task_index == 0)) and flags.train_url:
if not mox.file.is_directory(log_dir):
mox.file.make_dirs(log_dir)
else:
log_dir = None
model_meta = mox.get_model_meta(flags.model_name)
labels_offset = model_meta.default_labels_offset
num_workers = len(mox.get_flag('worker_hosts').split(','))
assert flags.bs_and_ims_strategy is not None
schduler = config_bs_ims(flags.bs_and_ims_strategy)
max_step = int(schduler[-1][0])
def input_fn(mode, **kwargs):
if not flags.synthetic:
ds_strategy_spec = []
ds_switch_steps = []
if flags.split_dataset_like_mxnet and mox.get_flag('job_name'):
if num_workers == 4:
file_pattern = 'train-*-of-*-node-%d-*-*' % task_index
elif num_workers == 8:
file_pattern = 'train-*-of-*-node-*-%d-*' % task_index
elif num_workers == 16:
file_pattern = 'train-*-of-*-node-*-*-%d' % task_index
else:
raise ValueError('num_workers should be 4, 8, 16')
else:
file_pattern = flags.file_pattern
for step, ims, bs in schduler:
# switch to next dataset 2 steps earlier because there are 2 pipeline prefetch queue
ds_switch_steps.append(step - 2)
if ims == 128:
ds_strategy_spec.append(
(os.path.join(imagenet_160_data,
file_pattern), bs, ims, 0.08))
elif ims == 224:
ds_strategy_spec.append(
(os.path.join(imagenet_data,
file_pattern), bs, ims, 0.087))
elif ims == 288:
ds_strategy_spec.append(
(os.path.join(imagenet_data,
file_pattern), bs, ims, 0.5))
else:
raise ValueError('image is not in [128, 224, 288]')
# The last stage of dataset does not need to be switched
ds_switch_steps.pop(-1)
tf.logging.info('Dataset will be switched at step: %s' %
ds_switch_steps)
dataset = ProgressiveImagenetDataset(
num_samples=flags.num_samples,
strategy_spec=ds_strategy_spec,
ds_switch_steps=ds_switch_steps,
shuffle=True,
num_parallel=flags.num_readers,
labels_offset=labels_offset,
private_num_threads=flags.private_num_threads,
shuffle_buffer_size=512 * 8 * 2)
image, label = dataset.get(['image', 'label'])
image_shape = tf.shape(image)[2]
batch_size = tf.shape(label)[0]
tf.summary.scalar(name='image_shape', tensor=image_shape)
tf.summary.scalar(name='batch_size', tensor=batch_size)
else:
import numpy as np
image = tf.constant(
np.random.randint(low=0,
high=255,
size=[flags.batch_size, 128, 128, 3],
dtype=np.uint8))
label = tf.constant(
np.random.randint(low=0,
high=999,
size=[flags.batch_size],
dtype=np.int64))
if flags.split_to_device:
input_spec = mox.InputSpec(split_to_device=True)
input_spec.new_input([image, label])
return input_spec
else:
return image, label
def model_fn(inputs, mode, **kwargs):
if not flags.gpu_synthetic:
if flags.split_to_device:
images, labels = inputs.get_input(0)
else:
images, labels = inputs
else:
import numpy as np
images = tf.constant(
np.random.randint(low=0,
high=255,
size=[flags.batch_size, 128, 128, 3],
dtype=np.uint8))
labels = tf.constant(
np.random.randint(low=0,
high=999,
size=[flags.batch_size],
dtype=np.int64))
if flags.fp16:
images = tf.cast(images, tf.float16)
def preprocess_fn(images, run_mode, *args):
images = images / 255.0
channels = tf.split(axis=3, num_or_size_splits=3, value=images)
for i in range(3):
channels[i] = (channels[i] - mean[i]) / std[i]
images = tf.concat(axis=3, values=channels)
if flags.data_format == 'NCHW':
images = tf.transpose(images, perm=(0, 3, 1, 2))
return images
model_kwargs = {}
if flags.model_name == 'resnet_v1_50_8k':
if flags.official_stride:
model_kwargs['official'] = True
if flags.fastai_initializer:
model_kwargs['weights_initializer_params'] = {
'factor': 2.0 / 1.3,
'mode': 'FAN_OUT'
}
mox_model_fn = mox.get_model_fn(name=flags.model_name,
run_mode=mode,
num_classes=1000,
preprocess_fn=preprocess_fn,
weight_decay=flags.weight_decay,
data_format=flags.data_format,
batch_norm_fused=True,
batch_renorm=False,
**model_kwargs)
logits, end_points = mox_model_fn(images)
labels_one_hot = slim.one_hot_encoding(labels, 1000)
loss = tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=labels_one_hot,
label_smoothing=0.0,
weights=1.0)
logits_fp32 = tf.cast(logits, tf.float32)
accuracy_top_1 = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits_fp32, labels, 1), tf.float32))
accuracy_top_5 = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits_fp32, labels, 5), tf.float32))
log_info = {
'ent_loss': loss,
'top-1': accuracy_top_1,
'top-5': accuracy_top_5
}
regularization_losses = mox.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
if len(regularization_losses
) > 0 and flags.use_optimizer != 'dymomentumw':
regularization_loss = tf.add_n(regularization_losses)
log_info['reg_loss'] = regularization_loss
loss = loss + regularization_loss
log_info['total_loss'] = loss
return mox.ModelSpec(loss=loss, log_info=log_info)
