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cifar10-sparsenet-bc.py
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cifar10-sparsenet-bc.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 *
from tensorpack.utils.gpu import get_nr_gpu
from tensorpack.callbacks import *
from tensorpack import logger, QueueInput, InputDesc, PlaceholderInput, TowerContext
from tensorpack.models import *
class Model(ModelDesc):
def __init__(self,k, path,num_block1, num_block2, num_block3,num_block4):
super(Model, self).__init__()
#self.N = int(layers_per_block)
self.growthRate = int(k)
self.num_path = int(path)
self.input_channel = 2*self.growthRate
self.num_block1 = int(num_block1)
self.num_block2 = int(num_block2)
self.num_block3 = int(num_block3)
self.num_block4 = int(num_block4)
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, nl=tf.identity):
return Conv2D(name, l, channel, 3, stride=stride,
nl=nl, use_bias=True,
W_init=tf.random_normal_initializer(stddev=np.sqrt(2.0/9/channel)))
def add_layer(name, l, block_idx):
with tf.variable_scope(name) as scope:
shape = l.get_shape().as_list()
in_channel = shape[3]
curr_growthRate = int(self.growthRate)
curr_input_channel = int(self.input_channel)
curr_num_block = (in_channel-curr_input_channel)/curr_growthRate + 1
with tf.variable_scope('bn2'):
c = tf.contrib.layers.batch_norm(l, decay=0.9, scale=True, is_training = get_current_tower_context().is_training, updates_collections=None, reuse=None)
#c = tf.nn.relu(c)
#c = conv('conv2', c, curr_growthRate, 1)
#bc_channel = (curr_num_block+1)//2*curr_growthRate
#bc_channel = curr_growthRate//2*curr_num_block
bc_channel = 4*self.growthRate
# if block_idx == 1:
# bc_channel = 12
# elif block_idx == 2:
# bc_channel = 24
c = Conv2D('conv2', c, bc_channel , 1, stride=1, use_bias=True, nl=tf.identity)
#c = BatchNorm('bn1', l)
with tf.variable_scope('bn1'):
c = tf.contrib.layers.batch_norm(c, decay=0.9, scale=True, is_training = get_current_tower_context().is_training, updates_collections=None, reuse=None)
c = tf.nn.relu(c)
c = conv('conv1', c, curr_growthRate, 1)
# print('curr_growthRate: %d' %(curr_growthRate))
# print('curr_input_channel: %d' %(curr_input_channel))
# print('in_channel %d' %(in_channel))
# print('curr_num_block %d' %((in_channel-curr_input_channel)/curr_growthRate + 1))
if(in_channel-curr_input_channel)%curr_growthRate != 0:
return
if curr_num_block > self.num_path:
split1, _, split2 = tf.split(l, [int(round(curr_num_block/2*curr_growthRate)), int(curr_growthRate), int(in_channel-curr_growthRate -round(curr_num_block/2*curr_growthRate))],3)
#split1, _, split2 = tf.split(l, [curr_num_block/2*curr_growthRate, curr_growthRate, in_channel-curr_growthRate -curr_num_block/2*curr_growthRate],3)
l = tf.concat([c, split1, split2],3)
else:
l = tf.concat([c, l], 3)
return l
def add_transition(name, l, idx):
shape = l.get_shape().as_list()
in_channel = shape[3]
curr_growthRate = int(self.growthRate)
curr_input_channel = int(self.input_channel)
curr_num_block = (in_channel-curr_input_channel)/curr_growthRate + 1
next_growthRate = int(self.growthRate)
next_input_channel = int(self.input_channel)
#out_channel = next_input_channel + (curr_num_block-1)*next_growthRate
#out_channel = next_input_channel
#out_channel = in_channel
# print("next_input_channel: %d" %(next_input_channel))
# print("next_growthRate: %d" %(next_growthRate))
#print("curr_num_block: %d" %(curr_num_block))
#print("in_channel: %d" %(in_channel))
# print("curr_growthRate: %d" %(curr_growthRate))
# print("curr_input_channel: %d" %(curr_input_channel))
out_channel=0
if curr_num_block%2 == 0:
out_channel = next_input_channel + (curr_num_block)*next_growthRate//2
else:
out_channel = next_input_channel + (curr_num_block-1)*next_growthRate//2
with tf.variable_scope(name) as scope:
#l = BatchNorm('bn1', l)
with tf.variable_scope('bn1'):
l = tf.contrib.layers.batch_norm(l, decay=0.9, scale=True, is_training = get_current_tower_context().is_training, updates_collections=None, reuse=None)
l = tf.nn.relu(l)
l = Conv2D('conv1', l, out_channel, 1, stride=1, use_bias=True, nl=tf.identity)
l = AvgPooling('pool', l, 2)
return l
def dense_net(name):
total = 0
l = conv('conv0', image, self.input_channel, 1, nl=tf.nn.relu)
with tf.variable_scope("blcok1") as scope:
for i in range(self.num_block1):
l = add_layer('dense_layer.{}'.format(i), l, 0)
l = add_transition("trasition1", l, 0)
with tf.variable_scope("block2") as scope:
for i in range(self.num_block2):
l = add_layer('dense_layer.{}'.format(i), l, 1)
l = add_transition("trasition2", l, 1)
with tf.variable_scope("block3") as scope:
for i in range(self.num_block3):
