# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py
import os, sys, time, random, argparse
import tensorflow as tf
from pathlib import Path
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
# self-lib
from tf_models import get_cell_based_tiny_net
from tf_optimizers import SGDW, AdamW
from config_utils import dict2config
from log_utils import time_string
from models import CellStructure
def pre_process(image_a, label_a, image_b, label_b):
def standard_func(image):
x = tf.pad(image, [[4, 4], [4, 4], [0, 0]])
x = tf.image.random_crop(x, [32, 32, 3])
x = tf.image.random_flip_left_right(x)
return x
return standard_func(image_a), label_a, standard_func(image_b), label_b
def main(xargs):
cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
# Add a channels dimension
all_indexes = list(range(x_train.shape[0]))
random.shuffle(all_indexes)
s_train_idxs, s_valid_idxs = all_indexes[::2], all_indexes[1::2]
search_train_x, search_train_y = x_train[s_train_idxs], y_train[s_train_idxs]
search_valid_x, search_valid_y = x_train[s_valid_idxs], y_train[s_valid_idxs]
#x_train, x_test = x_train[..., tf.newaxis], x_test[..., tf.newaxis]
# Use tf.data
#train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(64)
search_ds = tf.data.Dataset.from_tensor_slices((search_train_x, search_train_y, search_valid_x, search_valid_y))
search_ds = search_ds.map(pre_process).shuffle(1000).batch(64)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
# Create an instance of the model
config = dict2config({'name': 'GDAS',
'C' : xargs.channel, 'N': xargs.num_cells, 'max_nodes': xargs.max_nodes,
'num_classes': 10, 'space': 'nas-bench-201', 'affine': True}, None)
model = get_cell_based_tiny_net(config)
#import pdb; pdb.set_trace()
#model.build(((64, 32, 32, 3), (1,)))
#for x in model.trainable_variables:
# print('{:30s} : {:}'.format(x.name, x.shape))
# Choose optimizer
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
w_optimizer = SGDW(learning_rate=xargs.w_lr, weight_decay=xargs.w_weight_decay, momentum=xargs.w_momentum, nesterov=True)
a_optimizer = AdamW(learning_rate=xargs.arch_learning_rate, weight_decay=xargs.arch_weight_decay, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
#w_optimizer = tf.keras.optimizers.SGD(learning_rate=0.025, momentum=0.9, nesterov=True)
#a_optimizer = tf.keras.optimizers.AdamW(learning_rate=xargs.arch_learning_rate, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
####
# metrics
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
valid_loss = tf.keras.metrics.Mean(name='valid_loss')
valid_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='valid_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
@tf.function
def search_step(train_images, train_labels, valid_images, valid_labels, tf_tau):
# optimize weights
with tf.GradientTape() as tape:
predictions = model(train_images, tf_tau, True)
w_loss = loss_object(train_labels, predictions)
net_w_param = model.get_weights()
gradients = tape.gradient(w_loss, net_w_param)
w_optimizer.apply_gradients(zip(gradients, net_w_param))
train_loss(w_loss)
train_accuracy(train_labels, predictions)
# optimize alphas
with tf.GradientTape() as tape:
predictions = model(valid_images, tf_tau, True)
a_loss = loss_object(valid_labels, predictions)
net_a_param = model.get_alphas()
gradients = tape.gradient(a_loss, net_a_param)
a_optimizer.apply_gradients(zip(gradients, net_a_param))
valid_loss(a_loss)
valid_accuracy(valid_labels, predictions)
# TEST
@tf.function
def test_step(images, labels):
predictions = model(images)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
print('{:} start searching with {:} epochs ({:} batches per epoch).'.format(time_string(), xargs.epochs, tf.data.experimental.cardinality(search_ds).numpy()))
for epoch in range(xargs.epochs):
# Reset the metrics at the start of the next epoch
train_loss.reset_states() ; train_accuracy.reset_states()
test_loss.reset_states() ; test_accuracy.reset_states()
cur_tau = xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (xargs.epochs-1)
tf_tau = tf.cast(cur_tau, dtype=tf.float32, name='tau')
for trn_imgs, trn_labels, val_imgs, val_labels in search_ds:
search_step(trn_imgs, trn_labels, val_imgs, val_labels, tf_tau)
genotype = model.genotype()
genotype = CellStructure(genotype)
#for test_images, test_labels in test_ds:
# test_step(test_images, test_labels)
template = '{:} Epoch {:03d}/{:03d}, Train-Loss: {:.3f}, Train-Accuracy: {:.2f}%, Valid-Loss: {:.3f}, Valid-Accuracy: {:.2f}% | tau={:.3f}'
print(template.format(time_string(), epoch+1, xargs.epochs,
train_loss.result(),
train_accuracy.result()*100,
valid_loss.result(),
valid_accuracy.result()*100,
cur_tau))
print('{:} genotype : {:}\n{:}\n'.format(time_string(), genotype, model.get_np_alphas()))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NAS-Bench-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# training details
parser.add_argument('--epochs' , type=int , default= 250 , help='')
parser.add_argument('--tau_max' , type=float, default= 10 , help='')
parser.add_argument('--tau_min' , type=float, default= 0.1 , help='')
parser.add_argument('--w_lr' , type=float, default= 0.025, help='')
parser.add_argument('--w_weight_decay' , type=float, default=0.0005, help='')
parser.add_argument('--w_momentum' , type=float, default= 0.9 , help='')
parser.add_argument('--arch_learning_rate', type=float, default=0.0003, help='')
parser.add_argument('--arch_weight_decay' , type=float, default=0.001, help='')
# marco structure
parser.add_argument('--channel' , type=int , default=16, help='')
parser.add_argument('--num_cells' , type=int , default= 5, help='')
parser.add_argument('--max_nodes' , type=int , default= 4, help='')
args = parser.parse_args()
main( args )