if flags.strict_sync_replicas:
mox.set_flag('sync_replicas', False)
mox.set_flag('chief_inc_global_step', True)
def optimizer_fn():
global_step = tf.train.get_or_create_global_step()
decay_end = 1.0 - flags.cooldown
if flags.use_lr_schedule == 'lcd':
lr = linear_cosine_decay(flags.max_lr, flags.min_lr, global_step,
max_step, flags.warmup, decay_end)
print("Using Linear Cosine Decay Schedule")
elif flags.use_lr_schedule == 'poly':
lr = polynomial_decay(flags.max_lr, flags.min_lr, global_step,
max_step, flags.warmup, decay_end)
print("Using Polynomial Decay Schedule")
else:
raise ValueError("lr schedule not provided")
if flags.use_optimizer == 'dymomentum':
opt = DyMomentumOptimizer(lr,
flags.max_lr,
flags.min_lr,
max_mom=flags.max_mom,
min_mom=flags.min_mom,
global_step=global_step,
max_iteration=max_step,
use_nesterov=flags.use_nesterov,
cooldown=flags.cooldown,
use_lars=flags.use_lars,
weight_decay=flags.weight_decay)
print("Using Dynamic Momentum Optimizer")
elif flags.use_optimizer == 'dymomentumw':
opt = DyMomentumWOptimizer(lr,
flags.max_lr,
flags.min_lr,
max_mom=flags.max_mom,
min_mom=flags.min_mom,
global_step=global_step,
max_iteration=max_step,
use_nesterov=flags.use_nesterov,
cooldown=flags.cooldown,
use_lars=flags.use_lars,
weight_decay=flags.weight_decay)
print("Using Dynamic MomentumW Optimizer")
else:
raise ValueError("Optimizer not provided")
tf.summary.scalar(name='momentum', tensor=opt.get_momentum())
if flags.strict_sync_replicas:
from moxing.tensorflow.optimizer.simple_sync_optimizer import SimpleSyncOptimizer
opt = SimpleSyncOptimizer(opt,
num_workers=num_workers,
task_index=task_index)
return opt
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=optimizer_fn,
run_mode=flags.run_mode,
batch_size=flags.batch_size,
max_number_of_steps=max_step,
log_every_n_steps=flags.log_every_n_steps,
log_dir=log_dir,
auto_batch=False,
save_summary_steps=flags.save_summary_steps,
checkpoint_path=flags.checkpoint_url,
save_model_secs=flags.save_model_secs)
print('upload model finish at %s' % time.time())
if flags.local_cache == 'hard' and log_dir:
mox.file.copy_parallel(log_dir, flags.train_url)
print('Training job finish at: %s' % time.time())
def main(_):
# 获取当前使用的GPU数量和节点数量
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
data_meta = mox.ImageClassificationRawMetadata(base_dir=flags.data_url)
def input_fn(mode):
# 创建一个数据增强方法，该方法基于resnet50论文实现
augmentation_fn = mox.get_data_augmentation_fn(name='resnet_v1_50',
run_mode=mode,
output_height=224,
output_width=224)
# 创建`数据集读取类`，并将数据增强方法传入，最多读取20个epoch
dataset = mox.ImageClassificationRawDataset(
data_meta,
batch_size=flags.batch_size,
num_epochs=20,
augmentation_fn=augmentation_fn)
image, label = dataset.get(['image', 'label'])
return image, label
def model_fn(inputs, mode):
images, labels = inputs
# 获取一个resnet50的模型，输入images，输入logits和end_points，这里不关心end_points，仅取logits
logits, _ = mox.get_model_fn(name='resnet_v1_50',
run_mode=mode,
num_classes=data_meta.num_classes,
weight_decay=0.00004)(images)
# 计算交叉熵损失值
labels_one_hot = slim.one_hot_encoding(labels, data_meta.num_classes)
loss = tf.losses.softmax_cross_entropy(logits=logits,
onehot_labels=labels_one_hot)
# 获取正则项损失值，并加到loss上，这里必须要用mox.get_collection代替tf.get_collection
regularization_losses = mox.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
regularization_loss = tf.add_n(regularization_losses)
loss = loss + regularization_loss
# 计算分类正确率
accuracy = tf.reduce_mean(
tf.cast(tf.nn.in_top_k(logits, labels, 1), tf.float32))
# 返回MoXing-TensorFlow用于定义模型的类ModelSpec
return mox.ModelSpec(loss=loss,
log_info={
'loss': loss,
'accuracy': accuracy
})
def optimizer_fn():
# 使用分段式学习率，0-10个epoch为0.01，10-20个epoch为0.001
lr = learning_rate_scheduler.piecewise_lr(
'10:0.01,20:0.001',
num_samples=data_meta.total_num_samples,
global_batch_size=flags.batch_size * num_gpus * num_workers)
return tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=optimizer_fn,
run_mode=mox.ModeKeys.TRAIN,
max_number_of_steps=sys.maxint,
log_dir=flags.train_url)
import numpy as np
import pandas as pd
import tensorflow as tf
import moxing.tensorflow as mox
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Dense
from tensorflow.python.keras.layers import Dropout, Flatten, Activation, Concatenate
slim = tf.contrib.slim
NUM_SAMPLES_TRAIN = 1176
NUM_SAMPLES_EVAL = 295
NUM_SAMPLES_TEST = 8424
tf.flags.DEFINE_integer('batch_size', 16, 'Mini-batch size')
tf.flags.DEFINE_string('data_url', 's3://zxy/model/zzy', 'Dir of dataset')
tf.flags.DEFINE_string('log_dir', 's3://zxy/model/zzy/log', 'Dir of log')
tf.flags.DEFINE_boolean('is_training', True, 'True for train. False for eval and predict.')
