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cifar10-resnet-gn.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: cifar10-resnet.py
import argparse
import os
from tensorpack import *
from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
from tensorpack.utils.gpu import get_num_gpu
from tensorpack.dataflow import dataset
import tensorflow as tf
BATCH_SIZE = 128
NUM_UNITS = None
def GroupNorm(x, group, gamma_initializer=tf.constant_initializer(1.)):
"""
https://arxiv.org/abs/1803.08494
"""
shape = x.get_shape().as_list()
ndims = len(shape)
assert ndims == 4, shape
chan = shape[1]
assert chan % group == 0, chan
group_size = chan // group
orig_shape = tf.shape(x)
h, w = orig_shape[2], orig_shape[3]
x = tf.reshape(x, tf.stack([-1, group, group_size, h, w]))
mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
new_shape = [1, group, group_size, 1, 1]
beta = tf.get_variable('beta', [chan], initializer=tf.constant_initializer())
beta = tf.reshape(beta, new_shape)
gamma = tf.get_variable('gamma', [chan], initializer=gamma_initializer)
gamma = tf.reshape(gamma, new_shape)
out = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-5, name='output')
return tf.reshape(out, orig_shape, name='output')
@layer_register(use_scope=None)
def GNReLU(x, name=None):
x = GroupNorm(x, 8)
return tf.nn.relu(x, name=name)
class Model(ModelDesc):
def __init__(self, n):
super(Model, self).__init__()
self.n = n
def inputs(self):
return [tf.TensorSpec([None, 32, 32, 3], tf.float32, 'input'),
tf.TensorSpec([None], tf.int32, 'label')]
def build_graph(self, image, label):
image = image / 128.0
assert tf.test.is_gpu_available()
image = tf.transpose(image, [0, 3, 1, 2])
def residual(name, l, increase_dim=False, first=False):
shape = l.get_shape().as_list()
in_channel = shape[1]
if increase_dim:
out_channel = in_channel * 2
stride1 = 2
else:
out_channel = in_channel
stride1 = 1
with tf.variable_scope(name):
b1 = l if first else GNReLU(l)
c1 = Conv2D('conv1', b1, out_channel, strides=stride1, activation=GNReLU)
c2 = Conv2D('conv2', c1, out_channel)
if increase_dim:
l = AvgPooling('pool', l, 2)
l = tf.pad(l, [[0, 0], [in_channel // 2, in_channel // 2], [0, 0], [0, 0]])
l = c2 + l
return l
with argscope([Conv2D, AvgPooling, BatchNorm, GlobalAvgPooling], data_format='channels_first'), \
argscope(Conv2D, use_bias=False, kernel_size=3,
kernel_initializer=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
l = Conv2D('conv0', image, 16, activation=GNReLU)
l = residual('res1.0', l, first=True)
for k in range(1, self.n):
l = residual('res1.{}'.format(k), l)
# 32,c=16
l = residual('res2.0', l, increase_dim=True)
for k in range(1, self.n):
l = residual('res2.{}'.format(k), l)
# 16,c=32
l = residual('res3.0', l, increase_dim=True)
for k in range(1, self.n):
l = residual('res3.' + str(k), l)
l = GNReLU('bnlast', l)
# 8,c=64
l = GlobalAvgPooling('gap', l)
logits = FullyConnected('linear', l, 10)
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 = tf.to_float(tf.logical_not(tf.nn.in_top_k(logits, label, 1)), name='wrong_vector')
# monitor training error
add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
# weight decay on all W of fc layers
wd_w = tf.train.exponential_decay(0.0002, get_global_step_var(),
480000, 0.2, True)
wd_cost = tf.multiply(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
add_moving_summary(cost, wd_cost)
add_param_summary(('.*/W', ['histogram'])) # monitor W
return tf.add_n([cost, wd_cost], name='cost')
def optimizer(self):
lr = tf.get_variable('learning_rate', initializer=0.01, trainable=False)
opt = tf.train.MomentumOptimizer(lr, 0.9)
return opt
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.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 = MultiProcessRunner(ds, 3, 2)
return ds
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('-n', '--num_units',
help='number of units in each stage',
type=int, default=18)
parser.add_argument('--load', help='load model for training')
args = parser.parse_args()
NUM_UNITS = args.num_units
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
logger.set_logger_dir('train_log/GN-cifar')
dataset_train = get_data('train')
dataset_test = get_data('test')
config = TrainConfig(
model=Model(n=NUM_UNITS),
dataflow=dataset_train,
callbacks=[
ModelSaver(),
InferenceRunner(dataset_test,
[ScalarStats('cost'), ClassificationError('wrong_vector')]),
ScheduledHyperParamSetter('learning_rate',
[(1, 0.1), (82, 0.01), (123, 0.001), (300, 0.0002)])
],
max_epoch=400,
session_init=SaverRestore(args.load) if args.load else None
)
num_gpu = max(get_num_gpu(), 1)
launch_train_with_config(config, SyncMultiGPUTrainerParameterServer(num_gpu))