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cifar10-densenet.py
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cifar10-densenet.py
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#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import numpy as np
import tensorflow as tf
import argparse
import os
from tensorpack import *
from tensorpack.tfutils.symbolic_functions import *
from tensorpack.tfutils.summary import *
"""
CIFAR10 DenseNet example. See: http://arxiv.org/abs/1608.06993
Code is developed based on Yuxin Wu's ResNet implementation: https://github.com/ppwwyyxx/tensorpack/tree/master/examples/ResNet
Results using DenseNet (L=40, K=12) on Cifar10 with data augmentation: ~5.77% test error.
Running time:
On one TITAN X GPU (CUDA 7.5 and cudnn 5.1), the code should run ~5iters/s on a batch size 64.
"""
BATCH_SIZE = 64
class Model(ModelDesc):
def __init__(self, depth):
super(Model, self).__init__()
self.N = int((depth - 4) / 3)
self.growthRate =12
def _get_inputs(self):
return [InputDesc(tf.float32, [None, 32, 32, 3], 'input'),
InputDesc(tf.int32, [None], 'label')
]
def _build_graph(self, input_vars):
image, label = input_vars
image = image / 128.0 - 1
def conv(name, l, channel, stride):
return Conv2D(name, l, channel, 3, stride=stride,
nl=tf.identity, use_bias=False,
W_init=tf.random_normal_initializer(stddev=np.sqrt(2.0/9/channel)))
def add_layer(name, l):
shape = l.get_shape().as_list()
in_channel = shape[3]
with tf.variable_scope(name) as scope:
c = BatchNorm('bn1', l)
c = tf.nn.relu(c)
c = conv('conv1', c, self.growthRate, 1)
l = tf.concat([c, l], 3)
return l
def add_transition(name, l):
shape = l.get_shape().as_list()
in_channel = shape[3]
with tf.variable_scope(name) as scope:
l = BatchNorm('bn1', l)
l = tf.nn.relu(l)
l = Conv2D('conv1', l, in_channel, 1, stride=1, use_bias=False, nl=tf.nn.relu)
l = AvgPooling('pool', l, 2)
return l
def dense_net(name):
l = conv('conv0', image, 16, 1)
with tf.variable_scope('block1') as scope:
for i in range(self.N):
l = add_layer('dense_layer.{}'.format(i), l)
l = add_transition('transition1', l)
with tf.variable_scope('block2') as scope:
for i in range(self.N):
l = add_layer('dense_layer.{}'.format(i), l)
l = add_transition('transition2', l)
with tf.variable_scope('block3') as scope:
for i in range(self.N):
l = add_layer('dense_layer.{}'.format(i), l)
l = BatchNorm('bnlast', l)
l = tf.nn.relu(l)
l = GlobalAvgPooling('gap', l)
logits = FullyConnected('linear', l, out_dim=10, nl=tf.identity)
return logits
logits = dense_net("dense_net")
prob = tf.nn.softmax(logits, name='output')
cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
cost = tf.reduce_mean(cost, name='cross_entropy_loss')
wrong = prediction_incorrect(logits, label)
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
# weight decay on all W
wd_cost = tf.multiply(1e-4, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
add_moving_summary(cost, wd_cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
self.cost = tf.add_n([cost, wd_cost], name='cost')
def _get_optimizer(self):
lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
tf.summary.scalar('learning_rate', lr)
return tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
def get_data(train_or_test):
isTrain = train_or_test == 'train'
ds = dataset.Cifar10(train_or_test)
pp_mean = ds.get_per_pixel_mean()
if isTrain:
augmentors = [
imgaug.CenterPaste((40, 40)),
imgaug.RandomCrop((32, 32)),
imgaug.Flip(horiz=True),
#imgaug.Brightness(20),
#imgaug.Contrast((0.6,1.4)),
imgaug.MapImage(lambda x: x - pp_mean),
]
else:
augmentors = [
imgaug.MapImage(lambda x: x - pp_mean)
]
ds = AugmentImageComponent(ds, augmentors)
ds = BatchData(ds, BATCH_SIZE, remainder=not isTrain)
if isTrain:
ds = PrefetchData(ds, 3, 2)
return ds
def get_config():
log_dir = 'train_log/cifar10-single-fisrt%s-second%s-max%s' % (str(args.drop_1), str(args.drop_2), str(args.max_epoch))
logger.set_logger_dir(log_dir, action='n')
# prepare dataset
dataset_train = get_data('train')
steps_per_epoch = dataset_train.size()
dataset_test = get_data('test')
return TrainConfig(
dataflow=dataset_train,
callbacks=[
ModelSaver(),
InferenceRunner(dataset_test,
[ScalarStats('cost'), ClassificationError()]),
ScheduledHyperParamSetter('learning_rate',
[(1, 0.1), (args.drop_1, 0.01), (args.drop_2, 0.001)])
],
model=Model(depth=args.depth),
steps_per_epoch=steps_per_epoch,
max_epoch=args.max_epoch,
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.') # nargs='*' in multi mode
parser.add_argument('--load', help='load model')
parser.add_argument('--drop_1',default=150, help='Epoch to drop learning rate to 0.01.') # nargs='*' in multi mode
parser.add_argument('--drop_2',default=225,help='Epoch to drop learning rate to 0.001')
parser.add_argument('--depth',default=40, help='The depth of densenet')
parser.add_argument('--max_epoch',default=300,help='max epoch')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
nr_tower = 0
if args.gpu:
nr_tower = len(args.gpu.split(','))
# SyncMultiGPUTrainer(config).train()
launch_train_with_config(config, SyncMultiGPUTrainer(nr_tower))