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main_child_trainer.py
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main_child_trainer.py
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import os
import shutil
import sys
import time
import tensorflow as tf
import numpy as np
from utils import Logger
from utils import DEFINE_boolean
from utils import DEFINE_float
from utils import DEFINE_integer
from utils import DEFINE_string
from utils import print_user_flags
import data_utils
from micro_controller import MicroController
from micro_child import MicroChild
flags = tf.app.flags
FLAGS = flags.FLAGS
################## YOU Should write under parameter ######################
DEFINE_string("output_dir", "./output" , "")
DEFINE_string("train_data_dir", "./data/train", "")
DEFINE_string("val_data_dir", "./data/valid", "")
DEFINE_string("test_data_dir", "./data/test", "")
DEFINE_integer("channel",1, "MNIST: 1, Cifar10: 3")
DEFINE_integer("img_size", 32, "enlarge image size")
DEFINE_integer("n_aug_img",3 , "if 2: num_img: 55000 -> aug_img: 110000, elif 1: False")
DEFINE_float("child_lr_min", 0.00001, "for lr schedule")
DEFINE_string("child_fixed_arc", "1 2 1 3 0 1 0 4 1 1 1 1 0 1 0 1 1 0 0 1 0 1 0 4 1 0 2 0 0 3 1 1 0 0 0 0 4 1 1 0", "")
##########################################################################
DEFINE_boolean("reset_output_dir", True, "Delete output_dir if exists.")
DEFINE_string("data_format","NHWC", "'NHWC or NCHW'")
DEFINE_string("search_for", "micro","")
DEFINE_integer("batch_size",128,"")
DEFINE_integer("num_epochs",630," = (10 + 20 + 40 + 80 + 160 +320)")
DEFINE_integer("child_lr_dec_every", 100, "")
DEFINE_integer("child_num_layers", 15, "Number of layer. IN this case we will calculate 4 conv and 2 pooling layers")
DEFINE_integer("child_num_cells", 5, "child_num_cells +2 = Number of DAG'S Nodes")
DEFINE_integer("child_filter_size", 5, "")
DEFINE_integer("child_out_filters", 16, "")
DEFINE_integer("child_out_filters_scale", 1, "")
DEFINE_integer("child_num_branches", 5, "It should be same with number of kernel operation to calculate.")
DEFINE_integer("child_num_aggregate", None, "")
DEFINE_integer("child_num_replicas", 1, "")
DEFINE_integer("child_block_size", 3, "")
DEFINE_integer("child_lr_T_0", 10, "for lr schedule")
DEFINE_integer("child_lr_T_mul", 2, "for lr schedule")
DEFINE_integer("child_cutout_size", None, "CutOut size")
DEFINE_float("child_grad_bound", 5.0, "Gradient clipping")
DEFINE_float("child_lr", 0.1, "")
DEFINE_float("child_lr_dec_rate", 0.1, "")
DEFINE_float("child_keep_prob", 0.8, "")
DEFINE_float("child_drop_path_keep_prob", 0.6, "minimum drop_path_keep_prob")
DEFINE_float("child_l2_reg", 2e-4, "")
DEFINE_float("child_lr_max", 0.05, "for lr schedule")
DEFINE_string("child_skip_pattern", None, "Must be ['dense', None]")
DEFINE_boolean("child_use_aux_heads", True, "Should we use an aux head")
DEFINE_boolean("child_sync_replicas", False, "To sync or not to sync.")
DEFINE_boolean("child_lr_cosine", True, "Use cosine lr schedule")
DEFINE_float("controller_lr", 0.001, "")
DEFINE_float("controller_lr_dec_rate", 1.0, "")
DEFINE_float("controller_keep_prob", 0.5, "")
DEFINE_float("controller_l2_reg", 0.0, "")
DEFINE_float("controller_bl_dec", 0.99, "")
DEFINE_float("controller_tanh_constant", 1.50, "")
DEFINE_float("controller_op_tanh_reduce", 2.5, "")
DEFINE_float("controller_temperature", None, "")
DEFINE_float("controller_entropy_weight", 0.0001, "")
DEFINE_float("controller_skip_target", 0.8, "")
DEFINE_float("controller_skip_weight", 0.0, "")
DEFINE_integer("controller_num_aggregate", 10, "")
DEFINE_integer("controller_num_replicas", 1, "")
DEFINE_integer("controller_train_steps", 50, "")
DEFINE_integer("controller_forwards_limit", 2, "")
DEFINE_integer("controller_train_every", 1,"train the controller after this number of epochs")
DEFINE_boolean("controller_search_whole_channels", True, "")
DEFINE_boolean("controller_sync_replicas", True, "To sync or not to sync.")
