chengduoZH
commented
5 years ago @DianaZhang 你这种修改是没有问题，猜想问题是出在您的运行环境。NV在CUDA10.1才推出对ubunut18的支持。您的环境是ubuntu18 + cuda9，所以有可能是有问题的。 另外您使用的GPU卡是不一致的，不确定这样是否可以行。
Closed DianaZhang closed 4 years ago
chengduoZH
commented
5 years ago @DianaZhang 你这种修改是没有问题，猜想问题是出在您的运行环境。NV在CUDA10.1才推出对ubunut18的支持。您的环境是ubuntu18 + cuda9，所以有可能是有问题的。 另外您使用的GPU卡是不一致的，不确定这样是否可以行。
chengduoZH
commented
5 years ago @DianaZhang 你直接运行下面这段代码试试？
from __future__ import print_function
import os
import argparse
from PIL import Image
import numpy
import paddle
import paddle.fluid as fluid
def parse_args():
parser = argparse.ArgumentParser("mnist")
parser.add_argument(
'--enable_ce',
action='store_true',
help="If set, run the task with continuous evaluation logs.")
parser.add_argument(
'--use_gpu',
type=bool,
default=False,
help="Whether to use GPU or not.")
parser.add_argument(
'--num_epochs', type=int, default=5, help="number of epochs.")
args = parser.parse_args()
return args
def loss_net(hidden, label):
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
acc = fluid.layers.accuracy(input=prediction, label=label)
return prediction, avg_loss, acc
def multilayer_perceptron(img, label):
img = fluid.layers.fc(input=img, size=200, act='tanh')
hidden = fluid.layers.fc(input=img, size=200, act='tanh')
return loss_net(hidden, label)
def softmax_regression(img, label):
return loss_net(img, label)
def convolutional_neural_network(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
return loss_net(conv_pool_2, label)
def train(nn_type,
use_cuda,
save_dirname=None,
model_filename=None,
params_filename=None):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
if args.enable_ce:
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
startup_program.random_seed = 90
main_program.random_seed = 90
else:
train_reader = paddle.batch(
paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if nn_type == 'softmax_regression':
net_conf = softmax_regression
elif nn_type == 'multilayer_perceptron':
net_conf = multilayer_perceptron
else:
net_conf = convolutional_neural_network
prediction, avg_loss, acc = net_conf(img, label)
test_program = main_program.clone(for_test=True)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
def train_test(train_test_program, train_test_feed, train_test_reader):
acc_set = []
avg_loss_set = []
for test_data in train_test_reader():
acc_np, avg_loss_np = exe.run(
program=train_test_program,
feed=train_test_feed.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
avg_loss_set.append(float(avg_loss_np))
# get test acc and loss
acc_val_mean = numpy.array(acc_set).mean()
avg_loss_val_mean = numpy.array(avg_loss_set).mean()
return avg_loss_val_mean, acc_val_mean
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(startup_program)
epochs = [epoch_id for epoch_id in range(PASS_NUM)]
lists = []
step = 0
compiled_program = fluid.compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(loss_name=avg_loss.name)
for epoch_id in epochs:
for step_id, data in enumerate(train_reader()):
metrics = exe.run(
compiled_program,
feed=feeder.feed(data),
fetch_list=[avg_loss, acc])
if step % 100 == 0:
print("######Pass %d, Epoch %d, Cost %s" % (step, epoch_id,
metrics))
step += 1
# test for epoch
avg_loss_val, acc_val = train_test(
train_test_program=test_program,
train_test_reader=test_reader,
train_test_feed=feeder)
print("Test with Epoch %d, avg_cost: %s, acc: %s" %
(epoch_id, avg_loss_val, acc_val))
lists.append((epoch_id, avg_loss_val, acc_val))
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname, ["img"], [prediction],
exe,
model_filename=model_filename,
params_filename=params_filename)
if args.enable_ce:
print("kpis\ttrain_cost\t%f" % metrics[0])
print("kpis\ttest_cost\t%s" % avg_loss_val)
print("kpis\ttest_acc\t%s" % acc_val)
# find the best pass
best = sorted(lists, key=lambda list: float(list[1]))[0]
print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1]))
print('The classification accuracy is %.2f%%' % (float(best[2]) * 100))
def infer(use_cuda,
save_dirname=None,
model_filename=None,
params_filename=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
def load_image(file):
im = Image.open(file).convert('L')
im = im.resize((28, 28), Image.ANTIALIAS)
im = numpy.array(im).reshape(1, 1, 28, 28).astype(numpy.float32)
im = im / 255.0 * 2.0 - 1.0
return im
cur_dir = os.path.dirname(os.path.realpath(__file__))
tensor_img = load_image(cur_dir + '/image/infer_3.png')
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(
save_dirname, exe, model_filename, params_filename)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(
inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
lab = numpy.argsort(results)
print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
def main(use_cuda, nn_type):
model_filename = None
params_filename = None
save_dirname = "recognize_digits_" + nn_type + ".inference.model"
# call train() with is_local argument to run distributed train
train(
nn_type=nn_type,
use_cuda=use_cuda,
save_dirname=save_dirname,
model_filename=model_filename,
params_filename=params_filename)
infer(
use_cuda=use_cuda,
save_dirname=save_dirname,
model_filename=model_filename,
params_filename=params_filename)
if __name__ == '__main__':
args = parse_args()
BATCH_SIZE = 64
PASS_NUM = args.num_epochs
use_cuda = args.use_gpu
# predict = 'softmax_regression' # uncomment for Softmax
# predict = 'multilayer_perceptron' # uncomment for MLP
predict = 'convolutional_neural_network' # uncomment for LeNet5
main(use_cuda=use_cuda, nn_type=predict)
paddle-bot-old[bot]
commented
4 years ago Since you haven\'t replied for more than a year, we have closed this issue/pr. If the problem is not solved or there is a follow-up one, please reopen it at any time and we will continue to follow up. 由于您超过一年未回复，我们将关闭这个issue/pr。 若问题未解决或有后续问题，请随时重新打开，我们会继续跟进。
系统：ubuntu18.04，4核12G gpu：nvidia410，cuda9.0，cudnn7.3 paddle：1.4.1.post97，安装在python3.6的虚拟环境中 python：python3.6
使用模型，数字识别模型 代码修改部分
错误内容：
全部代码：