@ -25,7 +25,7 @@ import functools
import os
def vgg16_bn_drop ( input :
def vgg16_bn_drop ( input , is_train = True ):
def conv_block ( input , num_filter , groups , dropouts ) :
return fluid . nets . img_conv_group (
input = input ,
@ -46,13 +46,13 @@ def vgg16_bn_drop(input):
drop = fluid . layers . dropout ( x = conv5 , dropout_prob = 0.5 )
fc1 = fluid . layers . fc ( input = drop , size = 512 , act = None )
bn = fluid . layers . batch_norm ( input = fc1 , act = ' relu '
bn = fluid . layers . batch_norm ( input = fc1 , act = ' relu ' , is_test = not is_train )
drop2 = fluid . layers . dropout ( x = bn , dropout_prob = 0.5 )
fc2 = fluid . layers . fc ( input = drop2 , size = 512 , act = None )
return fc2
def get_model ( args :
def get_model ( args , is_train , main_prog , startup_prog ):
if args . data_set == " cifar10 " :
classdim = 10
if args . data_format == ' NCHW ' :
@ -65,57 +65,56 @@ def get_model(args):
data_shape = [ 3 , 224 , 224 ]
else :
data_shape = [ 224 , 224 , 3 ]
filelist = [
os . path . join ( args . data_path , f ) for f in os . listdir ( args . data_path )
]
with fluid . program_guard ( main_prog , startup_prog ) :
if args . use_reader_op :
data_file_handle = fluid . layers . open_files (
filenames = filelist ,
shapes = [ [ - 1 ] + data_shape , ( - 1 , 1 ) ] ,
lod_levels = [ 0 , 0 ] ,
dtypes = [ " float32 " , " int64 " ] ,
thread_num = 1 ,
pass_num = 1 )
data_file = fluid . layers . double_buffer (
fluid . layers . batch (
data_file_handle , batch_size = args . batch_size ) )
with fluid . unique_name . guard ( ) :
if args . use_reader_op :
images , label = fluid . layers . read_file ( data_file )
else :
images = fluid . layers . data (
name = ' data ' , shape = data_shape , dtype = ' float32 ' )
label = fluid . layers . data (
name = ' label ' , shape = [ 1 ] , dtype = ' int64 ' )
# Train program
net = vgg16_bn_drop ( images , is_train = is_train )
predict = fluid . layers . fc ( input = net , size = classdim , act = ' softmax ' )
cost = fluid . layers . cross_entropy ( input = predict , label = label )
avg_cost = fluid . layers . mean ( x = cost )
if args . use_reader_op :
filelist = [
os . path . join ( args . data_path , f ) for f in os . listdir ( args . data_path )
]
data_file = fluid . layers . open_files (
filenames = filelist ,
shapes = [ [ - 1 ] + data_shape , ( - 1 , 1 ) ] ,
lod_levels = [ 0 , 0 ] ,
dtypes = [ " float32 " , " int64 " ] ,
thread_num = args . gpus ,
pass_num = args . pass_num )
data_file = fluid . layers . double_buffer (
fluid . layers . batch (
data_file , batch_size = args . batch_size ) )
images , label = fluid . layers . read_file ( data_file )
else :
images = fluid . layers . data (
name = ' data ' , shape = data_shape , dtype = ' float32 ' )
label = fluid . layers . data ( name = ' label ' , shape = [ 1 ] , dtype = ' int64 ' )
# Train program
net = vgg16_bn_drop ( images )
predict = fluid . layers . fc ( input = net , size = classdim , act = ' softmax ' )
cost = fluid . layers . cross_entropy ( input = predict , label = label )
avg_cost = fluid . layers . mean ( x = cost )
# Evaluator
batch_size_tensor = fluid . layers . create_tensor ( dtype = ' int64 ' )
batch_acc = fluid . layers . accuracy (
input = predict , label = label , total = batch_size_tensor )
# inference program
inference_program = fluid . default_main_program ( ) . clone ( )
with fluid . program_guard ( inference_program ) :
inference_program = fluid . io . get_inference_program (
target_vars = [ batch_acc , batch_size_tensor ] )
# Optimization
optimizer = fluid . optimizer . Adam ( learning_rate = args . learning_rate )
# Evaluator
batch_size_tensor = fluid . layers . create_tensor ( dtype = ' int64 ' )
batch_acc = fluid . layers . accuracy (
input = predict , label = label , total = batch_size_tensor )
# Optimization
if is_train :
optimizer = fluid . optimizer . Adam (
learning_rate = args . learning_rate )
optimizer . minimize ( avg_cost )
# data reader
train_reader = paddle . batch (
if is_train :
reader = paddle . dataset . cifar . train10 ( ) \
if args . data_set == ' cifar10 ' else paddle . dataset . flowers . train ( )
else :
reader = paddle . dataset . cifar . test10 ( ) \
if args . data_set == ' cifar10 ' else paddle . dataset . flowers . test ( )
batched_reader = paddle . batch (
paddle . reader . shuffle (
paddle . dataset . cifar . train10 ( )
if args . data_set == ' cifar10 ' else paddle . dataset . flowers . train ( ) ,
buf_size = 5120 ) ,
reader , buf_size = 5120 ) ,
batch_size = args . batch_size * args . gpus )
test_reader = paddle . batch (
paddle . dataset . cifar . test10 ( )
if args . data_set == ' cifar10 ' else paddle . dataset . flowers . test ( ) ,
batch_size = args . batch_size )
return avg_cost , inference_program, optimizer , train_reader , test_reader , batch_acc
return avg_cost , optimizer , [ batch_acc ] , batched_reader , data_file_handle
sneaxiy
7 years ago