DEFINE_boolean("controller_training", False, "")
DEFINE_boolean("controller_use_critic", False, "")
DEFINE_integer("log_every", 50, "How many steps to log")
DEFINE_integer("eval_every_epochs", 1, "How many epochs to eval")
channel = FLAGS.channel
def get_ops(images, labels):
"""
Args:
images: dict with keys {"train", "valid", "test"}.
labels: dict with keys {"train", "valid", "test"}.
"""
ControllerClass = MicroController
ChildClass = MicroChild
child_model = ChildClass(
images,
labels,
use_aux_heads=FLAGS.child_use_aux_heads,
cutout_size=FLAGS.child_cutout_size,
whole_channels=FLAGS.controller_search_whole_channels,
num_layers=FLAGS.child_num_layers,
num_cells=FLAGS.child_num_cells,
num_branches=FLAGS.child_num_branches,
fixed_arc=FLAGS.child_fixed_arc,
out_filters_scale=FLAGS.child_out_filters_scale,
out_filters=FLAGS.child_out_filters,
keep_prob=FLAGS.child_keep_prob,
drop_path_keep_prob=FLAGS.child_drop_path_keep_prob,
num_epochs=FLAGS.num_epochs,
l2_reg=FLAGS.child_l2_reg,
data_format=FLAGS.data_format,
batch_size=FLAGS.batch_size,
clip_mode="norm",
grad_bound=FLAGS.child_grad_bound,
lr_init=FLAGS.child_lr,
lr_dec_every=FLAGS.child_lr_dec_every,
lr_dec_rate=FLAGS.child_lr_dec_rate,
lr_cosine=FLAGS.child_lr_cosine,
lr_max=FLAGS.child_lr_max,
lr_min=FLAGS.child_lr_min,
lr_T_0=FLAGS.child_lr_T_0,
lr_T_mul=FLAGS.child_lr_T_mul,
optim_algo="momentum",
sync_replicas=FLAGS.child_sync_replicas,
num_aggregate=FLAGS.child_num_aggregate,
num_replicas=FLAGS.child_num_replicas,
channel=FLAGS.channel)
if FLAGS.child_fixed_arc is None:
controller_model = ControllerClass(
search_for=FLAGS.search_for,
search_whole_channels=FLAGS.controller_search_whole_channels,
skip_target=FLAGS.controller_skip_target,
skip_weight=FLAGS.controller_skip_weight,
num_cells=FLAGS.child_num_cells,
num_layers=FLAGS.child_num_layers,
num_branches=FLAGS.child_num_branches,
out_filters=FLAGS.child_out_filters,
lstm_size=64,
lstm_num_layers=1,
lstm_keep_prob=1.0,
tanh_constant=FLAGS.controller_tanh_constant,
op_tanh_reduce=FLAGS.controller_op_tanh_reduce,
temperature=FLAGS.controller_temperature,
lr_init=FLAGS.controller_lr,
lr_dec_start=0,
lr_dec_every=1000000, # never decrease learning rate
l2_reg=FLAGS.controller_l2_reg,
entropy_weight=FLAGS.controller_entropy_weight,
bl_dec=FLAGS.controller_bl_dec,
use_critic=FLAGS.controller_use_critic,
optim_algo="adam",
sync_replicas=FLAGS.controller_sync_replicas,
num_aggregate=FLAGS.controller_num_aggregate,
num_replicas=FLAGS.controller_num_replicas)
child_model.connect_controller(controller_model)
controller_model.build_trainer(child_model)
controller_ops = {
"train_step": controller_model.train_step,
"loss": controller_model.loss,
"train_op": controller_model.train_op,
"lr": controller_model.lr,
"grad_norm": controller_model.grad_norm,
"valid_acc": controller_model.valid_acc,
"optimizer": controller_model.optimizer,
"baseline": controller_model.baseline,
"entropy": controller_model.sample_entropy,
"sample_arc": controller_model.sample_arc,
"skip_rate": controller_model.skip_rate,
}
else:
assert not FLAGS.controller_training, (
"--child_fixed_arc is given, cannot train controller")
child_model.connect_controller(None)
controller_ops = None
child_ops = {
"global_step": child_model.global_step,
"loss": child_model.loss,
"train_op": child_model.train_op,
"lr": child_model.lr,
"grad_norm": child_model.grad_norm,
"train_acc": child_model.train_acc,
"optimizer": child_model.optimizer,
"num_train_batches": child_model.num_train_batches,
}
ops = {
"child": child_ops,
"controller": controller_ops,
"eval_every": child_model.num_train_batches * FLAGS.eval_every_epochs,
"eval_func": child_model.eval_once,
"num_train_batches": child_model.num_train_batches,
}
return ops
def train():
images, labels = data_utils.read_data(FLAGS.train_data_dir,
FLAGS.val_data_dir,
FLAGS.test_data_dir,
FLAGS.channel,
FLAGS.img_size,
FLAGS.n_aug_img)
n_data = np.shape(images["train"])[0]
print("Number of training data: %d" % (n_data))
