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train.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import time
import shutil
import argparse
import functools
import numpy as np
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
import paddle.fluid as fluid
from model import network_cifar as network
import genotypes
import reader
import utility
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(utility.add_arguments, argparser=parser)
# yapf: disable
add_arg('use_multiprocess', bool, True, "Whether use multiprocess reader.")
add_arg('num_workers', int, 4, "The multiprocess reader number.")
add_arg('data', str, 'dataset/cifar10',"The dir of dataset.")
add_arg('batch_size', int, 96, "Minibatch size.")
add_arg('learning_rate', float, 0.025, "The start learning rate.")
add_arg('momentum', float, 0.9, "Momentum.")
add_arg('weight_decay', float, 3e-4, "Weight_decay.")
add_arg('use_gpu', bool, True, "Whether use GPU.")
add_arg('epochs', int, 600, "Epoch number.")
add_arg('init_channels', int, 36, "Init channel number.")
add_arg('layers', int, 20, "Total number of layers.")
add_arg('class_num', int, 10, "Class number of dataset.")
add_arg('trainset_num', int, 50000, "images number of trainset.")
add_arg('model_save_dir', str, 'eval_cifar10', "The path to save model.")
add_arg('cutout', bool, True, 'Whether use cutout.')
add_arg('cutout_length', int, 16, "Cutout length.")
add_arg('auxiliary', bool, True, 'Use auxiliary tower.')
add_arg('auxiliary_weight', float, 0.4, "Weight for auxiliary loss.")
add_arg('drop_path_prob', float, 0.2, "Drop path probability.")
add_arg('grad_clip', float, 5, "Gradient clipping.")
add_arg('image_shape', str, "3,32,32", "Input image size")
add_arg('arch', str, 'DARTS_PADDLE', "Which architecture to use")
add_arg('report_freq', int, 50, 'Report frequency')
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
# yapf: enable
def build_program(main_prog, startup_prog, is_train, args):
image_shape = [int(m) for m in args.image_shape.split(",")]
num_cells = 4
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
image = fluid.data(
name="image", shape=[None] + image_shape, dtype="float32")
label = fluid.data(name="label", shape=[None, 1], dtype="int64")
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=64,
use_double_buffer=True,
iterable=True)
drop_path_prob = None
drop_path_mask = None
if args.drop_path_prob > 0 and is_train:
drop_path_prob = fluid.data(
name="drop_path_prob",
shape=[args.batch_size, 1],
dtype="float32")
drop_path_mask = fluid.data(
name="drop_path_mask",
shape=[args.batch_size, args.layers, num_cells, 2],
dtype="float32")
genotype = eval("genotypes.%s" % args.arch)
do_drop_path = args.drop_path_prob > 0
logits, logits_aux = network(
x=image,
is_train=is_train,
c_in=args.init_channels,
num_classes=args.class_num,
layers=args.layers,
auxiliary=args.auxiliary,
genotype=genotype,
do_drop_path=do_drop_path,
drop_prob=drop_path_prob,
drop_path_mask=drop_path_mask,
name='model')
top1 = fluid.layers.accuracy(input=logits, label=label, k=1)
top5 = fluid.layers.accuracy(input=logits, label=label, k=5)
loss = fluid.layers.reduce_mean(
fluid.layers.softmax_with_cross_entropy(logits, label))
if is_train:
if args.auxiliary:
loss_aux = fluid.layers.reduce_mean(
fluid.layers.softmax_with_cross_entropy(logits_aux,
label))
loss = loss + args.auxiliary_weight * loss_aux
step_per_epoch = int(args.trainset_num / args.batch_size)
learning_rate = fluid.layers.cosine_decay(
args.learning_rate, step_per_epoch, args.epochs)
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=args.grad_clip)
optimizer = fluid.optimizer.MomentumOptimizer(
learning_rate,
args.momentum,
regularization=fluid.regularizer.L2DecayRegularizer(
args.weight_decay),
grad_clip=clip)
optimizer.minimize(loss)
outs = [loss, top1, top5, learning_rate]
else:
outs = [loss, top1, top5]
return outs, data_loader
def train(main_prog, exe, epoch_id, train_loader, fetch_list, args):
loss = utility.AvgrageMeter()
top1 = utility.AvgrageMeter()
top5 = utility.AvgrageMeter()
for step_id, data in enumerate(train_loader()):
devices_num = len(data)
if args.drop_path_prob > 0:
feed = []
for device_id in range(devices_num):
image = data[device_id]['image']
label = data[device_id]['label']
num_cells = 4
drop_path_prob = np.array(
[[args.drop_path_prob * epoch_id / args.epochs]
for i in range(args.batch_size)]).astype(np.float32)
drop_path_mask = 1 - np.random.binomial(
1,
drop_path_prob[0],
size=[args.batch_size, args.layers, num_cells, 2]).astype(
np.float32)
feed.append({
"image": image,
"label": label,
"drop_path_prob": drop_path_prob,
"drop_path_mask": drop_path_mask
})
else:
feed = data
loss_v, top1_v, top5_v, lr = exe.run(
main_prog, feed=feed, fetch_list=[v.name for v in fetch_list])
loss.update(loss_v, args.batch_size)
top1.update(top1_v, args.batch_size)
top5.update(top5_v, args.batch_size)
if step_id % args.report_freq == 0:
logger.info(
"Train Epoch {}, Step {}, Lr {:.8f}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}".