l = add_layer('dense_layer.{}'.format(i), l, 2)
#l = BatchNorm('bnlast', l)
with tf.variable_scope('bnlast'):
l = tf.contrib.layers.batch_norm(l, decay=0.9, scale=True, is_training = get_current_tower_context().is_training, updates_collections=None, reuse=None)
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')
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
wd_cost = tf.multiply(1e-4, l2, 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()
pc_mean = np.array([125.3, 123.0, 113.9])
pc_std = np.array([63.3, 62.1, 66.7])
if isTrain:
augmentors = [
imgaug.MapImage(lambda x: (x - pc_mean)/(pc_std)),
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 - pc_mean)/(pc_std))
#imgaug.MapImage(lambda x: x - pp_mean)
]
ds = AugmentImageComponent(ds, augmentors)
nr_tower = args.gpu.split(',')
BATCH_SIZE = 64
BATCH_SIZE /= len(nr_tower)
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-bc-k[%d]-path[%d]-[%d-%d-%d-%d]-' % ( int(args.k), int(args.path), int(args.block1), int(args.block2),int(args.block3), int(args.block4))
logger.set_logger_dir(log_dir, action='n')
# prepare dataset
dataset_train = get_data('train')
dataset_test = get_data('test')
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
# config = tf.ConfigProto(allow_soft_placement = True,gpu_options=gpu_options)
# config.gpu_options.allow_growth=True
# config.gpu_options.per_process_gpu_memory_fraction = 0.4
callbacks = []
callbacks.append(ModelSaver())
nr_tower = len(args.gpu.split(','))
print('nr_tower = {}'.format(nr_tower))
steps_per_epoch = dataset_train.size()//nr_tower
if nr_tower == 1:
# single-GPU inference with queue prefetch
callbacks.append(InferenceRunner(dataset_test,
[ScalarStats('cost'), ClassificationError()]))
else:
# multi-GPU inference (with mandatory queue prefetch)
callbacks.append(DataParallelInferenceRunner(
dataset_test, [ScalarStats('cost'), ClassificationError()], list(range(nr_tower))))
#callbacks.append(InferenceRunner(dataset_test,
#[ScalarStats('cost',prefix="testing"), ClassificationError(summary_name='validataion_error1')]))
# callbacks.append(DataParallelInferenceRunner(
# dataset_test, [ScalarStats('cost'), ClassificationError()], list(range(nr_tower))))
callbacks.append(ScheduledHyperParamSetter('learning_rate', [(0, 0.1), (args.drop_1, 0.01), (args.drop_2, 0.001),(args.drop_3, 0.0002)]))
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),(args.drop_2, 0.0001)])
# ],
callbacks=callbacks,
model=Model(args.k, args.path, args.block1, args.block2, args.block3,args.block4),
steps_per_epoch=steps_per_epoch,
max_epoch=args.max_epoch,
#session_config = config,
nr_tower=nr_tower,
)
if __name__ == '__main__':
#BATCH_SIZE = 64
parser = argparse.ArgumentParser()
parser.add_argument('--gpu',default='0', 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=200,help='Epoch to drop learning rate to 0.001')
parser.add_argument('--drop_3',default=250,help='Epoch to drop learning rate to 0.0002')
parser.add_argument('--max_epoch',default=280,help='max epoch')
parser.add_argument('--k', default=8, help='number of output feature maps for each dense block')
parser.add_argument('--path', default=7,help='number of paths to each layer')#12
parser.add_argument('--block1', default=4, help='number of layers for the first block')
parser.add_argument('--block2', default=6, help='number of layers for the second block')
parser.add_argument('--block3', default=8, help='number of layers for the third block')
parser.add_argument('--block4', default=0, help='number of layers for the fourth block')
parser.add_argument('--flops', action='store_true', help='print flops and exit')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
config = get_config()
#config.gpu_options.allow_growth = True
if args.load:
config.session_init = SaverRestore(args.load)
if args.gpu:
config.nr_tower = len(args.gpu.split(','))
#config.nr_tower=max(get_nr_gpu(), 1),
#SyncMultiGPUTrainer(config).train()
#SyncMultiGPUTrainerParameterServer(config).train()
if args.flops:
# manually build the graph with batch=1
input_desc = [InputDesc(tf.float32, [1, 32, 32, 3], 'input'),
InputDesc(tf.int32, [1], 'label')
]
# input_desc = [
# InputDesc(tf.float32, [1, 224, 224, 3], 'input'),
# InputDesc(tf.int32, [1], 'label')
# ]
input = PlaceholderInput()
input.setup(input_desc)
with TowerContext('', is_training=True):
config.model.build_graph(*input.get_input_tensors())
tf.profiler.profile(
tf.get_default_graph(),
cmd='op',
options=tf.profiler.ProfileOptionBuilder.float_operation())
#sess.run(run_metadata=tf.RunMetadata())
else:
trainer = SyncMultiGPUTrainerParameterServer(len(args.gpu.split(',')))
launch_train_with_config(config, trainer)