flags = tf.flags.FLAGS
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
steps_per_epoch = int(math.ceil(float(NUM_SAMPLES_TRAIN) / (flags.batch_size * num_gpus * num_workers)))
submission = pd.DataFrame(columns=['id', 'is_iceberg'])
def input_fn(run_mode, **kwargs):
if run_mode == mox.ModeKeys.TRAIN:
num_samples = NUM_SAMPLES_TRAIN
num_epochs = None
shuffle = True
file_pattern = 'iceberg-train-*.tfrecord'
else:
num_epochs = 1
shuffle = False
if run_mode == mox.ModeKeys.EVAL:
num_samples = NUM_SAMPLES_EVAL
file_pattern = 'iceberg-eval-*.tfrecord'
def main(*args, **kwargs):
import time
st = time.time()
num_gpus = mox.get_flag('num_gpus')
num_workers = len(mox.get_flag('worker_hosts').split(','))
exclude_list = ['global_step']
model_meta = mox.get_model_meta(flags.model_name)
exclude_list.append(model_meta.default_logits_pattern)
checkpoint_exclude_patterns = ','.join(exclude_list)
mox.set_flag('checkpoint_exclude_patterns', checkpoint_exclude_patterns)
data_meta = mox.ImageClassificationRawMetadata(base_dir=flags.data_url)
labels_list = data_meta.labels_list
mox.set_flag('loss_scale', 1024.0)
def input_fn(mode, **kwargs):
data_augmentation_fn = mox.get_data_augmentation_fn(name=flags.model_name,
run_mode=mode)
dataset = mox.ImageClassificationRawDataset(data_meta,
batch_size=flags.batch_size,
num_epochs=20,
augmentation_fn=data_augmentation_fn,
reader_class=mox.AsyncRawGenerator)
images, labels = dataset.get(['image', 'label'])
return images, labels
def model_fn(inputs, mode, **kwargs):
images, labels = inputs
# cpu cannot support model infer with `NCHW`, gpu support both
if mode == mox.ModeKeys.EXPORT:
data_format = 'NHWC'
else:
data_format = 'NCHW'
mox_model_fn = mox.get_model_fn(
name=flags.model_name,
run_mode=mode,
num_classes=data_meta.num_classes,
weight_decay=0.00004,
data_format=data_format,
batch_norm_fused=True)
images_fp16 = tf.cast(images, tf.float16)
with mox.var_scope(force_dtype=tf.float32):
logits, _ = mox_model_fn(images_fp16)
labels_one_hot = slim.one_hot_encoding(labels, data_meta.num_classes)
loss = tf.losses.softmax_cross_entropy(labels_one_hot, logits=logits)
regularization_losses = mox.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
regularization_loss = tf.add_n(regularization_losses)
loss = loss + regularization_loss
logits_fp32 = tf.cast(logits, tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.nn.in_top_k(logits_fp32, labels, 1), tf.float32))
export_spec = mox.ExportSpec(inputs_dict={'images': images},
outputs_dict={'logits': logits_fp32},
version='model')
return mox.ModelSpec(loss=loss,
log_info={'loss': loss, 'accuracy': accuracy},
export_spec=export_spec)
def optimizer_fn():
lr = learning_rate_scheduler.piecewise_lr('10:0.01,20:0.001',
num_samples=data_meta.total_num_samples,
global_batch_size=flags.batch_size * num_gpus * num_workers)
opt = tf.train.MomentumOptimizer(learning_rate=lr, momentum=0.9)
return opt
mox.run(input_fn=input_fn,
model_fn=model_fn,
optimizer_fn=optimizer_fn,
run_mode=mox.ModeKeys.TRAIN,
log_dir=flags.train_url,
checkpoint_path=flags.checkpoint_url,
max_number_of_steps=sys.maxint,
export_model=mox.ExportKeys.TF_SERVING)
# for model infer in ModelArts console
with mox.file.File(os.path.join(flags.train_url, 'model', 'labels.txt'), 'w') as f:
f.write('\n'.join(labels_list))
print(time.time() - st)