format(epoch_id, step_id, lr[0], loss.avg[0], top1.avg[0],
top5.avg[0]))
return top1.avg[0]
def valid(main_prog, exe, epoch_id, valid_loader, fetch_list, args):
loss = utility.AvgrageMeter()
top1 = utility.AvgrageMeter()
top5 = utility.AvgrageMeter()
for step_id, data in enumerate(valid_loader()):
loss_v, top1_v, top5_v = exe.run(
main_prog, feed=data, fetch_list=[v.name for v in fetch_list])
loss.update(loss_v, args.batch_size)
top1.update(top1_v, args.batch_size)
top5.update(top5_v, args.batch_size)
if step_id % args.report_freq == 0:
logger.info(
"Valid Epoch {}, Step {}, loss {:.6f}, acc_1 {:.6f}, acc_5 {:.6f}".
format(epoch_id, step_id, loss.avg[0], top1.avg[0], top5.avg[
0]))
return top1.avg[0]
def main(args):
devices = os.getenv("CUDA_VISIBLE_DEVICES") or ""
devices_num = len(devices.split(","))
is_shuffle = True
startup_prog = fluid.Program()
train_prog = fluid.Program()
test_prog = fluid.Program()
train_fetch_list, train_loader = build_program(
main_prog=train_prog,
startup_prog=startup_prog,
is_train=True,
args=args)
valid_fetch_list, valid_loader = build_program(
main_prog=test_prog,
startup_prog=startup_prog,
is_train=False,
args=args)
logger.info("param size = {:.6f}MB".format(
utility.count_parameters_in_MB(train_prog.global_block()
.all_parameters(), 'model')))
test_prog = test_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
train_reader = reader.train_valid(
batch_size=args.batch_size,
is_train=True,
is_shuffle=is_shuffle,
args=args)
valid_reader = reader.train_valid(
batch_size=args.batch_size, is_train=False, is_shuffle=False, args=args)
places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
train_loader.set_batch_generator(train_reader, places=places)
valid_loader.set_batch_generator(valid_reader, places=place)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 4 * devices_num
build_strategy = fluid.BuildStrategy()
if args.with_mem_opt:
for i in range(len(train_fetch_list)):
train_fetch_list[i].persistable = True
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
parallel_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=train_fetch_list[0].name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
test_prog = fluid.CompiledProgram(test_prog)
def save_model(postfix, program):
model_path = os.path.join(args.model_save_dir, postfix)
if os.path.isdir(model_path):
shutil.rmtree(model_path)
logger.info('save models to %s' % (model_path))
fluid.io.save_persistables(exe, model_path, main_program=program)
best_acc = 0
for epoch_id in range(args.epochs):
train_top1 = train(parallel_train_prog, exe, epoch_id, train_loader,
train_fetch_list, args)
logger.info("Epoch {}, train_acc {:.6f}".format(epoch_id, train_top1))
valid_top1 = valid(test_prog, exe, epoch_id, valid_loader,
valid_fetch_list, args)
if valid_top1 > best_acc:
best_acc = valid_top1
save_model('cifar10_model', train_prog)
logger.info("Epoch {}, valid_acc {:.6f}, best_valid_acc {:.6f}".format(
epoch_id, valid_top1, best_acc))
if __name__ == '__main__':
args = parser.parse_args()
utility.print_arguments(args)
utility.check_cuda(args.use_gpu)
main(args)