def get_trainer(self):
# 数据大小
datadim = 3 * 32 * 32
# 获得图片对于的信息标签
lbl = paddle.layer.data(name="label",
type=paddle.data_type.integer_value(10))
# 获取全连接层,也就是分类器
out = mobile_net(datadim, 10, 1.0)
# 获得损失函数
cost = paddle.layer.classification_cost(input=out, label=lbl)
# 使用之前保存好的参数文件获得参数
# parameters = self.get_parameters(parameters_path="../model/mobile_net.tar.gz")
# 使用损失函数生成参数
parameters = self.get_parameters(cost=cost)
'''
定义优化方法
learning_rate 迭代的速度
momentum 跟前面动量优化的比例
regularzation 正则化,防止过拟合
'''
momentum_optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
learning_rate=0.1 / 128.0,
learning_rate_decay_a=0.1,
learning_rate_decay_b=50000 * 100,
learning_rate_schedule="discexp")
'''
创建训练器
cost 分类器
parameters 训练参数,可以通过创建,也可以使用之前训练好的参数
update_equation 优化方法
'''
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=momentum_optimizer)
return trainer
# coding=utf-8
from paddle.utils.merge_model import merge_v2_model
# 导入神经网络
from mobilenet import mobile_net
from vgg import vgg_bn_drop
if __name__ == "__main__":
# 图像的大小
img_size = 3 * 32 * 32
# 总分类数
class_dim = 10
net = mobile_net(img_size, class_dim)
param_file = '../model/mobile_net.tar.gz'
output_file = '../model/mobile_net.paddle'
merge_v2_model(net, param_file, output_file)
def train_parallel_exe(args,
learning_rate,
batch_size,
num_passes,
init_model=None,
pretrained_model=None,
model_save_dir='model',
parallel=True,
use_nccl=True,
lr_strategy=None,
layers=50):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if args.model is 'se_resnext':
out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
else:
out = mobile_net(img=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
avg_cost = fluid.layers.mean(x=cost)
test_program = fluid.default_main_program().clone(for_test=True)
if "piecewise_decay" in lr_strategy:
bd = lr_strategy["piecewise_decay"]["bd"]
lr = lr_strategy["piecewise_decay"]["lr"]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(boundaries=bd,
values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
elif "cosine_decay" in lr_strategy:
step_each_epoch = lr_strategy["cosine_decay"]["step_each_epoch"]
epochs = lr_strategy["cosine_decay"]["epochs"]
optimizer = fluid.optimizer.Momentum(
learning_rate=cosine_decay(learning_rate=learning_rate,
step_each_epoch=step_each_epoch,
epochs=epochs),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
else:
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
opts = optimizer.minimize(avg_cost)
if args.with_mem_opt:
fluid.memory_optimize(fluid.default_main_program())
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
fluid.default_startup_program.random_seed = 1000
exe.run(fluid.default_startup_program())
if init_model is not None:
fluid.io.load_persistables(exe, init_model)
if pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(pretrained_model, var.name))
fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
train_reader = paddle.batch(flowers.train(), batch_size=batch_size)
test_reader = paddle.batch(flowers.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
test_exe = fluid.ParallelExecutor(use_cuda=True,
main_program=test_program,
share_vars_from=train_exe)
fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]
train_speed = []
for pass_id in range(num_passes):
train_info = [[], [], []]
test_info = [[], [], []]
pass_time = 0
pass_num = 0
pass_speed = 0.0
for batch_id, data in enumerate(train_reader()):
t1 = time.time()
loss, acc1, acc5 = train_exe.run(fetch_list,
feed=feeder.feed(data))
t2 = time.time()
period = t2 - t1
pass_time += period
pass_num += len(data)
loss = np.mean(np.array(loss))
acc1 = np.mean(np.array(acc1))
acc5 = np.mean(np.array(acc5))
train_info[0].append(loss)
train_info[1].append(acc1)
train_info[2].append(acc5)
if batch_id % 10 == 0:
print("Pass {0}, trainbatch {1}, loss {2}, \
acc1 {3}, acc5 {4} time {5}"
.format(pass_id, \
batch_id, loss, acc1, acc5, \
"%2.2f sec" % period))
sys.stdout.flush()
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
pass_speed = pass_num / pass_time
train_speed.append(pass_speed)
if pass_id == num_passes - 1:
train_acc_top1_kpi.add_record(train_acc1)
train_acc_top5_kpi.add_record(train_acc5)
train_cost_kpi.add_record(train_loss)
mean_pass_speed = np.array(pass_speed).mean()