g = tf.Graph()
with g.as_default():
ops =get_ops(images, labels)
child_ops = ops["child"]
controller_ops = ops["controller"]
saver = tf.train.Saver(max_to_keep=2)
checkpoint_saver_hook = tf.train.CheckpointSaverHook(
FLAGS.output_dir, save_steps=child_ops["num_train_batches"], saver=saver)
hooks = [checkpoint_saver_hook]
if FLAGS.child_sync_replicas:
sync_replicas_hook = child_ops["optimizer"].make_session_run_hook(True)
hooks.append(sync_replicas_hook)
if FLAGS.controller_training and FLAGS.controller_sync_replicas:
sync_replicas_hook = controller_ops["optimizer"].make_session_run_hook(True)
hooks.append(sync_replicas_hook)
print("-" * 80)
print("Starting session")
config = tf.ConfigProto(allow_soft_placement=True)
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
start_time = time.time()
while True:
run_ops = [
child_ops["loss"],
child_ops["lr"],
child_ops["grad_norm"],
child_ops["train_acc"],
child_ops["train_op"]]
loss, lr, gn, tr_acc, _ = sess.run(run_ops)
global_step = sess.run(child_ops["global_step"])
if FLAGS.child_sync_replicas:
actual_step = global_step * FLAGS.num_aggregate
else:
actual_step = global_step
epoch = actual_step // ops["num_train_batches"]
curr_time = time.time()
if global_step % FLAGS.log_every == 0:
log_string = ""
log_string += "epoch = {:<6d}".format(epoch)
log_string += "ch_step = {:<6d}".format(global_step)
log_string += " loss = {:<8.6f}".format(loss)
log_string += " lr = {:<8.4f}".format(lr)
log_string += " |g| = {:<8.4f}".format(gn)
log_string += " tr_acc = {:<3d}/{:>3d}".format(
tr_acc, FLAGS.batch_size)
log_string += " mins = {:<10.2f}".format(
float(curr_time - start_time) / 60)
print(log_string)
if actual_step % ops["eval_every"] == 0:
if (FLAGS.controller_training and
epoch % FLAGS.controller_train_every == 0):
print("Epoch {}: Training controller".format(epoch))
for ct_step in range(FLAGS.controller_train_steps *
FLAGS.controller_num_aggregate):
run_ops = [
controller_ops["loss"],
controller_ops["entropy"],
controller_ops["lr"],
controller_ops["grad_norm"],
controller_ops["valid_acc"],
controller_ops["baseline"],
controller_ops["skip_rate"],
controller_ops["train_op"],
]
loss, entropy, lr, gn, val_acc, bl, skip, _ = sess.run(run_ops)
controller_step = sess.run(controller_ops["train_step"])
if ct_step % FLAGS.log_every == 0:
curr_time = time.time()
log_string = ""
log_string += "ctrl_step = {:<6d}".format(controller_step)
log_string += " loss = {:<7.3f}".format(loss)
log_string += " ent = {:<5.2f}".format(entropy)
log_string += " lr = {:<6.4f}".format(lr)
log_string += " |g| = {:<8.4f}".format(gn)
log_string += " acc = {:<6.4f}".format(val_acc)
log_string += " bl = {:<5.2f}".format(bl)
log_string += " mins = {:<.2f}".format(
float(curr_time - start_time) / 60)
print(log_string)
print("Here are 10 architectures")
for _ in range(10):
arc, acc = sess.run([
controller_ops["sample_arc"],
controller_ops["valid_acc"],
])
if FLAGS.search_for == "micro":
normal_arc, reduce_arc = arc
print(np.reshape(normal_arc, [-1]))
print(np.reshape(reduce_arc, [-1]))
else:
start = 0
for layer_id in range(FLAGS.child_num_layers):
if FLAGS.controller_search_whole_channels:
end = start + 1 + layer_id
else:
end = start + 2 * FLAGS.child_num_branches + layer_id
print(np.reshape(arc[start: end], [-1]))
start = end
print("val_acc = {:<6.4f}".format(acc))
print("-" * 80)
print("Epoch {}: Eval".format(epoch))
if FLAGS.child_fixed_arc is None:
ops["eval_func"](sess, "valid")
ops["eval_func"](sess, "test")
if epoch >= FLAGS.num_epochs:
break
def main(_):
print("-" * 80)
if not os.path.isdir(FLAGS.output_dir):
print("Path {} does not exist. Creating.".format(FLAGS.output_dir))
os.makedirs(FLAGS.output_dir)
elif FLAGS.reset_output_dir:
print("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
print("-" * 80)
log_file = os.path.join(FLAGS.output_dir, "stdout")
print("Logging to {}".format(log_file))
sys.stdout = Logger(log_file)
print_user_flags()
train()
if __name__ == "__main__":
tf.app.run()