/
search_arc_cifar.py
executable file
·372 lines (336 loc) · 15.9 KB
/
search_arc_cifar.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
import os
import sys
import shutil
import sys
import time
import tensorflow as tf
import numpy as np
import keras.backend as K
from enas.utils import Logger
from enas.utils import DEFINE_boolean
from enas.utils import DEFINE_float
from enas.utils import DEFINE_integer
from enas.utils import DEFINE_string
from enas.utils import print_user_flags
import enas.data_utils
from enas.micro_controller import MicroController
from enas.micro_child import MicroChild
from enas.data_utils_cifar import read_data
flags = tf.app.flags
FLAGS = flags.FLAGS
# ----- Experiment Settings -----
DEFINE_string("output_dir", "./output-cifar-search" , "")
DEFINE_string("data_path", "./data/cifar10", "")
DEFINE_integer("channel",3, "MNIST: 1, Cifar10: 3")
DEFINE_integer("img_size", 32, "enlarge image size")
DEFINE_integer("n_aug_img",1 , "if 2: num_img: 55000 -> aug_img: 110000, elif 1: False")
DEFINE_boolean("reset_output_dir", True, "Delete output_dir if exists.")
# ------------------------------
# ----Child Model Settings-----
DEFINE_string("data_format","NHWC", "'NHWC or NCHW'")
DEFINE_string("search_for", "micro","")
DEFINE_integer("batch_size",128,"")
DEFINE_integer("num_epochs", 150," = (10+ 20+ 40+ 80)")
DEFINE_integer("child_lr_dec_every", 100, "")
DEFINE_integer("child_num_layers", 3, "Number of layer. IN this case we will calculate 4 conv and 2 pooling layers")
DEFINE_integer("child_num_cells", 3, "child_num_cells +2 = Number of DAG'S Nodes")
DEFINE_integer("child_filter_size", 5, "")
DEFINE_integer("child_out_filters", 20, "")
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", 1, "0.9")
DEFINE_float("child_drop_path_keep_prob", 1, "minimum drop_path_keep_prob = 0.6")
DEFINE_float("child_l2_reg", 0, "")
DEFINE_float("child_lr_max", 0.05, "for lr schedule")
DEFINE_float("child_lr_min", 0.0005, "for lr schedule")
DEFINE_string("child_skip_pattern", None, "Must be ['dense', None]")
DEFINE_string("child_fixed_arc", 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")
# --------------------------
# ------ Controller Settings ------
DEFINE_float("controller_lr", 0.0035, "")
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", 1.10, "")
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", 30, "")
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", True, "")
DEFINE_boolean("controller_use_critic", False, "")
# --------------------------
# ------ Logger Settings ------
DEFINE_integer("log_every", 25, "How many steps to log")
DEFINE_integer("eval_every_epochs", 1, "How many epochs to eval")
# --------------------------
channel = FLAGS.channel
def print_all_vars():
for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES ):
print(i)
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="adam",
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():
if FLAGS.child_fixed_arc is None:
images, labels = read_data(FLAGS.data_path)
else:
images, labels = read_data(FLAGS.data_path, num_valids=0)
print("shape images train: ", images['train'].shape)
print("shape labels train: ", labels['train'].shape)
print("shape images test: ", images['test'].shape)
print("shape labels test: ", labels['test'].shape)
print("shape images valid: ", images['valid'].shape)
print("shape labels valid: ", labels['valid'].shape)
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)
config = tf.ConfigProto(allow_soft_placement=True)
print_all_vars()
print("-" * 80)
print("Starting session")
with tf.train.SingularMonitoredSession(
config=config, hooks=hooks, checkpoint_dir=FLAGS.output_dir) as sess:
K.set_session(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()