train_speed_kpi.add_record(mean_pass_speed)
for data in test_reader():
t1 = time.time()
loss, acc1, acc5 = test_exe.run(fetch_list, feed=feeder.feed(data))
t2 = time.time()
period = t2 - t1
loss = np.mean(np.array(loss))
acc1 = np.mean(np.array(acc1))
acc5 = np.mean(np.array(acc5))
test_info[0].append(loss)
test_info[1].append(acc1)
test_info[2].append(acc5)
if batch_id % 10 == 0:
print("Pass {0},testbatch {1},loss {2}, \
acc1 {3},acc5 {4},time {5}"
.format(pass_id, \
batch_id, loss, acc1, acc5, \
"%2.2f sec" % period))
sys.stdout.flush()
test_loss = np.array(test_info[0]).mean()
test_acc1 = np.array(test_info[1]).mean()
test_acc5 = np.array(test_info[2]).mean()
print("End pass {0}, train_loss {1}, train_acc1 {2}, train_acc5 {3}, \
test_loss {4}, test_acc1 {5}, test_acc5 {6}, pass_time {7}, train_speed {8}"
.format(pass_id, \
train_loss, train_acc1, train_acc5, test_loss, test_acc1, \
test_acc5, pass_time, pass_num / pass_time))
sys.stdout.flush()
train_acc_top1_kpi.persist()
train_acc_top5_kpi.persist()
train_cost_kpi.persist()
train_speed_kpi.persist()
def main():
datadim = 3 * 224 * 224
classdim = 102
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1, gpu_id=1)
momentum_optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * BATCH),
learning_rate=0.005 / BATCH,
#learning_rate_decay_a=0.1,
#learning_rate_decay_b=50000 * 50,
learning_rate_schedule='constant')
image = paddle.layer.data(name="image",
type=paddle.data_type.dense_vector(datadim))
net = mobile_net(image)
# option 2. vgg
#net = vgg_bn_drop(image)
out = paddle.layer.fc(input=net,
size=classdim,
act=paddle.activation.Softmax())
'''
out = paddle.layer.img_conv(
input=net,
filter_size=1,
num_filters=classdim,
stride=1,
act=paddle.activation.Linear())
'''
lbl = paddle.layer.data(name="label",
type=paddle.data_type.integer_value(classdim))
cost = paddle.layer.classification_cost(input=out, label=lbl)
# Create parameters
parameters = paddle.parameters.create(cost)
with gzip.open('Paddle_mobilenet.tar.gz', 'r') as f:
fparameters = paddle.parameters.Parameters.from_tar(f)
for param_name in fparameters.names():
if param_name in parameters.names():
parameters.set(param_name, fparameters.get(param_name))
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 50 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
# save parameters
with gzip.open('mobilenet_params_pass_%d.tar.gz' % event.pass_id,
'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(
paddle.dataset.flowers.test(), batch_size=10),
feeding={
'image': 0,
'label': 1
})
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# Create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=momentum_optimizer)
trainer.train(reader=paddle.batch(paddle.reader.shuffle(
paddle.dataset.flowers.train(), buf_size=50000),
batch_size=BATCH),
num_passes=200,
event_handler=event_handler,
feeding={
'image': 0,
'label': 1
})
def train_parallel_do(args,
learning_rate,
batch_size,
num_passes,
init_model=None,
pretrained_model=None,
model_save_dir='model',
parallel=True,
use_nccl=True,
lr_strategy=None,
layers=50):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if parallel:
places = fluid.layers.device.get_places()
pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl)
with pd.do():
image_ = pd.read_input(image)
label_ = pd.read_input(label)
if args.model is 'se_resnext':
out = SE_ResNeXt(input=image_,
class_dim=class_dim,
layers=layers)
else:
out = mobile_net(img=image_, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label_)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label_, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label_, k=5)
pd.write_output(avg_cost)
pd.write_output(acc_top1)
pd.write_output(acc_top5)
avg_cost, acc_top1, acc_top5 = pd()
avg_cost = fluid.layers.mean(x=avg_cost)
acc_top1 = fluid.layers.mean(x=acc_top1)
acc_top5 = fluid.layers.mean(x=acc_top5)
else:
if args.model is 'se_resnext':
out = SE_ResNeXt(input=image, class_dim=class_dim, layers=layers)
else:
out = mobile_net(img=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
inference_program = fluid.default_main_program().clone(for_test=True)
if "piecewise_decay" in lr_strategy:
bd = lr_strategy["piecewise_decay"]["bd"]
lr = lr_strategy["piecewise_decay"]["lr"]
optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(boundaries=bd,
values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
elif "cosine_decay" in lr_strategy:
step_each_epoch = lr_strategy["cosine_decay"]["step_each_epoch"]
epochs = lr_strategy["cosine_decay"]["epochs"]
optimizer = fluid.optimizer.Momentum(
learning_rate=cosine_decay(learning_rate=learning_rate,
step_each_epoch=step_each_epoch,
epochs=epochs),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
else:
optimizer = fluid.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
opts = optimizer.minimize(avg_cost)
if args.with_mem_opt:
fluid.memory_optimize(fluid.default_main_program())
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
if init_model is not None:
fluid.io.load_persistables(exe, init_model)
if pretrained_model:
def if_exist(var):
return os.path.exists(os.path.join(pretrained_model, var.name))
fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
train_reader = paddle.batch(reader.train(), batch_size=batch_size)
test_reader = paddle.batch(reader.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
for pass_id in range(num_passes):
train_info = [[], [], []]
test_info = [[], [], []]
for batch_id, data in enumerate(train_reader()):
t1 = time.time()
loss, acc1, acc5 = exe.run(
fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
t2 = time.time()
period = t2 - t1
train_info[0].append(loss[0])
train_info[1].append(acc1[0])
train_info[2].append(acc5[0])
if batch_id % 10 == 0:
print("Pass {0}, trainbatch {1}, loss {2}, \
acc1 {3}, acc5 {4} time {5}"
.format(pass_id, \
batch_id, loss[0], acc1[0], acc5[0], \
"%2.2f sec" % period))
sys.stdout.flush()
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
for data in test_reader():
t1 = time.time()
loss, acc1, acc5 = exe.run(
inference_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
t2 = time.time()
period = t2 - t1
test_info[0].append(loss[0])
test_info[1].append(acc1[0])
test_info[2].append(acc5[0])
if batch_id % 10 == 0:
print("Pass {0},testbatch {1},loss {2}, \
acc1 {3},acc5 {4},time {5}"
.format(pass_id, \
batch_id, loss[0], acc1[0], acc5[0], \
"%2.2f sec" % period))
sys.stdout.flush()
test_loss = np.array(test_info[0]).mean()
test_acc1 = np.array(test_info[1]).mean()
test_acc5 = np.array(test_info[2]).mean()
print("End pass {0}, train_loss {1}, train_acc1 {2}, train_acc5 {3}, \
test_loss {4}, test_acc1 {5}, test_acc5 {6}"
.format(pass_id, \
train_loss, train_acc1, train_acc5, test_loss, test_acc1, \
test_acc5))
sys.stdout.flush()
model_path = os.path.join(model_save_dir + '/' + args.model,
str(pass_id))
if not os.path.isdir(model_path):
os.makedirs(model_path)
fluid.io.save_persistables(exe, model_path)
import cv2
import numpy as np
from keras.applications.resnet50 import decode_predictions
from mobilenet import mobile_net, preprocess_input
# Set up model
mobilenet = mobile_net(input_size=(224, 224, 3), include_top=True)
mobilenet.load_weights(
'../../weights/feature_extractors/mobilenet_1_0_224_tf.h5')
mobilenet.summary()
def predict_one_example():
# Prepare data input
img_path = '../../test_imgs/cat.jpg'
img = cv2.imread(img_path)
img = cv2.resize(img, (224, 224))
x = preprocess_input(img)
x = np.expand_dims(x, axis=0)
preds = mobilenet.predict(x)
print('Predicted:', decode_predictions(preds, top=3)[0])
# Measure performance
import timeit
performance = timeit.repeat('predict_one_example()',
"from __main__ import predict_one_example",
number=1,
repeat=50)
lab = np.argsort(-probs)
# 返回概率最大的值和其对应的概率值
return lab[0][0], probs[0][(lab[0][0])]
if __name__ == '__main__':
# 开始预测
paddle.init(use_gpu=False, trainer_count=1)
# VGG模型
# out = vgg_bn_drop(3 * 32 * 32, 10)
# with gzip.open("../model/vgg16.tar.gz", 'r') as f:
# parameters = paddle.parameters.Parameters.from_tar(f)
# MobileNet模型
out = mobile_net(3 * 32 * 32, 10)
with gzip.open("../model/mobile_net.tar.gz", 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
image_path = "../images/truck1.png"
# 总开始预测时间
start_time = int(round(time.time() * 1000))
for i in range(10):
result, probability = to_prediction(image_path=image_path,
out=out,
parameters=parameters)
print '预测结果为:%d,可信度为:%f' % (result, probability)
# 总的预测时间
end_time = int(round(time.time() * 1000))
# 打印平均预测时间